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CHAPTER ONE
INTRODUCTION
1.1 INTRODUCTION
The relationship between interest rate and stock market has become the focus of
interest among recent researchers in identifying the long-run implications between
these two variables. This study has become essential in stock market after the 1997
stock market crash in Asean countries. The interest rate are said to be linked with
the deposit rates, lending rates and discount rates .Interest rates are also
fundamental to a capitalist society with variables like investment, inflation and
unemployment. As for stock market, it normally serves as a channel to direct funds
from individuals to investors by mobilizing individual owned resources. This
implies that stock market must have a significant relationship with real and
financial sectors and the entire economy. The main stock prices in ASEAN-5
countries are namely Malaysia (KLCI), Indonesia (IDDOW), Singapore (SGDOW),
Philippines (PSEi) and Thailand (SET).
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1.1.1 Stock Market
A stock market is a place where stocks, shares and other financial instruments are
traded through exchanges and over the counter.1A stock market normally serves
as a channel to direct the funds from individuals to investors by mobilizing
individual owned resources.2 Therefore, through these function of stock market,
changes in interest rate can give a major effect to the performance of the financial
sectors of the economy. It is a public market for the trading of company stock and
derivatives at an agreed price; these are securities listed on a stock exchange as well
as those only traded privately.
__________________________1 Wikipedia2 Euro Journals
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1.1.2 Interest Rate
An interest rate is the rate at which interest is paid by a borrower for the use of
money that borrows from a lender. 3Interest rates are fundamental to a capitalist
society. Interest rates are normally expressed as a percentage rate over the period of
one year. Interest rates are also a vital tool of monetary policy and are taken into
account when dealing with variables like investment, inflation and unemployment.
The proxies of interest rates are deposit rate, lending rate and discount rate.
________________________3 Wikipedia
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1.1.3 ASEAN -5
The term ASEAN-5 countries namelyMalaysia, Indonesia, Singapore, Philippines
and Thailand was founded in August 1967 and known as a region of promising
potential. Association for South East Asian Nations (ASEAN) is a political and
economic organization of 10 countries in South East Asia formed originally by
Indonesia, Malaysia, Philippines, Singapore, and Thailand.4 The ASEAN-5 has now
undergone transformations in economic development, in which each country has
experienced economic growth due to the adoption of export-oriented trade policy,
the rapid flow of foreign direct investment (FDI), and sound macroeconomic
policies.5
________________________
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4Asia Econ5E.U-Tokyo
1.2 PROBLEM STATEMENT
A study on interest rate and the stock market has been a major issue in economics.
This study has been undertaken to examine the impacts of interest rate on stock
market in selected ASEAN- 5 countries. If, the stock market has a relationship with
interest rate, hence, the stock market may no longer efficient. As well, statistical
methods of measurement have been employed in order to comprehend the
correlation between interest rate and stock market return in a particular country.
The stock market reaction towards interest rate is observed to identify the
movement of this co integration in short term causality and long term linkages.
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1.3 RESEARCH OBJECTIVES
General objective
To analyze the relationship between interest rate and stock market return.
Specific objective
To observe the long run cointegration relationship between interest rate and
stock market return.
To examine the causality between interest rate and stock market return.
1.4 SIGNIFICANCE OF THE STUDY
This study has central idea for understanding how interest rate can influence the
stock market, since interest rate is one of the macroeconomic indicator that plays an
important role of emerging stock market as well as stock market is considered as
the primary indicator of a countrys economic strength and development.
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1.5 SCOPE OF THE STUDY
In this study, the research is conducted in term to study the correlation among
interest rate and stock market in Asian five countries which consists of Malaysia,
Indonesia, Singapore, Philippines and Thailand. These founding members are
developed countries in term of their financial instruments and one of the leading
countries. It begins with resource and report of data, which comprises year of the
study from January 2000 until December 2009, as monthly in these countries.
Subsequently is the clear explanation to the model specification, which, the sign of
coefficient is affirmed based to earlier theoretical frameworks. As a final point, the
causality test is used to examine on the efficiency of the stock prices, where the
hypothesis stated that if there is a causal relationship between stock prices and
interest rate, hence, the stock market is consider no longer efficient.
