ANALYSING MACROECONOMIC EFFECTS OF FINANCIAL SHOCK …

ANALYSING MACROECONOMIC EFFECTS OF FINANCIAL SHOCK DURING GLOBAL

FINANCIAL CRISIS AND ITS CONTAGION ON EMERGING MARKETS

A dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy

By

Hummaira Jabeen

DEPARTMENT MANAGEMENT SCIENCES Faculty of Commerce, Economics and Management

Sciences ISRA UNIVERSITY, HYDERABAD

December 2019

ANALYSING MACROECONOMIC EFFECTS OF FINANCIAL SHOCK DURING GLOBAL

FINANCIAL CRISIS AND ITS CONTAGION ON EMERGING MARKETS

By

Hummaira Jabeen

NAME OF SUPERVISOR AND CO- SUPERVISOR

Dr. Nadeem Qureshi (Supervisor) Assistant Professor

Dr. Hakimzadi Wagan (Co-Supervisor) Assistant Professor

PROF. DR. Hameedullah Kazi (Co-Supervisor)

iii

ACKNOWLEDGEMENTS

I would like to express my very great appreciation to assistant Professor

Dr. Muhammad Nadeem Qureshi; my research supervisor, who gave me the

golden opportunity to do this thesis and for his valuable and constructive

suggestions during the planning and development of this research work.

My deep gratitude to Professor Hakimzadi Wagan, research Co-

supervisor, for her patient guidance, enthusiastic encouragement and useful

advice and critiques on this research work and assistance in keeping my progress

on schedule. She helped me a lot in doing this Research and under her

supervision I came to know about so many new things. I am really thankful to her.

Her willingness to give her time so generously has been very much appreciated.

My special thanks to Prof. Dr. Hameedullah Kazi, my research co-

supervisor for his professional guidance and valuable support.

Finally, I wish to thank my parents for their support and encouragement

throughout my study.

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ABSTRACT

Financial system working at a global level is in a constant state of evolution.

This state of Globalization carry benefits for the economies but with the same

token also carries cascading defaults and failures.

The purpose of this study is to explore the transmission of U.S. monetary

policy shocks during mortgage crisis 2007 to emerging countries. In this study

classification of emerging markets by Financial Times Stock Exchange (FTSE) is

employed. FTSE classifies emerging markets into advance emerging markets

which include Brazil, Czech Republic, Hungary, Malaysia, Mexico, Poland, South

Africa, Taiwan, Thailand and Turkey and the secondary emerging markets namely

Chile, China, Colombia, Egypt, India, Indonesia, Pakistan, Peru, Philippines,

Russia and UAE.

For working on the attainment of the purpose, VAR methodologies are used.

At first level, transmission of monetary policy is focused; this has been studied at

two levels. Transmission arising from national monetary policy and the impact of

monetary policy shock arising from United States on the economy of emerging

markets in a time varying context. This objective is achieved using the TVP-VAR

model with stochastic volatility.

At second level, impact of financial shock has been studied. One of the key

outcomes of the US financial global crisis is that due to financial innovations we

are unable to capture the broader horizon of financial conditions with just few

variables. Keeping this view in front, objective of this study is to offer an empirical

v

assessment of the effects of the financial conditions of the United States upon

macro-economy of the emerging economies using standard Vector Auto-

Regression (VAR) Models. This objective is achieved by utilizing Financial

Conditions Index of Brave and Butter (2011) for the assessment of impact upon

macro-economy of the emerging markets as being classified by Financial Times

Stock Exchange (FTSE).

At third level, in this study, an index is created using wide range of macro-

economic and financial variables over a long horizon for the Pakistan using a time

varying model developed by Koop and Korobilis (2014). This method develops

and forecasts financial conditions index.

It is being found that ISLM framework is partly applicable in many cases.

Prize puzzle exists more in county specific monetary policy as compared to

international contagion. Response of output is aligning with the theory at country

level but not in international level. Hungary, Turkey, Malaysia at country level fully

aligns with the theory. Mexico and Turkey fully deviate from theory in the case of

contagion impact.

It is also found that countries do have impact of financial conditions of the

US but in many cases, Impact die off with the time. Extend vary from county to

country but impact does exist on the macro-economy of the emerging countries.

The countries that are having Free Trade Agreement with the US are having

strong and long-term response. Bilateral partners' response dies off with the

passage of time except Russia and Hungary. In the last but not the least, financial

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condition index of the Pakistan is able to give a true picture of the economy.

Forecasting of the index of the macroeconomic variables is close to the reality.

Key Words

Financial conditions, Bayesian analysis, financial forecasts, monetary policy

shock, emerging economies, TVP-VAR analysis, financial conditions, emerging

markets, SVAR.

JEL Classification

C53, G17, G19, C58, E02, F62, G01

vii

ABBREVIATION AND SYMBOLS

Symbol/Abbreviation Term

𝐴𝑦------------------------------------------------------------------------------------general matrix

A, Fi,𝐵𝑖 ,𝐶𝑖 , 𝑔𝑖, 𝐷𝑖-------------------------------------------------- vector/matrices of coefficients

𝐺0 ---------------------------------------------------------------------- (n*1) vector of constants

𝐺1-------------------------------------------------------------------- (n*n) matrix of coefficients

𝑝𝑡---------------------------------------------variable indicating the Monetary Policy stance

𝑣𝑡𝑝, 𝑣𝑡

𝑦, ut, εt----------------------------------------------------------------------structural shock

𝑥𝑡----- (n*1) vector of financial and economic variables for the construction of FCI

yt ---------------------------------------------------------- (k*1) vector of observed variables

𝑍𝑡-------------------------------------------vector of non-policy macroeconomic variables

Σ (Sigma)-----------------------------------------------------------------------------------addition

⊗------------------------------------------------------------------------------ Kronecker product

𝜆𝑡𝑦--------------------------------------------------------------------------regression coefficients

𝜆𝑡𝑓------------------------------------------------------------------------------------factor loadings

𝑓𝑡----------------------------------------------------------------latent factor interpreted as FCI

yt--------------------------------------------------------------vector of observed variables

ct---------------------------------------------------------------------------------------------intercept

BIS---------------------------------------------------------Bank of international settlements

viii

CPEC----------------------------------------------------China Pakistan economic corridor

DOH---------------------------------------------------Dornbusch overshooting hypothesis

EKF---------------------------------------------------------------------Extended Kalman filter

EME-------------------------------------------------------------Emerging market economics

FATVPVAR-Factor Augmented-Time varying parameters vector auto regression

FAVAR----------------------------------------Factor augmented vector auto regression

FCI-------------------------------------------------------------------Financial condition index

FOMC------------------------------------------------------Federal open market committee

FTSE--------------------------------------------------------Financial times stock exchange

GARCH----------------Generalized Autoregressive Conditional Heteroskedasticity

GDP-------------------------------------------------------------------Gross domestic product

GFC-----------------------------------------------------------------------Global financial crisis

IFS------------------------------------------------------------International financial statistics

IMF---------------------------------------------------------------International monetary fund

IPI------------------------------------------------------------------Industrial production index

IS-LM---------------------------------------------------Investment savings-liquidity money

KFS---------------------------------------------------------------------Kalman filter smoother

KSE-------------------------------------------------------------------Karachi stock exchange

MCI------------------------------------------------------------------Monetary condition index

MCMC-------------------------------------------------------------Markov chain Monte Carlo

NFCI------------------------------------------------------National financial condition index

OECD--------------------Organization for Economic Cooperation and Development

OLS----------------------------------------------------------------------Ordinary least square

OPEC-----------------------------------Organization of Petroleum Exporting Countries

ix

PCA------------------------------------------------------------Principal component analysis

PSX------------------------------------------------------------------Pakistan stock exchange

REER-----------------------------------------------------------Real effective exchange rate

S&P----------------------------------------------------------------------Standard’s and poor’s

SBP---------------------------------------------------------------------State bank of Pakistan

SDG---------------------------------------------------------Sustainable development goals

SME----------------------------------------------------------Small and medium enterprises

SRO-----------------------------------------------------------------Statutory regulatory order

SVAR------------------------------------------------------Structural vector auto regression

TVPFAVAR-Time varying Parameters-Factor augmented vector auto regression

TVP-VAR----------------------------Time varying parameters-vector auto regression

UK--------------------------------------------------------------------------------United Kingdom

US/USA------------------------------------------United States/ United states of America

VAR---------------------------------------------------------------------Vector auto regression

x

TABLE OF CONTENTS

Page

ACKNOWLEDGEMENTS ------------------------------------------------- iii

ABSTRACT ------------------------------------------------------------------- IV

ABBREVIATIONS & SYMBOLS ----------------------------------------- VII

TABLE OF CONTENTS --------------------------------------------------- X

LIST OF TABLES ----------------------------------------------------------- XV

LIST OF FIGURES --------------------------------------------------------- XVI

CHAPTER - I ………………………………………………………. INTRODUCTION ……………………………………………………

01

1. Shock and Contagion---------------------------------------------------- 02 1.1 Historical Context of shock and Contagion ------------------ 03 1.2 Types of Shock ------------------------------------------------------ 04

1.2.1 Monetary Policy Shock ------------------------------------ 04 1.2.1.1 Theories on Monetary Policy ------------------- 04 1.2.1.2 Transmission of Monetary Policy -------------- 07 1.2.1.3 Monetary Policy Anomalies --------------------- 08

1.2.2 Financial Shock --------------------------------------------- 09

2. Emerging Markets ------------------------------------------------------- 10

2.1 Emerging Markets Classification ------------------------------- 11 3. Gap in the literature ----------------------------------------------------- 14 4. Problem Statement ------------------------------------------------------ 15

5. Objectives ------------------------------------------------------------------ 15

6. Scope of the Study ------------------------------------------------------ 16

7. Structure of the Study--------------------------------------------------- 17

CHAPTER II…………………………………………………………. LITERATURE REVIEW…………………………………………….

18

1. Monetary Policy Shocks------------------------------------------------ 18

2. Financial shocks---------------------------------------------------------- 25

2.1 Literature on Development of FCI------------------------------- 25

2.2 Literature review on FCI Transmission ------------------------ 29

3. Gap in the Literature----------------------------------------------------- 32

CHAPTER III ……………………………………………………….. METHODOLOGY……………………………………………………

33

1. Methodology of the First Objective ---------------------------------- 33

1.1 Hypotheses of First Objective ----------------------------------- 33

xi

1.2 Scope of the Objective -------------------------------------------- 36

1.3 Methodology --------------------------------------------------------- 37

2. Methodology of the Second Objective ------------------------------ 39


2.2 Methodology --------------------------------------------------------- 42

2.2.1 VAR Model --------------------------------------------------- 42

3. Methodology of the Third Objective --------------------------------- 44

3.1 Rational of the Index ----------------------------------------------- 45


3.3 Econometric Method ----------------------------------------------- 47

3.3.1 Multivariate Models of TVP-FAVAR -------------------- 49

4. Conclusion ----------------------------------------------------------------- 50

CHAPTER IV ……………………………………………………….. MONETARY POLICY SHOCK TRANSMISSION IN EMERGING MARKETS…………………………………………….

51

1. Introduction----------------------------------------------------------------- 51

2. Identification of Monetary Policy (MP) Shock---------------------- 54

2.1 Theoretical Framework-------------------------------------------- 55

2.2 Data & Choice of Variable----------------------------------------- 56

3. Results---------------------------------------------------------------------- 59

3.1 Simultaneous Relation--------------------------------------------- 60

3.2 Stochastic Volatility------------------------------------------------- 60

3.3 Impulse Responses------------------------------------------------- 60

3.3.1 Impulse Response of Domestic Markets-------------- 61

3.3.1.1 Brazil ------------------------------------------------- 62

3.3.1.2 Colombia--------------------------------------------- 63

3.3.1.3 Czech Republic------------------------------------- 64

3.3.1.4 Hungary ---------------------------------------------- 65

3.3.1.5 Malaysia --------------------------------------------- 66

3.3.1.6 Mexico ----------------------------------------------- 67

3.3.1.7 Pakistan---------------------------------------------- 68

3.3.1.8 Peru --------------------------------------------------- 69

3.3.1.9 Phillipines ------------------------------------------- 70

3.3.1.10 Poland ---------------------------------------------- 71

3.3.1.11 Russian Federation------------------------------ 72

3.3.1.12 Turkey ---------------------------------------------- 73

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3.3.2 Impulse Response of International Contagion------- 74

3.3.2.1 US to Brazil------------------------------------------ 74

3.3.2.2 US to Colombia ------------------------------------ 75

3.3.2.3 US to Czech Republic---------------------------- 76

3.3.2.4 US to Hungary ------------------------------------- 77

3.3.2.5 US to Malaysia ------------------------------------- 78

3.3.2.6 US to Mexico --------------------------------------- 79

3.3.2.7 US to Pakistan-------------------------------------- 80

3.3.2.8 US to Peru ------------------------------------------ 81

3.3.2.9 US to Phillipines ----------------------------------- 82

3.3.2.10 US to Poland -------------------------------------- 83

3.3.2.11 US to Russian Federation---------------------- 84

3.3.2.12 US to Turkey -------------------------------------- 85

4. Conclusion------------------------------------------------------------------ 86

CHAPTER V………………………………………………………… UNITED STATES’ FINANCIAL CONDITIONS AND MACRO-ECONOMY OF EMERGING MARKETS………………………….

88

1. Introduction ---------------------------------------------------------------- 88

2. Identification---------------------------------------------------------------- 91

2.1 Financial Conditions and Forecasts of Macro-Economy-- 91

2.2 Selection of Variables---------------------------------------------- 91

3. Results---------------------------------------------------------------------- 99

3.1 Brazil ------------------------------------------------------------------- 99

3.2 Chile -------------------------------------------------------------------- 101

3.3 Czech Republic ----------------------------------------------------- 103

3.4 Greece ----------------------------------------------------------------- 104

3.5 Hungary --------------------------------------------------------------- 106

3.6 India -------------------------------------------------------------------- 108

3.7 Malaysia --------------------------------------------------------------- 110

3.8 Mexico ----------------------------------------------------------------- 112

3.9 Pakistan---------------------------------------------------------------- 114

3.10 Poland --------------------------------------------------------------- 116

3.11Russian Federation ----------------------------------------------- 118

3.12 South Africa -------------------------------------------------------- 120

4. Conclusion------------------------------------------------------------------ 122

CHAPTER VI ………………………………………………………. Construction of Financial Condition Index for Pakistan……

124

xiii

1. Introduction----------------------------------------------------------------- 124

2. Data-------------------------------------------------------------------------- 126

3. Construction of Financial Condition Index-------------------------- 128

3.1 Estimating financial Position using FCI------------------------ 131

3.1.1 1971-77 Era--------------------------------------------------- 131

3.1.2 1977-88 Era--------------------------------------------------- 132

3.1.3 The Era of Structural Adjustments --------------------- 133

3.1.4 2001 to onwards -------------------------------------------- 134

4. Forecasting of Macro-economic Variables------------------------- 138

4.1 Inflation----------------------------------------------------------------- 138

4.2 Exchange Rate------------------------------------------------------- 139

4.3 Monetary policy (short term interest rate) -------------------- 141

4.4 Gross Domestic Product (GDP) -------------------------------- 142

4.5 Stock Market---------------------------------------------------------- 143

4.6 Forecasting under other Variants of the Model-------------- 153

5. Conclusions---------------------------------------------------------------- 147

CHAPTER VII ………………………………………………………. DISCUSSIONS ……………………………………………………...

148 148

CHAPTER VIII ……………………………………………………… CONCLUSIONS …………………………………………………….

168 168

CHAPTER IX ………………………………………………………. RECOMMENDATIONS ……………………………………………

173

REFERENCES………………………………………………………

179

Appendix IV-A-----------------------------------------------------------------

192

Appendix IV-B----------------------------------------------------------------- 216

Appendix IV-C ---------------------------------------------------------------- 232

xiv

LIST OF TABLES Chapter Page

I-1 Country Classification of FTSE ------------------------------------------ 13

III-1 Hypotheses of First Objective -------------------------------------------- 35

III-2 Hypotheses of Second Objective --------------------------------------- 41

III-3 Hypotheses of Third Objective ------------------------------------------- 44

IV-1 Variables and Transformation-------------------------------------------- 57

IV-2 Results of Transmission of Monetary Policy ------------------------- 87

V-1 Variables and Transformation-------------------------------------------- 93

V-2 Results ------------------------------------------------------------------------- 122

VI-1 Variables and Transformation-------------------------------------------- 127

VI-2 Chronical Exchange rate in Pakistan ----------------------------------- 140

VI-3 Chronical GDP --------------------------------------------------------------- 142

VIII-1 Status of Hypotheses of First Objective ------------------------------- 169

VIII-2 Status of Hypotheses of Second Objective --------------------------- 170

VIII-3 Status of Hypotheses of Third Objective ------------------------------ 172

IX-1 Findings and Recommendations----------------------------------------

173

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LIST OF FIGURES Chapter Page

IV-1 Posterior means of time-varying impulse response of

Brazil---------------------------------------------------------------------

62


Colombia----------------------------------------------------------------

63


Czech Republic--------------------------------------------------------

64


Hungary-----------------------------------------------------------------

65


Malaysia-----------------------------------------------------------------

66


Mexico-------------------------------------------------------------------

67


Pakistan-----------------------------------------------------------------

68


Peru---------------------------------------------------------------------

69


Philippines -------------------------------------------------------------

70


Poland ------------------------------------------------------------------

71

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Russian Federation -------------------------------------------------

72


Turkey -------------------------------------------------------------------

73

IV-13 Posterior means of time-varying impulse response from

US to Brazil------------------------------------------------------------

74


US to Colombia-------------------------------------------------------

75


US to Czech Republic----------------------------------------------

76


US to Hungary--------------------------------------------------------

77


US to Malaysia -------------------------------------------------------

78


US to Mexico ---------------------------------------------------------

79


US to Pakistan -------------------------------------------------------

80


US to Peru ------------------------------------------------------------

81


US to Philippines ----------------------------------------------------

82

xvii


US to Poland ---------------------------------------------------------

83


US to Russian Federation-----------------------------------------

84


US to Turkey ---------------------------------------------------------

85

V-1 Transmission to Brazil----------------------------------------------- 100

V-2 Transmission to Chile------------------------------------------------ 102

V-3 Transmission to Czech Republic---------------------------------- 103

V-4 Transmission to Greece--------------------------------------------- 105

V-5 Transmission to Hungary------------------------------------------- 107

V-6 Transmission to India------------------------------------------------ 109

V-7 Transmission to Malaysia------------------------------------------- 111

V-8 Transmission to Mexico -------------------------------------------- 113

V-9 Transmission to Pakistan ------------------------------------------ 115

V-10 Transmission to Poland -------------------------------------------- 117

V-11 Transmission to Russian Federation ---------------------------- 119

V-12 Transmission to South Africa ------------------------------------- 121

VI-1 Factors estimation using TVP-FAVAR-------------------------- 128

VI-2 Factors estimation using FAVAR--------------------------------- 129

VI-3 Factors estimation using FA-TVP-VAR-------------------------

-

130

xviii

VI-4 FCI estimation using TVP-FAVAR-------------------------------- 135

VI-5 FCI estimation using FAVAR-------------------------------------- 136

VI-6 FCI estimation using FA-TVP-VAR------------------------------- 137

VI-7 Forecasted Inflation ---------------------------------- 139

VI-8 Forecasted and Actual Exchange Rate------------------------- 141

VI-9 Forecasted and Actual Discount Rate--------------------------- 142

VI-10 Forecasted and Actual Gross Domestic Product------------- 143

VI-11 Forecasted and Actual Stock Market --------------------------- 143

VI-12 Forecasting using FA-TVP-VAR---------------------------------- 144

VI-13 Forecasting using FAVAR------------------------------------------ 145

VI-14 Forecasting using TVP-FAVAR----------------------------------- 146

1

CHAPTER I

INTRODUCTION

"Finance is, as it were, the stomach of the country, from which all the other

organs take their tone." (William Edward Gladstone, 1858)

This chapter shed light upon conceptual framework. After elaborating

conceptual framework, problem statement and structure of the study are

discussed.

The financial system is the combination of banking, non-banking institutes,

different types of financial markets and regulatory authorities. This has a crucial

part in the market-based economy. This part of the economy utilizes idle

resources for the sake of capital formation by using a wide range of different

financial tools (James, 2007). This function of finance is not the recent

phenomena; the history and economic literature are full of such examples that

have acknowledged the crucial role of finance for economic development. Walter

Bagehot (1873) had long ago accepted the crucial role of finance for the growth

of the economy specifically for England. Some other worth studying work on this

area are of Goldsmith (1969), Mckinnon (1973) and last but not the least King and

Levine (1993) who established the association between financial and economic

development in 80 countries.

A sound economy requires such a financial system that would facilitate the

smooth circulation of funds between different agents of the economy with the help

2

of different instruments. This can only be happening when the economy is

functioning properly. Financial institutes take the risk at times and work in a

dynamic environment. This state of evolution of the financial system is resulting

in deregulation, innovation, and globalization. But this positive side carries its cost

with it.

Financial system working at a global level is in a constant state of evolution

that at a time results in bubble burst, system failure, the crisis that results in the

spiral death of the institute. These failures of financial systems at an economy

level cost very high. Most recent manifestation can be seen in the form of the

recent crisis of mortgage that started as the local problem in the United States

and sooner spread to other regions of the world. This contagion nature of financial

failure costs very high for the many economies around the world. So its very

crucial for the authorities to maintain and work for the stability of the financial

system (Ghani, 2013).

1. Shock and Contagion

Likewise, to earthquakes, countries do face sudden movements. These

types of turbulence in the economy are known as shocks that may result in crises

(Zumbach, et al. 2000). Due to globalization, such a crisis does not have

implication for a single country. They do have an impact on other financial and

economic institutes and at times may create a death spiral. Open economies are

more prone to get affected by these events. When such correlations of economic

events in neighboring and cross-border economies rise exceptionally in crisis

3

times comparative to the links during normal eras, this is known as contagion.

Contagion impact among economies can be spread through diverse networks.

1.1 Historical Context of Shock and Contagion

Financial history is full of examples of the financial crisis. A quick inspection

of economic history recommends that misconduct of money and credit has led to

financial crisis and several explosions over the centuries. This is true in both

advanced and emerging markets.

Prominent events are great depression in 1930s, the savings and loan

debacle of the 1980s, the Continental Illinois Bank and Trust Company in 1984,

stock market crash in 1987, the Wall Street Crash of 1987, the dotcom bubble,

European Exchange Rate Mechanism Attack in 1992, Mexican Peso Collapse in

1994, East Asian Crisis in 1997, Long Term Capital Management (LTCM) crisis

in 1998, Turkey (2001-02), Argentina (2001), Russian collapse in 1998, Brazilian

devaluation in 1999, technological crisis in 2000, East Asian currency crisis in

1997-98, which grew in Thailand then overcame the Indonesia, Korea, and

Malaysia and last but not the least US mortgage crisis 2007 that sooner spread

in other regions of the world and are considered to be the most damaging financial

shifts since the Great Depression of 1930. Due to its intensity and monetary cost

around the world, the 2007 Global Financial Crisis (GFC) has some similarities to

the great depression of the 1930s (Ghani, 2013). One common element that is

found in all these crises is how these spread from one country to others countries

not of a similar nature but of different size and structure around the world.

4

1.2 Types of Shock

This study is an attempt to study the shock on emerging markets. There may

be many types of shock. Specifically, the focus of this study is the following types

of shock:

Monetary policy shock

Financial shock

1.2.1 Monetary Policy Shock

Monetary management is the central activity of almost all central banks.

Economic fluctuations due to monetary policy are known as monetary policy

shock. In order to understand monetary policy shock, we need to know the

science of monetary policy. For this reason, following session will discuss theories

on monetary policy, anomalies of monetary policy and transmission of monetary

policy that ultimately result in shock.

1.2.1.1 Theories on Monetary Policy

Classical economists (Prominently Adam Smith and David Ricardo) wrote

extensively on the money. The quantity theory of money is amongst oldest

surviving doctrines in economics. It describes the relationship between monetary

and real variables. In the classical model, aggregate demand is equal to

aggregate supply. Price and aggregate demand are having inverse relation and

no relationship exists between price and aggregate supply because the real

output is equal to potential output. This is also known as the neutrality of money.

It means the change in money stock does not lead to change in the level of

production, employment or income. Monetary policy thus cannot be an effective

5

means of influencing real macroeconomic variables such as output, employment

or investment (Meenai, 2012).

According to Karl Marx, money has not any use value but It serves as

universal equivalence. It is acknowledged representative of wealth in a capitalist

society. On account of the crisis, he said that money in simple circulation gives

rise to the monetary crisis. It promises to pay are not realized at a large scale, this

will lead to a chain reaction. within the context of an advanced financial system,

the monetary crisis might be caused by breaks in industrial and commercial

transactions. Origin of crises lies in contradictions of capitalist production, which

manifest themselves in persistent tendencies of overaccumulation and

underconsumption. The crisis appears as a monetary phenomenon and money

become scared or worthless. Creditworthiness collapses, financial markets

plummet (Meenai, 2012).

Keynesian Orthodox Model was presented by John Maynard Keynes who

presented it as an attempt to overthrow the conventional wisdom of those who

claimed to be the inheritors of the classical model. He criticized the classical

model on following grounds:

1. Potential output is not equal to actual output; classical economist ignores

the problem of lack of effective demand and existence of under full employment

equilibrium in a capitalist economy.

2. In long run we all are dead; classicist ignores the time of adjustment.

3. The classical model ignores the problem of monopolization.

6

4. Determination of aggregate demand is a complex process and depends

upon many other factors. Classicists have oversimplified it.

5. Real income and profit expectations are more important than interest

rate as a determinant of savings and investments.

According to Keynes money is not neutral. Increasing money supply,

lowering interest rate can influence aggregate production, employment, and

investment. Governments by proactive monetary and fiscal policy can eliminate

unemployment and enable a capitalist economy to achieve full employment

equilibrium. Capitalism is not a self-regulatory system. It needs regulations

(Meenai, 2012).

Modern Neo Classical Monetary Theory or The monetarists, led by Milton

Friedman and Alan Meltzer, emerged as the response of failure of the Keynesian

system and started reaffirming faith in the quantity theory of money. Monetarism

drew inspiration from several empirical studies during the 1970s (Meenai, 2012).

In summary, although money-related concepts existed from the diminishing

of barter system. John Maynard Keynes was the first person that worked on formal

monetary policy and stressed its role in economic stability. According to him, the

central bank by controlling money supply can have an impact on the economy.

