Bank Indonesia, Financial Stability Review No. 12 March 2009

The preparation of the Financial Stability Review (FSR) is one of the avenues

through which Bank Indonesia achieves its mission ≈to safeguard the stability of the Indonesian

Rupiah by maintaining monetary and financial system stability for sustainable national

economic development∆.

Publisher :

Bank Indonesia

Jl. MH Thamrin No.2, Jakarta

Indonesia

Information and Orders:

This edition is published in Maret 2009 and is based on data and information available as of December 2008, unless stated

otherwise.

The PDF format is downloadable from: http://www.bi.go.id

For inquiries, comments and feedback please contact:

Bank Indonesia

Directorate of Banking Research and Regulation

Financial System Stability Bureau

Jl.MH Thamrin No.2, Jakarta, Indonesia

Phone : (+62-21) 381 8902, 381 8075

Fax : (+62-21) 351 8629

Email : [email protected]

FSR is published biannually with the objectives:

To improve public insight in terms of understanding financial system stability.

To evaluate potential risks to financial system stability.

To analyze the developments of and issues within the financial system.

To offer policy recommendations to promote and maintain financial system stability.

Financial Stability Review( No. 12, March 2009 )

ii

iii

Foreword vi

Overview 3

Chapter 1 Macroeconomic Conditions and

the Real Sector 9

Macroeconomic Conditions 9

Real Sector Conditions 12

Box 1.1. Indonesian Household Balance Sheet Survey

2008 15

Box 1.2. Corporate Sector Credit Risk: Credit Default

Swaps (CDS) 17

Box 1.3. Transition Matrices: The Risk Potential of

Corporate Credit of Three Sectors 18

Chapter 2 The Financial Sector 21

Indonesian Financial System Structure 21

Financial Stability Index 22

The Banking Industry 22

Funding and Liquidity Risk 22

Credit Growth and Credit Risk 24

Market Risk 30

Profitability and Capital 32

Nonbank Financial Institutions and the Capital Market 35

Finance Companies 35

Capital Market 38

Box 2.1. Chronology of the 2008 Financial Sector

Shocks and Policy Responses 44

Box 2.2. Bank Century»s Takeover, Bank Indover»s

Closure and Financial System Stability 45

Table of Contents

Box 2.3. Segmentation in the Interbank Money Market

(PUAB) 46

Box 2.4. Structured Products and Offshore Products:

Their Impact to the Stability of the Financial

System 48

Box 2.5. The Impact of Foreign Debt to Financial

System Stability 50

Chapter 3 Financial Infrastructure and Risk

Mitigation 53

Payment System Performance 53

Credit Bureau 56

Financial System Safety Net 60

Box 3.1. The Financial System Stability and PERPPU

on the Amendments to the Law on Bank

Indonesia 63

Box 3.2. Best Practices of Systemic Impact Analysis

towards the Financial System 64

Chapter 4 Prospects of the Financial System in

Indonesia 67

Economic Prospects and Risk Perception 67

Bank Risk Profile: Level and Direction 68

Prospect of the Indonesian Financial System 69

Articles

Article 1 Impact of Contagion Risk on the Indonesian

Capital Market 73

Article 2 Corporate Balance Sheet Modelling:

Determinants of Indonesian Corporate

Debt 83

iii

iv

List of Tables and Figures

Tables

1.1 Global Economic Indicators 10

2.1 Banking Profit and Loss 33

2.2 Financing Growth of Finance Companies 36

2.3 Financial Ratios of Finance Companies 36

2.4 NPL of Finance Companies 37

2.5 Price Index Perfomance of Several Stock

Exchanges in the Region 39

2.6 Sectoral Price Index 40

3.1 Debtor Identification Number (DIN) Data

(2006-2008) 58

3.2 Financial Safety Net Framework 61

4.1 Projection of Several Economic Indicators 67

4.2 Risk Perception of Indonesia 68

Box Tables :