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1.6 FORMAT OF PROJECT PAPER
This paper has been organized into a few chapters in order to make it convenient to
be used as a reference in future studies.
Chapter Two reviews the relevant literature done in previous studies. In this
chapter, the literature reviews summarize related studied and analysis to provide a
better understanding as well as increase breadth of knowledge in undertaking the
study.
Chapter Three provides a brief description on the data and methodology employed
in the analysis of this study. This chapter further presents the empirical results and
findings with illustration of tables and graph.
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CHAPTER TWO
LITERATURE REVIEW
2.1 PREVIOUS STUDIES
Gupta and Sayekt (1997) examined the relationship between the interest rate, exchange rate
and stock price in the Jakarta Stock exchange from 1993 to 1997.This study was
undertaken by applying Granger-Causality tests and auto regressive integrated moving
average (ARIMA) model testing as the nature of time series data is necessary to test
stationary of each variable. The empirical evidence suggests that in most of the instances
there were no strong causal relationship between stock price and the interest rate or
exchange rate.Also,in this research, the results of Granger Causality indicate that only in
sub period one and three causality relationship between the variables under study have
been observed. The findings also indicate that the interest rates trend to have more
causality relationship with the stock price rather than exchange rate.Finally,causality
evidence fails to establish that historical information generally offers sufficient significant
a significant short-term predictive content for stock price.
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Khrawish et al. (2010) studied on the effect of interest rates on stock market
capitalization rate in Amman Stock Exchange (ASE) from 1999 to 2008.This study
was undertaken by applying Multiple Regression Model, Simple Regression Model
and Ordinary Least-Square (OLS) regression method. In this study, they found that
there is significant and positive relationship between government prevailing interest
rate and stock market capitalization rate. They also found that government stock
rate exerts negative influence on stock market capitalization rate where it shows
significant and negative relationship between government prevailing interest rate
and government development stock rate.Hence, they conclude that policy directions
might encourage the supply of investment funds through significant favoring
control of interest rate in order to stimulate the growth of stock market.
Ciffer et al. (2007) conducted a study to investigate the impacts of changes in
interest rates on stock returns by applying Wavelet analysis, Granger-Causality test,
Unit root test and Cointegration test. In this study, they found that interest rates
have a crucial role in determining stock returns. They also found that by using daily
closing values of the ISE 100 Index and compounded interest rates, it is proven that
starting with 9 days time-scale effect, interest rate is granger cause of ISE 100
Index and the effects of interest rates on stock return increases with higher time-
scales.Finally, the evidence of bond market has significant long-term effect on
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stock market for Turkey and traders should consider long-term money market
changes as well as short-term changes.
Alam and Uddin (2009) revised on market efficiency of fifteen countries and also
effect of interest rate on share price and changes of interest rate on changes of share
price. They analyzed the market efficiency by utilizing on country-wise Time
Series Analysis, Panel Data Analysis and market efficiency test. In this study, they
applied Random Walk Model. The randomness of stock return is the basic
assumption of Efficient Market Hypothesis that is violated for all countries where
these markets are not efficient in weak form. From this study, they found that for all
countries interest rate has significant negative relationship with changes of share
price.Further, if the interest rate is controlled for these countries, it will benefit in
share market.
Ologunde et al. (2006) investigated the relationships between stock market
capitalization rate and interest rate. They analyzed the relationships from 1981 to
2000 by utilizing on Time Series data and Stock Market Efficiency obtained from
Central Bank of Nigeria (CBN) and Nigeria Stock Exchange (NSE).In this study,
they used Ordinary Least-Square (OLS) regression method to investigate the
relationships between stock market and interest rate. They found that interest rate
and government development stock rate of the stock market is controlled as well as
the great benefit of the stock market will be enhanced. They drawn a conclusion
that in all stages of economic growth in Nigeria, great reliance has been placed on
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the stock market as the medium interaction of long term funds within the units to
achieve optimal financial growth.