Modern Neo Classical monetary overthrew Keynesian theory because according

to the Keynesian systems of monetary management does only work in the short

run, it is not applicable in long run (Meenai 2010). This process does not end here

but it's in continual progression; researchers and economists are working in this

7

area on a continual basis and working on understanding the nature of monetary

policy using theoretical and empirical approach.

1.2.1.2 Transmission of Monetary Policy

“Describes how policy-induced changed in nominal money stock or short-term

nominal interest rate impact real variables such as aggregate output and

employment.” Ireland (2005)

One main task of the central bank is to maintain price stability. Most countries

adopt explicit inflation targeting. Structural structure and list of tools the central

bank uses for achieving its targets are known as monetary policy. The most

central bank uses qualitative policy. Its instruments help in direction-finding

market interest rate and handling interbank liquidity.

Transmission channels are not working disjointedly but jointly intensifying

their outcomes. Working depends upon the current structure of the economy and

financial system (Klacso, 2013). The regime of monetary policy is chosen

according to the structure and current stance of the economy. Regimes offer an

arrangement of monetary policy choices making process and also make it stress-

free to connect decisions to the public. Basic monetary policy positions are:

Nominal anchor

Monetary targeting

Exchange rate targeting

Inflation targeting

It is a general agreement amongst many economists that in short run

monetary policy may have a noteworthy impact on real economy. Indeed, many

findings have confirmed Friedman and Schwartz (1963) that movement that

8

comes in real output by monetary policy actions last for two years (Christiano et.

al 1994). But the problem is how monetary policy exercises its effect, on this

pointless agreement exists among economists. To a huge degree, empirical

examination of results of monetary policy has treated the monetary transmission

method as “Black Box” (Bernanke and Gertler, 1995).

The conservative view is that monetary policymakers use short-term rates

for influencing the cost of capital and consequently sending functions. But

estimation indicates that it has an impact on the long run. This gap led to exploring

other means (Bernanke & Gertler, 1995). From the above discussion, it can be

said that monetary policy is a complex phenomenon. It is not transmitted through

one channel but many.

12.1.3 Monetary Policy Anomalies

Monetary policy does not transmit to all the economies evenly and according

to theories presented earlier. Transmission process varies from economy to

economy and at the point, serious deviations are being observed from standards.

Few major anomalies in the transmission process are as follows:

a. Exchange Rate Overshooting:

The exchange rate overshooting or Dornbusch overshooting hypothesis was

presented by Rudiger Dornbusch in 1976. That time this was considered as the

birth of modern international macroeconomics (Rogoff, 2002). For studying

exchange rate movements under this hypothesis, the hypothetical

macroeconomic framework is built with the purpose of developing a theory that

may be employed for observing mega fluctuations in exchange rate. This model

9

is a hybrid model of Mundell-Fleming in the short run and in the long run having

features of flexible price model with endogenous expectations. According to this

hypothesis, the exchange rate will overshoot when the short run response to a

disturbance is higher than long-run response (Dornbusch, 1976).

b. Prize Puzzle:

According to many standard macroeconomic theories (e.g. IS-LM framework

or Monetarist), the contractionary monetary policy will result in declining prices.

But Sims (1992) using real data empirically proved deviation of these phenomena

and found that contractionary monetary policy is resulting in rising prices. He

named this behavior price as a prize puzzle. After him, many studies confirm this

behavior and found supporting evidence of the prize puzzle (Javid and Munir,

2010).

The preceding session has shed light on the monetary policy on different

aspects. Transmission of monetary policy specifically in turmoil time period to

other sectors of the economy is known as monetary policy transmission. Similar

nature of shocks is being studied in this study and how they have a

macroeconomic impact.

1.2.2 Financial Shock

Movements in financial condition index are named financial shock in this

study. In the second half of the study financial shocks are being studied. In this

study meaning of financial shock is movements in financial condition index (FCI).

FCI is being studied at two levels in this study. At first level transmission to

emerging markets are being studied. For the transmission purpose, FCI

developed by Brave and Butters (2011) is employed. This index is updated on

10

weekly basis on the official website of the federal reserve bank of Chicago titled

National Financial Conditions index. This is a weighted average of 105 indicators

of financial activity of broader coverage of money, debt, equity market and

traditional and shadow banking. It’s a useful indicator for monitoring financial

stability and forecasting purposes. Positive values of this index mean tighter than

average and negative values indicate looser than average conditions adjusted for

economic conditions. At second level a case of emerging market is being taken

and the index is formed for that country. After the construction of FCI, it is being

tested and it impacts upon macroeconomic variables are being studied.

2. Emerging Markets

In 1981 Antonie Van Agtmael; an economist at World Bank’s International

Corporation devised the term ‘Emerging market’ in a conference referring for

those countries, which cannot be defined existing criteria’s such as Asian Tigers

of that time namely, Thailand, South Korea, Taiwan and Hong Kong were playing

a major part in the global economy. So these sort of countries needs a different

classification. Moreover, that time he was working on ‘Third World Equity Fund’

for that he needs an attractive name for the investor’s attention. So Emerging

economies were considered those countries that were in shift from developing to

developed economies. (Serban, Borisov and Dobrea 2012).

2.1 Emerging Markets Classification

In this study emerging markets’ classification of Financial Times Stock

Exchange (FTSE) Rusell is being followed. This Global Equity Index series was

started in 1985 when FT-actuaries world index was formed. This was a joint

11

venture of financial times and institute of actuaries. In the start country

classification was not done, index only used to cover countries.

In the start division between developed and emerging markets were more

based on subjective analysis and the major focus was wealth. A transparent

system was not available. In 2003, a consultant proposed a structural framework

for the classification of markets. This was:

1. Quality of the market (of rules and regulations);

2. Materiality (country needed to be of a significant size for the inclusion);

3. Consistency and Predictability (for this purpose ‘watch list’ was formed that

would serve as a barometer for the promotion and demotion of the country);

4. Cost limitation (consideration of cost while the implementation of any change

while assessing any country);

5. Stability (phase based approach for the country introduction in the list and its

promotion);

6. Market access (liquidity for the international investors).

The outcome of the meeting was available in 2003. These criteria provided

strong support for the assessment of the quality of the market. These rules were

implemented in 2004. FTSE’s formal procedure for the assessment of the markets

is as follows:

1. Quality of market matrix that would work as a benchmark for the market

judgment;

2. Questionnaire for the regulatory bodies; whose response would help in market

assessment; a new FTSE Russell country classification advisory committee was

12

formed who would report FTSE Russell Policy Advisory Board. This committee

would conduct an objective assessment against benchmark;

3. Watchlist for the country was formed;

4. Engagement with market policy was formed;

5. Annual basis judgment system was formed;

6. Clear communication and implementation timetable was schedule for taking

necessary actions.

Since rules formed back in 2003, the transparent country classification

system is being implemented at FTSE global equity indexes. Review of the

country is being done on annual basis (September of each year). As an outcome

of these exercise countries is being classified into developed, advanced emerging

markets, secondary emerging markets and frontier markets. (FTSE Russell 2015)

Evaluation is being done on annual basis, however, in September 2016 it was

decided that FTSE Russell would not change the classification of the countries in

September 2017. Countries classification in September 2016 is given in table II-

1. (FTSE Russell 2016)

13

Table I-1: Country Classification of FTSE (September 2016)

Developed Advanced

Emerging

Countries

Secondary

Emerging

Frontier

Australia

Austria

Belgium/Luxembourg

Canada

Denmark

Finland

France

Germany

Hong Kong

Ireland

Israel

Italy

Japan

Netherlands

New Zealand

Norway

Portugal

Singapore

South Korea

Spain

Sweden

Switzerland

UK

USA

Brazil

Czech Republic

Greece

Hungary

Malaysia

Mexico

Poland

South Africa

Taiwan

Thailand

Turkey

Chile

China

Colombia

Egypt

India

Indonesia

Pakistan

Peru

Philippines

Qatar

Russia

UAE

Bahrain

Bangladesh

Botswana

Bulgaria

Côte d’Ivoire

Croatia

Cyprus

Estonia

Ghana

Jordan

Kenya

Latvia

Lithuania

Macedonia

Malta

Mauritius

Morocco

Nigeria

Oman

Palestine

Romania

Serbia

Slovakia

Slovenia

Sri Lanka

Tunisia

Vietnam

Source: FTSE Russell 2016

14

3. Gap in the Literature

In the light of reviewed literature, it can be stated that studies are available

of monetary shock but studies covering emerging markets specifically national

and international context still are lacking or are in less number. This is an area

where this study fits and will cover.

From the literature, it can be established that FCI does have implications for

the economies but it can also be seen that a great deal of studies is on advanced

economies. There is a lack of studies covering emerging economies. Secondly, a

major focus of the studies is on forecasting economic activity or growth (e.g. GDP)

or interest rates. Other strong variables like exchange rate are not employed.

Furthermore, there is a lack of studies on response analysis of emerging markets

of the financial conditions of the advanced countries. This study adds the literature

on shocks transmission using the SVAR model with bootstrap after bootstrap

method.

As an advanced version of MCI, FCI’s with the passage of time, by using

more variables for the index formation seems a wise strategy for gauging

economic and financial statements and as a policy tool and seems to have

improved the forecasting power (Hatzius, et al. 2010). With its limitations, still, in

an evolving state, it’s serving as a more realistic tool for decision makers and as

a policy tool especially in a time of crisis.

As it is evident, there is a rich literature covering FCI. But little consideration

is giving to the factor of time variation and emerging market. By filling the gap for

emerging market FCI, this study offers a contribution to the literature on emerging

15

markets specifically Pakistan.

Major GAP in the existing literature is as follows:

1. Studies on advanced countries;

2. Lack of studies on transmission of financial conditions of advanced country

and macro-economy of emerging markets;

3. Use of weak proxies;

4. Models based upon the assumption of homoscedasticity;

5. Non-availability of FCI for emerging countries;

6. FCI development using the assumption of homoscedasticity and constant

parameters.

4. Problem Statement

Globalization carries with it increased financial interdependencies among

many countries. Such linkages may result in cascading defaults and failures. This

creates the need to study the linkages among the financial markets and the impact

of these crises on the markets. Understanding the major reasons and the extent

of these crises is very much important for designing regulatory responses that

may defuse cascades before they happen.

5. Objectives

In the light of Gap in the literature and problem statement, this study aims

to work on the following objectives:

1. To gauge the time-varying effect of the monetary policy;

2. To find out the effect of U.S. financial shocks on emerging markets;

3. To develop and test FCI for Pakistan.

16

6. Scope of the Study

Testing for shock and contagion assists stakeholders and watchdogs to

understand how information special effects move to unconnected economies.

During crisis time period, such as in mortgage crisis, a cross-border contagion of

financial conditions may have strong implications for the financial stability. So it is

required to provide timely assessment of the correlation, transmission, and

contagion of policy actions and financial and economic conditions. So that

authorities may develop contingency plans for mitigating negative consequences

of these events. Transmission of volatility and contagion are hot areas of debate

and research due to their strong implications for monetary policy, financial

landscape, risk measurement, capital requirements, asset pricing and economic

assessment.

Current thesis majorly based upon saltwater economics. The term salt

water and fresh water was first employed by Robert E. Hall in 1976 to differentiate

between two major schools of thoughts of economics. Freshwater economists are

believer of free market while saltwater economists are believers of Keynesian

Economics (Gordon 2003).

Moreover, this study is on emerging countries. Advanced emerging

markets and Secondary Emerging as classified by FTSE (Financial Times Stock

Exchange) are part of this study. Quarterly data is being employed and necessary

transformation is done in the light of objective (details are available in relevant

chapter). The time span is also set according to objective and availability of data;

as data availability in case of emerging markets is a real challenge. Moreover, for

17

analysis purpose time series econometrics and Bayesian econometrics is being

employed. With this scope, shock and contagion arising from monetary policy and

financial condition index are being studied in this thesis. Following headings

discuss in length major definitions and classification mechanism in use.

7. Structure of the Study

A rise of the global crisis and sooner its spread to other regions of the world

has highlighted the need to study the shock transmission. With this background,

this study is an attempt to study the shock transmission from different

perspectives. This study is covering a wide range of shock related issue in major

three chapters such as monetary policy shock transmission both at national and

international transmission, financial shock transmission at international level

transmission and in the last an index is developed for an emerging economy and

its transmission on the economy is studied.

Chapter two and chapter three are linked with each other. Chapter two

covers the literature review. This chapter covers major studies available on

transmission and contagion. While chapter three covers methodology employed

in attainment of all three objectives. Chapter four till six discuss results of the

cases of the transmission and contagion followed by discussion, conclusion, and

recommendations chapters respectively.

This chapter has presented background knowledge of the major concepts

of the study. Moreover, it discusses problem statement and how study has been

structured.

18

CHAPTER II

LITERATURE REVIEW

In this chapter, literature will be discussed by following theoretical

background. This chapter will be presenting critical analysis of the available

literature upon monetary and financial shock.

In the recent history of finance, we may found different incidents of financial

stress. But the very little amount of studies is available directed towards

understanding the impact of such crisis on monetary policy transmission and

transmission of a recently developed tool financial condition index and their

impact upon economic activity. This study purposes to link two aspects of the

literature on the national and international impact of:

Monetary policy shocks

Financial shocks

Preceding sections shed light in the light of the literature on both types of

shocks.

1. Monetary Policy Shocks

Monetary policy transmission is an area of study from a number of

decades. This study is an attempt to bridge two major areas of the monetary policy

transmission; preceding literature covers theoretical and empirical literature on

monetary policy transmission. Both kinds of literature have developed and

employed the standard method and time-varying models for the study of the

transmission mechanism.

19

Theoretical literature has tried to develop relationships in different ways.

Sims (1980) criticizing the existing exercises of studying macroeconomy; offer

new field that produced better results. This started a new era in the macro-

economy study and using method developed in 1980, Sims (1992) studied the

monetary policy transmission in the light of existing theories. He found deviation

at points in the response of macroeconomic variables and named that behavior

as prize puzzle. Later in the same study, he also proposed a solution for the

removal of the puzzle. Bernanke and Gertler (1995) developed a model showing

the shock arising from the conditions of the balance sheet that were resulting in

output fluctuations and also found that negative shocks are having a greater

impact than positive shocks. Azariadis and Smith (1998) developed a model

where the economy was able to switch between different regime namely higher

interest rates, worsen balance sheet conditions, weaker banking lending and a

finance free zone with low financial stress. They found that the result of a

response to all the situations of shocks is non-linear in nature.

Allen and Gale (2000) conducted a study for the development of

contagious model. They displayed financial contagion as an equilibrium

phenomenon, aims to provide micro-economic foundation of financial contagion

by focusing upon liquidity preference shocks. Liquidity preference shock

imperfectly correlated across regions.

Ciccarelli and Rebucci (2003) measured contagion by using Bayesian

Time Varying Coefficient Model. Here timing of the contagion was unknown and

heteroskadicity and omitted variables were presented, they modeled cross-

market linkages changing randomly upon simulated and actual data. They applied

20

the framework upon full and limited information set and used for investigating

positive and negative contagion. They found contagious impact of Argentinian

crisis upon Chilean economy.

Rigobon and sack (2004) asserted that with changes in monetary policy

response arising in asset prices is problematic by the endogeneity of policy

decisions and also that interest rate and asset prices do react to many other

variables. For this reason, they developed an estimator based on the

heteroscedasticity that occurs in high-frequency data. They demonstrated that

response to changes in monetary policy of asset prices can be identified based

upon surge in the variance of policy shock on the day of Federal Open Market

Committee (FOMC)’s meeting and Chairman’s monetary policy testimony. They

establish that a rise in short-term interest rate stock prices decline and the yield

curve goes upward but with the passage of time it gets smaller. Results also

indicated that estimations of event-study contain biases that make estimated

effect appear on T-bill yield large and on stock prices smaller.

Macro-economy does have time variation in its impact. With this objective,

Primiceri (2004) developed a model for studying the impact of monetary policy on

growth and inflation. He proved that monetary policy does have time variation in

its transmission.

Gai (2013) developed a contagion model of financial systems by using

network theory. His model captured two important channels of contagion in

financial systems. Glasserman and Young (2013) proposed a framework that

focused upon the network defined by liabilities between financial institutions. They

analyzed the probability of contagion and the expected losses generated by

21

contagion when the joint distribution of shock is given. Elliott et.al (2014) modeled

contagions and cascades of failures among organizations.

Empirical evidence does have mixed results. Goldstein (2005) examined

the impact of the growth slowdown in China and US and its link with the global

financial conditions. He found that growth slowdown in theses countries could

result financial crisis in emerging countries.

Neri and Nobili (2010) studied the transmission of US monetary policy

shock to the Eurozone. They found that international transmission works through

exchange rate, commodity price, short term interest rate and balance sheet and

they found that contractionary monetary policy decreases the value of Euro and

commodity prices that create demand in euro area and result in expansion in euro

area.

Bagliano and Morana (2010) assessed the mechanism of great recession

spillover to advance and emerging countries using FVAR.

Nakajima (2011) conducted a study on Japanese economy for finding the

time-varying impact of the monetary policy. He estimated monetary policy over

three decades using a TVP-VAR model with stochastic volatility. He found

evidence of performance difference that clearly indicating that during three

decades Japanese economy has gone in major structural shift thus highlighting

the strong impact of monetary policy upon macro-economy. Yuksel et.al (2013)

studies the Taylor-type monetary policy rule with TVP specifications on the

Turkish economy. Time-Varying Parameters of the model was estimated using

structural Extended Kalman Filter (EKF). In the light of the consequences, they

claim that changes in the risk preferences of the firms and household need to

22

reflect in monetary policy including market interest rate as the risk attitude of the

households can do this. Moreover, they found that EKF performs better than

standard Kalman Filter for the phenomena under study.

Todorov (2012) conducted a study for finding the international linkages and

transmission of shocks between US and frontier markets. His study focused on

stock market assessment. By using Generalized Auto-regressive Conditional

Heteroscedasticity (GARCH model on daily data from 20 countries, he found the

limited exposure of frontier markets to US shock. Results from a TVP model

indicated that statistically strong impact of US returns on frontier markets.

Kazi et.al (2013) conducted a study for finding the changing transmission

of monetary policy shock in 14 (OECD) nations. By employing TVP-FAVAR

method they studied the 265 variables’ response. They found that US monetary

policy is having a strong negative impact upon growth. They also found that

transmission to growth has increased since the 1980s and size of the impact of

monetary policy shock during turmoil time was higher than normal periods and

kept on changing over time. Shock decreased the share price in many OECD

countries and asset, trade and interest rate channel were the prominent ones for

the shock propagation to the rest of the economy.

Fornari and Stracca (2013) conducted a study for finding the quantitative

impact of financial shocks on financial and real variables. Their study comprised

of a panel of 21 advanced countries from the time period of 1985 till 2011. They

found that financial shocks can be classified from other shock kinds and also that

they do have a strong effect upon macro-economy. It was also found that financial

structure and development is not a strong contributor to the shock transmission.

23

Moreover, it was found, financial shocks have an impact not only in crisis but also

in normal time.

Ghani (2013) conducted a study for finding out macroeconomic impact of

global financial crisis (GFC) upon emerging countries and policy response by

emerging market economies (EMEs). Global financial crisis impacted EMEs with

different intensity. GFC exposed strengths and weaknesses of paradigm of

development in EMEs based upon liberalized capital account and improved

macroeconomic conditions. EMEs are exposed to crisis in the presence of

financial liberalization reforms without adequate regulatory framework and

country specific characteristics also play a role.

Hab et.al, (2014) analyzed two types of contagion namely information

spillover and liquidity risk premium. These both shocks were initiated by US sub

prime segments and have impacted price determination in other markets. They

studied these shocks upon open-ended property funds. They found that in the

beginning, liquidity risk premium plays a role of contagion but with the passage of

time information spillover comes in action. As a result, they confirm that both types

of shocks are main drivers of the contagion across markets.

`Fu & Lio (2015) analyzed the influence of monetary policy on the direction

then spread of investment dynamic adjustment in the china. Marfatia (2015)

studied the influence of monetary policy on yield curve using the Cooley and

Prescott (1976)’s process of time-varying response coefficients. Results indicate

that there exists noteworthy time variation in response to bond rates during the

1989-2008-time period. Yiu et.cl (2010) conducted a study for finding the

relationship between Asian and US stock market by using the principal

24

component method. Results of Asymmetric Conditional Correlation model

indicated that US market is having a contagion impact upon Asian markets.

Barakchian (2015) studied the spillover of US monetary policy upon

Canada using global vector auto regression (GVAR). Kim (2001) concluded that

expansionary monetary policy results positive output in G6 but Bluedorn and

Bowdler (2001) in case of G7 and Scrimgeour (2010) found that positive monetary

policy results in positive short-term interest rate in four countries in America’s.

Cross and Nguyen (2016) studied global oil price shock upon china’s output using

time varying parameter vector auto regression (TVPVAR) model. They found that

impact is small and temporary in nature.

Rogers et.al (2018) assessed the relationship between monetary policy

and macro economy at the time of zero lower bound using the structural vector

auto regression model. They calculated effects of monetary policy shocks upon

expectations.

Furceri (2018) studied the impact of monetary policy shock upon income

inequality. They found that contractionary monetary policy increases income

inequality.

Arias (2019), with the help of SVAR, studied the impact of monetary policy

shocks. They found that with the increase of rate output decrease and they also

found that, during great moderation, policy shocks are contractionary in nature.

In studying the shock transmission to the stock market, Yiu et.cl (2010)

conducted a study for finding the relationship between Asian and US stock market

by using the principal component method. Results of Asymmetric Conditional

Correlation model indicated that US market is having a contagion impact upon

25

Asian markets. Todorov (2012) conducted a study for finding the international

linkages and transmission of shocks between US and frontier markets. His study

focused on stock market assessment. By using Generalized Auto-regressive

Conditional Heteroscedasticity (GARCH model on daily data from 20 countries,

he found the limited exposure of frontier markets to US shock. Results from a TVP

model indicated that statistically strong impact of US returns on frontier markets.

Ehramann and Fratzsher (2009) analyzed the transmission of US Monetary Policy

Shock to global equity market by taking data of 50 economies. They found that a

100bp increase in monetary policy results 2.7 decreases in return on average.

They also found heterogeneity of the transmission and also found that the

economies, which are open and relatively liquid markets are more prone to the

transmission. Markwat et.al (2009) proved that stock market contagion operates

as domino effect. He found that global crashes do not occur all of sudden but are

preceded by local and regional crashes.

2. Financial Shock

In this study, financial shock means changes in financial conditions index

(FCI). In case of transmission from US to emerging markets, index developed by

Brave and Butter (2012) for the US is in use.

2.1 Literature on Development of FCI

Its been long in practice to use a single variable as a policy tool. Scholars

have used different variables for serving this purpose. In this area like Friedman

and Anna (1963) used monetary aggregates for measuring monetary policy

shocks. Sims (1992), and Bernanke and Blinder (1992) employed interest rate,

26

use of quantity of non-borrowed reserves by Christiano and Eichanbaum (1992),

usage of M1 by Fung and Kasumovish (1998), and use of term spread by Oliner

& Rudebusch (1996). Use of single variable with its easiness have many

limitations and in many cases results in different types of puzzles namely

exchange rate, price or interest rate puzzle [Sims (1992); Dornbusch (1976)]. So

due to lack of agreement on true representative as policy tool raises the question

of the validity of this practice.

In this scenario, a composite measure of any sort seems to be an obvious

solution to this problem. Such an effort started with the development of monetary

condition index (MCI). This use of this measure has been in practice in many

central banks like Canada and New Zealand. But after time, questions start raising

on its validity also as both variables changes so quickly, so in this scenario, it is

hard to find out the tight or loose monetary conditions. Some also criticized the

ground that there may be some other strong variables in place of these for policy

tool (Freedman 1994).

Future attempts were made by using more variables for index formation

like Bernanke and Mihov (1998) developed an index for the United States and this

method was applied to Canada by Fung and Yuan. Hatzius (2000) used a large

number of variables for index formation. Stock and Watson (2002) using Dynamic

Factor Models developed an index for forecasting purpose. Bernanke et.al, (2005)

developed Factor-Augmented Vector Auto Regression (FAVAR) models using

two different methods for the effective study of monetary policy and concluded

that this approach results in better results. The concept of time variation is also

27

being studied in index formation. Koop and Korobilis (2014) developed multiple

indexes for the forecasting purpose.

Updated and according to macroeconomic variables, indexes were formed

later on by Guichard and Turner (2008), Goodhard and Hofmann (2001), Gauthier

et.al, (2004) Mayes and Viren (2001) and Swiston (2008) by using VAR developed

an index for the United States.

Beaton (2009) developed two growth based FCIs. One was developed

using a structural vector error correction model (VECM) and other using

macroeconomic modeling approach. They concluded that contractionary financial

conditions do have an impact on the economy. They studied the link between

financial shock and economic growth at zero lower bounds. They created

equivalency of FCI with interest rate. They concluded that tight financial conditions

do impact upon GDP growth till 40 percent.

Hatzius et.al, (2010) explored the connection amongst financial conditions

and economic activity. They studied prevailing practices based on single

indicators and FCI then proposed a new method for FCI development. The

analysis represented the strong predictive power of FCI.

Gomez et.al, (2011) constructed FCI for Colombia using 21 variables with

the help of the PCA method. They evaluated the predictive power of FCI and

found that it performs better than individual variables. They also found that it could

serve as a leading indicator for early warning indicator thus can be used as a

useful indicator for representing financial stability and macro-prudential

supervision. Matheson (2011) construed FCI for the US and Euro area using DFM

techniques and found good forecasting abilities of the FCIs.

28

Nombulelo et.al, (2012) constructed FCI for South Africa. FCI outperforms

than the benchmark in forecasting exercise.

Debuque-Gonzales and Gochoco-Bautista (2013) construed FCI for Asian

countries namely Hong Kong, China, Japan, Korea, Malaysia and Singapore

using PCA by the following the methodology of Hatzius et.al, (2010). They also

constructed regional based FCI. They found the strong predictive ability of FCI

than AR based models.

Angelopolou (2013) constructed two different FCIs for the euro area using

a wide range of the variable. One FCI with monetary policy and second without

monetary policy. Moreover, country-based FCI was also constructed. This

practice was done to see the impact of monetary policy. The indices represented

a true picture of financial conditions of the eurozone since its creation. In this

practice, the symmetric impact of monetary policy was observed.