1.3.1 Collectability of Debtor Migration of Three

Sectors 18

2.1.1 Chronology of Shocks to the Indonesian

Financial Sector in 2008 44

2.1.2 Policy Response 44

2.3.1 Daily Average Transaction Volume of Rupiah

PUAB from January to December 2008 46

2.3.2 Daily Average Transaction Volume of Domestic

Foreign Exchange PUAB 46

2.5.1 Private Foreign Debt Maturing in 2009 50

1.1 Business Confidence Indicators 9

1.2 Price Index of several Commodities 10

1.3 GDP Growth of Industrial Countries 10

1.4 GDP Growth of Several Emerging Market

Countries 11

1.5 Global Stock Price Index 11

1.6 Rupiah Exchange Rate against the US Dollar 11

1.7 Inflation in ASEAN-5 and Vietnam 11

1.8 Real Interest Rate in Indonesia, US and

Singapore 12

1.9 ROA and ROE of Nonfinancial Public Listed

Companies 12

1.10 DER and TL/TA of Nonfinancial Public Listed

Companies 12

1.11 Probability of Default (PD) of Nonfinancial Public

Listed Companies 13

1.12 Unemployment Rate in ASEAN 13

1.13 Structure of Household Income Sources 14

2.1 Assets of Financial Institutions 21

2.2 Financial Stability Index 22

2.3 Performance of Deposits 23

2.4 Performance of Foreign Exchange Deposits 23

2.5 Growth of Foreign Exchange Deposits vs Rp

Exchange Rate to US Dollar 23

2.6 Excess of Bank Liquidity 23

2.7 Transaction Volume of PUAB (daily average) 24

2.8 Credit Growth (yoy) 25

2.9 Credit Growth during 2007-2008 25

2.10 Credit Growth by Bank Group (y-t-d) 25

2.11 Credit Growth by Usage (y-t-d) 26

2.12 Credit Growth by Economic Sector 26

2.13 Growth of Housing loans, Credit Cards and

Others 26

2.14 Growth of Property Credit 26

2.15 Credit Growth by Its Initial Denomination 26

Figures

iv

v

2.16 Credit Share by Usage 27

2.17 Growth of MSM Credit 27

2.18 Non Performing Loans 28

2.19 Credit, NPL and Provisions 28

2.20 Gross NPL Ratio by Bank Group 28

2.21 Gross NPL Ratio by Economic Sector 28

2.22 Gross NPL Ratio by Credit Usage 28

2.23 Gross NPL Ratio of Consumer Credit 29

2.24 Gross NPL Ratio of Property Credit 29

2.25 Gross NPL Ratio of Credit in Rupiah and Foreign

Exchange 29

2.26 Gross NPL Ratio of MSM and Non MSM Credit 30

2.27 Gross NPL Ratio of MSM Credit 30

2.28 Rupiah Interest Rate and Exchange Rate 31

2.29 Rupiah Maturity Profile 31

2.30 Foreign Exchange Maturity Profile 31

2.31 Net Open Positions 31

2.32 SUN Portfolio of Banking Industry 32

2.33 Performance of SUN Owned by Banks 32

2.34 Bank Profitability 33

2.35 Bank Interest Income 33

2.36 ROA by Bank Groups 33

2.37 Ratio of Interest Expense to Interest Income by

Bank Group 34

2.38 Capital, Risk-Weighted Assets and CAR 34

2.39 Integrated Stress Test on CAR of 15 Major Banks 34

2.40 Interbank Stress Test 35

2.41 Business Activities of Finance Companies 35

2.42 Finance Companies Source of Funds 35

2.43 Composition of Financing by Finance Companies

(Nov «08) 36

2.44 NPL of Financing by Finance Companies 36

2.45 Developments of NPL Value 36

2.46 Cash Flow of Private Finance Companies 37

2.47 Cash Flow of Joint Venture Finance Companies 37

2.48 Bank Exposure 37

2.49 The Decrease of NPL of Bank Subsidiary Finance

Companies 38

2.50 The Increase of NPL of Bank Subsidiary Finance

Companies 38

2.51 Foreign Investment: SBI √ SUN √ Stocks 38

2.52 Foreign Placements: SBI √ SUN √ Stocks 38

2.53 SUN and SBI Ownership by Foreign Investors 39

2.54 SUN Absorption by Domestic and Foreign Financial

Institutions 39

2.55 Performance of JCI, Global and Regional Index

(Based on Index per 31 Dec 2005) 39

2.56 Volatility of Asian Stock Indices (30 days) 40

2.57 Stock Transaction Value of Domestic and Foreign

Investors 40

2.58 Capitalization and Issuance Value 40

2.59 Stock Price Performance of Several Banks 41

2.60 P/E Ratio of Bank Stocks 41

2.61 Price Performance of Several FR Series Bonds 41

2.62 Yield of 1 to 30 year SUN 41

2.63 Government Bonds: Market Liquidity of Various

Tenors 42

2.64 Issuance and Position of Corporate Bonds 42

2.65 Net Asset Value of Mutual Funds 42

2.66 Mutual Funds: Redemptions-Subscriptions-NAV 42

2.67 Mutual Fund: NAV-Participating Units 43

2.68 Performance of Fund Collection of Mutual Funds 43

3.1 Performance of BI-RTGS Transactions 53

3.2 Performance of Bank Indonesia National

Clearing System 54

3.3 Performance of Card Based Payment Instruments 54

3.4 E-Money Transactions 54

3.5 Role of Credit Bureau 57

3.6 Credit Bureau Strategic Policy 58

4.1 Bank Risk Profile and Outlook 69

Figures included in Boxes:

1.1.1 Composition of Household Debt Percentage of

Total Debt 15

1.1.2 Purpose of Household Credit 16

1.2.1 CDS Price in Indonesia 17

1.2.2 CDS Spread in Indonesia 17

v

vi

I welcome the publication of the Financial Stability Review No. 12 March 2009. This edition is critically important as

there recently have been many developments which need our analysis regarding their impact to financial system stability

as a whole.

Our analysis has revealed that the resilience of the Indonesian financial sector during semester II 2008, in general,

has been relatively maintained, despite the sharp increase in pressure to the financial system stability the global crisis has

brought. One of the indicators of the increased pressure is the Financial Stability Index (FSI) surpassing the indicative

maximum level of 2 in November and December 2008. In the capital market, the increase in pressure was indicated by the

drop of the Jakarta Composite Index (IHSG), while government bonds (SUN) were marred by a drop in their prices.