Ahmad et al. (2010) had investigated the relationship between stock return, interest
rate and exchange rate in Pakistani economy over the period of 1998 to 2009.They
estimated data of short term interest rate and stock market returns (KSE-100) by
using Multiple Regression Model, to test the significance of change in interest rate
and exchange on stock returns. Their findings show that both the change in interest
rate and change in exchange rate has a significant impact on stock returns over the
sample period.Moreover,they discovered that changes in interest rate has a negative
impact while change in exchange rate has positive impact. They concluded that the
increase interest rate increases the cost of business and lowers the returns while a
decline in interest rate gives a positive sign to the a stock market and stock returns
increases eventually.
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Banerjee and Adhikary (2006) studied the dynamic effects of interest rate on
Bangladesh Stock Market (Dhaka Stock Exchange) returns. The data used for this
study consists of monthly data from January 1983 to December 2006.They used
Cointegration method and Vector Error Correction model (VECM) to see the causal
relationship between dependent variable and two independent variable. Their
findings shows that the long-run equilibrium relationship among the variables from
interest rate and stock market returns in Bangladesh does exist.Further, they seem
to have no significant influences on the stock market in the short run of interest rate
in Bangladesh.
Kazi (2009) conducted a study to identify the influential risk factors for the
Australian Stock Market. The data used in the analysis consists of quarterly data
from 1983 to 2002.They applied Cointegration technique, Philip-Perron (PP),
Augmented Dickey-Fuller (ADF) and Unit root test to check for stationary for the
interest rate and stock market index series. From this study, they drawn a
conclusion that the bank interest rate, dividend yield, and global market influence
are significant for the Australian Stock Market returns in the long-run whereas the
stock prices are adjusted each quarter by its own market performance where the
interest rate and global stock market movements of previous quarter.
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Hamrita et al. (2008) had examined the multi-scale relationship between the interest
rate, exchange rate and stock price for the period of 1990:1-2008:12.This study was
undertaken by applying Wavelet Cross-Correlation transform and Granger causality
test. Their finding shows that the relationship between interest rate is not
significantly different from zero at all scales.Hence, the relationship between
interest rate returns and stock index returns is significantly different zero only at the
highest scales.
Chiarella et al. (2002) investigated well known model of the stock market, interest
rate and output interaction. This study was undertaken by applying Blanchard
model, RBC models and VAR models. Their finding shows that the model captures
a number of features of the data. They illustrates that the changes in stock market is
dependent on the state of the economy. They concluded that the nonlinear models
perform reasonably well on most of the measures which is the interest rate, stock
price and output interaction.
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2.1.1 Literature Review Summary Table
SOURCE/ YEAR PURPOSE OF
STUDY
METHODS OF
ANALYSIS
FINDINGS
Gupta and Sayekt
(1997)
To examined the
relationship
between interest
rate,
exchange rate
and stock price in
Jakarta
Stock Exchange
from
1993 to 1997.
Granger-
Causality test
Auto
Regressive
Integrated
Moving
Average
(ARIMA)
The analysis shows
that interest rates
trend to have more
causality
relationship with
the stock price
rather than
exchange rate.Khrawish et al.(2010) To study on the
effects of interest
rates on stock market
capitalization rate in
Amman Stock
Exchange (ASE) from
1999 to 2008.
Multiple
Regression Model
Simple
Regression Model
Ordinary Least-
Square (OLS)
regression method
Government stock rate
exerts negative
influence on stock
market capitalization
rate where it shows
significant and
negative relationship
between interest rate
and stock rate.
Cifter et al. (2007) To investigate the
impacts of changes in
interest rates on stock
Granger-Causality
test
Unit Root Test
The interest rate is
granger cause of
ISE 100 Index and
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returns by applying
Wavelet Analysis.
Co Integration
test
the effects of
interest rates on
stock returns
increases with
higher time-scales.
Alam and Uddin
(2009)
To investigate the
market efficiency of
fifteen countries and
also effect of interest
rate on share price
and changes of interest rate on
changes of share
price.
Random Walk
(model)
They found that for all
countries interest rates
has significant negative
relationship with
changes of share price.