Erdem and Tsatsaronis (2013) constructed an index for forecasting

purpose. They found that financial factors do have strong implications for GDP

but weak for the inflation.

Koop and Korobilis (2014) developed FCI using FAVAR models with TVP

and stochastic volatility for forecasting purpose. Time variations allowed a change

in weights attached with variables and DMA/DMS method allow the variable

change over time. They concluded that this way of studying macro-economy

produces better results. Areosa and Dutra (2016) constructed an FCI using the

methodology of Brave and Butters (2011) and the same was later applied for the

forecasting purpose in the case of Brazil.

29

Muraru (2015) developed an index for Romania using three different

methods namely weighted average, Principal Component Analysis, and Dynamic

Factor Model. Results indicate that regardless of the method employed index is

working as an instrument capturing a broader picture of the financial situation of

the economy. So it can be employed for forecasting purposes.

Authorities have also created an index for studying the financial conditions

of economies. Hong Kong Monetary (2010) built an index for the Hong Kong and

China for studying the episodes of stress in mentioned economies. Another index

formed by Monetary Authority of Singapore (2009) for studying the economic

conditions of Asian countries (China, Republic of China, Thailand, Taipei,

Philippines, Malaysia, Korea, Republic of Korea, Indonesia, and India).

International Monetary Fund has also constructed Asia based index for studying

the economic conditions of Asian countries.

Use of principal component analysis for the index formation has been the

practice of many. English et. al (2005) by using more than forty variables

estimated index for the US, UK, and Germany. Hatzius et.al (2010) developed an

index for the US by using 45 variables covering all major financial and economic

variables. Brave and Butters (2010)’s index comprises more than 100 variables

and captures a broad horizon of the economic and financial landscape.

2.2 Literature Review on Shock Transmission

This study aims to add on the literature on the impact of financial condition

index on economies. Mortgage crisis 2007 with its significant negative impact on

leading economies has highlighted the need for the better understanding of the

30

link between financial conditions and macro-economy. For this reason, a

considerable work is available from world’s eminent researchers. Brave and

Butters (2011) constructed an FCI for the USA using a large number of variables

and prove that this index is able to forecast short term and medium term economic

activity. Gumata et.al (2012) constructed an index for South Africa and also found

that this index is having strong predictive power in the short run. Hatzius et.al

(2010) also constructed an index for the USA and found that relative predictive

power of the index is unstable and this index performs well in unusual financial

stress time period and this is able to forecast economic conditions especially

during stress time. Gonzales and Bautista (2013) constructed FCI for five Asian

Markets. They concluded that FCI predicts economy more than benchmark AR

models. FCI was helpful in forecasting economy.

Eickmeier et.al, (2011) employed the FCI developed by Hatzius et.al,

(2010) for studying the international transmission during 1971-2009 of financial

shock using the TVP-FAVAR method. They found that positive US financial

shocks do have a positive impact upon the growth of countries under study (US,

Canada, the UK, France, Germany, Italy, Spain, Japan and Australia). Moreover,

they found that transmission has increased since the 1980s, size of shock has

varied over time indicating time variation is shock transmission and changing

financial landscape in last few years in the US are major reasons for the

international transmission specifically during the crisis.

Alessandria and Mumtaz (2017) hypothesized that the links between credit

markets and real economy tighten in a crisis. Balcilara et.al (2016) used a

previously constructed index for finding its ability to forecast the South African

31

economy. They found that the response of economy is non-linear to financial

conditions. While discussing the response of individual variables they found that

among T-bills, output and inflation response of inflation are highest during the

crisis.

Beaton et.al (2009) studies the effect of financial shocks at zero lower

bound like in the current crisis on real activity. They found that impact may be

amplified at higher interest rates during the financial crisis. Opschoor et.al (2014)

studied to find the impact of financial conditions on the stock market by using

Bloomberg FCI. They found that worst financial conditions are associated with

high volatility and correlation between stock return.

Shocks do not arise from the index; shocks arising from policies are well-

studied phenomena and it’s being proven specifically in case of transmission. Kazi

et.al (2013) conducted a study for finding the changing transmission of monetary

policy shock in 14 (OECD) nations. By employing TVP-FAVAR method they

studied the 265 variables’ response. They found that US monetary policy is having

a strong negative impact upon growth. They also found that transmission to

growth has increased since the 1980s and size of the impact of monetary policy

shock during turmoil time was higher than normal periods and kept on changing

over time. Shock decreased the share price in many OECD countries and asset,

trade and interest rate channel were the prominent ones for the shock propagation

to the rest of the economy.

Todorov (2012) conducted a study for finding the international linkages and

transmission of shocks between US and frontier markets. His study focused on

stock market assessment. By using Generalized Auto-regressive Conditional

32

Heteroscedasticity (GARCH model on daily data from 20 countries, he found the

limited exposure of frontier markets to US shock. Results from a TVP model

indicated that statistically strong impact of US returns on frontier markets.

3. Gap in the Literature

In the light of reviewed literature, it can be stated that studies are available

of monetary shock but studies have majorly focused upon advanced economies

whereas crisis do not know about the boundaries. Moreover, crisis has been

studied using static method whereas impact is time varying. In keeping these

issues in front, this study has been done that covers emerging economies using

time varying methods.

Moreover, from literature it can be established that FCI do have implications

for the economies but it can also be seen that a great deal of studies are on

advanced economies. There is a lack of studies covering emerging economies.

Secondly, major focus of the studies is on forecasting economic activity or growth

(e.g. GDP) or interest rates. Other strong variables like exchange rate are not

employed. Furthermore, there is a lack of studies on response analysis of

emerging markets of the financial conditions of the advanced countries. This study

adds the literature on shocks transmission using SVAR model with bootstrap after

bootstrap method.

This chapter discusses literature upon monetary and financial shock. It

discuss in length literature upon the development and transmission mechanism

of the financial shock. After presenting critically analyzed literature, gap in the

literature has been discussed.

33

CHAPTER III

METHODOLOGY

This chapter will discuss about the research methodology used in each

objective. Study consists of three objectives and after setting objectives

hypotheses is being develop for each objective followed by methodology to test

the hypotheses. Objectives of this study are as follows:

1. To gauge the time varying effect of the monetary policy

2. To find out the effect of U.S. financial shocks on emerging markets

3. To develop and test FCI for Pakistan

1. Methodology of the First Objective

Hypotheses are developed in the light of objectives; now onwards-objective

wise methodology is shared.

1.1 Hypotheses of First Objective

This section develops hypotheses for the first objective that is ‘To gauge

the time-varying effect of the monetary policy’. This objective is further break down

into following sub-objectives:

1. To find out the time varying impact of country specific monetary policy

before, after and during crisis time period upon growth and inflation.

2. To find out the time varying impact of US monetary policy shocks in

emerging countries before, after and during crisis time period upon growth

and inflation.

34

In the light of sub-objectives four hypotheses have been developed. First

two hypotheses are addressing country specific shock and last two hypotheses

are addressing case of contagion.

1. A time-varying contractionary monetary policy has a negative impact

on growth.

Weise (1999) on UK data and Atanasova (2003) on US data found that

contractionary and expansionary monetary shocks do have an impact on the

economy. McCallum (1991) found that output responds more to a contractionary

monetary policy. Cover (1992) found that due to contractionary monetary policy

output declines whereas expansionary monetary policy does not have a

significant impact upon output. Similar results were found by Morgan (1993);

Thoma (1994); Rhee and Rich (1995); Kandil (1995); Karras (1996) and Balke

(2000). Economies behave differently in the time of crisis as compared to the

normal time period. While Nakajima (2011) confirmed the time-varying nature of

the Japanese market but Yio (2010) found no strong evidence of time-varying

nature of volatility on the US & Asian markets. So it can be inferred that different

opinion exists on the transmission of monetary policy.

2. Price puzzle exists in monetary policy transmission.

Sims (1992) found prize puzzle in monetary policy transmission while

Hanson (2004) did not found the evidence of the prize puzzle in monetary policy

transmission. So it can be said that the presence of puzzles is proven in the light

of light but the greater agreement does not exist.

35

3. The systematic US monetary policy has a positive impact on the

growth of emerging economies.

International transmission does exist but it is degree vary from county to

country e.g. Todorov (2012) and Kazi et.al (2013).

4. The expansionary monetary in the US creates prize puzzle in emerging

economies.

As stated above monetary policy transmission to the prizes do have mixed

results in the transmission.

Table III-1: Hypotheses of First Objective

S# Hypotheses Test Impulse variable

Response variable

1. A contractionary monetary policy has inverse impact on growth.

TVP-VAR Model on

Normalized variables

Monetary policy

Growth and

inflation


TVP-VAR Model on


Monetary policy

Growth and

inflation

3. The systematic US monetary policy has positive impact on growth of emerging economies.

TVP-VAR Model on


Monetary policy

Growth and

inflation

4. The expansionary monetary in US creates prize puzzle in emerging economies.

TVP-VAR Model on


Monetary policy

Growth and

inflation

Source: Author’s compilation

36

1.2 Scope of the Objective

Time span for the objective is 1995Q1-2012Q2. Countries from Advance

emerging countries are Brazil, Czech Republic, Hungary, Malaysia, Mexico,

Poland, Turkey and countries from Secondary emerging markets are Colombia,

Pakistan, Peru, Philippines, and Russia.

Impulse variable in the study is monetary policy. Monetary Policy

Instruments for the study are Money market rate, Central Bank Policy

Rate/Discount rate, and T-bill rate. Prominent researchers have also used these

variables. E.g. Use of Money market rate by Rosoiu & Rosoiu (2013); use of

Central Bank Policy Rate/Discount rate by Nakajima (2011) & Modenesi & Araujo

(2012); and use of T-bill rate Sims (1992) as a policy tool for the monetary policy.

Response variables are growth and inflation. Proxy for the Growth is Gross

Domestic Product (Real Index); same variable has been used by Bernanke &

Gertler (1995) and proxy for the Inflation is Consumer Price Index (Real) just like

have been employed by the Sims (1992), and Winkelried & Gutierrez (2015).

37

1.3 Methodology

VAR is a method used for forecasting purpose. VAR models are like

simultaneous equations. Here we consider many endogenous variables together

but each variable is explained by its lagged value, usually there is no exogenous

variable in this (Gujarati, 2008).

VAR models have gone through many developments and still, this process

is going on. Cogley and Sargent (2001) was the pioneer who developed VAR

model with time-varying coefficients. This model was criticized by the Stock (2001)

on the ground of the assumptions employed in the study related to the constant

variance of the VAR’s structural shock. In response to this criticism, Cogley and

Sargent (2001) modified their model using Stochastic Volatility. Stochastic

Volatility originally proposed by Black (1976) is having a significant place in TVP-

VAR models. In the line with this, Primiceri (2005) proposed the TVP-VAR model

with time-varying parameters. In the context of Bayesian inference of TVP-VAR

model with stochastic volatility, employment of Markov Chain Monte Carlo

(MCMC) method makes estimations feasible. Bayesian econometrics is based

upon the rules of probability. All the econometric analysis such as parameter

estimation, model comparison, prediction and such activities follow same

probability rules. So we may say that rules of probability are universal in nature

(Koop, 2003).

To work on the stated objectives that is to estimate Monetary Policy shock

in a time-varying nature. To work on this, TVP-VAR model proposed by Nakajima

(2011) is estimated in this study. For illustrating identification of structural shock

38

in TVP-VAR model it is convenient to present non-policy and policy variables by

a k-dimensional vector of variables (yt).

In this case stated two equations earlier can be written in following TVP-

VAR model proposed by Joushi Nakajima (2011) is as follows:

Ayt = F1yt−1 + ⋯+ Fsyt−s + ut, t=s+1,…,n, (Equation III-1)

Where;

yt= (k*1) vector of observed variables,

A, F1, …, Fs= (k*k) matrices of coefficients

ut=structural shock

it is assumed that ut~ N(0, ΣΣ) where

Σ = (

σ1 0 ⋯ 00 ⋱ ⋱ ⋮⋮ ⋱ ⋱ 00 ⋯ 0 σk

)

Simultaneous relations of the structural shock are specified by recursive

identification, assuming that A is lower-triangular,

A = (

1 0 ⋯ 0a21 ⋱ ⋱ ⋮⋮ ⋱ ⋱ 0

ak1 ⋯ ak,k−1 1

)

Equation (3) can be rewritten as the following reduced form VAR model:

yt = ct + B1yt−1 + ⋯+ Bsyt−s + A−1Σεt, εt~N(0, Ik), (Equation III-2)

Where

Bi = A−1Fi for i=1,….,s.

Stacking the elements in the rows of the Bi’s to form β (k2s ∗ 1 vector) and

defining Xt = Ik ⊗ (yt−1′ , …… , yt−s

′ ) where ⊗ denotes the Kronecker product, the

model can be written as

39

yt = Xtβ + A−1Σεt, (Equation III-3)

All the parameters of equation (2) are constant. In order to extend this

model to TVP-VAR, parameters need to be time-varying.

Consider the TVP-VAR model stochastic volatility specified by

yt = Xtβt + A−1Σtεt, t=s+1,….,n, (Equation III-4)

where the coefficients and the parameters are all time-varying. To model

the process of time-varying parameters, Primiceri (2005) is being followed, let at =

(a21, a31, a32, a41, … , ak,k−1)′ be a stacked vector of the lower-triangular elements

in At and ht = (h1t , … , hkt )′ with hjt = log σjt2 , for j=1,…,k, t=s+1,….,n. it is

assumed that the parameters if equation (3) follow a random walk process as

follows:

βt+1 = βt + uβt, at+1 = at + uat, ht+1 = ht + uht,

(

εt

uβt

uat

uht)

~

(

0,(

I 0 0 00 Σβ 0 0

0 0 Σa 00 0 0 Σh

)

)

For t=s+1,…,n, where

βs+1~N(uβo, Σβ0), as+1~N(uao, Σa0) and hs+1~N(uho, Σh0). For more details, refer

Nakajima (2011).

2. Methodology of the Second Objective

This section develops hypotheses for the first objective that is ‘To find-out

the effects of the financial conditions of the United states upon macro-economy

of the emerging economies’.

40

a. There is an exchange rate puzzle like Dornbusch’s exchange rate

overshooting in the transmission mechanism.

Link of the foreign exchange rate with other rates has been an area of

interest of scholar around the world. Such as Sanchez (2005) established a link

between the exchange rate and interest rate in the small open economy. His

results indicated that an increase in interest rate results in the contractionary

foreign exchange rate and vice versa. Sichei (2005) confirmed Dornbusch’s

Hypothesis in the case of South Africa; while Tu & Feng (2009) rejected this

hypothesis in the case of U.S. and Germany.

b. FCI reflects information of stock market in the long run.

Zeng (2010) found the strong response of monetary policy to the monetary shock.

Todorov (2012) found that lagged US stock data don’t have an impact on the frontier

market but expected to have implications for frontier markets.

c. FCI reflects information on short-term interest rate in the short run.

Montagnoli and Napolitano (2005) found that financial condition index is a good

indicator for giving information in the short run. Brave and Butters (2011) found that FCI

can be used for short and medium term forecasting.

d. FCI reflects information on long-term interest rate in the long run.

This chapter covered in-depth literature on the subject under study. After

identifying a gap in the literature, objectives were set that preceded with

hypotheses. Upcoming chapters will deal with individual objective and empirically

will test the hypotheses.

41

Table III-2: Hypotheses of Second Objective

S# Hypotheses Test Impulse variable

Response variable

1. There is exchange rate puzzle like Dornbusch’s exchange rate overshooting in transmission mechanism.

SVAR model on

normalized variables

Financial condition

index

Exchange rate

2. FCI reflects information of stock market in the long run.

SVAR model on normalized variables

Financial condition index

Stock market

3. FCI reflects information of short term interest rate in the short run.



Short term interest rate

4. FCI reflects information of long term interest rate in the long run.



Long term interest rate



Countries from the advanced emerging markets are Brazil, Czech

Republic, Hungary, Malaysia, Mexico, Poland, and South Africa and countries

from Secondary emerging markets are Chile, Greece, India, Pakistan, and

Russian Federation. Impulse variable is Financial condition index developed by

Brave & Butters (2011) and response variables are Economic & financial variables

namely Short term Interest rate, Long term interest rate (same variable have been

used by nakajima (2011) in his study), Stock prices (similar variable can be found

in the study of Agha et.al (2005)), Real effective exchange rate (same variable for

the same purpose have been used by Correa & Caetano (2013).

42

2.2 Methodology

To analyze the spread of shock to the economies majorly two methods are

in use namely Structural Macro Models and VAR models. Sims (1980) criticized

the structural models in his seminal work to a great deal and VAR models are

proposed as alternatives. VAR models are in great use by notable researchers for

the study on transmission mechanism (e.g. Brave and Butters (2011) and many

others).

2.2.1 VAR Model

Vector Autoregression Models (VAR) is employed for economic analysis.

In this study the vector, autoregression model proposed by Barsky and Sims

(2012) employed. VAR models have been employed to analyze shocks of

different natures such as Sims and Zha (2006) used VAR model to study the

money impact upon output; Blanchard and Perotti (2002) fiscal policy impact; and

by Gali (1999) to study the relation between technology shocks and worked hours.

VAR models multivariate and linear demonstration of a vector of observables on

its own lags and in other case other variables as constant or trend. In VAR models,

we make explicit identification assumption for isolating estimation of the behavior

under study.

𝑦𝑡 = [𝑦1,𝑡𝑦2,𝑡 …𝑦𝑛,𝑡]′ (Equation III-5)

Where:

𝑦𝑡= Vector with the value of n variables at time t

As reduced form VAR, it can be written as:

43

𝑦𝑡 = 𝐺0 + 𝐺1𝑌𝑡−1 + 𝐺1𝑌𝑡−1+…. +𝐺1𝑌𝑡−1 + 𝜀𝑡 (Equation III-6)

Where:

• 𝐺0=(n*1) vector of constants

• 𝐺1=(n*n) matrix of coefficients

• 𝜀𝑡=(n*1) vector of white noise innovation

• 𝐸[𝑒𝑡] = 0

• 𝐸[𝑒𝑡𝑒𝑡′] = Ω(𝑛𝑜𝑡 𝑑𝑖𝑎𝑔𝑜𝑛𝑎𝑙)𝑖𝑓 𝑡 = 𝜏 𝑜𝑡ℎ𝑒𝑟𝑤𝑖𝑠𝑒 0

Assumption about error term:

• 𝐸[𝑒𝑡𝑒𝑡′] = Ω for t ≠ τ

In matrix notation:

• 𝑦𝑡 = 𝐺1𝑌𝑡−1 + 𝑒𝑡

𝑖𝑛 𝑡ℎ𝑖𝑠 𝑠𝑡𝑢𝑑𝑦:

𝑦𝑡 =

[ 𝑦1,𝑡

𝑦2,𝑡

𝑦3,𝑡

𝑦4,𝑡

𝑦5,𝑡]

=

[ 𝐹𝐶𝐼𝐼𝑅

𝐺𝐵𝑅𝑅𝑆 ]

(Equation III-7)

IR is the short-term interest rate, GBR is the Government Bond Rate (long-

term interest rate), R is the exchange rate and S represents stocks. VARs are

performed by ordinary least square (OLS) equation by equation. Residuals take

on recursive ordering.

44

3. Methodology of the Third Objective

This section develops hypotheses for the first objective that is ‘To develop

and test FCI for Pakistan’.

a. FCI helps in measuring financial shocks.

Hatzius (2011) found that FCI is a good representative of economic activity.

Koop and Korobilis (2014) and many others found the similar results.

Table III-3: Hypotheses of Third Objective

S # Hypotheses Test Impulse variable

Response variable

1. FCI helps in measuring financial shocks timely

TVP-FAVAR with

its restrictions

on normalized variables

Index 1. Consumer price index

2. Discount rate 3. KSE 100 index 4. Gross domestic

product 5. Real effective

exchange rate


45

3.1 Rational of the Index

Financial Condition Index (FCI) may serve for many purposes. For

example, it can be used to find out early signs of bad financial conditions Gomez

et.al, (2011); Muraru (2015) or could serve as a forecaster of the economy

(Nombulelo et.al, (2012); Bautista (2013); Erdem and Tsatsaronis (2013). It is now

in practice of many financial institutes (IMF, Goldman Sachs, and Bloomberg) and

authorities (federal reserve bank of Chicago and many other banks) to develop

FCI for the market watch. Estimation of FCI ranges from simple weighted average

method to developed sophisticated methodology. Keeping in mind the existing

practice of developing the financial index, the chief empirical input in the literature

of this study is to develop an FCI for an emerging market using the most recent

approach.

Development and usage of FCI deal with the variable choice for FCI and

its link with macro-economy. These need to think about changing state. For this

reason, a method of index development by Koop and Korobilis (2014) is utilized.

Indexes are created using a wide range of macroeconomic and financial variables

over a long horizon for Pakistan. They developed a method using extensions of

Factor models and presented multiple forms of the index. The rationale of using

this method is that it is able to capture the time-varying nature of the variables so

can give a better picture of financial conditions.

46


FCI for the Pakistan from 1969 Q1-2016Q1 using following variables:

1. Equities

2. Gold

3. Import volume

4. Export volume

5. Goods, deflator/unit value of export

6. Goods, deflator/unit value of import

7. Industrial production index

8. Deposit rate

9. Government Bond rate

10. Money market rate

11. Producer price index

12. Total reserve excluding gold & foreign reserves

13. Total consumption

14. T-bill rates

15. KMI-30

16. All Share Index

17. M1 (Currency)

18. National Saving Amount Outstanding

19. Schedule Bank Amount outstanding

20. Total fixed capital formation

47

3.3 Econometric Method

This study employs the methodology of Koop and Korobilis (2014) for the

development of FCI. In their method, the factor model is based on two connecting

equations. Equation one helps in extracting FCI from financial and economic

variables 𝑥𝑡 and second equation deals with interconnectivity of FCI and macro-

economic variables 𝑦𝑡.

TVP-FAVAR model

𝑥𝑡 = 𝜆𝑡𝑦𝑦𝑡 + 𝜆𝑡

𝑓𝑓𝑡 + 𝑢𝑡 (Equation III-8)

[𝑦𝑡

𝑓𝑡] = 𝑐𝑡 + 𝐵𝑡,1 [

𝑦𝑡−1

𝑓𝑡−1] + ⋯…………+ 𝐵𝑡,𝑝 [

𝑦𝑡−𝑝

𝑓𝑡−𝑝] + 𝜀𝑡 (Equation III-9)

Where

𝑥𝑡=An (n*1) vector of financial and economic variables for the construction of FCI

𝑦𝑡=An (s*1) vector of macroeconomic variables [in this empirical work 𝑦𝑡 =

(𝑐𝑜𝑛𝑠𝑢𝑚𝑒𝑟 𝑝𝑟𝑖𝑐𝑒 𝑖𝑛𝑑𝑒𝑥, 𝐾𝑆𝐸 100 𝑖𝑛𝑑𝑒𝑥, 𝐷𝑖𝑠𝑐𝑜𝑢𝑛𝑡 𝑅𝑎𝑡𝑒, 𝐺𝑟𝑜𝑠𝑠 𝐷𝑜𝑚𝑒𝑠𝑡𝑖𝑐 𝑃𝑟𝑜𝑑𝑢𝑐𝑡,

𝑅𝑒𝑎𝑙 𝐸𝑓𝑓𝑒𝑐𝑡𝑖𝑣𝑒 𝐸𝑥𝑐ℎ𝑎𝑛𝑔𝑒 𝑟𝑎𝑡𝑒)′]

𝜆𝑡𝑦=Regression coefficients

𝜆𝑡𝑓=Factor loadings

𝑓𝑡=Latent factor interpreted as FCI

48

𝑐𝑡= Intercept

𝐵𝑡,1, … , 𝐵𝑡,𝑝=VAR coefficients

𝑢𝑡 𝑎𝑛𝑑 𝜀𝑡=Zero-mean Gaussian disturbances with time-varying

covariance𝑉𝑡 𝑎𝑛𝑑 𝑄𝑡. For more details, refer Koop and Korobilis (2014).

49

3.3.1 Multivariate Models of TVP-FAVAR

The complete model defined in equations (one and two) is the TVP-

FAVAR. some limits on the TVP-FAVAR are also considered here that as a result

give other famous multivariate models. Those are:

Factor-augmented time-varying parameter VAR (FA-TVP-VAR):

The specification is gained from the TVP-FAVAR model beneath the limit

that the loadings are constant. In this case, the first equation in the earlier model

defines a normal factor model, whereas the other equation is a TVP-VAR

augmented with the FCI.

Factor-augmented VAR (FAVAR): This model is obtained from the TVP-FAVAR

under the restriction that both𝜆𝑡and 𝛽𝑡are time-invariant.

All presented models are having heteroskedastic covariances.

Algorithm for the calculation of TVP-FAVAR is as follows:

1. Initialization of all the parameters, 𝜆0, 𝛽0, 𝑓0,𝑉0,𝑄0and gaining of the

principal components estimates of the factors, 𝑓��.

2. Estimation of the time-varying parameters𝜃𝑡 given 𝑓��,than estimation of 𝑉𝑡,

𝑄𝑡, 𝑅𝑡, 𝑊𝑡using Variance Discounting and finally estimate 𝜆𝑡and 𝛽𝑡, given

(𝑉𝑡, 𝑄𝑡, 𝑅𝑡, 𝑊𝑡), using the Kalman Filter and Smoother

3. Estimate the factors ft given 𝜃𝑡using the Kalman Filter Smoother (KFS).

50

4. Conclusion

This chapter discuss in length methodology being employed in the

attainment of each objective.

This chapter discusses hypotheses developed in the light of literature

followed by scope and methodology. All three objectives are interconnected yet

differ.

That’s why three different methods have been employed for studying the

objectives of the study.

51

CHAPTER IV

MONETARY POLICY SHOCK TRANSMISSION IN

EMERGING MARKETS

1. Introduction

Central banking has been transformed, in practice and in theory……(Wolf,

2012)

After setting objectives in last chapter, this chapter will be discussing first

objective in length. This chapter will be discussing results of the first objective.

Global financial crisis (2007) started initially as a subprime mortgage

problem in the United States (US). With a high default rate of the subprime

mortgage, economies have suffered disastrous losses in coming years [Fornari

and Stracca (2013); Olmo and Sanso-Navarro (2014)]. In its initial state, this fear

was there that this crisis would spill over to the rest of the world economies [see

e.g. Ciccarelli et.al (2013); wolf (2012)]. With time, many fears come true and

largely negative impact was seen in debt markets, real estate, and bond market

and upon other macroeconomic variables. This is a true case of contagion

indicating how a local problem turns into a global crisis [(see, e.g., Hab (2014);

Wagan and Ali (2014)]. This global response of crisis has initiated a heated debate

among researchers on the causes of the crisis. It is believing of many researchers

and market watchers while discussing this global level response of financial crisis

that monetary policy is actively responsible for the transmission [Fatima (2013)]

and possibly it’s a source of contagion in time of crisis [Kazi et.al (2013)].

52

With this background, this can be said that responsibilities of the

central/federal/state (state bank henceforth) banks are higher than past; they

need to consider the international impact in the form of contagion in their decision-

making process in order to minimize impact at the macroeconomic level. It

believes that this approach of decision-making will result in the better conduct of

Monetary Policy objectives [Blinder (2010); Borio (2009)].

Above discussion indicates that among many of the objectives of the state

bank, one is to work on achieving financial stability hence economic growth. In

order to work in this objective, state bank authorities require accurate measure on

the effect of Monetary Policy upon economy then possible sources of contagion.

Studies available on this line majorly have employed Vector Auto-Regression

(VAR) models for empirical evidence [Luporini (2008); Best (2013); Rodofo

Cermeno and Polo (2012); Aleem and Lahiani (2011); Zakir and Malik (2013);

Phiromswad (2015)]. VAR approach is an econometric method mainly used for

economic analysis. In the line with it, TVP-VAR is a new approach of this area for

studying economic issues. It was proposed by Primiceri (2005) who employed in

t for studying the systematic and non-systematic Monetary Policy of the United

state.

This study reassures the argument of the Primiceri (2005) that the state of

the economy tends to vary over time so is true for the transmission and contagion

process. By permitting parameters to change over an interval, Monetary Policy

shocks may closely be observed. This can be done using TVP-VAR models where

it is assumed that parameters for the low first-order random walk process that

allow them a long-lasting and short-lived shift in parameters and the economic

53

structure can be studied in a flexible and vigorous manner.

Motivated by the influential work of the Primiceri (2005), the objective of

this paper is to present an empirical proof on the Monetary Policy shock in a time-

varying method in emerging markets. Moreover, this study is also having a base

upon the study of Sims (1992) in that he asserts that economists and researchers

have not clear clue about the size and extent of the effects of the Monetary Policy

on aggregate activity. This view is still true in the case of many countries.

Economists have agreed on this part that monetary authorities are capable

enough to control short-term interest rate hence can have an influence on

aggregate activity. There is formal statistical and theoretical evidence on this view

(e.g. (Nakajima, 2011); and many others.) in this study this hypothesis is being

tested at two levels; firstly as contagion arising from United State from Monetary

Policy; secondly transmission at country level from Monetary Policy. Both are

being tested on emerging markets.

Evidence available in this area is majorly in the case of advanced countries

[Jannsen et.al (2015); Marfatia (2015)]. This study is an attempt to fill the gap in

the case of emerging markets. This study addresses the following questions:

1. How enormous is the influence of US Monetary Policy shock on the

aggregate economy of the emerging markets?

2. How big is the effect of the Monetary Policy shock at country level on the

aggregate economy in case of emerging markets?

For studying these questions, I employed TVP-VAR model, proposed by

Nakajima (2011). This model will create an impulse response of the economy

54

arising from the Monetary Policy Shocks. This is being studied by giving

contractionary Monetary Policy Shocks. It is being found that Monetary Policy

shocks at both levels are being transmitted to the aggregate economy both at

national and international level. Extent may vary from country to country but its

true for all the emerging countries.

The rest of the chapter is structured as follows.

Section 2 deals with identification of monetary shock.

Section 3 deals with the results section.

Section 4 with conclusion.

2. Identification of Monetary Policy (MP) Shock

In literature, there is no consensus on the identification problem of

exogenous Monetary Policy shock from endogenous components of the Monetary

Policy. In this study, the identification strategy proposed by Bernanke and Mihov

(1998) is employed. In light of this strategy, it is assumed that some good single

measure for Monetary Policy is available. In this scenario, the “true” structure of the

economy can be modeled as follows:

𝑍𝑡 = ∑ 𝐵𝑖𝑍𝑡−𝑖 +𝑘𝑖=0 ∑ 𝐶𝑖𝑝𝑡−𝑖 +𝑘

𝑖=0 𝐴𝑦𝑣𝑡𝑦 (Equation IV-1)

𝑝𝑡 = ∑ 𝐷𝑖𝑍𝑡−1 +𝑘𝑖=0 ∑ 𝑔𝑖𝑝𝑡−𝑖 +𝑘

𝑖=1 𝑣𝑡𝑝 (Equation IV-2)

Where

𝑍𝑡=Vector of non-policy macroeconomic variables

𝑝𝑡=Variable indicating the Monetary Policy stance.

55

𝐵𝑖 ,𝐶𝑖 ,𝑔𝑖, 𝐷𝑖=vector/matrices of coefficients

𝑣𝑡𝑝, 𝑣𝑡

𝑦=Structural shock

𝐴𝑦=General matrix (following Bernanke (1986), in eq. 1 structural shock is

multiplied by general matrix, so that shock may enter in more than one equation).

Monetary policy shock is defined as the unexpected change in the short-

term interest rate of the central banks. The inspiration to use short-term interest

rate as a proxy of Monetary Policy comes from Sims (1992) who have used for the

study of Monetary Policy transmission mechanism in the USA. Apart from him,

many prominent researchers have used short-term interest rate as a proxy of

Monetary Policy namely Nakajima (2011); Primiceri (2005) and Bernanke and

Blinder (1992) among many.

2.1 Theoretical Framework

Majorly it was in the 50s and 60s when standards emerge those emphases

on the role of Monetary Policy in the economic structure. The Keynesian and

Monetarist school of thought in length have discussed role of Monetary Policy. As

a result t, ISLM framework has emerged. In this framework, it is assumed that any

innovation in Monetary Policy does have an impact on the economy. In this study

as it is earlier identified that short-term interest rate will represent Monetary Policy

Shocks. Under monetarists and ISLM, explanation monetary contraction will

create declining output and monetary contraction is deflation. If these responses

were not created, then it would know as puzzle e.g. price puzzle.

56

2.2 Data & Choice of Variable

For studying the Monetary Policy shock variables include the gross domestic

product (GDP) and inflation. The time span for the study is 1995Q1–2012Q2. All

series are downloaded from the website of the International monetary fund (IMF),

FRED — St. Louis Fed, Bank for International Settlements (BIS) accounts and

State Bank of Pakistan. For finding stationary in series Phillips Perron (2001) test

is employed. The data consists of quarterly variables for the US and emerging

countries namely advanced emerging markets and Secondary Emerging as

classified by FTSE (Financial Times Stock Exchange). Detail on variable and

transformation is given in table IV-1.

57

Table IV-1: Variables and Transformation

Country Name

Variable Name Transformation Source

Brazil Money Market Rate Log difference IMF

Gross Domestic Product Natural Logarithms IMF

Consumer Price Index Natural Logarithms IMF

Colombia Discount Rate Log difference IMF

Gross Domestic Product Log difference IMF


Czech Republic Money Market Rate Log difference IMF


Consumer Price Index Log difference IMF

Hungary Discount Rate Log difference IMF



Malaysia Money Market Rate Log difference IMF



Pakistan Money Market Rate Log difference IMF

Gross Domestic Product Natural Logarithms SBP-

Paper

(2013) &

Arby

(2008)


Mexico T-Bill Rate Log difference IMF



Peru Discount Rate Natural Logarithms IMF



58

Philippines Money Market Rate Natural Logarithms IMF



Poland Money Market Rate Log difference IMF



Russian

Federation

Money Market Rate Natural Logarithms IMF



South Africa Central Bank-Policy

Rate

Log difference IMF



Thailand Money Market Rate Log difference IMF



Turkey Discount Rate Log difference IMF




As a proxy of the growth, we have used GDP (gross domestic product, Real,

Seasonally Adjusted-index in units), downloaded from IMF except in case of

Pakistan, for Pakistan data on GDP is taken from a paper by Hanif et. al (2013)

who have quartered national accounts of Pakistan. Consumer Price Index (CPI)

(index in units of the base year 2010) as a proxy of inflation taken from IMF for all

the countries.

59

3. RESULTS

This section covers the results of TVP-VAR model for the emerging

markets. Model is based on three variables and data frequency is quarterly.

Number of lags is four and it is assumed that Σβ is a diagonal matrix. The following

priors are assumed for the i-th diagonals of the covariance matrices:

(Σβ)i−2 ~ Gamma (20, 0.01)

(Σa)i−2 ~ Gamma (4, 0.01)

(Σh)i−2 ~ Gamma (4, 0.01)

For the initial state of the time-varying parameter, rather flat priors are set;

μβ0=μa0 = μh0=0, and Σβ0=Σa0 = Σh0=10*1. For computing the posterior

estimates, M=10,000 are sampled from where 1,000 are discarded. The results

in table and figure show that the MCMC algorithm produces posterior draws

efficiently (for details refer appendix IV-A).

Table and Figure report the estimation results for the selected parameters

of the TVP-VAR model for the variable set of the model for each country. The

results in table and figure show that the MCMC algorithm produces posterior

draws efficiently. Part one indicates the results of sample autocorrelations,

sample paths in part two, and posterior densities in part three.

This is done at two levels:

1. At the domestic level

2. At the international level

60

3.1 Simultaneous Relation

One of the characteristics of TVP-VAR model is a time-varying

simultaneous relation. Simultaneous relation of all the variables stays constant

over time (for details refer appendix IV-B).

This is also divided into two parts

3. At the domestic level

4. At the international level

3.2 Stochastic Volatility

The stochastic volatility of inflation and output exhibit a stable trend in all

variables. (For details refer appendix IV-C).

3.3 Impulse Responses

The impulse response is used to observe the dynamics of the model. for

the TVP-VAR model, the responses are computed at all points in time using the

estimated time-varying parameters. The impulse response is generated in the

following way:

Before the crisis at 1998 Q4-represented by the dotted line

During crisis at 2008 Q4-represented by dash line

After crisis 2012 Q2-represented by the solid line

This is done on the following:

Impact of Domestic Monetary Policy upon economic variables

Impact of US. Monetary policy upon economic variables

61

3.3.1 Impulse Response of Domestic Markets

The responses are drawn in a time- series manner by showing the size of

the impulses for before crisis, during the crisis and after the crisis over time.

Impulse response before the crisis is somewhat different but during the crisis and

after crisis response is the same. Impulse response of prices is shown in graph

title 𝜀𝑖 ↑→ 𝑝. Impulse response of the inflation is visible in graph title 𝜀𝑖 ↑→ 𝑥.

62

3.3.1.1 Brazil

Figure 1 shows the impulse responses of the time-varying responses for

the TVP-VAR model. The impulse responses of output to a positive interest rate

shock (𝜀𝑖 ↑→ 𝑥) stay negative and volatile most of the time and impulse response

of inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is also negative and of varying

nature. After the crisis response goes down after initial positive response while

response before the shock initially goes negative followed by a positive response

that stays negative afterward. In summary, the response of both variables is

negative most of the time.

Fig.IV-1: Posterior means of the time-varying impulse response of Brazil

Note: Time-varying responses for Before crisis at 1998 Q4 (Dotted Line),

During Crisis at 2008 Q4 (Dashed Line), and after crisis at 2012 Q1 (Solid Line)

horizons for the TVP-VAR model where impulse variable is monetary policy (εi)

and output (x) and inflation (p) are response variables.

63

3.3.1.2 Colombia

Figure 2 is showing the impulse response of the Colombian economy. The

impulse responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) is followed

by a negative in the start that shows positive followed by the stable response of

zero boundaries. Before the crisis, response shows a heap sort of response in the

medium term that soon dies off. The response of price (𝜀𝑖 ↑→ 𝑝) is volatile in

nature and negative most of the time. Before the crisis response shows a positive

response but of volatile.

Fig.IV-2: Posterior means of the time-varying impulse response of Colombia





64

3.3.1.3 Czech Republic

Figure 3 is showing the impulse response of Czech-Republican economy.

The impulse responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) shows

the highest volatility but the response is positive most of the time. The response

of price (𝜀𝑖 ↑→ 𝑝) is also positive but less volatile as compared to output. In

summary, the response of both variables is positive and volatile.

Fig.IV-3: Posterior means of the TVP impulse response of Czech Republic





65

3.3.1.4 Hungary

Figure 4 is showing the impulse response of the Hungarian economy. The

impulse responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) stay

negative and volatile most of the time and impulse response of inflation to positive

interest rate shock (𝜀𝑖 ↑→ 𝑝) is positive and less volatile. The response of

aftershock is of a stable nature.

Fig.IV-4: Posterior means of the time-varying impulse response of Hungary





66

3.3.1.5 Malaysia

Figure 5 is showing the impulse response of the Malaysian economy. The



interest rate shock (𝜀𝑖 ↑→ 𝑝) is mixed and of varying nature.

Fig.IV-5: Posterior means of the time-varying impulse response of Malaysia





67

3.3.1.6 Mexico

Figure 6 is showing impulse response of Mexican economy. The impulse

responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) stay negative and

volatile most of the time. The response of before shock is positive compared to

other shocks. Same behavior in case of price (𝜀𝑖 ↑→ 𝑝) is observed, volatile and

negative and positive response of before of the crisis.

Fig.IV-6: Posterior means of the time-varying impulse response of Mexico





68

3.3.1.7 Pakistan

Figure 7 is showing impulse response of Pakistani economy. The impulse

responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) stay positive and

volatile most of the time and impulse response of inflation to positive interest rate

shock (𝜀𝑖 ↑→ 𝑝) is also positive and of varying nature; this response is less volatile

compared to price.

Fig.IV-7: Posterior means of the time-varying impulse response of Pakistan





69

3.3.1.8 Peru

Figure 8 is showing the impulse response of Peru economy. The impulse

responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) is mixed and volatile

most of the time and impulse response of inflation to positive interest rate shock

(𝜀𝑖 ↑→ 𝑝) is volatile in the short run followed by a stable response; shock in during

crisis remains positive all the time while before and after crisis response goes

towards stability after initial negative response.

Fig.IV-8: Posterior means of the time-varying impulse response of Peru





70

3.3.1.9 Philippines

Figure 9 is showing the impulse response of the Philippines economy. The



interest rate shock (𝜀𝑖 ↑→ 𝑝) is negative in short run while positive in long run and

of stable nature. Before crisis response after initial positive response turns in a

negative response and is of a stable nature.

Fig.IV-9: Posterior means of the time-varying impulse response of

Philippines





71

3.3.1.10 Poland

Figure 10 is showing the impulse response of Poland economy. The

impulse responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) is mixed

and volatile most of the time and impulse response of inflation to positive interest

rate shock (𝜀𝑖 ↑→ 𝑝) is also positive and of varying nature.

Fig.IV-10: Posterior means of the time-varying impulse response of Poland





72

3.3.1.11 Russian Federation

Figure 11 is showing the impulse response of the Russian economy. The



interest rate shock (𝜀𝑖 ↑→ 𝑝) is also negative in short run followed by positive and

this response is of varying nature.

Fig.IV-11: Posterior means of the time-varying impulse response of Russian

Federation





73

3.3.1.12 Turkey

Figure 12 is showing the impulse response of Turkey economy. The



interest rate shock (𝜀𝑖 ↑→ 𝑝) is mixed and of varying nature.

Fig.IV-12: Posterior means of the time-varying impulse response of Turkey





74

3.3.2 Impulse Response of International Contagion

3.3.2.1 US to Brazil

Figure 13 is showing the impulse response of the Brazilian economy to US

Monetary Policy. The impulse responses of output to a positive interest rate shock

(𝜀𝑖 ↑→ 𝑥) is positive and volatile most of the time; the response of before crisis is

more volatile than other responses and impulse response of inflation to positive

interest rate shock (𝜀𝑖 ↑→ 𝑝) is positive and of stable nature. Before the crisis

response is initially positive followed by a negative response after an initial

positive response that turns into a positive response that’s stable in nature. During

and after response initially negative than remain positive afterward.

Fig.IV-13: Posterior means of time-varying impulse response from the US to

Brazil





75

3.3.2.2 US to Colombia

Figure 14 is showing the impulse response of the Colombian economy to

US Monetary Policy. The impulse responses of output to a positive interest rate

shock (𝜀𝑖 ↑→ 𝑥) is positive and near zero; the response of before response is more

volatile compared to other responses. The impulse response of inflation to

positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is positive but of varying nature and same

like output, response of price before crisis is more volatile.

Fig.IV-14: Posterior means of time-varying impulse response US to Colombia





76

3.3.2.3 US to Czech Republic

Figure 15 is showing the impulse response of the Czech Republican

economy to US Monetary Policy. The impulse responses of output to a positive

interest rate shock (𝜀𝑖 ↑→ 𝑥) is positive and volatile most of the time and impulse

response of inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is also positive and

of varying nature.

Fig.IV-15: Posterior means of time-varying impulse response from the US to the

Czech Republic





77

3.3.2.4 US to Hungary

Figure 16 is showing the impulse response of the Hungarian economy to


shock (𝜀𝑖 ↑→ 𝑥) stay positive and volatile most of the time and impulse response

of inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is also positive and of varying

nature.


Hungary





78

3.3.2.5 US to Malaysia

Figure 17 is showing the impulse response of the Malaysian economy to



of inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is also positive but of stable

nature; the response of before crisis is somewhat volatile.


Malaysia





79

3.3.2.6 US to Mexico

Figure 18 is showing the impulse response of the Mexican economy to the



of inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is negative and of varying

nature but less as compared to output.


Mexico





80

3.3.2.7 US to Pakistan

Figure 19 is showing the impulse response of Pakistani economy to US


(𝜀𝑖 ↑→ 𝑥) stay positive and volatile most of the time and impulse response of

inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is mixed and of varying nature.

Fig.IV-19: Posterior means of time-varying impulse response from the US

to Pakistan





81

3.3.2.8 US to Peru

Figure 20 is showing the impulse response of Peru economy to US Monetary

Policy. The impulse responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) stay

negative and volatile most of the time and impulse response of inflation to positive interest

rate shock (𝜀𝑖 ↑→ 𝑝) is also negative but of stable nature; during crisis response shows

a positive response followed by a stable negative response.


to Peru





82

3.3.2.9 US to Philippines

Figure 21 is showing the impulse response of Philippines economy to US


(𝜀𝑖 ↑→ 𝑥) is positive and volatile most of the time and impulse response of inflation

to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is positive and of varying nature; the

response of before crisis is more volatile compared to other responses.


the Philippines





83

3.3.2.10 US to Poland

Figure 22 is showing the impulse response of Poland economy to US


(𝜀𝑖 ↑→ 𝑥) is positive and volatile most of the time; before crisis response shows

highest volatility and impulse response of inflation to positive interest rate shock

(𝜀𝑖 ↑→ 𝑝) is positive but of stable nature.


to Poland





84

3.3.2.11 US to Russian Federation

Figure 23 is showing the impulse response of the Russian economy to the


shock (𝜀𝑖 ↑→ 𝑥) is positive and volatile most of the time and impulse response of

inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is negative initially that is followed

by near zero response of stable nature.


to Russian Federation





85

3.3.2.12 US to Turkey

Figure 24 is showing the impulse response of the Turk economy to US


(𝜀𝑖 ↑→ 𝑥) stay positive and volatile most of the time and impulse response of

inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is also positive but of stable

nature. The response is near zero.


to Turkey





86

4. Conclusion

The key driver of this study is to identify the impact of Monetary Policy upon

major economic variables of emerging markets. For this purpose, the predictive

power of Monetary Policy is tested upon major macroeconomic variables namely

Growth and prices (inflation). Impulse response affirms the predictive power of

the MP of macroeconomic movements in emerging markets.

Under the monetarist/ISLM, framework interest rate surprises represent

Monetary Policy shock. This states that monetary contraction (positive Monetary

Policy shock) creates declining output and increasing prices. From the results it

can be seen that diversity of the result is visible under the ISLM framework.

Results has been summarized in table IV-2.

87

Table IV-2: Results of Transmission of Monetary Policy

National Monetary Policy International Monetary Policy

Country Output Price Puzzle

ISLM Output Price Puzzle

ISLM

Advanced Emerging Countries

Brazil Yes Yes Partly

Applicable No No

Partly Applicable

Czech Republic

No No Partly

Applicable No No

Partly Applicable

Hungary Yes No Fully

Applicable No No

Partly Applicable

Malaysia Yes No Fully

Applicable No No

Partly Applicable

Mexico Yes Yes Partly Applicable

No Yes Not Applicable

Poland No No Partly Applicable

No No Partly Applicable

Turkey Yes No Fully Applicable

No Yes Not Applicable

Secondary Emerging Countries

Colombia No Yes Partly Applicable


Pakistan No No Partly Applicable


Peru No No Partly Applicable

Yes Yes Partly Applicable

Philippines Yes Yes Partly Applicable


Russia Yes Yes Partly Applicable

No Yes Partly Applicable


This chapter discusses monetary policy shock in length. This chapter also

present results at national monetary policy and contagion arising from US

monetary policy towards emerging economies.

88

CHAPTER V

UNITED STATES’ FINANCIAL CONDITIONS AND

MACRO-ECONOMY OF EMERGING MARKETS

1. Introduction

“when the US sneezes, the rest of the world catches a cold” Barachian

(2015).

This chapter is in continuation of last chapter. This chapter will be

discussing second objective in length.

There is mounting evidence that economies around the globe are more

integrated especially in the last few years. Financial development and

liberalization is a major reason for this integration. Due to financial integration, we

may observe co-movement among variables at international level; extent may

vary but its true for many countries especially at the macroeconomic level. This

co-movement is also true in the time of turmoil such as the collapse of housing

price resulted not the only collapse of investment domestically but also a strong

wave of crisis not in nearby countries but also far away countries were also

observed. One possible reason that seems at the front is that transmission of

shock results in contagion effects (Kazi et.al 2013).

It’s a well-established believe of many researchers and economists that

key reason behind this contagion is the active role of U.S. policies e.g. Ehramann

and Fratzscher (2009); Carmassi et.al (2009). One more strong reflection of crisis

is that in the presence of financial innovations, its difficult to capture financial

89

horizon with the help of a small number of variables as traditionally being

applied. Policymakers, researchers, regulators and other market watchers have

all acknowledged the significance of the interconnectivity of traditional and newly

developed financial markets and its link with the economy. Thus it can be said

that monitoring of financial stability requires an understanding of the evolving

paradigm of the financial world and its link with the economy (Brave and Butters

(2010). After the initial hit of the crisis in advanced countries, financial turmoil hit

to emerging economies; where stock market, exchange rate and sovereign debt

all came under pressure [(Balakrishnan et.al, 2009); (Calvo et.al, 2008) and many

others].

The question of whether financial conditions predict economic activity has

a long history in economics. The conclusion by Stock and Watson (2003) “some

asset prices predict inflation or output growth in some countries in some periods”

epitomizes the common view among econometricians that financial indicators are

too noisy and erratic to be exploited for macroeconomic forecasting. Yet

macroeconomists have got to the conclusion that financial shocks are a vital

source of business cycles [(Balcilara et.al 2016); (Alessandria and Mumtaz 2017);

(Opschoor 2014); (Jermann and Quadrini 2012)]. This suggests that financial

information should be utilized in the right conditions for the right prediction of

macroeconomic fluctuations. In order to work on this objective, most of the

empirical evidence is based upon vector auto-regressions (VAR) models (Potts

and Yerger, (2010); Owyang and Wall (2009); Boivin and Giannoni (2006) among

many). It is being observed that little attention is a pain on the question to what

extent financial conditions of advanced countries have an impact upon macro-

90

economy of the emerging economies.

The identified gap in the literature is filled by studying the transmission of

U.S. financial conditions in emerging markets. Having strong implications for the

emerging markets, this thesis is an effort to offer an empirical assessment of the

outcomes of the financial conditions of the United States upon macro-economy of

the emerging economies. To work on this objective, the paper investigates

whether brave and Butters (2011)’s FCI has an influence on the exchange rate,

interest rates, and stock markets.

Barsky and Sims (2012) use Structural Vector Auto Regression (SVAR)

method to study the response to confidence innovations of news, animal spirit and

pure noise in the New Keynesian framework. In the same way, the SVAR model

is used to examine the impact of United States’ financial conditions upon macro-

economy of emerging economies. Barsky and Sims (2012) model use confidence

innovations’ shock to other variables. Whereas, in this study, FCI is used and

response of macroeconomic variables is studies. The core drive of employing U.S.

financial condition jolts is to recognize the predictive power of financial conditions

of major economic variables of emerging markets. For this purpose, the predictive

power of financial conditions is tested upon major macroeconomic variables (short

term and long term interest rate, exchange rate and stock markets). Impulse

response affirms the predictive power of the financial conditions of

macroeconomic movements in emerging markets. This finding echoes the

findings of Brave and Butters (2011) who established that it is likely to use a

financial condition index to increase upon predictions of events of economic

activity over short and medium forecast horizons. Similar findings can be seen in

91

the study by Koop and Korobilis (2014), Hatzius et.al (2010), Debuque and

Bautista (2013) and many others.

This study offers following contribution to the literature. First, most of the

studies on the transmission of financial conditions are available in advanced

countries such as Hatzius (2010), Brave and Butter (2011) and many others. This

study covers emerging countries for studying the possible contagion impact

arising from advanced countries. Secondly, previous studies have majorly

focused upon aggregate activity for studying the macroeconomic impact, in this

study other major macroeconomic variables are being used for the assessment

of contagion impact.

This section is followed by segment 3 identification, segment 3 results and

finally by segment 4 discussion and conclusion.

2. Identification

2.1 Financial Conditions and Forecasts of Macro-Economy

One of the objectives of this thesis is to discover the response of

macroeconomic variables arising from a surprise move in united states’ financial

conditions. It is established in the literature that to work on such objective it is

required to run a VAR model so in the same way a VAR model is developed

comprising FCI and macroeconomic variables and for considering the partial

derivatives of macroeconomic conditions at different horizons with respect to

innovations in Financial conditions. This can be taken like a generalized impulse

response function in Pesaran and Shin (1998). Here orthogonalize is the only

shock means FCI not ordering of macroeconomic variables. It is orthogonalized

92

first in the model.