Ologunde et al.
(1998)
To study on the
relationships between
stock market
capitalization rate
and interest rate from
1981 to 2000.
Ordinary Least-
Square (OLS)
Regression
method
Interest rate and
government
development stock rate
of the stock market is
controlled as well as the
great benefit of the
stock market will be
enhanced.
Ahmad et al.(2010) To investigate the
relationships between
stock return, interest
Multiple
Regression(model)
The increase interest
rate increases the cost
of business and lowers
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rate and exchange rate
in Pakistani economy
over the period of
1998 to 2009.
the returns while a
decline in interest rate
gives a positive sign to
the stock market and
stock returns increases
eventually.
Banerjee and
Adhikary (2006)
To study the
dynamic effects
of interest rate
on Bangladesh
stock market
(Dhaka StockExchange)
returns consists
of monthly data
from January
1983 to
December
2006.
Co Integration
method
Vector Error
Correction
Model (VECM)
Long run
equilibrium
relationship among
the variables from
interest rate and
stock market
returns in
Bangladesh does
exist.
Kazi (2009) To identify the
influential risk factors
for Australian Stock
Market.
Philip- Perron
(PP)
Augmented
Dickey-Fuller
(ADF)
Unit Root Test
Co Integration
method
The interest rate
are significant for
the Australian
stock
Market returns in
the long-run.
Hamrita et al. (2008) To examine the multi-
scale relationship
between interest rate
and stock price for the
period of 1990:1 to
Wavelet Cross-
Correlation
transform
Granger-Causality
The relationship
between interest rate
returns and stock index
returns is significantly
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2008:12. test different zero only at
the highest scales.
Chiarella et al.(2002) To investigate well-
known model of stock
market, interest rate
and output
interaction.
Blanchard model
RBC model
VAR model
Nonlinear models
performs
reasonably well on
most of the
measures which is
the interest rate,
stock price and
output interaction.
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CHAPTER THREE
DATA AND METHODOLOGY
3.1 INTRODUCTION
This chapter provides the facts on the method that will be applied in this study,
where the procedures is clearly stated and defined. It starts with source and
description of data, which includes year of the study from January 2000 until
December 2009 in ASEAN-5 countries, as well as background of the data.
Subsequently, is the explanation to the model specification, where the sign of
coefficient is stated based to earlier theoretical frameworks. Finally, efficiency of
the stock market is tested by using co integration test, whereby the hypothesis
stated that if there is a causal relationship between stock market return and interest
rate, hence, the stock market is consider no longer efficient.
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3.2 SOURCE OF DATA
The data employed in this study are consisting of monthly observations of changes
in stock price; Malaysia (INDEX: KLCI), Indonesia (INDEX: IDDOW), Singapore
(INDEX: SGDOW), Philippines (INDEX: PSEi) and Thailand (INDEX: SET) and
interest rate, from 2000: 1 to 2009: 12. The data of market index for each country
are obtained from the International Financial Statistic, IMF.
3.2.1 DEFINITION OF THE VARIABLES
Independent variable
Independent variable defined as variable whose quantitative value is independently
controlled by the researcher. Independent variable for this study will be the changes
in stock price which set on monthly basis for Malaysia, Indonesia, Singapore,
Philippines and Thailand.
Dependent variable
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Dependent variable defined as variable whose quantitative value is expected to
depend on the effects of the independent variable. In this study, there is main
selected dependent variable, namely interest rate.
3.2.2 MODEL SPECIFICATION
The objective of this study is to test the co integration between interest rate ( fi )
and stock market ( SP ).The investigation on the long run relationship between
interest rate fluctuations and stock market is based on simple general model where
the stock market ( SP ) is dependent variables, and is a function of interest rate
( fi ).Changes in interest rate may affect stock prices through changes in portfolio
substitution or inflationary expectation. The model has been illustrated as below:
SPt = f ( fit) (3.1)
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Regression Model
Where SPrepresents changes in stock prices andfi represents the interest rate; tis
the time trend. As for interest rate, in general it has been indicated to have negative
or positive effect on stock market return. A study done by Banerjee and Adhikary
(2006) has proved that interest rate is confirmed to have a long run equilibrium
relationship with stock price. Prior to the analysis, all data are transformed into
natural linear regressions, as such the equation becomes:
Y= 0 + 1 I1 + (3.2)
In this case, 0, 1 are the parameters to be estimated; is the missing or error
values, y represents dependant variable (stock market return) and I represents
independent variables (I
= interest rate).