Furthermore, impulse response analysis is done. It traces the effects of

structural shocks on endogenous variables. By impulse response function we may

see the mechanism by which shock spread over time. For finding impulse

response we have employed Kilian (1998) bootstrap after bootstrap method. For

this method VAR is estimated using OLS and 1000 draws for impulse response

are generated for bootstrap, then the bias-corrected estimator is calculated that

later on is employed for generating 2000 new draws using bootstrap.

2.2 Selection of Variables

Transmission channels are not working separately but their mutual effect

gets amplified, moreover, this depends upon the state of the financial system and

economy (Klacso 2013). Mapping link between the economy and the financial

system has great significance and urgency, especially after the crisis. Majority of

econometric models for forecasting majorly have used interest rate. This strategy

may work in the normal time period but in crisis time period, with this single

variable, we are unable to seize all the connections between financial structure

and economy. For this reason, many authors have suggested using an index

indicating a financial condition for the study of the transmission mechanism.

The aim of the study is to study the impulse response of emerging markets

of the financial market of the United States. In order to work on it, Vector

autoregressive (VAR) systems are estimated for data from Brazil, Chile, Czech

Republic, Greece, Hungary, India, Malaysia, Pakistan, Mexico, Poland, Russian

Federation, and South Africa. Data is taken from the International Financial

Statistics (IFS) database and from other official sources. detailed descriptions

93

about the data source, time frame and transformation is given in the following

table.

Table V-1: Variable and Transformation

Country Name and

Time Span

Variable Name

indicator Transformation Source

US Chicago Fed National Financial Conditions Index, Index, Weekly, Not Seasonally Adjusted

index (weekly)

Percentage Change

FRED

Brazil 1993Q1-2016Q2

Money Market Rate

Percent per Annum

Level IMF

Interest Rates, Savings Rate

Percent per annum

Level IMF

The total share price for All shares

Logarithm difference

FRED

Real Effective Exchange Rate, based on Consumer Price Index

index Logarithm difference

IMF

Chile 2004Q3-2016Q1

Discount Rate

Percent per Annum


IMF

Savings Rate

Percent per Annum


IMF

The total share price


FRED

94

for All shares



IMF

Czech republic 2000Q2-2016Q1

Money Market Rate

Percent per Annum


IMF

Government Bonds

Percent per Annum


IMF



FRED



IMF

Greece 1997Q3-2016Q4

T-bill rate Percent per Annum

Level IMF

Government Bonds

Percent per Annum


IMF



FRED



IMF

Hungary 2001Q1-2016Q1

Discount Rate

Percent per Annum

Level IMF

Government Bonds

Percent per Annum


IMF

95



FRED



IMF

India 2005Q1-2015Q3

Discount rate

Percent per Annum


IMF

Government Bonds

Percent per Annum


IMF



FRED

BIS effective exchange rate-Real (CPI-based), Broad Indices

Monthly averages; 2010=100


BIS

Malaysia 2000Q2-2016Q3

Money Market Rate

Percent per Annum


IMF

Government Bonds

Percent per Annum

Level IMF

KLCI Bursa Malaysia


Finance.Yahoo



IMF

Pakistan 1997Q3-2016Q1

Money Market Rate

Percent per Annum


IMF

96

Government Bonds

Percent per Annum


IMF

KSE 100 index


SBP



IMF

Mexico 1995Q1-2016Q2

T-Bill Rate Percent per Annum


IMF

Government Bonds

Percent per Annum


IMF



FRED



IMF

Poland 2001Q1-2016Q2

Money Market Rate

Percent per Annum

Level IMF

Government Bonds

Percent per Annum

Level IMF



FRED



IMF

Russia 1999Q1-2016Q2

Money Market Rate

Percent per Annum

Level IMF

97

Government Bonds

Percent per Annum

Level FRED



FRED



IMF

South-Africa 1990Q1-2016Q2

Central Bank-Policy Rate

Percent per Annum

Level IMF

Government Bonds

Percent per Annum


IMF



FRED



IMF


The estimation is done on quarterly data with four lags using the rule of

thumb. For unit root analysis Ng and Perron (2001) are employed. All datasets

are standardized.

Proxy for the financial condition is FCI from the USA developed by Brave

and Butters (2012), it comprises a weighted average of 105 indicators of financial

activity those wide-ranging exposure of the financial system of Money markets,

Debt and Equity markets, Traditional and “Shadow” banking system and useful in

monitoring financial stability and forecasting.

98

For each country, proxy for short interest rate R is money market rate for

Brazil, Czech Republic, Malaysia, Pakistan, Poland Russian Federation and

discount rate for Chile, Hungary, India, T-bill rate for Greece, Mexico and central

bank policy rate for South Africa, proxy for long-term interest rate is saving rate

for the Brazil and a government bond rate (GBR) for the rest of the countries, a

real effective exchange rate (index) REER as proxy of exchange rate, and a stock

prices S proxy of stock market. for Pakistan and Greece government bond was

missing at some points. It was handled using interpolation. For this econometric

method is employed named cubic spline. High-frequency variables are converted

to low-frequency variables using period-end values.

99

3. Results

Here, it is tried to lengthen the time span of the data to assess how the

U.S. financial conditions are transmitted to emerging markets. For this reason, in

this study impact of FCI to emerging economies upon following channels is

studied:

Interest rate channel (short term and long term)

Exchange rate channel

Stock market channel

In the current study, it is being claimed that financial conditions have

predictive power of macroeconomic variables. In this session, results of financial

shocks on economies are presented. The shaded zones signify one standard

error bias-corrected bootstrap confidence bands of Kilian (1998).

3.1 Brazil

The impulse response functions in case of Brazil are presented in figure 1.

Time span is 1993q1-2016q2. This allows seeing the financial condition index’s

(FCI) impact mechanism clarifying by demonstrating the response of the system

to a shock in the measure of financial condition index. An innovation to FCI has

implications for all the variables in short term. A one standard deviation innovation

in FCI in short-term interest rate is followed by strong impact volatile and rapidly

building but a temporary response. In terms of long-term interest rate response is

also volatile but strong, highly positive in the start and rapidly building like short-

term interest rate but temporary in nature. Exchange rate overshoot in the start,

the initial negative response is being followed by a positive response that ends

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shortly. The response of the stock market is also low and volatile and temporary

in nature. The response of all the variable is temporary in nature.

Fig V-1: Transmission to Brazil

Note: These are Impulse Response Functions from a five-variable VAR

with FCI, short term interest rate (IR), long term interest rate (GBR), exchange

rate (ER) and stock market (S). FCI is ordered first. The shaded areas are one-

standard-error confidence bands.

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3.2 Chile

The time span for the study is 2004 Q3 - 2016 Q1. The reason behind the

shorter span is the unavailability of data. The impulse response functions in Chile

is presented in figure 2. This allows seeing the financial condition index’s impact

mechanism clarifying by illustrating the response of the system to a shock in the

measure of financial condition index. An innovation to FCI has implications for all

the variables in long run. A one standard deviation innovation in FCI in short-term

interest rate is followed by strong and volatile and rapidly building, the response

is of a permanent nature and positive in long run. The response of long-term

interest rate is negative in the start being followed by positive stable and

permanent response. The response of exchange rate is like Dornbusch

overshoot; it overshoots in the start but stabilize in the long run. Stock markets

respond volatility in the start but the positive and stable response in long run. The

response of all the variables is of a permanent nature. The response of all the

variable is temporary in nature.

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Fig V-2: Transmission to Chile





103

3.3 Czech Republic

The impulse response functions in case of Czech Republic is presented in

figure 3. The time span for the study is 2000 q2-2016q1. This allows seeing the

financial condition index transmission mechanism clarifying by illustrating the

response of the system to a shock in the measure of financial condition index. An

innovation to FCI has implications for the variables in short term. The response of

interest rate is volatile and negative in the short run but positive and stable in long

run. Long-term interest rate responds positively in short-term but this response is

of a temporary nature. Exchange rate behaves negatively in short-term but

stabilizes in the long run while the stock market behaves positively in the short

run and stabilize afterward. The response of variables is of a temporary nature.

The response of all the variable is temporary in nature.

Fig V-3: Transmission to the Czech Republic

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standard-error confidence bands. Chile

3.4 Greece

The impulse response functions in the case of Greece is presented in figure

4. The time span is 1993 q3-2016q4. This allows seeing the financial condition

index transmission mechanism clarifying by illustrating the response of the system

to a shock in the measure of financial condition index. A one standard deviation

innovation to FCI has strong implications for the short-term interest rate in short-

term, after the initial hike, it is being followed by a positive and stable response in

the long run. The response of long-term interest rate, exchange rate, and the stock

market is of a temporary nature, volatile in the start but stable in the long run. The

response of exchange rate is overshooting; Response of all the variable is

temporary in nature.

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Fig V-4: Transmission to Greece





106

3.5 Hungary

The impulse response functions in the case of Hungary is presented in

figure 5. The time span is 2001q1-2016q1. This allows seeing the financial

condition index transmission mechanism clarifying by illustrating the response of

the system to a shock in the measure of financial condition index. A one standard

deviation innovation to FCI has implications for the variables in the long term. The

response of short-term interest rate is strongly positive in short-term followed by

a negative response that stabilizes in the long run, the response is of a permanent

nature. The response of long-term interest rate and the stock market is volatile

throughout the time span; a positive high response being followed by negative

making a V-shaped response in short-term that keeps a volatile movement in the

long run. Exchange rate responds negatively initially followed by volatile but long-

run relation. The response of all the variable is temporary in nature.

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Fig V-5: Transmission to Hungary





108

3.6 India

The impulse response functions in the case of India is presented in figure

6. time span 2005 q1-2015q3. This allows seeing the financial condition index

transmission mechanism clarifying by illustrating the response of the system to a

shock in the measure of financial condition index. An innovation to FCI has

implications for the variables in the long run. The response of short-term interest

rate is negative initially being followed by a positive response that’s permanent

nature. Response if the long-term interest rate is volatile in the short term that

tends to stabilize in the long run. This is true in the case of the exchange rate and

a stock market that is volatile initially but strong and positive in the long run. The

response of all the variable is temporary in nature.

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Fig V-6: Transmission to India





110

3.7 Malaysia

The impulse response functions in the case of Malaysia is presented in

figure 7. Time span 2000q2-2016q3. This allows seeing the financial condition



implications for the variables in short term. One standard deviation of innovation

in FCI is being followed by a negative response in short-term but positive in the

long run that faded away with time, response of long-term interest rate is volatile

in short-term that also faded away in the long run, exchange rate is highly volatile

in the short run that tends to fade away in long run and response of stock market

a positive initial response is being followed by stable response that tends to fade

away. The response of all the variable is temporary in nature.

111

Fig V-7: Transmission to Malaysia





112

3.8 Mexico

The impulse response functions in the case of Mexico is presented in figure

8. 1995 q1-2016q2. This allows seeing the financial condition index transmission


measure of financial condition index. An innovation to FCI has implications for the

variables in short term. The response of short-term interest rate is negative

followed by a positive, the long-term interest rate is volatile, the exchange rate is

positive than negative and the stock market is negative followed by positive. The

response is of short-term nature of all the variables. The response of all the

variable is temporary in nature.

113

Fig V-8: Transmission to Mexico





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3.9 Pakistan

The impulse response functions in the case of Pakistan is presented in

figure 9 with the time span of 1997q3-2016q1. This allows seeing the financial


the system to a shock in the measure of financial condition index. An innovation

to FCI has implications for the variables in short term. The one standard deviation

shock arising in FCI is followed by the volatile response of short-term interest rate

that faded away with time. The response of long-term interest rate dies after initial

positive and negative wave. Exchange rate dies also the first negative than

positive response. The response of stock market is strong in the start but die off

with the time. The response of all the variable is temporary in nature.

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Fig V-9: Transmission to Pakistan





116

3.10 Poland

The impulse response functions in case of Poland is presented in figure

10. 2001q1-2016q2. This allows seeing the financial condition index transmission


measure of financial condition index. An innovation to FCI has implications for the

variables in long term. The small impact effects are followed by quickly building

response short term and long term interest rate and it’s of permanent nature. The

response of exchange rate volatility and the stock market is volatile that tend to

stabilize with time. The response of all the variable is temporary in nature.

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Fig V-10: Transmission to Poland





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3.11 Russian Federation

The impulse response functions in case of Russian Federation is

presented in figure 11. Russia 1999q1-2016q2. This allows seeing the financial


the system to a shock in the measure of financial condition index. An innovation

to FCI has implications for the variables in long run. The response of interest rate

is strong, after an initial positive response it tends to stabilize and it’s of permanent

nature. The response of the exchange rate and the stock market is volatile but it's

of a temporary nature. The response of all the variable is temporary in nature.

119

Fig V-11: Transmission to Russian Federation





120

3.12 South Africa

The impulse response functions in the case of South Africa is presented in

figure 12. Time span 1990q1-2016q2. This allows seeing the financial condition



implications for the variables is mixed. One standard deviation shock of FCI is

being followed by the strong response of short-term interest rate that is permanent

in nature. The response of long-term interest rate, exchange rate, and stock

market dies off after the initial response. Exchange rate overshoots. The response

of all the variable is temporary in nature.

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Fig V-12: Transmission to South Africa





122

4. Conclusion

The main purpose of using U.S. financial condition shocks is to identify the

predictive power of major economic variables of emerging markets. For this

purpose, the predictive power of financial conditions is tested upon major

macroeconomic variables namely short term and long term interest rate,

exchange rate and stock markets. Impulse response affirms the predictive power

of the financial conditions of macroeconomic movements of the US in emerging

markets. Results have given in the following table.

Table V-2: Results

Short Term

Interest Rate

Long Term

Exchange Rate

Stock Die Off With Time

Country

Advanced Emerging Countries

Brazil Temporary and Weak

Temporary and Weak

Temporary and Weak

Temporary and Weak

Yes

Czech Republic

Weak but Permanent

Weak but Permanent

Weak but Permanent

Weak but Permanent

Yes

Hungary Strong and Permanent

Moderate and Permanent



No

Malaysia Moderate and Permanent




Yes

Mexico Weak but Permanent

Weak but Permanent

Weak but Permanent

Weak but Permanent

Yes

Poland Strong and Permanent

Strong and Permanent



No

South Africa

Yes Except In Case Of Short Term

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Interest Rate

Secondary Emerging Countries

Chile Strong and Permanent




No

Greece Strong and Permanent




Yes

India Moderate and Permanent




No

Pakistan Temporary and Weak

Temporary and Weak

Temporary and Weak

Temporary and Weak

Yes

Russia Moderate and Permanent


Weak but Permanent

Weak but Permanent

No


This chapter discusses transmission of the US financial shock to the

emerging countries. This chapter also discuss results of the second objective of

the study.

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CHAPTER VI

CONSTRUCTION OF FINANCIAL CONDITION

INDEX FOR PAKISTAN

1. Introduction

This chapter will be discussing last objective. An index is created for the

special case on the emerging countries named Pakistan. The construction and

prediction of the index is also presented.

One of the learning lessons of recent mortgage crisis commonly known as

the global financial crisis is that broad financial conditions due to innovations in

financial landscape are difficult to capture by using a small number of variables

that cover only a few traditional financial markets. In the light of different episodes

of crises policymakers, regulators and financial market stake-holders have

affirmed the link between traditional and newly developed markets and the link

between financial and nonfinancial market. Closer watch on financial stability is

essential for understanding such links. Moreover, many of the econometric

system, that can be used to forecast or simulate a shock’s impact, do have the

very weak financial background. In many situations, they are based on single

variables; interest rate in many cases. This practice creates problems especially

in a time of crisis time periods. For this reason, index on financial conditions are

the best for this purpose (Brave and Butters 2010), (Hatzius, et al. 2010); Koop

and Korobilis (2014).

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Financial Condition Index (FCI) may serve for many purposes. For

example, it can be used to find out early signs of bad financial conditions Gomez

et.al, (2011); Muraru (2015) or could serve as a forecaster of the economy

(Nombulelo et.al, (2012); Bautista (2013); Erdem and Tsatsaronis (2013). It is now

in practice of many financial institutes (IMF, Goldman Sachs, and Bloomberg) and

authorities (federal reserve bank of Chicago and many other banks) to develop

FCI for the market watch. Estimation of FCI ranges from simple weighted average

method to developed sophisticated methodology. Keeping in mind the existing

practice of developing the financial index, the chief empirical input in the literature

of this study is to develop an FCI for an emerging market using the most recent

approach.

Development and usage of FCI deal with the variable choice for FCI and

its link with macro-economy. This need to think about changing state. For this

reason, a method of index development by Koop and Korobilis (2014) is utilized.

Indexes are created using a wide range of macroeconomic and financial variables

over a long horizon for Pakistan. They developed a method using extensions of

Factor models and presented multiple forms of the index. The rationale of using

this method is that it is able to capture the time-varying nature of the variables so

can give a better picture of financial conditions.

Results show that constructed FCIs do have predictive power for

macroeconomic variables. This index correctly forecasts major macroeconomic

variables and indicates that they both moves in the same direction and correctly

forecast.

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FCI constructed here may serve as a decision-making tool in place of

monetary policy stances those are employed as a policy tool. This FCI can also

be utilized for identifying the historical development of any phenomena and the

current state of the system and may utilize for forecasting other macroeconomic

sectors.

Following segment deals with the econometric method of FCI

development, followed by data and model sets, estimation of FCI and forecasting

of macro-economy and in the last but not the least conclusion and discussion.

2. Data

In this study, for the index formation twenty variables covering major

financial and economic variables. All the variables are stationary. Data is being

stationary using Phillips Perron. Table 5.1 provides detail on the stationary, data

source and other detail related to data. The time span for the study is 1969 Q1 till

2016 Q1. All models use four lag. Forecasting is done of macroeconomic

variables namely Consumer price index, gross domestic product, real effective

exchange rate, discount rate and KSE 100 index. Some variables do not start

from 1969 and some have missing values; this is being dealt with Kalman filter.

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Table VI-1: Variables and Transformation

S# Name Transformation Source

1 Equities Logarithm difference IMF

2 Gold Logarithm difference IMF

3 Import Volume Logarithm difference IMF

4 Export Volume Logarithm difference IMF

5 Goods, Deflator/unit value of import

Logarithm difference IMF

6 Goods, Deflator/unit value of export

Logarithm difference IMF

7 Industrial Production Index Logarithm IMF

8 Discount Rate First difference IMF

9 Bond Rate First difference IMF

10 Money market Rate Level IMF

11 Producer Price Index Logarithm difference IMF

12 Total Reserves Logarithm IMF

13 Currency Logarithm difference IMF

14 CPI Logarithm difference IMF

15 T-bill rate First difference IMF

16 KSE 100 index Logarithm difference Finance.Yahoo

17 GDP Natural Logarithms SBP-Paper (2013) & Arby (2008)

18 Real Effective Exchange Rate Logarithm difference IMF


To summarize, the models which yield FCI are the TVP-FAVARs, FA-TVP-

VARs, FAVARs.

Hyperparameters and initial conditions in the paper are being set as set by

Koop and Korobilis (2014). Following them, TVP-FAVAR models and its restricted

versions are being obtained by setting following forgetting/decay factors:

TVP-FAVAR: 1-0.96; 2-0.96; 3-0.99; 4-0.99

Heteroscedastic FAVAR: 1-0.96; 2-0.96; 3-1; 4-1

FA-TVP-VAR:1-0.96; 2-0.96; 3-1; 4-0.99

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3. Construction of Financial Condition Index

FCI is being constructed using all three methods of FAVAR. Factors are

being estimated and in the same order, FCI is being estimated for the time span

of 1969 Q1 till 2016 Q1.

Figure 1 till 3 are representing factors estimated using all the variables. As

it is depicted that estimates are quite similar in all models namely TVP-FAVAR,

and-TVP-VAR.at some points minor difference does exist but these differences

are not very strong.

Fig VI-1: Factors estimation using TVP-FAVAR

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Fig VI-2: Factors estimation using FAVAR

130

Fig VI-3: Factors estimations using FA-TVP-VAR

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3.1 Estimating financial Position using FCI

In order to find out the validity of constructed FCI, its movement is being

checked with historical events and if it is succeeded to illustrate the true shape of

financial history, that index is considered to be strong. To work on it, Figure 4 to

figure 6 shows the construction of the FCI. The estimates from TVP-FAVAR,

FAVAR, FA-TVP-VAR are quite similar. This indicates that all the indices are

showing similar conditions of financial and economic history. Next job is to closely

monitor history, for this, we need to have a look at history and see whether it

covers major events of the history.

3.1.1 1971-77 Era (Post Dhaka Fall period)

Sample time frame starts from the end of a decade of development and

start of bad luck years. In its background, the second five-year plan (1960-65) was

a huge accomplishment due to political stability. It was generally believed that

South Korea acquired many ideas from the second five-year plan and

implemented these to achieve a high degree of success. During the 1960s,

Pakistan achieved food autarky. New variations in wheat and rice were

introduced. The Gross National Product was 8.3%, second highest in Asia after

Japan. The payback periods were the shortest and in many cases only seven

months. Pakistan achieved record growth and as a result of examining the record,

this decade could be said best performing in the history of Pakistan. Pakistan was

considered to be a model capitalist economy in the 1960s. But the 1965 war with

India proved an economic setback for Pakistan. Socio-economic tensions and

social upheavals and industrial strikes coupled with several other factors besieged

the country.

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The decade of development was followed by Dhaka fall due to that

industrial base shrinks. In December 1973 inflation reaches its highest 37.8

percent. Bhutto’s economic program was considered to be a failure by his critics.

In this time rupee was devalued by 120 percent till may 1972. In 1973,

Organization of the Petroleum Exporting Countries(OPEC) price increases played

havoc with Pakistan’s import bill and balance of payments deteriorated.

The period after 1973 saw a serious worldwide recession affecting

Pakistan’s exports.

Recurrent domestic cotton crop failures and floods in 1973, 1974 (along

with pest attacks) and 1976 affected Pakistan’s main exports. The 1970s were a

turbulent period in Pakistan’s economic history. Due to socio-economic

compulsions, industries, financial institutes, agro-based sectors, social

infrastructure projects, shipping services and many other productive sector

industries were nationalized without appropriate cushioned logistics. Payments to

wage earners went up without productivity increase.

The outcome of nationalization measures ended incentives for private

investment. Hundreds of public enterprises were established with political motives

resulting in corruption and inefficiency (Saeed, 2013); (Zaidi, 2010).

3.1.2 1977-88 era (The second military regime)

Martial law was imposed in 1977 and the rulers never addressed the

economic agenda as a priority. General Zia’s time period was more liberal in

economic terms than predecessors. Remittances from the middle east and aid

from abroad helped launch Pakistan’s second economic revolution. 1984

government launched money whitening scheme. 1988 first structural adjustment

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agreement with IMF. The results were that towards the latter part of the 1970s

and a major part of 1980s, Pakistan operated on the famous principle of ‘business

as usual’.

3.1.3 The era of structural Adjustments (1988-2001)

During the 1990s, the country lost on several fronts, especially in economic

growth. The economic performance was dismal. Despite public announcements

of self-reliance, the government’s actions continued to undermine their intentions.

Borrowings increase but export declined. Loan defaulters were not prepared to

return borrowed money. There were about 5,000 sick industrial units. Fixed

income earners received no incentives to protect their wages. The business

community was exasperated with the multiplicity of taxes at different levels.

Indeed, the decade of the 1990s excluding may 28, 1998 when Pakistan became

a nuclear power, didn’t emerge as the economically sound period for the country.

after nuclear test many nations imposed economic sanctions on Pakistan. More

precisely 90’s era is known as return of democracy and era of structural

adjustment. In this time period, Economic liberalization and stabilization were

adopted by the authorities and privatization was encouraged, among these for the

encouragement of the exports tariff rates were lessen. Moreover, during this time

macroeconomic crisis happened, high taxation was in action, trade reforms

resulted in deindustrialization and rupee devalued on a continual basis, inflation

rates were high and privatization was done without proper policy (Saeed 2013).

in such a volatile time period, volatility can also be seen in the index. Its been

changing all the time.1991-PM begun the economic liberalization program. 1991-

KSE 100 indexes were launched.

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3.1.4 2001 to Onwards

2008-government enters IMF stabilization program to ward off the balance

of payment crisis. The stock market hit 15760 points on April 20 but in just four

months KSE plunges 55pc, wiping 136.9 billion off market value. Market touched

the lowest point. In the end of 90’s and early years of 2000’s acceleration in

economic growth is seen, along with this industrial production increased, export

earnings raised, upsurge in investment, and foreign exchange reserved increased

(Zaidi 2005) This can be seen from the graph that during this time index goes up

but again is in volatile condition.in the mid-2000’s economic growth picked up

more, in this time period sound macroeconomic fundamentals were achieved.

(Zaidi 2005). From the graph, we may see fluctuation but towards an upward

trend. In last of 2000’s impact of global crisis started to show its impact upon

Pakistan’s economy and downward trend was observed in major variables. This

is observable from the graph. 2013-China Pakistan Economic Corridor(CPEC)

formalized, IMF approves $6.7 billion loan package to help Pakistan revive the

ailing economy. A recovery phase can be seen after the impact of the crisis that

sustained till 2016, fluctuating and with the upward movement of the index.

Overall, significant periods of crisis in financial history are well captured as are

periods of relative calm.

The volatility of all the models is the same for all figures. This is indicative

of the graph that financial conditions are volatile and changing over time. This is

true for Pakistan. Throughout the history due to political instability, financial

conditions are not sound over time in Pakistan. More precisely, year wise following

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are significant events in the time span for which index is created. This timeline is

truly indicating up and down in the history and at the same time fluctuations can

be seen in the graph of the FCI.

Fig VI-4: FCI estimation using TVP-FAVAR

136

Fig VI-5: FCI estimation using FAVAR

137

Fig VI-6: FCI estimation using FA-TVP-VAR

138

4. Forecasting of Macroeconomic Variables

Preceding session test the index by its ability to forecasts the major

macro-economic variables.

4.1 Inflation

Pakistan has traditionally been a low inflation country. Consumer price

index annual changes on average 2 to 3 percent during the 1950s and 1960s.

during the Bhutto Government in the 1970s, the CPI rose on average by 20

percent every year. The 1980s saw a major deceleration of inflationary pressure

and price increases were restricted to 7% per annum. On the other hand, policy

liberalization period up to the end 1990s observed acceleration. Inflation was 10%

on average during mild liberalization and about 12% per annum during the era of

intensive liberalization. The rate of inflation was halved during 1998-2003. The

Musharraf period (1999-2008) can be divided into inflationary episodes. During

the ending period in 2004, inflation was low but it doubled during 2004-08,

reaching double figures in 2008 (Meenai, 2010).

Figure VI-7 is showing the results of forecasted inflation. Movements in

inflation are almost similar to forecasted and actual. Ups and downs are almost

the same. So, it can be said that index is a true representative of the economy.

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Fig VI-7: Forecasted Inflation

4.2 Exchange Rate

The exchange rate is a policy instrument which can be used to affect

almost all constituents of the balance of payments. A depreciating currency may

or may not stimulate exports, discourage imports, workers’ remittances, jack up

interest rates stimulating external capital inflows and raising the burden of

government debt (Meenai, 2010). The Pakistani rupee has depreciated from Rs.

3.3 to the dollar in August 1947 to Rs. 116 to the dollar in March 2018. (source:

Official Website of Statistical Bureau of Pakistan).

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Table VI-2: Chronical Exchange Rate in Pakistan

Year Exchange Rate Regime PKR per

US $

1955 Fixed Exchange Rate (1947-1982) 4.76

1972 11

1973 9.9

1982 10.1

1982 10.55

1983 Managed Float (1982-1998) 12.7

1984 13.4

1985 15

1986 16

1987 17

1988 17.59

1989 19.2

1990 21.4

1991 22.4

1992 22.4

1993 25.9

1994 30.1

1995 30.85

1996 33.56

1997 39.99

1998 43.195

1999 Two-Tier Exchange Rate System (Multiple Exchange Rate from July 1998 till may 1999)

50.05

2000

Dirty Float: State Bank Pakistan (SBP) defending the Exchange Rate within a Narrow Band from 19 may 1999 till July 2000; from Managed Float to Floating Exchange Rate Regime since 20 July 2000 (Free Float Regime since 2000)

51.77

2001 58.4

2002 61.4

2003 58.4995

2004 57.57

2005 59.657

2006 59.85

2007 61

2008 71.46


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Movements in the exchange rate are almost similar to forecasted and

actual. It can be said that Index has given a realistic view of the exchange rate.

Fig VI-8: Forecasted Real Effective Exchange Rate

4.3 Monetary policy (short-term interest rate)

In mid of the 1990s, SBP shifted monetary policy management method.

Before the 1990s it used to change monetary policy on an ad hoc basis but after

1990s it shifted towards market-oriented monetary management. Despite sincere

efforts from monetary authorities’ monetary policy have failed to meet its objective

in Pakistan (Meenai, 2012). As it is evident from the graph that monetary policy is

kept throughout the time.

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Fig VI-9: Forecasted Discount Rate

4.4 Gross Domestic Product (GDP)

Historically, the growth rate of Pakistan has been good. On average, it has

been near to five percent annually during the last six decades. In its regional level,

its been at two percent from the 1960s till 1980s; however, since 1993 its low then

the regional average.

Table VI-3: Chronical GDP

1950s 1960s 1970s 1980s 1990s 2000-06 1950-2006

GDP 3.5 6.8 4.8 6.5 4.6 5.4 5.2

Source: Official Website of Statistical Beaurue of Pakistan

It is evident from graph that FCI is able to forecast GDP.

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Fig VI-10: Forecasted Gross Domestic Product

4.5 Stock Market

The stock market of Pakistan has been volatile in most of the time as

depicted from the following graph.

Fig VI-11: Forecasted Stock Market

144

4.4 Forecasting under other Variants of the Model

Fig VI-12: Forecasting using FA-TVP-VAR

145

Fig VI-13: Forecasting using FAVA

146

Fig VI-14: Forecasting using TVP-FAVAR

Forecasting graph is similar under all the models and constructed FCI is

able to closely predict the macroeconomic situation of Pakistan. Thus, can be

used for the prediction of Pakistan’s economy.

147

5. Conclusions

After mortgage crisis or more commonly known as global crisis 2008, it’s

been core job of policymakers and other stakeholders to have an eye on the

financial conditions of the economy for better study of the economic situation of

any country. A recently developed tool serving this purpose is financial condition

index. Literature has no clear rule for the variable selection and method for the

index formation but its importance has been acknowledged and backed by

empirical support.

By knowing such an important job of the Financial Condition Index, this

study is an attempt to develop an index for Pakistan as Pakistan don’t have such

an instrument for market and economy watch. Using the financial and economic

variables those are considered representative of a developing state develops the

index.

This chapter has presented in length methodology and analysis of the

index. After presenting index, prediction of the major macroeconomic variables is

also presented.

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CHAPTER VII

DISCUSSIONS

This chapter will be presenting discussion upon all the objectives. All the

objectives will be discussed in length.

1. Discussions of First Objective

The key driver of this objective is to identify the impact of Monetary Policy

upon major economic variables of emerging markets. For this purpose, the

predictive power of Monetary Policy is tested upon major macroeconomic

variables namely Growth and prices (inflation). A transmission of monetary policy

is being analyzed at country level and for studying contagion impact of US

monetary policy upon emerging markets has been studied. This has been

conducted at two groups of countries namely advanced emerging countries and

secondary emerging countries.

1.1 Advanced emerging countries

Brazilian government introduced Plano Real in 1994. This plan

transformed economy completely and brought sustainability of economic growth.

Economy was praised after this plan due to price stability, fiscal responsibility,

rapid growth, was result of macro-economic reforms started in 1990’s in the form

of real plan, banking reforms, privatization, greater openness of the economy

towards foreign direct investment. As a result of all these efforts, inflation came

down. As a result of real plan, increasing interest rates stabilized currency. This

149

attracted foreign investment. After success of this initiative, formal inflation

targeting policy was introduced. By looking at its, growth rate during 1995-2003,

it was weaker by international standards but during 2000’s, remarkable growth

can be seen in Brazil. A slump in growth during 2009 due to global crisis but a

jump in 2010 is also observable. After first decade of 2000’s Brazil experienced

decline in economic situation (Amann & Baer, 2012).

In the study of monetary policy transmission of Brazil, direct response is

observable in both variables. The impulse responses of output to a positive

interest rate shock (𝜀𝑖 ↑→ 𝑥) is not aligned with theory as it stays negative and

volatile most of the time but impulse response of inflation to positive interest rate

shock (𝜀𝑖 ↑→ 𝑝) is align with theory as it is negative most of the time. Luporini

(2012) found similar results while analyzing the monetary policy transmission in

Brazilian economy using VAR model. He found that tight monetary policy results

in downward trend of GDP growth rate and inflation. Inflation and exchange rate

does have an impact but after an interval. In the study of United State’s monetary

policy transmission in Brazil, the impulse responses of output to a positive interest

rate shock (𝜀𝑖 ↑→ 𝑥) is positive and volatile and impulse response of inflation to

positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is positive and of stable nature.

1997’s currency crisis shattered Czech Republic’s economy severely. As

a mitigating plan, two austerity packages and revitalization programs were

introduced from the government. As a result of these efforts, growth can be seen

during first decade of 2000. Growth rate of the economy was on decreasing trend

from 2009 till 2013 and from 2014 to onwards; it again started towards increasing

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trend. In 2015, its GDP was ranked as highest in European Union. Despite of

being part of European union, Global financial crisis doesn’t have an impact upon

this economy mainly due to two reason; first strong banking sector as a result of

1990’s crisis and presence of national currency. (Havlat, Havrlant, Kuenzel, &

Monks, 2018); and (Abdel-Salam, 2017).

In the study of monetary policy transmission of Czech Republic, direct

response is observable in both variables. The impulse responses of output to a

positive interest rate shock (𝜀𝑖 ↑→ 𝑥) shows the highest volatility but the response

is positive most of the time that is not align to the standard. The response of price

(𝜀𝑖 ↑→ 𝑝) is also positive but less volatile as compared to output. Response of

both variables deviates from standard in both cases. Both response deviates from

the standard. Koerner (2015) found opposite results while analyzing the

transmission of monetary policy in Czech Republic using recursive VAR, SVAR

and structural vector error correction model (SVECM). He found in all the models

that tightening monetary policy results in decline of output and inflation. In the

study of United State’s monetary policy transmission to the Czech Republic, the

impulse responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) is positive

and volatile most of the time and impulse response of inflation to positive interest

rate shock (𝜀𝑖 ↑→ 𝑝) is also positive.

As a result of transition from 1990 till 2004, Hungary shifted from planned

economy to market economy. In the form of reform, government announced

Bokros package for saving country from the financial collapse and aims to

stabilize economy. Many of the resulting measures worked as shock therapy and

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put the country on the road of sustainable growth and in succession country joined

European Union in 2004 (Csizmadia, 2008).

In the study of monetary policy transmission, direct response is observable

in both variables. The impulse responses of output to a positive interest rate shock

(𝜀𝑖 ↑→ 𝑥) are aligned with theory as it stays negative but impulse response of

inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) deviates from standard that is

positive. Balazs (2005) found partly similar results while analyzing the monetary

policy transmission in Hungarian economy using SVAR method. He found that

contractionary monetary policy results in appreciation of exchange rate, lowering

output and inflation. In the study of United State’s monetary policy transmission,

the impulse responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) stay

positive and impulse response of inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝)

is also positive.

Malaysia is an economic sound country and outperforming in the region

since 1970. Since 1970, economy has gone to a number of transformations. As a

result of Asian financial crisis in 1997-98, country strengthen its macro-prudential

policies and many other policies aims toward stability of the financial and

economic sector. These reforms also helped in recovering from global financial

crisis of 2008 and country managed to have a sound economic growth (Koen,

Asada, Nixon, Rahuman, & Arif, 2017).

The impulse responses of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥)

stay negative and impulse response of inflation to positive interest rate shock (𝜀𝑖 ↑

→ 𝑝) is mixed. In the study of United State’s monetary policy transmission, the


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positive and volatile most of the time and impulse response of inflation to positive

interest rate shock (𝜀𝑖 ↑→ 𝑝) is also positive.

Mexican economy improved macroeconomic policies as a result of 1994

crisis that’s why country was not much affected by South American Crisis 2002.

However, country was severely hit by the mortgage crisis of 2008. Mexico is an

export oriented economy (OECD , 2017).


stay negative and volatile most of the time. The response of before shock is

positive compared to other shocks. Same behavior in case of price (𝜀𝑖 ↑→ 𝑝) is

observed, volatile and negative and positive response of before shock. In the

study of United State’s monetary policy transmission, the impulse responses of

output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥) stay positive and impulse

response of inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is negative.

Poland, eighth largest economy of the European Union (EU), is pursuing

policy of economic liberalization since 1990. Poland was the only country in

European union to avoid recession of 2007. Till 2017, Polish economy was the

only country of the EU that was able to maintain economic growth since 26 years

(OECD, 2018).

The impulse responses of output to a positive interest rate shock (εi ↑→ x)

is mixed and volatile most of the time and impulse response of inflation to positive

interest rate shock (εi ↑→ p) is also positive and of varying nature. In the study of

United State’s monetary policy transmission, the impulse responses of output to

a positive interest rate shock (εi ↑→ x) is positive and impulse response of inflation

to positive interest rate shock (εi ↑→ p) is positive.

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Turkey is at center of Eurasia (political and economic area where Europe,

Former Soviet Union and Middle East intersect). Geography of the Turkey creates

unique business opportunities for the country. Since 1994, country has

experienced three major economic downturn, among them crisis in 2001 was the

severest (Arguden, 2007). Following crisis of 2001, country experienced

economic growth as a result of structural change policies. From 2007 to onwards

government growth slowed down as government spending was the mainstay of

the economy (Acemoglu & Ucer, 2015).


stay negative and volatile most of the time and impulse response of inflation to

positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is mixed and of varying nature. Durran et.al

(2012) estimated the monetary policy transmission in turkey using GMM model.

They found that increase in monetary policy results in decline of stock price and

increase in government bond yield. In the study of United State’s monetary policy

transmission, the impulse responses of output to a positive interest rate shock

(𝜀𝑖 ↑→ 𝑥) stay positive and impulse response of inflation to positive interest rate

shock (𝜀𝑖 ↑→ 𝑝) is also positive.

1.2 Secondary Emerging Markets

Between 1989-1992 Colombia went on an unprecedented period of

change in economic policy and reforms. As a result of these initiatives, country

enjoyed fairly good economic growth in first half of 1990s, but country faced its

first economic recession in late 1990s in the midst of Asian and Russian crises.

This recession and real estate bubble resulted in major banking crisis. In early

years of 2000, economy started to recover (Steiner & Vallejo, 2010).

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The impulse response of output to a positive interest rate shock (𝜀𝑖 ↑→ 𝑥)

is negative in the start but positive afterwards. The response of price (𝜀𝑖 ↑→ 𝑝) is

volatile in nature and negative most of the time. In the study of United State’s

monetary policy transmission, the impulse response of output to a positive interest

rate shock (𝜀𝑖 ↑→ 𝑥) is positive and the impulse response of inflation to positive

interest rate shock (𝜀𝑖 ↑→ 𝑝) is positive.

During the 1990s, the country lost on several fronts, especially in economic

growth. The economic performance was dismal. Indeed, the decade of the 1990s

excluding may 28, 1998 when Pakistan became a nuclear power, didn’t emerge

as the economically sound period for the country. After nuclear test many nations

imposed economic sanctions on Pakistan. More precisely 90’s era is known as

return of democracy and era of structural adjustment. In this time period, the

authorities adopted Economic liberalization and stabilization and privatization was

encouraged, among these for the encouragement of the exports tariff rates were

lessen. Moreover, during this time macroeconomic crisis happened (Saeed,

2013). In the end of 90’s and early years of 2000’s acceleration in economic

growth is seen, along with this industrial production increased, export earnings

raised, upsurge in investment, and foreign exchange reserved increased (Zaidi,

2005).


stay positive and volatile most of the time and impulse response of inflation to

positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is also positive and of varying nature. Agha

et.al (2005) found opposite results while studying the monetary policy

transmission in Pakistan using VAR method and found that tightening monetary

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policy results in reduction of price and output. In the study of United States’

monetary policy transmission, the impulse responses of output to a positive

interest rate shock (𝜀𝑖 ↑→ 𝑥) stay positive and volatile most of the time and

impulse response of inflation to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is mixed and

of varying nature.

Inflation in Peru in the form of Fuji shock resulted in macro-economic

stability, prudent fiscal spending, High international reserve accumulation,

External debt reduction, Achievement of investment grade status and Fiscal

surpluses (Martinelli & Vega, 2018). Being such a sound country Peru is having

direct impact of the United State’s monetary policy upon its output. This behavior

may be attribute to the trade agreement that happens between United States of

America and Peru on April 2006 (Federal Register, 2009). Under this agreement,

obstacles to trade were eliminated, and measures were taken to fostering private

investment in and between United States and Peru.


is mixed and volatile most of the time and impulse response of inflation to positive

interest rate shock (εi ↑→ p) is volatile in the short run followed by a stable

response. In the study of United State’s monetary policy transmission, the impulse

responses of output to a positive interest rate shock (εi ↑→ x) stay negative and


shock (εi ↑→ p) is also negative but of stable nature.

The Philippines once was a model of development and second to japan in

East Asian countries. It hit badly with global financial crisis of 2008. But economy

started to recover in 2010s and it started to continue after wards (Hays, 2008).

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positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is negative in short run while positive in long

run and of stable nature. In the study of United State’s monetary policy

transmission, the impulse responses of output to a positive interest rate shock

(𝜀𝑖 ↑→ 𝑥) is positive and volatile most of the time and impulse response of inflation

to positive interest rate shock (𝜀𝑖 ↑→ 𝑝) is positive and of varying nature.

Starting years of transition from Soviet Central Planned Economy were not

easy for Russia. It was a time of economic chaos. Years 1999-2008 can be seen

with impressive Russian economic growth. This process ends with the hit of global

financial crisis and resulted recession in the country (Cooper, 2009).



positive interest rate shock (εi ↑→ p) is also negative in short run followed by

positive in the long run. Ono (2013) found the monetary transmission in Russian

economy using VAR method that monetary shocks have positive impact upon

growth. In the study of United State’s monetary policy transmission, the impulse

responses of output to a positive interest rate shock (εi ↑→ x) are positive and


shock (εi ↑→ p) is negative initially but weak positive response afterwards.

In studying the monetary policy transmission of US to the rest of the world,

researchers are having dynamic results but mostly endorsing this fact that

contagion exist despite the usage of method and data. Extent of contagion may

vary from sector to sector, economy to economy but in the light of evidences it

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can be said that contagion does exist specially of the last Global financial crises.

Neri and Nobili (2010) found using the VAR method the evidence of the

transmission between Eurozone and US. Bagliano and Morana (2010) found the

evidence of spillover of US financial shocks to the advanced and emerging

countries using FVAR method. Todorov (2012) found the evidence of spillover

effects in financial markets of frontier markets from the US using the TVP-VAR

model. Kazi et.al (2013) found the evidence of US monetary policy shock to the

OECD nations in the light of TVP-FAVAR model. Fornari and Stracca (2013) in

studying the advance economies found the propagation of shocks not in turmoil

time period but in normal time. Ghani (2013) concluded that emerging economies

were exposed to shock from advance countries due to the weakness in regulatory

framework. Hab et.al (2014) found the evidence on information spillover and

liquidity spillover from the US to the open-ended property funds of other

economies. Yiu et.cl (2010) conducted a study for finding the relationship between

Asian and US stock market by using the principal component method. Results

indicated that US market is having a contagion impact upon Asian markets.

Barakchian (2015) studied the spillover of US monetary policy upon Canada using

global vector auto regression (GVAR). Kim (2001) concluded that expansionary

monetary policy results positive output in G6 but Bluedorn and Bowdler (2001) in

case of G7 and Scrimgeour (2010) found that positive monetary policy results in

positive short-term interest rate in four countries in America’s. Cross and Nguyen

(2016) studied global oil price shock upon china’s output using time varying

parameter vector auto regression (TVPVAR) model. They found that impact is

small and temporary in nature.

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In studying the shock transmission to the stock market, Yiu et.cl (2010)

conducted a study for finding the relationship between Asian and US stock market

by using the principal component method. Results of Asymmetric Conditional

Correlation model indicated that US market is having a contagion impact upon

Asian markets. Ehramann and Fratzsher (2009) analyzed the transmission of US

Monetary Policy Shock to global equity market by taking data of 50 economies.

They also found heterogeneity of the transmission and also found that the

economies, which are open and relatively liquid markets are more prone to the

transmission. Markwat et.al (2009) proved that stock market contagion operates

as domino effect. He found that global crashes do not occur all of sudden but are

preceded by local and regional crashes.

Impulse responses of the emerging markets affirm the predictive power of

the Monetary Policy of macroeconomic movements in emerging markets. This

finding echoes the findings of Nakajima (2011), Primiceri (2005) who established

that Monetary Policy is being transmitted upon macro-economy.

Under the monetarist/ISLM, framework interest rate surprises represent

Monetary Policy shock. This states that monetary contraction (positive Monetary

Policy shock) creates declining output and increasing prices.

Looking at the individual variable response at the national level, with

positive Monetary Policy shock output declines in Brazil, Hungary, Malaysia,

Mexico, Philippines, Russian Federation and Turkey. Economies those deviate

from the theory are Colombia, Czech Republic, Pakistan, Peru, and Poland.

At the international level, positive Monetary Policy shock creates declining

output only in Peru, for the rest of the countries namely Brazil, Colombia, Czech-

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republic, Hungary, Malaysia, Mexico, Pakistan, Philippines, Poland, Russian-

federation and turkey standard deviate. Inflation in Peru in the form of Fuji shock

resulted in macro-economic stability, prudent fiscal spending, High international

reserve accumulation, External debt reduction, Achievement of investment grade

status and Fiscal surpluses (Martinelli & Vega, 2018). Being such a sound country

Peru is having direct impact of the United State’s monetary policy upon its output.

This behavior may be attribute to the trade agreement that happens between

United States of America and Peru on April 2006 (Federal Register, 2009). Under

this agreement, obstacles to trade were eliminated, and measures were taken to

fostering private investment in and between United States and Peru. Direct

response of the output as a result of monetary policy shock may be due to this

agreement.

The response of Price to positive Monetary Policy shock is positive in Czech

Republic, Hungary, Pakistan, Peru, Philippines, Poland, Russian Federation and

Turkey. Standard deviates in Brazil, Colombia, and Mexico, and somehow in the

short run in Philippines and Russian Federation. In international level, prices

increase in Brazil, Colombia, Hungary, Malaysia, Pakistan, Philippines, and

Poland. The response is mixed in Czech-republic, Russian Federation, and

turkey. Prize puzzle exists in Mexico, Peru and somewhat in Russian Federation.

Sims (1992) conducted a study on the US by using short-term interest rate and

CPI and Industrial Production Index (IPI) and provides evidence of prize puzzle.

This study also affirms the results of agha et.al (2005).

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From a theoretical perspective, this study provides evidence for prize

puzzle in emerging markets to some extent. From a practical perspective, this

study is beneficial for the researchers, economists and market practitioners.

This study is being limited by the availability of data. This study was started

with the objective to work in emerging markets. Emerging markets do lag due to

lack of specialized and well-maintained data. This study may be extended in the

future for broader results and application with the availability of data. We were

limited by data time span. Future work can be more reliable once these limitations

are overcome.

This study is an attempt to study the international transmission of US

Monetary Policy and standard Monetary Policy on macro-economic of emerging

markets. It is being found that Monetary Policy does have an impact upon macro-

economy of emerging markets; the extent may vary but its true for all the

countries. Past studies majorly covering advance countries; this study is an

attempt to extend the studies on emerging markets.

2. Discussions of Second Objective

2.1 Advanced Emerging Countries

Brazil, Czech Republic, Hungary, Malaysia, Poland and South Africa are

having bilateral relations with the US (Executive Office of the President, ). For that

reason, financial conditions of the US are having on impact upon the Brazil in

short term yet volatile. A one standard deviation innovation in FCI creates strong

but temporary response of short-term interest rate, long-term interest rate, and

exchange rate and of the stock market. Response of the macro-economy of

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Czech Republic is of short term in nature and more stable as compared to Brazil.

Long run impact of US financial conditions is observable in the macro economy

of Hungary. Malaysia and the US are having bilateral relations. Impact of US

financial conditions upon macro-economy of the Malaysia is of temporary and

short-term nature. An innovation to FCI has implications for the Polish economy

in the long term. Response of South African economy upon the financial

conditions of the US is mixed. Short-term interest rate respond in the long run

while other macro economic variables behave in the short run. Mexico and the

US are having foreign relations, for that reason, an innovation to FCI has

implications for the macro economy of the Mexico in short term.

2.2 Secondary Emerging Countries

Chile a stable and prosperous nation, in 2006 it was having highest

nominal GDP per capita in Latin America (World Economic Forum, 2009). Chile

is having Free Trade Agreements and on strong bilateral relations with the US

(Executive Office of the President, ).

Chile is strategic alley of the US. That’s why; an innovation to FCI has

implications for the macro-economy of the Chile in long run.

Greece has been center of Eurozone debt crisis. It has the highest level of

public debt in Eurozone and high budget deficit. It was the first country that came

under intense market pressure and turn to international monetary fund (IMF) and

other states for financial assistance. By adopting Euro as currency, borrowing cost

decreased dramatically. Interest rate on government bond dropped till 18% from

1993-98. As a result of European Union membership, capital inflow increase and

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convergence criteria was adopted. But with the passage of time investors lost its

confidence and it was on its peak in 2009. Global financial crisis and related

downturn of economies worsen the situation (Nelson, Belkin, & Mix, 2011). US

and Greece are on bilateral relations. Response of the macro economy of the

Greece upon the financial conditions the US is temporary in nature except stock

market.

Relation of the US and India are international relations. An innovation to

FCI has implications for the macro economy of the India in the long run. Pakistan

and Russian federation are having bilateral relations with the US. Innovations to

the financial conditions of the US are having implications for the macro economy

of the Pakistan in short term while in case of Russia response is in the long run.

Financial conditions does have implications for the economies and can be

used a representative of the economy. Major studies are available at country level.

Eickmeier et.al, (2011) employed the FCI developed by Hatzius et.al, (2010) for

studying the international transmission during 1971-2009 of financial shock using

the TVP-FAVAR method. They found that positive US financial shocks do have a

positive impact upon the growth of countries under study (US, Canada, the UK,

France, Germany, Italy, Spain, Japan and Australia).

Alessandria and Mumtaz (2017) hypothesized that the links between credit

markets and real economy tighten in a crisis; financial indicators might be

particularly useful in forecasting the macroeconomic outcomes associated with

episodes of financial distress. To capture the state of financial markets they

employed the Financial Condition Index (FCI) constructed and maintained by the

Chicago Fed. Balcilara et.al (2016) used a previously constructed index for finding

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its ability to forecast the South African economy. They found that the response of

economy is non-linear to financial conditions. Opschoor et.al (2014) studied to

find the impact of financial conditions on the stock market by using Bloomberg

FCI. They found that worst financial conditions are associated with high volatility

and correlation between stock return.

The main purpose of using U.S. financial condition shocks is to identify the

predictive power of major economic variables of emerging markets. For this

purpose, the predictive power of financial conditions is tested upon major

macroeconomic variables namely short term and long-term interest rate,

exchange rate and stock markets. Impulse response affirms the predictive power

of the financial conditions of macroeconomic movements in emerging markets.

This finding echoes the findings of Brave and Butters (2011) who established that

FCIs do have strong predictive power in short-term and long-term horizons.

Similar findings can be seen in the study of Koop and Korobilis (2014), Hatzius

et.al (2010), Debuque and Bautista (2013) and many others.

Looking at the individual variable response, we can see that response of

short-term interest rate is initially negative and volatile in the start but positive and

stable in the long run in case of Brazil, Chile, Czech Republic, Greece, India,

Malaysia, Mexico, Pakistan, Poland, and South Africa. The exception of this trend

is Hungary and the Russian Federation where the initial positive response is being

followed by a negative response that with time shift into a positive and stable

response.

The response of long-term interest rate is volatile that tends to stabilize with

time and this is of permanent nature in case of Chine, Czech Republic, Greece,

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Hungary, India, Poland, and Russian Federation. Volatile yet of the temporary

nature of response can be seen in the case of Brazil, Malaysia, Pakistan, and

South Africa.

The response of stock market is volatile in short-term in case of Brazil,

Czech Republic, Greece, Malaysia, Mexico, Pakistan, Russian Federation, and

South Africa while volatile and of permanent nature in Chile, Hungary, India, and

Poland.

The response of the exchange rate is like overshooting in many countries.