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3.3 FRAMEWORK OF ANALYSIS
3.3.1 Stationary and Unit Root Test
Whether a variable is stationary or non- stationary is very crucial. This is the case
where if variables in a model are non- stationary, then the usual standard
econometrics methods do not apply. Hence, it is very important to check the time
series properties (integration and co integration) not only to see whether the
variables establish a long- run equilibrium but even more important is to avoid
spurious and misleading inference. Therefore, stationarity of a time series is
important implications upon modeling of time series analysis. The shock that
occurred in stationary time series is impermanent in nature and would dispel
(Enders, 1995). Then it will revert to its long- run mean level, in which the long-
term forecasts of this stationary time series would converge to its unconditional
mean. In one hand, a stationary time series has a finite variance that is time-
invariant and theoretical correlogram that will diminish as lag length increases, and
mean reversion fluctuates around a constant long-run mean. On the other hand, a
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non- stationary series posses a permanent component that did not possess a long run
mean to which the series returns, and its theoretical autocorrelation may not
decompose, but in finite samples, the correlogram samples will eliminate slowly. In
addition, it has time dependant variance that goes to infinity as time approaches
infinity. Simple approach defining stationary as below;
The variableytis stationary if it possesses the following three properties;
a. E(Yt) = , the mean valueytis constant and independent of time,
b. Var(Yt) = 2 , the variance ofyt, is constant across time,
c. Cov(Yt , Yt-s) = s , the covariance is dependant only on the distance s between
the observation and independent of time t
It should be noted that the variable is non- stationary if one or more than one of the
above mentioned conditions are not fulfilled. If the variables in an econometric
model are stationary then the standard distribution of the test statistics are valid.
However, this is not the case if the variables are non- stationary. That is the
application of conventional econometric techniques to non- stationary (integrated)
time series can give rise to misleading inference. It is therefore essentially
important to test for non-stationary. One way to make a non-stationary time series
to a stationary time series is to take differences of the variables. However, this will
lead to lots of long-run information.
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In order to test the stationary of a time series, unit root test is applied. To illustrate
the concept of unit root, let consider that the real interest rate series sp followed an
autoregressive one process:
(spt- ) = (spt-1 - ) + t (3.3)
wherespt is stock prices in logarithm form at time t, is the constant term and t
is the error term with properties of zero mean, no serial correlation and has constant
variance. Interest rate rin considered to be stationary if 0
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Augmented Dickey Fuller (ADF)
Initially, the unit root test was tested using Dickey Fuller (DF), which developed by
Dicker and Fuller in 1979. However, due to it less superiority as compared to ADF
(Dickey and Fuller, 1981) which was taken place in testing the unit root test.
Therefore, for a unit root test in each series is conducted with and without a
deterministic trend (t) by using ADF methodology. The general form of ADF test is
estimated by the following regression is used to determine whether the variables are
trend stationary using Equation (3.4) and whether the variables are stationary
without trend using Equation 3.5;
Xt= 0 + xt-1 + iXt-i + t (3.4)
Xt= 0+ t + xt-1 + ni=1 iXt-i + t (3.5)
where t
is white noise, andn
is the number of lagged changes inXt
. If the
variables are non-stationary, then it will be tested for the presence of unit root by
differencing at first difference using equation below;
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2Xt= 0 + Xt-1 + ni=1 i2Xt-i + t (3.6)
2Xt= 0 + t + Xt-1 + ni=1 i2Xt-i + t (3.7)
where t is white noise, and n is the number of lagged changed inXt. Equation (3.6)
denotes the regression in ADF without a trend and Equation (3.7) denotes the
regression without a trend.
The null hypothesis (H0) is tested against the alternative hypothesis (H1) where,
H0:Xtis nonI(0)
H1:XtisI(0)
The null hypothesis (H0) is rejected and the series are shown to be stationary or
integrated of order zero when the ADF statistics are more than their critical values
for Equation (3.6), however, when null hypothesis is rejected, the series are shown
to be stationary or integrated of first orderI(1) when the tabulated ADF statistics
are more than their critical values for Equation (3.7).
Additionally, enough lagged differences are included to include ensuring that the
error term becomes white noise. Besides, if the autoregressive representation ofYt
contains a unit root, the t ratio for 1 should be consistent with the hypothesis,
which is
1 =0. Otherwise, if the coefficient is significantly different from zero then
the hypothesis that Ytcontains a unit root is rejected and thus it accepted as Yt is
stationary rather than integrated. In the ADF test, the variables are tested whether
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unit roots are present with and without a deterministic time trend. Stock price (SP),
M1 and M2 series are tested in their levels and in their first difference.
Phillips- Perron (PP)
As mentioned above, the unit roots tests, which have developed by Phillips (1987),
and Phillips and Peron (1988), is taken into consideration, on the levels and first
differences of real interest rates. The Phillips- Perron 1988 (PP) test, is able to
identify serial correlation and time dependant heteroscedasticity and if there is a
structure break within a series. This gives an added advantage over the ADF test,
therefore in this study; PP test is also used to test on the data series to further verify
the presence of unit root in the series. The PP test is a test of the hypothesis =1 in
the equation;
Xt= 1 +Xt-1 +1t (3.8)
Xt= 2 + t + X t-1 +2t (3.9)
whereXt represents the first difference ofXt-1, tdenotes the trend of the variable
andt is the white noise.
The purpose of the PP test is the same as the ADF test, which is to test on the
stationarity of the variable. Thus, the hypothesis for the test is as such;
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H0: Xtis non- stationary
H1:Xt is stationary
The tabulated t-statistics results for the PP test should be negative and significantly
different from zero forXt to be stationary in which the null hypothesis (H0) is
rejected. This is generalization to DF procedure, however it is a powerful test to
moderate and small sample size. As compared to ADF test, PP test has no lagged
difference terms. Instead the equation is estimated by ordinary least squares and the
t- statistic of the coefficient is corrected for serial correlation in t. In Eviews, it uses
the Newey- West (1987) procedure to adjust the standard error.
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3.3.2 Co integration Test
This study is attempted to use of the technique Johansen and Juselius (1990) who
devised a method to estimate whether two or more variables are co integrated via
multivariate maximum- likelihood procedure that overcomes many of the
limitations of the bivariate test of Engle and Granger (1987). These limitations
require that one of the two variables is considered exogenous, while these test do
not have well- defined limiting distribution and, therefore, their critical values are
sensitive to sample sizes (Hall, 1989).
Johansen maximum likelihood procedure begins by expressing a process, ifY1is a
vector ofn variables, all of which areI(I) processes; there exist klag VECM with
Gaussian errors of the following form:
Y1= + k-1i-1 1Yt-1 +kYt-k+ (3.10)
where is vector of drift, the matrixs are matrices of parameters, andt is a white
noise vector.
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The confined point of conducting Johansens co integration test is to determine the
rank (r) of matrixk. There are three possible outcomes, which it can be of full rank
(r-n), which imply that the interest rate series is a stationary process that would
contradict the earlier finding of non- stationary. Besides that, the rank ofk can be
zero (r=0), which case it indicates that there is no long run relationship among the
national nominal interest rates. In particular case, when k is of either full rank or
zero rank, it will be appropriate to estimate the model in either level of first
differences, respectively. Finally, in the intermediate case when there are most rco
integrating vectors 0 r n (i.e. reduced rank) it suggests that there are (n-r)
common stochastic trends.