These finding echoing the findings of overshooting hypothesis proposed by

Dornbusch (1976) who established that immediate response of exchange rate

towards a disturbance is higher than its long-run response. Such response is

being observed in the case of Brazil, Chile, Greece, Hungary, India, Malaysia,

Mexico, Pakistan, Poland, Russian Federation, and South Africa. Exception from

this response is the Czech Republic who does not overshoot whose reasons are

unknown now.

From a theoretical perspective, this study confirms the hypothesis being

tested by notable researchers’ that state that financial condition index does have

predictive power of macro-economy and specifically confirms Dornbusch’s

exchange rate overshooting hypothesis in most of the economy.

From a practical perspective, this study is beneficial for the researchers,

economists and market practitioners.

This study is being limited by the availability of data. This study has started

the objective to work in emerging markets. Emerging markets due to lack of

specialized and well-maintained data. This study may be extended in the future

165

for broader results and application with the availability of data. We were limited by

data time span. It was after 2000 that data on most countries was available.

Furthermore, data were available on different frequencies that also limit us with

the application. Future work can be more reliable once these limitations are

overcome.

This study is an attempt to study the international transmission of US

financial conditions on macro-economic of emerging markets. It is being found

that financial conditions do have an impact upon macro-economy of emerging

markets; the extent may vary but its true for all the countries. Past studies majorly

covering advance countries; this study is an attempt to extend the studies on

emerging markets.

3. Discussion of Third Objective

After mortgage crisis or more commonly known as global crisis 2008, it’s

been core job of policymakers and other stakeholders to have an eye on the

financial conditions of the economy for better study of the economic situation of

any country. A recently developed tool serving this purpose is financial condition

index. Literature has no clear rule for the variable selection and method for the

index formation but its importance has been acknowledged and backed by

empirical support.

By knowing such an important job of the Financial Condition Index, this

study is an attempt to develop an index for Pakistan as Pakistan does not have

such an instrument for market and economy watch. Using the financial and

economic variables those are considered representative of a developing state

develops the index. A developing state does have an issue with the data. This

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data issue limits us with the variable choice. The developed index is able to serve

as a barometer of the economy as it’s so near to historical event of Pakistan. So

it may say that it may serve better than monetary policy based estimation results

and can give a more realistic picture of the economy both at short term and long-

term basis. It is constructed over a long horizon that it could serve a better

representative of the economy.

Gaglianone and Areosa (2016) constructed a FCI for the Brazilian

economy using the method of Brave and Butters (2011) and Aramonte et.al

(2013). They use the developed index for studying the economic conditions and

employed for forecasting purpose. Gomez et.al (2011) construed a FCI for the

Colombia using PCA method and used it for forecasting purpose of the macro-

economy. Goodhard and Hofmann (2001), Mayes and Viren (2001), Gauthier

et.al, (2004). Guichard and Turner (2008) and Swiston (2008), Brave and Butters

(2011), Hatzius et.al (2010) constructed an FCI for the USA, Gumata et.al (2012)

constructed an index for South Africa, Gonzales and Bautista (2013) constructed

FCI for five Asian Markets and concluded that these index can be used for

forecasting purpose. Authorities have also created an index for studying the

financial conditions of economies. Hong Kong Monetary (2010) built an index for

the Hong Kong and China for studying the episodes of stress in mentioned

economies. Another index formed by Monetary Authority of Singapore (2009) for

studying the economic conditions of Asian countries (China, Republic of China,

Thailand, Taipei, Philippines, Malaysia, Korea, Republic of Korea, Indonesia, and

India). International Monetary Fund has also constructed Asia based index for

studying the economic conditions of Asian countries. This chapter has presented

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discussion upon each objective. All three objectives have been discussed in

length.

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CHAPTER VIII

CONCLUSIONS

This chapter will be presenting conclusion of the study. Conclusion

gained from each objective will be discussed.

1. Objective-wise Findings

Succeeding the evaluation of literature (Chapter 3), three key research

objectives for the thesis were identified.

1. To gauge the time varying effect of the monetary policy

2. To find out the effect of U.S. financial shocks on emerging markets

3. To develop and test FCI for Pakistan

We now raise back to these objectives, summarizing the procedures used

to approach them and the key points of conclusion that can be drawn from the

research.

1.1 Findings of First Objective

Mortgage financial crisis that sooner converted into global crisis affirms the

evidences of the transmission and contagion. For this reason, its being an attempt

to study the transmission and contagion arising from monetary policy. At first level,

transmission of monetary policy towards macro-economy is being studied and at

second level, contagion of monetary policy shock arising from United States on

the economy of emerging markets is being studied in a time varying context. This

objective is achieved using the TVP-VAR model with stochastic volatility proposed

by Jouchi Nakajima (2011). Results are indicating significant impact of shock

169

upon growth and price of the emerging markets and evidences for prize

puzzle and deviation from standards are also found in transmission mechanism

in some countries. This objective was further broken down into four hypotheses.

Status of the hypotheses is given in table 6.1.

Table: VIII-1: Status of Hypotheses of First Objective

S # Hypotheses Status

1. A contractionary monetary policy has inverse impact on growth.

Accepted in case of Hungary, Malaysia, Mexico, Philippines, Russian Federation, and Turkey.


Accepted in case of Czech Republic, Hungary, Malaysia, Pakistan, Poland, Russian Federation, and Turkey.

3. The systematic US monetary policy has positive impact on growth of emerging economies.

Accepted only in case to US to Peru

4. The expansionary monetary in US creates prize puzzle in emerging economies.

Accepted in case of Brazil, Colombia, Czech Republic, Hungary, Philippines, Poland.


1.2 Findings of Second Objective

One of the key outcomes of the recent global crisis is that due to financial

innovations we are unable to capture the broader horizon of financial conditions

with just few variables. Policymakers, regulators, market participants and

researchers have affirmed this conjunction and have emphasis to work on this

part for enhancing our level of understanding on this part. Keeping this view in

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front, another objective of this study is to offer an empirical assessment of the

effects of the financial conditions of the United States upon macro-economy of

the emerging economies using standard Vector Auto-Regression (VAR) Models.

This objective is achieved by utilizing financial conditions index of Brave and

Butter (2011) for the assessment of impact upon macro-economy of the emerging

markets as being classified by Financial Times Stock Exchange (FTSE). It is being

found that macro-economic variables do respond on the financial conditions of

the united stated however magnitude varies from country to country. This

response is of short-term nature rather of long term. Status of the hypotheses is

given in table 6.2.

Table: VIII-2: Status of Hypotheses of Second Objective

S# Hypotheses Status

1. Dornbusch’s exchange rate overshooting hypothesis exists in emerging markets in transmission mechanism

Accepted in case of Brazil, Chile, Hungary, India, Mexico, Pakistan, Poland, Russian Federation, South Africa.

2. FCI reflects information of stock market in the long run.

Accepted in case of Chile, Czech republic, Greece, Hungary, India, Malaysia, Pakistan, Poland, Russian Federation, and South Africa.

3 FCI reflects information of short-term interest rate in the short run.

Accepted only in case of Brazil

4 FCI reflects information of long-term interest rate in the long run.

Accepted in case of Chile, Czech republic, Greece, Hungary, India, Malaysia, Poland, and Russian Federation.


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1.3 Findings of Third Objective

One of the learning lesson of recent mortgage crisis is that broad financial

conditions due to innovations in financial landscape is difficult to capture by using

small number of variables that cover only few traditional financial markets. In the

light of recent crisis policy makers, regulators and financial market stakeholders

has affirmed the link between traditional and newly developed markets and link

between financial and non-financial market. Closer watch on financial stability is

essential for understanding such links. For this reason, index on financial

conditions are the best for this purpose. With this background, in this study, an

index is created using wide range of macro-economic and financial variables over

a long horizon for the Pakistan using a time varying model developed by Koop

and Korobilis (2014). This method develops and forecasts financial conditions

index.

Overall, significant periods of economic growth and crisis in financial

history are well captured by the index. By looking at different statistics of the

dynamic forecast, it can be derived that fit is good and graph of the forecasts

closely follows financial conditions, indicating that this index is having strong

predictive power of the major macro-economic variables. This study proposes

new dimension for the policy studies and the constructed index in this study may

help regulators, policy makers and scholars for finding the true state of the

financial conditions. Index is able to closely mark major episodes in the financial

history of the Pakistani economy, particularly those characterized by large

172

external financial and economic shocks. Status of the hypotheses is given in table

6.3.

Table: VIII-3: Status of Hypotheses of Third Objective

S# Hypothesis Status

1. FCI helps in measuring financial shocks. Accepted


This chapter discuss result objective wise and present comprehensively

conclusion on each objective.

173

CHAPTER IX

RECOMMENDATIONS

This is the last chapter of the study. This will be discussing

recommendations, implications and limitations of the study.

1. Policy Recommendation

This study discusses in length case of transmission and contagion arising

from US. This study specifically follows salt-water school of economics and

majorly employ ISLM framework for studying the impact of monetary policy.

Moreover, for studying the impact of financial conditions, an index (representative

of US economy) and second index (created for the Pakistan) both have been

employed for studying macroeconomic response. As a result of this exercise and

findings of the study, following are the recommendations of this study.

Table IX-1: Findings and Recommendations

Findings Recommendations ISLM framework is partly applicable in many cases.

Monetary policy objectives need to revise. Revision of monetary objectives in accordance of time is need of time.

prize puzzle exist more in county specific monetary policy as compared to international contagion

Contagion arising from international link may be studied under the framework of ISLM.

Response of output is aligning with the theory at country level but not in international level.

Transmission arising from the monetary policy shed light on the output response in the light of ISLM framework. So, specifically in this area, ISLM can be employed.

Hungary, Turkey, Malaysia at country level fully align with the theory

These countries may be studied and policies in these countries may be designed using ISLM Framework

174

Mexico and Turkey fully deviate from theory in the case of contagion impact

These both countries should not be studied under ISLM framework in studying contagion impact. The reason of this complete deviation is known. Use of another salt water theory may define the behavior of these countries

Countries do have impact of financial conditions of the US but in many cases, Impact die off with the time.

Financial conditions of the US should be taken into consideration while designing policies specifically short-term policies.

Financial conditions of the US do have implications for the emerging countries. Extend vary from country to country but impact does exist on the macro-economy of the emerging countries.

In globalization, consideration of impact of other economies will help deals better with the disaster situations. This could be achieved by the Inclusion of financialization in decision-making.

The countries who are having Free Trade Agreement with the US are having strong and long term response

Strong international relation with any country is having its own merits and demerits. Inclusion of any clause or any such component that would act as buffer in case of disaster could help in this matter.

Bilateral partners' response dies off with the passage of time except Russia and Hungary (These both countries shifted from planned economy towards capital economy and both were badly hit from the crisis.

Initiatives are needed to eliminate the dominancy of foreign capital economies for lessen the impact of crisis. It is being found that the countries that were stick to their currency faced low extend of the crisis.

Interest rates (both short and long term) are more responsive towards financial conditions of the US

It is proven that macro-economy respond, but Interest rate responded more. So while designing interest rates, consideration of financialisation and globalization may result more realistic rates.

Financial condition index of the Pakistan is able to give a true picture of the economy.

Decision making process of the authorities should take input from Multi-factors. Decision based on single or few variables results in biased results.

forecasting of the index of the macroeconomic variables is close to the reality.

use of index may give better insight in the study of macroeconomic fluctuations.


175

2. Implications

Implications of this study may be divided into two groups as this study addresses

transmission upon macro-economy from two areas namely financial condition index and

monetary policy.

2.1 Implications of Financial Condition index

During last few decades, we may see enormous growth in financial

landscape. Due to surge in financial engineering and innovation, Investors are

having more option to avail for efficient allocation of funds than ever before. But

this blessing has also its cost with it. It’s a challenge for the market and economy

stakeholder as how to deal with innovations that operate at global level.

As the results of recent crisis that is seen at global level, we may say that

more closely watched instrument is required for having mitigating plans.

Policy makers for designing more robust and realistic market plans and

mitigating strategies can use Index. Moreover, recent trend led by Basel III in

terms of regulation and supervision is to work in more systematic and macro-

prudential framework. So it can be say that a tool like FCI can serve this purpose

well and can be considered a step towards this also.

FCI is able to mimic the behavior of the economy in a more robust way. So

it can be employed as a tool to study the economy in a more realistic way.

2.2 Implications of Monetary Policy

Effectiveness of monetary policy has been under question from decades

especially under crisis. Along with it, its possible contagion is also under question.

There is a great deal of available literature covering both aspects. Thus

highlighting its importance. Timely information on monetary policy transmission is

176

useful for the policy makers and regulators for developing relevant plans.

By these objectives in hand, key contribution of this thesis is to study the

adverse impact of shocks namely monetary and financial on economy and

possible contagion arising from the origin of crisis towards emerging economies.

Results indicate that impact of shock do exist both at national level and

international level in the form of contagion. It’s also found that Financial condition

index may serve as early warning indicator in both situations.

This study shed light on monetary policy transmission at national level and

also at international level in a time varying nature meaning transmission is not

static in nature. Monetary policy transmitted in a time varying way so monetary

objectives should be designed accordingly keeping this fact in front.

Moreover, it is found that transmission process is not aligned with the

theory. Certain deviation exists from theory in transmission mechanism.

Theoretical background of this study is ISLM framework. It was found that it is

partly applicable to the most of the economies. So monetary authorities should

consider this finding while designing their policies.

Moreover, this study does have implications and address following

Sustainable Development Goal (SDGs). Sustainable development goals are set

by United Nations for the better future. This project will address Goal number eight

that state “Promote sustained, inclusive and sustainable economic growth, full

and productive employment and decent work for all” in general and specifically its

8.10 part that state “Strengthen the capacity of domestic financial institutions to

encourage and expand access to banking, insurance and financial services for

all” (United Nation General Assembly, 2014).

177

3. Limitations and Future Work

This study is a comprehensive in nature in studying the shock in emerging

markets and it has tried to cover many aspects. But no study is perfect. It is true

for this study. This study is being limited by the availability of data. Limit the

number of countries, data time span, shorter time span; data was available on

different frequencies that also limit us with the application and interpolation.

Future direction of this study can be taken by using following means:

• Study on monetary policy rules and its implications for the economy;

• FCI with more important variable e.g. CPEC, Small and Medium

Enterprises (SMEs) sector;

• High frequency FCI;

• Regional and international comparison of FCI;

• Study on different crisis and comparison.

So its plan to work on these areas in future for better study of subject under

study subject to the removal of limitations. Moreover, a good index is one that

keep on updating with time as variables impact keep on changing. It is true for the

FCI. The FCI cannot always precisely identify the direction of the evolutionof

economic activity, as GDP growth can also be affected by exogenous factors (for

example productivity shocks). Moreover, the good indicator properties of the index

are conditional on the stability of the association between real and financial

variables and of the allocated weights – therefore, given their changing nature,

the index needs to be periodically reassessed so as to capture the most relevant

combination of indicators, both in terms of their selection and of the assigned

178

weights, as the relationships between the variables change. In this regard,

estimation using Dynamic Model Averaging techniques can be quite helpful.

With this chapter, this study comes to an end formally. This chapter

discusses policy recommendations, implications and limitations of the study.

179

REFERENCES

ABDEL-SALAM, N. (2017), “Diverging Migration Tendencies of the Youth in Central Europe Case Studies of Hungary and the Czech Republic”, Master Thesis, Charles University in Prague, Faculty of Arts, Prague.

ACEMOGLU, D., AND UCER, M. (2015), “The Ups and Downs of Turkish Growth,

2002-2015: Political Dynamics, the European Union and the Institutional Slide”.

AGHA, A. I., AHMED, N., MUBARIK, Y. A., & SHAH, H. (2005), “Transmission

Mechanism Of Monetary Policy In Pakistan”, SBP-Research Bulletin, 1(1), pp. 1-23.

ALEEM, A., AND LAHIANI, A. (2011), “Monetary policy rules for a developing

country: Evidence from Pakistan”, Journal of Asian Economies, vol. no. 22, pp. 483-494.

ALESSANDRIA, P., AND MUMTAZ, H. (2017), “Financial conditions and density

forecasts for US output and inflation”, (Elsevier, Ed.) Review of Economic Dynamics, vol. no. 24, pp. 66-78.

ALLEN, F., AND GALE, D. (2000, February), “Financial Contagion”, The Journal

Of Political Economy, pp. 1-33. AMANN, E., AND BAER, W. (2012), “Brazil as an Emerging Economy: A New

Economic Miracle?”, Brazilian Journal of Political Economy, vol. no. 32(3(128)), pp. 412-423.

ANG, JAMES B. (2007), “A Survey of Recent Developments in the Literature of

Finance and Growth”, Discussion Paper, Monash University, Department of Economics.

ANGELOPOULOU, E., BALFOUSSIA, H., AND GIBSON, H. (2013), “Building a

Financial conditions index for the euro area and Selected euro area countries What does it tell us about the crisis?”, Working Paper, European Central Bank, macro Prudential research network, Frankfurt am Main.

AREOSA, W. P., AND DUTRA, W. (2016, May), “Financial Condition Indicators

for Brazil”, Working Paper, Central Bank of Brazil. ARIAS, J. E., CALDARA, D., RUBIO-RAMÍREZ , J.F., (2019), “The systematic

component of monetary policy in SVARs: An agnostic identification procedure”, Journal of Monetary Economics, vol. no. 101, pp. 1-13.

https://www.sciencedirect.com/science/journal/03043932

180

ARGUDEN, Y. (2007), “An Overview of the Turkish Economy: Outlook and Current Perspectives”, Policy Brief, Middle East Institute.

BACCHETTA, P., AND BALLABRIGA, F. (2000), “The Impact of Monetary Policy

and Banks' Balance Sheets: Some International Evidence”, Applied Financial Economics (10), pp. 15-26.

BAGEHOT, WALLET (1873), “Lombard Street: A Description of the Money

Market”, Great Britain: Henry S. King & Co. BAGLIANO, F., AND MORANA, C. (2010), “The Great Recession: US Dynamics

And Spillovers To The World Economy”, Working Paper, Center For Research On Pension And Welfare Policies.

BALAKRISHNAN, R., DANNINGER, S., ELEKDAG, S., AND TYTELL, I. (2009,

June), “The Transmission of Financial Stress from Advanced to Emerging Economies”, IMF Working Paper, 3.

BALCILARA, M., THOMPSON, K., GUPTAB, R., AND EYDEN, R. V. (2016),

“Testing the asymmetric effects of financial conditions in South Africa: A nonlinear vector autoregression approach”, Journal of International Financial Markets, Institutions & Money.

BARAKCHIAN, S. MAHDI (2015), "Transmission of US monetary policy into the

Canadian economy: A structural cointegration analysis." Economic Modelling 46: pp. 11-26.

BARSKY, R., AND SIMS, E. (2011), “News shocks and business cycles”, Journal of Monetary Economics, pp. 273-289.

BARSKY, ROBERT B; SIMS, ERIC R. (2012), “Information, Animal Spirits, and

the Meaning of Innovations in Consumer Confidence”, American Economic Review, 102(4), pp. 1343-1377.

BEATON, K., LALONDE, R., AND LUU, C. (2009), “A Financial Conditions Index

for the United States”, Discussion Paper, Bank of Canada, International Economic Analysis Department, Ontario.

BELONGIA, M. T., AND IRELAND, P. N. (2006), “The Own-Price of Money and

the Channels of Monetary Transmission”, Journal of Money, Credit, and Banking, 38(2), pp. 429-445.

BERNANKE, B. S. (1983), “Irreversibility, uncertainty, and cyclical adjustment”,

The Quarterly Journal of Economics, vol. no. 98(1), pp. 85-106.

181

BERNANKE, B. S., AND BLINDER, A. S. (1992, September), “The Federal Funds Rate and the Channels of Monetary Transmission”, American Economic Review (82), pp. 901-921.

BERNANKE, B., AND GERTLER, M. (1995), “Inside the Black Box: The Credit

Channel of Monetary Policy Transmission”, Journal of Economic Perspectives , vol. no. 9(4), pp. 27-48.

BERNANKE., B., AND MIHOV, I. (1998), “Measuring Monetary Policy’, Quarterly

Journal of Economics, vol. no. 113(3), pp. 869-902. BLACK, F. (1976), “Studies of Stock Market Volatility Changes”, Proceedings of

the American Statistical Association, pp. 177-181. BLANCHARD, O. J., AND PEROTTI, R. (2002), “An Empirical Characterization of

the Dynamic Effects of Changes in Government Spending and Taxes on Output”, Quarterly Journal of Economics, vol. no. 117, pp. 1329-1368.

BLANCHARD, O. J., AND QUAH, D. (1989), “The Dynamic Effects of Aggregate

Demand and Supply Disturbances”, American Economic Review, vol. no. 79, pp. 655-673.

BLANCHARD, OLIVIER J., HAMID FARUQEE, AND MITALI DAS. (2010), "The

Initial Impact of the Crisis on Emerging Market Countries." Brookings Papers on Economic Activity, pp. 263-323.

BEST, G. (2013), “Fear Of Floating Or Monetary Policy As Usual? A Structural

Analysis Of Mexico’s Monetary Policy”, North American Journal Of Economics And Finance , vol. no. 24, pp. 45-62.

BLINDER, A. S. (2010), “How Central Should The Central Bank Be?”, Journal Of Economic Literature, vol. no. 48(1), pp. 123-133.

BLOOM, N., BOND, S., AND REENEN, J. V. (2007), “Uncertainty and Investment Dynamics”, Review of Economic Studies, vol. no. 74(2), pp. 391-415.

BLUEDORN, J., AND BOWDLER, C. (2011), “The Open Economy

Consequences Of U.S. Monetary Policy”, Journal Of International Monetary Finance, vol. no. 30, pp. 309-336.

BOIVIN, J., AND GIANNONI, M. P. (2006, August), “Has Monetary Policy become more Effective?”, The Review of Economics and Statistics, vol. no. 88(3), pp. 445–462.

BORIO, C. (2008). Policy Analysis, pp. 251. BOUIS, R., RAWDANOWICZ, L., RENNE, J.-P., WATANABE, S., AND

CHRISTENSEN, A. K. (2013), “The effectiveness of monetary policy since the onset of the financial crisis”, OECD Economics Department Working Paper No. 1081.

182

BRAVE, S., AND BUTTERS, R. A. (2010), “Gathering Insights on the Forest from the Trees: A New Metric for Financial Conditions:, Chicago: Federal Reserve Bank of Chicago.

BUCH, C. M., BUCHHOLZ, M., AND TONZER, L. (2014), “Uncertainty and

international banking international Banking: Micro foundations and Macroeconomic Implications”, International Monetary Fund (IMF) and the Dutch National Bank (DNB).

CALVO, G. A., IZQUIERDO, A., AND MEJA, L.-F. (2008), “Systemic Sudden

Stops: The Relevance Of Balance-Sheet Effects And Financial Integration”, Massachusetts: National Bureau of Economic Research.

CARMASSI, JACOPO, DANIEL GROS, AND STEFANO MICOSSI (2009), "The

Global Financial Crisis: Causes and Cures", Journal of Common Market Studies 47: pp. 977–996.

CHRISTIANO, L., AND EICHENBAUM, M. (1992), “Identification and the Liquidity

effect of Monetary Policy Shocks”, Political Economy, Growth, and Business Cycles, pp. 335-370.

CICCARELLI, M., MADDALONI, A., & PEYDRO, J.-L. (2013), “Heterogeneous transmission mechanism monetary Policy and financial fragility in the euro area”, Frankfurt am Main: European Central Bank.

CLAESSENS, S., DORNBUSCH, R., AND PARK, Y. (2001), “Contagion: why

crises spread and how this can be stopped, in International Financial Contagion. Boston: S. Clasessens and K”, Forbes, Kluwer Academic Publishers.

COGLEY, T., AND SARGENT, T. J. (2001), “Evolving Post-World War II U.S.

Inflation Dynamics”, NBER Macroeconomics Annual, vol. no. 16, pp. 331–373.

COOPER, W. H. (2009), “Russia’s Economic Performance and Policies and Their

Implications for the United States”, Congressional Research Service. CROCKETT, A. (1997), “Why is Financial Stability a Goal of Public Policy?”, FRB

Economic Review, Q IV, pp. 5-22. CROSS, J., AND NGUYEN, B. H. (2016), “The Relationship Between Global Oil

Price Shocks And China’s Output: A Time-Varying Analysis”, Journal of Energy Economics.

CSIZMADIA, L. (2008), “The Transition Economy of Hungary between 1990 and 2004”, MSc. in Finance and International Business, University of Aarhus, Aarhus School of Business.

183

DEDOLA, L., AND LIPPI, F. (2005), “The monetary transmission mechanism:

Evidence from the industries of five OECD countries”, European Economic Review, vol. no. 49(6), pp. 1543–1569.

DIXIT, A., AND PINDYCK, R. (1994), “Investment under Uncertainty”, Princeton

University Press. DORNBUSCH, R. (1976, December), “Expectations and Exchange Rate

Dynamics”, Journal of Political Economy, vol. no. 84, pp. 1161-1176.

DURAN, M., ÖZCANB, G., OZLU, P., AND ÜNALMIS, D. (2012), “Measuring the impact of monetary policy on asset prices in Turkey”, Economic Letters, vol. no. 114, pp. 29–31.

EHRMANN, M., AND FRATZSCHER, M. (2009), “Global Financial Transmission of Monetary Policy Shocks”, Oxford Bulletin of Economics And Statistics, vol. no. 71(6), pp. 739-759.

EICHENGREEN, B., AND LINDERT, P. H. (1992), “The International Debt Crisis in Historical Perspective”, Cambridge: MA: MIT Press.

EICKMEIER, S., LEMKE, W., AND MARCELLINO, M. (2011), “The changing

international transmission of financial shocks: evidence from a classical time-varying FAVAR”, Discussion Paper, Deutsche Bundesbank.

ELLIOTT, M., GOLUB, B., AND JACKSON, M. O. (2014), “Financial Networks

And Contagion”, American Economic Review Journal, vol. no. 104(10), pp. 3115-3153.

ERDEM, M., AND TSATSARONIS, K. (2013, March), “Financial conditions and economic activity: a statistical approach”, Quarterly Review, pp. 37-51.

“Executive Office of the President, Countries and Regions”, Retrieved 2018, from

Office of the United States Trade Representative: https://ustr.gov/countries-regions.

“Executive Office of the President. Free Trade Agreements”, Retrieved 11 14, 2018, from United States Trade Representative: https://ustr.gov/trade-agreements/free-trade-agreements.