The co integration procedure yields two likelihood ratio test statistics, referred to as
the Trace test and the Maximum eigenvalue ( max) test, which will help determine
which of the three possibilities is supported by the data. The Johansen trace test
statistic of null hypothesis that are at most rco integrating vectors 0 r n , and
therefore (n-r) common stochastic trends is
trace = -T ni=r+1 ln (1- i), (3.11)
whereis are the n-r smallest square canonical correlations of Yt-k with respect to
Y1 series corrected for lagged differences of the Yt and T is the sample size
actually used for estimation. The max statistic is given by:
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max = Tln (1- r+ 1) (3.12)
Since the asymptotic distribution of the Trace and and max test statistic follow 2
distribution, a simulation procedure is needed to identify proper critical values for
each test.11
3.3.3 TECM Granger Causality Test
Granger causality test is a technique for examining the causal relations among
variables that are non-stationary and co integrated. Otherwise a VAR model is used
in the case of no co integration found among the variables (Granger, 1969). The
standard Granger Causality test examines if there is exist feedback (bi directional)
or one- way causality between variables. Considering two series, Xt and Yt , the
Granger Causality test is in the form as follows:
Xt = 1 + n1
i=1 11 (i) Xt-i+ m1j=1 12 (j) Yt-j + Xt (3.13)
Yt = 2 + 21 (i) Xt-i+ m2j=1 22 (j) Yt-j + Yt (3.14)
Where Xt and Yt are stationary random processes intended to capture other
pertinent information not contained in lagged values of Xtand Yt. The lag lengths,
n and m, are decided by AIC in this study. Therefore, the series Yt fails to Granger
cause Xt if12 (j) = 0(j=1, 2,3,m1) ; and these series Xtfails to cause Ytif21(i) =
0(i=1, 2,3,n1).
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3.4 HYPOTHESIS
Hypothesis can be divided into two types:
a. Null hypothesis (H0)
Null hypothesis states there is no difference between the parameters.
b. Alternative hypothesis (H1, H2, H3, Hx)
Alternative hypothesis states there exists a difference between the parameters.
3.4.1 Unit Root Test
H0: The data series are non stationary.
H1: The data series are stationary.
3.4.2 Co integration Test
H0: There is no existence of co integration between interest rate and stock market
return.
H1: There is an existence of co integration between interest rate and stock market
return.
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3.4.3 TECM Granger Causality Test
Hypothesis 1
H0: Interest rate do not Granger Cause stock market return.
H1: Interest rate do Granger Cause stock market return.
Hypothesis 2
H0: Stock market return does not Granger Cause interest rate.
H1: Stock market return does Granger Cause interest rate.
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TABLE OF CONTENT
CONTENT PAGES
CHAPTER 1: INTRODUCTION
1.1 Introduction
1.1.1 Stock Market1.1.2 Interest Rate1.1.3 ASEAN-5
1.2 Problem Statement
1.3 Research Objectives
1.3.1 General Objectives
1.3.2 Specific Objectives1.4 Significance of the Study
1.5 Scope of the Study
1.6 Format of Project Paper
1-9
CHAPTER 2: LITERATURE REVIEW
2.1 Previous Studies
2.1.1 Summary of Literature Review
10-22
CHAPTER 3: DATA AND METHODOLOGY
3.1 Introduction
3.2 Source of Data3.3 Model Specification
3.4 Methodology
3.4.1 Stationarity and Unit Root Test3.4.2 Co Integration Test
3.4.3 TECM Granger Causality Test
3.5 Hypothesis
23-38
GANTT CHART
39
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UNIVERSITI TENAGA NASIONAL
COLLEGE OF BUSINESS MANAGEMENT AND ACCOUNTING
DEPARTMENT OF FINANCE AND ECONOMICS
BACHELOR OF FINANCE (Hons.)
SEMESTER 1, 2010/2011
PROJECT PAPER IN FINANCE
(FICB 344)
PROJECT PROPOSAL
A STUDY ON THE RELATIONSHIP BETWEEN INTEREST RATE AND STOCK
MARKET RETURN: EVIDENCE FROM ASEAN- 5 COUNTRIES
SUPERVISED BY:
PN SUZAIDA BTE BAKAR
PREPARED BY,SHALINI A/P RAGOO (BF 081216)