FATIMA, K. (2013), “Globalization, inflation and monetary policy”, Thesis,

University of Glasgow, Adam Smith Business School, Glasgow. FEDERAL REGISTER (2009, January 22), The President. Vol. no.74, pp. 13.

https://ustr.gov/trade-agreements/free-trade-agreements

https://ustr.gov/trade-agreements/free-trade-agreements

184

FORNARI, FABIO, AND LIVIO STRACCA (2013), “What does a Financial Shock do? First International Evidence”, Working Paper, Macroprudential Research Network, European Central Bank.

FREEDMAN, C. (1994), “The use of indicators and the monetary conditions index

in Canada”, (T. B. Cottarelli, Ed.) Frameworks for monetary stability. FRIEDMAN, M., AND ANNA, J. S. (1963), “A Monetary History of United States”,

pp. 1867–1960. FTSE RUSSELL (2016), “FTSE Annual Country Classification Review”. FTSE RUSSELL (2015), “FTSE country classification process”, Research Report. FU, Q., AND LIU, X. (2015), “Monetary policy and dynamic adjustment of

corporate investment: A policy transmission channel perspective”, China Journal of Accounting Research, vol. no. 8, pp. 91-109.

FURCERI, D., PRAKASH LOUNGANI, P., ZDZIENICKA, A. (2018), “The effects

of monetary policy shocks on inequality”, Journal of International Money and Finance, vol. no. 85, pp. 168-186.

FUNG, B. S., AND KOSUMOVICH, M. (1998), “Monetary Shocks in G-6 Countries

is There a Puzzle?”, Journal of Monetary Economics, vol. no. 42(3), pp. 575-592.

GAI, P. (2013), “The Robust Yet Fragile Nature Of Financial Systems. In P. Gai,

Systemic Risk”, United Kingdom: Oxford University Press, vol. no. 1, pp. 9-28.

GAGLIANONE, W. P., AND AREOSA, W. D. (2016), “Financial Conditions Indicators for Brazil”, Central bank of Brazil.

GALI, J. (1999), “Technology, Employment, and the Business Cycle: Do Technology Shocks Explain Aggregate Fluctuations?”, American Economic Review, vol. no.89, pp. 249-271.

GAUTHIER, C., CHRISTOPHER, G., AND LIU, Y. (2004), “Financial Conditions Indexes for Canada”, Bank of Canada Working Paper, pp. 2004-22.

GHANI, SHAZIA (2013), “Global Financial Crisis of 2007 Analysis of Origin and

Assessment of Contagion to Emerging Economies Lessons and Challenges for Financial Regulation”, PhD Thesis, Grenoble: University of Grenoble.

GLASSERMAN, P., AND YOUNG, H. P. (2013), “How Likely Is Contagion In

Financial Networks?”, Discussion Paper Series, University Of Oxford, Department Of Economics.

185

GOLDSMITH, R. W. (1969), “Financial structure and development”, New Haven, CT: Yale University Press.

GOLDSTEIN, M. (2005), “What Might The Next Emerging-Market Financial Crisis

Look Like?”, Working Paper, Institute For International Economics, Washington, DC.

GOMEZ, E., MURCIA, A., AND ZAMUDIO, N. (2011), “Financial Conditions index:

early and leading indicator for Colombia”, Essays on Economic Policy, vol. no. 29(66), pp. 177-220.

GONZALES, M. D., AND BAUTISTA, M. S. (2013), “Financial Conditions Indexes

for Asian Economies”, Asian Development Bank. ADB Economics. GOODFRIEND, M. (1991), “Interest Rates and the Conduct of Monetary Policy”,

Carnegie- Rochester Conference Series(Spring), pp. 7-30. GOODHART, C., AND HOFMANN, B. (2001, march 2-3), “Asset Prices, Financial

Condition of Transmission of Monetary Policy”, Conference on Assets Prices, Exchange Rates, and Monetary Policy. Stanford University.

GORDON, ROBERT J. (2003), “Productivity Growth, Inflation, and

Unemployment”, Cambridge University Press, pp. 226–227. GUICHARD, S., AND TURNER, D. (2008), “Quantifying the Effect of Financial

Conditions on US Activity”, Economics Department. OECD Economics Department.

GUJARATI, D. N., AND PORTER, D. C. (2009), “Basic Econometrics (Fifth ed.)”,

New York: McGraw-Hill Companies. GUMATA, N., KLEIN, N., AND NDOU, E. (2012), “A Financial Conditions Index

for South Africa”, Working Paper, International Monetary Fund, African Department.

HANIF, M. N., IQBAL, J., AND MALIK, M. J. (2013), “Quaternization of National

Income Accounts of Pakistan”, SBP Research Bulletin, vol. no. 9(1). HANSON, M. S. (2004), “The “Price Puzzle” Reconsidered”, Journal of Monetary

Economics, pp. 1-38. HAB, L. H., KOZIOL, C., & SCHWEIZER, D. (2014), “What Drives Contagion in

Financial Markets? Liquidity Effects versus Information Spill‐Over”, European Financial Management, vol. no. 20(3), pp. 548-573.

186

HATZIUS, J., HOOPER, P., MISHKIN, F., SCHOENHOLTZ, K., AND WATSON, M. (2010), “Financial Conditions Indexes: A Fresh Look After the Financial Crisis”, NBER Working Paper Series.

HAVLAT, M., HAVRLANT, D., KUENZEL, R., AND MONKS, A. (2018), “Economic

Convergence in the Czech Republic and Slovakia”. Economic Brief, European Commission, Directorate-General for Economic and Financial Affairs.

HAYS, J. (2008), “Economic History of the Philippines”, Retrieved from

http://factsanddetails.com/southeast-asia/Philippines/sub5_6g/entry-3916.html.

JANNSEN, N., POTJAGAILO, G., AND WOLTERS, M. H. (2015), “Monetary

Policy during Financial Crises: Is the Transmission Mechanism Impaired?”, Working Paper, Kiel Institute for the World Economy, Kiel.

JAVID, M., AND MUNIR, K. (2010), “The Price Puzzle and Monetary Policy

Transmission Mechanism in Pakistan: Structural Vector Autoregressive Approach”, The Pakistan Development Review, pp. 449–460.

JERMANN, U., AND QUADRINI, V. (2012), “Macroeconomic effects of financial

shocks”, The American Economic Review, vol. no.102(1), pp. 238–271. JIMENEZ, G., ONGENA, S., PEYDRO, J.-L., AND SAURINA, J. (2014),

“Hazardous times for monetary policy: What do twenty-three million bank loans say about the effects of monetary policy on credit risk-taking?”, J. Econometrica vol.no. 82(2), pp. 463–505.

KAZI, I. A., WAGAN, H., AND AKBAR, F. (2013). “The changing international

transmission of U.S. monetary policy shocks: Is there evidence of contagion effect on OECD countries”, Economic Modelling, vol. no. 30(6), pp. 90–116.

KILIAN, L. (1998), “Small Sample Confidence Intervals for Impulse Response Functions”, The Review of Economics and Statistics, pp. 218-230.

KIM, S. (2001), “International Transmission Of U.S. Monetary Policy Shocks:

Evidence From VAR'S”, Journal Of Monetary Economics, vol. no.48, pp. 339–372.

KING, R. G., AND R. LEVINE. (1993), "Finance and growth: Schumpeter Might Be Right", Quarterly Journal of Economics, vol. no. 108: pp. 717-737.

KLACSO, JAN. (2013), "Mathematical Analysis of the Transmission Mechanism of Monetary Policy of the National Bank of Slovakia." Ph. D. Thesis, Department of Applied Mathematics and Statistics, Comenius University, Slovakia.

http://factsanddetails.com/southeast-asia/Philippines/sub5_6g/entry-3916.html

http://factsanddetails.com/southeast-asia/Philippines/sub5_6g/entry-3916.html

187

KOEN, V., ASADA, H., NIXON, S., RAHUMAN, M. R., AND ARIF, A. Z. (2017), “Malaysia's Economic Success Story and Challenges”, Working Paper, Organization for Economic Co-operation and Development.

KOERNER, J. (2015), “Monetary Transmission in the Czech Republic after the

Transformation”, Eastern European Business and Economics Journal, vol. no. 1(3), pp.19-47.

KOOP, G. (2003), “Bayesian Econometrics”.Chichester: John Wiley & Sons Ltd. KOOP, G., AND KOROBILIS, D. (2014), “A new index of financial conditions”,

European Economic Review, vol. no. 71(C), pp. 101-116. KSEL, E. Y., METIN-OZCAN, K., AND HATIPOGLU, O. (2013), “A survey on time-

varying parameter Taylor rule: A model modified with interest rate pass-through”, J. Economic Systems, vol. no. 37, pp. 122-134.

LAURENT, R. D. (1988), “An Interest Rate Based Indicator of Monetary Policy”,

J. Economic Perspective, pp. 3-14. LUPORINI, V. (2008), “The Monetary Transmission Mechanism in Brazil:

Evidence from a VAR Analysis”, J. EST. ECON., SAO PAULO, vol. no. 38(1), pp. 7-30.

MARFATIA, H. A. (2015), “Monetary policy’s time-varying impact on the US bond

markets: Role of financial stress and risks”, North American Journal of Economics and Finance, vol. no. 34, pp.103-123.

MARKWAT, T., KOLE, E., AND DIJK, D. V. (2009), “Contagion As Domino Effect

In Global Stock Markets”, ERIM Report Series Research in Management, Rotterdam.

MARTINELLI, C., AND VEGA, M. (2018),”Monetary and Fiscal History of Peru 1960-2010: Radical Policy Experiments, Inflation and Stabilization”, George Mason University, Interdisciplinary Center for Economic Science.

MATHESON, T. (2011), “Financial Conditions Indexes for the United States and

Euro Area”, International Monetary Fund. MAYES, D. G., AND VIREN, M. (2001), “Financial condition Index”, Bank of

Finland, Discussion Paper, 17/01. MCKINNON, R. I. (1973), “Money and Capital in Economic Development”,

Washington, DC: Brookings Institution. MEENAI, S. A. (2010), “Money and Banking in Pakistan’, (J. A. Ansari, Ed.)

Karachi: Oxford University Press.

188

MORGAN, D. P. (1993), “Asymmetric effects of monetary policy”, vol. no. 78, pp.

21-33. MURARU, A. (2015), “Building a Financial Conditions Index for Romania”,

Occasional Paper, National Bank of Romania. NAKAJIMA, J. (2011, November), “Time-Varying Parameter VAR Model with

Stochastic Volatility: An Overview of Methodology and Empirical Applications”, Monetary and Economic Studies Journal, pp.107-142.

NELSON, R. M., BELKIN, P., & MIX, D. E. (2011), “Greece’s Debt Crisis:

Overview, Policy Responses, and Implications”, Congressional Research Service.

NERI, S., AND NOBILI, A. (2010), “The Transmission Of US Monetary Policy To

The Euro Area”, International Finance Journal, vol. no. 13(1), pp. 55–78. OECD. (2017), “OECD Economic Surveys Mexico”. OECD. (2018), “OECD Economic Surveys Poland”. OLINER, S. D., AND RUDEBUSCH, G. D. (1996), “Is There Broad Credit Channel

of Monetary Policy?”. OLMO, J., AND SANSO-NAVARRO, M. (2015, August), “Changes in the

transmission of monetary policy during crisis episodes: Evidence from the euro area and the U.S. Economic Modelling”, vol. no.48, pp. 155-166.

ONO, S. (2013), “The effects of foreign exchange and monetary policies in Russia”, Economic Systems Journal , vol. no. 37, pp. 522-541. OPSCHOOR, A., DIJK, D. V., AND WEL, M. V. (2014, December), “Predicting

Volatility and Correlations with Financial Conditions Indexes”, Journal of Empirical Finance, vol. no. 29, pp. 435-447.

OWYANG, M. T., AND WALL, H. J. (2009), “Regional VARs and the Channels of

Monetary Policy”, Applied Economics Letters, vol. no. 16(12), pp. 1191–1194.

PEERSMAN, G. (2004), “The transmission of monetary policy in the euro area:

are the effects different across countries?”, Oxford Bulletin of Economics and Statistics, vol. no. 66(3), pp. 285–308.

PESARAN, M. H., AND SHIN, Y. (1998), “Generalized Impulse Response

Analysis in Linear Multivariate Models”, Economics Letters, vol. no. 58(1), pp. 17–29.

189

PHIROMSWAD, P. (2015), “Measuring Monetary Policy with Empirically Grounded Restrictions: An Application to Thailand”, Journal of Asian Economies, vol. no. 38, pp. 104-113.

POTTS, T., AND YERGER, D. (2010), “Variations across Canadian regions in the

sensitivity to U.S. monetary policy”, Atlantic Economic Journal, vol. no. 38, pp. 443–454.

PRIMICERI, G. E. (2005), “Time varying structural vector auto regressions and

monetary policy”, The Review of Economic Studies, vol. no. 72(3), pp. 821–852.

RAJAN, R. G. (2005), “Has financial development made the world riskier?”,

Proceedings-Economic Policy Symposium, pp. 313–369. Kansas: Jackson Hole, Federal Reserve Bank of Kansas City.

REINHART, C. M., AND ROGOFF, K. S. (2008), “Is the 2007 US sub-prime

financial crisis so different? an international historical comparison”, American Economic Review, vol. no. 98(2), pp. 339-344.

REINHART, C., AND ROGOFF, K. (2010), “This time is different: Eight Centuries

of Financial Folly”, Princeton University Press. RIGOBON, R., AND SACK, B. (2004), “The impact of monetary policy on asset

prices”, Journal of Monetary Economics, vol. no. 51, pp. 1553–1575. RODOLFO CERMENO, F. A., AND POLO, J. O. (2012), “Monetary Policy Rules

in A Small Open Economy: An Application to Mexico”, Journal of Applied Economics, vol. no. 15(2), pp. 259-286.

JOHN H. ROGERS, J. H., SCOTTI, C., WRIGHT, J. H., (2018), “Unconventional

Monetary Policy and International Risk Premia”, Journal of Money, Credit and Banking, vol. no. 50(8), pp. 1827-1850.

ROGOFF. (2002), “Dornbusch’s Overshooting Model after Twenty-Five Years”,

Retrieved from IMF.Org: http://www.imf.org/external/np/speeches/2001/112901.html.

SAEED, K. A. (2013), “The Economy of Pakistan. Karachi”, Oxford University

Press. SERBAN, E., BORISOV, D., AND DOBREA, R. (2012), “Emerging Markets:

Concept Evolution and Business Environment Particularities”, Management Research and Practice Journal, vol. no. 4(2), pp. 5-19.

SICHEI, G. A. (2005), “An Econometric Model of the Rand-US Dollar Nominal

Exchange Rate”, Retrieved from Web.up.ac.za.:

http://www.imf.org/external/np/speeches/2001/112901.htm

190

http://www.up.ac.za/dspace/bitstream/2263/4509/1/Sichei_Econometric%282005%29.pdf.

SIMS, C. A. (1980), “Macroeconomics and Reality”, J. Econometrica, vol. no. 48,

pp. 1-48. SIMS, C. A. (1992, June), “Interpreting the Macroeconomic Time Series Facts:

The Effects of Monetary Policy”, European Economic Review (36), pp. 975-1000.

SIMS, C. A., AND ZHA, T. (2006), “Does Monetary Policy Generate

Recessions?”, Journal Macroeconomic Dynamics (second), pp. 231-272. STEINER, R., AND VALLEJO, H. (2010), "The Economy. In Colombia: A Country

Study”, (Rex A. Hudson ed.). Library of Congress Federal Research Division.

STOCK, J. H. (2001), “Discussion of Sargent and Cogley’s ‘Evolving Post-World

War II U.S. Inflation Dynamics”, NBER Macroeconomics Annual, vol. no. 16, pp. 379-387.

STOCK, J. H., AND WATSON, M. W. (2002, April), “Macroeconomic Forecasting

using Diffusion Indexes”, Journal of Business and Economic Statistics, vol. no. 20(2), pp. 147-162.

STOCK, J., AND WATSON, M. (2003), “Forecasting output and inflation: the role

of asset prices”, Journal of Economic Literature, vol. no. 41(3), pp. 788–829.

SWISTON, A. (2008), “A US Financial Conditions Index: Putting Credit Where

Credit is Due”, IMF Working Paper. TESOGLU, H. S. (2004), “Monetary transmission-federal funds rate and prime

rate”, Journal of Post Keynesian Economics, vol. no. 26(2), pp. 357-362. TODOROV, GALIN KOSTADINOV. (2012), “A Study of Stock Market Linkages

between the US and Frontier Markets”, PhD Thesis, Florida International University.

TU, W., AND FENG, J. (2009, February), “An Overview Study on Dornbusch

Overshooting Hypothesis”, International Journal of Economics and Finance, vol. no. 1(1), pp. 110-116.

UNITED NATION GENERAL ASSEMBLY (2014), “Open Working Group of the

General Assembly on Sustainable Development Goals”, United Nation.

http://www.up.ac.za/dspace/bitstream/2263/4509/1/Sichei_Econometric%282005%29.pdf

http://www.up.ac.za/dspace/bitstream/2263/4509/1/Sichei_Econometric%282005%29.pdf

191

VALENCIA, F. (2013), “Aggregate uncertainty and the supply of credit”, IMF Working Paper 13/241.

VAVRA, J. (2014), “Inflation dynamics and time-varying volatility: New evidence

and an SS interpretation”, Quarterly Journal of Economics, vol. no. 129(1), pp. 215-258.

W., D., AND HATZIUS, J. (2000), “The Goldman Sachs Financial Conditions

Index: The Right Tool for a New Monetary Policy Regime”, Global Economics Paper, pp. 44.

WAGAN, H., AND ALI, Z. (2014), “Are Emerging Markets Exposed to Contagion

from the United States: Evidence from Stock and Sovereign Bond Markets”, In Mohammed El Hedi Arouri, S. Boubaker, and D. K. Nguyen (Eds.), Emerging Markets and the Global Economy pp. 509-536, Academic Press.

WOLF, M. (2012, May 1), “The Financial Times”. WORLD ECONOMIC FORUM (2009), “The Global Competitiveness Report”. Y., A., GAMBACORTA, L., AND MARQUES-IBANEZ, D. (2014, March), “Does

monetary policy affect bank risk?”, International Journal of Central Banking, pp. 95-135.

YIU, MATTHEW S., WAI-YIP ALEX HO, AND DANIEL F. CHOIC. (2010),

"Dynamic correlation analysis of financial contagion in Asian markets in global financial turmoil." Applied Financial Economics Journal, vol. no. 20, pp. 345-354.

VONNÁK, B. (2005), “Estimating the effect of Hungarian monetary policy within a

structural VAR framework”, Working Paper. YUKSEL, E., METIN-OZCAN, K., AND HATIPOGLU, O. (2013), “A survey on

time-varying parameter Taylor rule: A model modified with interest rate pass-through”, Economic Systems Journal, vol. no. 37, pp. 122-134.

ZAIDI, S. A. (2005), “Issues in Pakistan's Economy”, Karachi: Oxford University

Press. ZAKIR, N., AND MALIK, W. S. (2013), “Are the effects of monetary policy on

output asymmetric in Pakistan?”, Economic Modelling Journal, vol. no. 32, pp. 1-9.

ZUMBACH, M. S., DACOROGNA, M. M., OLSEN, J. L., AND OLSEN, R. B.

(2000), “Measuring Shock In Financial Market”, International Journal Of Theoretical And Applied Finance, vol. no. 3(3), pp. 347-355.

192

APPENDICES Appendix IV-A

Part A: Domestic Transmission

Brazil

Table 1: Estimation results for the parameters of the TVP-VAR model ---------------------------------------------------------------------- Parameter Mean Stdev 95%U 95%L Geweke Inef. ---------------------------------------------------------------------- sb1 0.1714 0.1046 0.0413 0.4459 0.000 231.53 sb2 0.1533 0.0756 0.0586 0.3469 0.418 143.75 sa1 0.0055 0.0017 0.0034 0.0097 0.073 22.60 sa2 0.0055 0.0016 0.0034 0.0097 0.689 11.96 sh1 0.0054 0.0015 0.0034 0.0093 0.253 20.85 sh2 0.0057 0.0018 0.0034 0.0101 0.568 21.44

Source: Author’s Compilation

Figure (1) – Results of MCMC draws in Brazil- Sample autocorrelations (top), sample paths (middle), and posterior densities (bottom)

193

Colombia

Table 2: Estimation results for the parameters of the TVP-VAR model ---------------------------------------------------------------------- Parameter Mean Stdev 95%U 95%L Geweke Inef. ---------------------------------------------------------------------- sb1 0.1111 0.0448 0.0454 0.2160 0.045 85.24 sb2 0.2512 0.1266 0.0755 0.5481 0.000 150.22 sa1 0.0056 0.0018 0.0034 0.0100 0.761 32.64 sa2 0.0056 0.0019 0.0034 0.0097 0.349 26.08 sh1 0.0055 0.0014 0.0034 0.0090 0.252 15.09 sh2 0.0056 0.0017 0.0034 0.0100 0.664 23.45 Source: Author’s Compilation

Figure (2) – Results of MCMC draws in Colombia- Sample autocorrelations (top), sample paths (middle), and posterior densities (bottom)

194

Czech Republic



Figure (3) – Results of MCMC draws in Czech Republic- Sample autocorrelations (top), sample paths (middle), and posterior densities (bottom)

195

Hungary



Figure (4) – Results of MCMC draws in Hungary- Sample autocorrelations (top), sample paths (middle), and posterior densities (bottom)

196

Malaysia



Figure (5) – Results of MCMC draws- Sample autocorrelations (top), sample paths (middle), and posterior densities (bottom)

197

Mexico




198

Pakistan


Figure (7) –

Results of MCMC draws- Sample autocorrelations (top), sample paths (middle), and posterior densities (bottom)

199

Peru


-------------------------------------------------------------- Source: Author’s Compilation


200

Philippines

Table 9: Estimation results for the parameters of the TVP-VAR model

---------------------------------------------------------------------- Parameter Mean Stdev 95%U 95%L Geweke Inef. ---------------------------------------------------------------------- sb1 0.1223 0.0640 0.0339 0.2715 0.001 179.52 sb2 0.2207 0.1247 0.0462 0.4946 0.991 247.99 sa1 0.0056 0.0024 0.0033 0.0101 0.838 26.73 sa2 0.0056 0.0018 0.0034 0.0102 0.685 30.40 sh1 0.0062 0.0104 0.0034 0.0101 0.198 35.53 sh2 0.0057 0.0023 0.0034 0.0104 0.773 29.45



201

Poland




202

Russian Federation




203

Turkey



204

Part B: International Transmission

US to Brazil:




205

US to Colombia:




206

US to Czech Republic:




207

US to Hungary:




208

US to Malaysia:



Figure (17) –

Results of MCMC draws- Sample autocorrelations (top), sample paths (middle), and posterior densities (bottom)

209

US to Mexico:




210

US to Pakistan:




211

US to Peru:



212

US to Philippines:




213

US to Poland:




214

US to Russian Federation:

Table 23: Estimation results for the parameters of the TVP-VAR model ---------------------------------------------------------------------- Parameter Mean Stdev 95%U 95%L Geweke Inef. ---------------------------------------------------------------------- sb1 0.2592 0.1552 0.0395 0.5933 0.000 285.33 sb2 0.5321 0.2465 0.0951 1.0534 0.000 271.79 sa1 0.0053 0.0015 0.0033 0.0089 0.525 15.74 sa2 0.0056 0.0018 0.0035 0.0100 0.002 28.46 sh1 0.0055 0.0016 0.0034 0.0094 0.373 25.89 sh2 0.0056 0.0018 0.0034 0.0101 0.982 23.44 -------------------- Source: Author’s Compilation


215

US to Turkey:



216

Appendix IV-B

a) Simultaneous Relation in domestic countries:

Figure (1) – Simultaneous Relation of Brazil

217

Figure (2) Simultaneous Relation of Colombia

Figure (3) Simultaneous Relation of Czech Republic

218

Figure (4) Simultaneous Relation of Hungary

Figure (5) Simultaneous Relation of Malaysia

219

Figure (6) Simultaneous Relation of Mexico

Figure(7) – Simultaneous Relation of Pakistan

220

Figure (8) Simultaneous Relation of Peru

Figure (9) Simultaneous Relation of Philippines

221

Figure (10) Simultaneous Relation of Poland

222

Figure (11) Simultaneous Relation of Russian Federation

Figure (12) Simultaneous Relation of Turkey

223

b) Simultaneous Relation in International Linkage:

Figure (13) – Simultaneous Relation from US to Brazil

Figure (14) Simultaneous Relation from US to Colombia

224

Figure (15) – Simultaneous Relation from US to Czech Republic

Figure (16) Simultaneous Relation from US to Hungar

225

Figure (17) Simultaneous Relation from US to Malay

Figure (18) Simultaneous Relation from US to Mexico

226

Figure (19) Simultaneous Relation from US to Pakistan

Figure (20) Simultaneous Relation from US to Peru

227

Figure (21) Simultaneous Relation from US to Philippines

Figure (22) Simultaneous Relation from US to Poland

228

Figure (23) Simultaneous Relation from US to Russian Federation

Figure (24) Simultaneous Relation from US to Turkey

229

Appendix IV-C

Stochastic Volatility

Figure 25- Stochastic Volatility of Brazil

Figure 26- Stochastic Volatility of Colombia

230

Figure 27- Stochastic Volatility of Czech Republic

Figure 28- Stochastic Volatility of Hungary

Figure 29- Stochastic Volatility of Malaysia

231

Figure 30- Stochastic Volatility of Mexico

Figure 31- Stochastic Volatility of Pakistan

Figure 32- Stochastic Volatility of Peru

232

Figure 33- Stochastic Volatility of Philippines

Figure 34- Stochastic Volatility of Poland

233

Figure 35- Stochastic Volatility of Russian Federation

Figure 36- Stochastic Volatility of Turkey

Figure 37- Stochastic Volatility of US-Brazil

Figure 38- Stochastic Volatility of US-Colombia

234

Figure 39- Stochastic Volatility of US-Czech Republic

Figure 40- Stochastic Volatility of US-Hungary

235

Figure 41- Stochastic Volatility of US-Malaysia

Figure 42- Stochastic Volatility of US-Mexico

236

Figure 43- Stochastic Volatility of US-Pakistan

Figure 44- Stochastic Volatility of US-Peru

237

Figure 45- Stochastic Volatility of US-Philippines

Figure 46- Stochastic Volatility of US-Poland

Figure 47- Stochastic Volatility of US-Russian Federation

238

Figure 48- Stochastic Volatility of US-Turkey

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