In the banking sector, the pressure manifests itself in the form of increases in liquidity risk, particularly from August

to September 2008. Liquidity pressures surface not only from the global crisis, but also from expansive growth of credit

which was funded by banks» secondary reserves as opposed to being funded from increases in deposits. Concomitantly,

the banking sector also faced increases in exchange rate risk as the rupiah weakened. As we neared the end of 2008, we

saw pressures to the financial system stability start to subside, although not completely returning to levels prior the crisis.

The decrease in pressures was attributed to the various policies taken, both by the government and Bank Indonesia.

Although lowered in intensity, still left on our plates, among others is the issue of segmentations in the interbank money

market (PUAB).

Even though pressures to the financial sector has increased, the most dominant industry of the financial sector, i.e.

the banking industry, has been able to maintain relatively solid performance. At the end of December 2008, the banking

industry»s capital adequacy ratio (CAR) remained at a high 16.2% while asset quality was well maintained as indicated by

low levels of NPL, i.e. 3.8% (gross) and 1.5% (net).

Looking forward, we must continue to be vigilant to various sources of instability, including the potential of increase

in credit risk and the possibility of liquidity pressure returning. Another potential pressure source is the increasing signs of

a credit crunch in the banking industry. Such can, in turn, disrupt the performance of the real sector, both at corporate

and household levels. Disruptions in the real sector will only come back to the banking industry in the form of credit risk

increases.

Foreword

vii

The increase of challenges in the financial sector needs to be anticipated by our continuous efforts to improve and

increase surveillance quality to support an early warning mechanism. By knowing risk potentials early in advance, we will

be able to strategically prepare mitigating measures and thus enable us to minimize losses. Such accentuates the impor-

tance of the publication of this edition as it serves as an important medium to communicate surveillance results to our

stakeholders. It is our hope that the Review succeeds in its mission and the information contained within will be of great

use to all its readers.

Jakarta, March 2009

DEPUTY GOVERNOR OF BANK INDONESIA

Muliaman D. Hadad Muliaman D. Hadad Muliaman D. Hadad Muliaman D. Hadad Muliaman D. Hadad

viii

1

Overview

Overview

2

Overview

This page is intentionally blank

3

Overview

Financial system stability was well maintained during semester II 2008.

Pressures intensified on the financial sector during the period as a result of

the global economic crisis. The JSX Composite (IHSG) slid sharply and

government bonds (SUN) experienced a significant decline. The banking sector

also suffered liquidity pressures, due in part to the global liquidity crisis but

also to expansive credit growth that lasted until October 2008 and was

primarily funded by secondary reserves. In addition, rupiah depreciation since

the beginning of October 2008 further aggravated financial sector risk. Such

volatility in the financial sector triggered a steep rise in the Financial Stability

Index during the reporting semester, exceeding the indicative 2-point

maximum limit in November and December 2008. To maintain financial system

stability, the government issued a regulation in lieu of law (PERPPU), and

Bank of Indonesia promulgated several new regulations, including an

amendment to the minimum reserve requirement. Such measures improved

bank liquidity and alleviated exchange rate volatility, however, not to the

levels recorded prior to October 2008. Nearing year end 2008 and into the

new year of 2009 there were indications that bank credit growth was slowing

down. If this situation persists, it could adversely impact the economy

considering that the banking sector is the primary source of funds. Looking

forward, the financial system is projected to remain stable despite the

burgeoning challenges attributable to the unrelenting economic slowdown.

1. SOURCES OF INSTABILITY

1.1. Global financial crisis

The global financial crisis represents the major root

of instability. This is because Indonesia»s economy is

increasingly integrated with the global economy.

Furthermore, foreign sources of funds have become more

important for banks and nonbank sectors alike.

Consequently, the financial turmoil currently impacting a

multitude of countries can potentially reach Indonesia»s

shores. Such financial volatility will not only destabilize the

Overview

4

Overview

domestic financial sector but also the corporate sector. As

a result, businesses will have difficulty obtaining foreign

funds. Furthermore, the real sector, which relies heavily

on foreign funding, will face disruptions thus reducing its

debt repayment capacity. In the banking sector, these

constraints will trigger an increase in nonperforming loans

(NPL) and undermine credit growth and other

disbursements in foreign exchange, which are required to

support economic activity.

1.2. Macroeconomic conditions

Macroeconomic stability is the foremost prerequisite

in achieving financial system stability. Some experts have

predicted that the domestic macro economy in 2009 will

not improve compared to that of 2008, principally due to

the global economic slowdown. Deteriorating

macroeconomic conditions will encumber financial stability

due to the inherent increase in NPL. In addition, the banking

sector will become more selective when extending credit,

which may spur a credit crunch. Accordingly, anticipatory

measures are required in order to avoid an increase in bank

risk due to tightening macroeconomic conditions, including

intensifying monitoring and accelerating credit

restructuring for debtors affected by the global crisis.

1.3. Real sector conditions and infrastructure

Instability may also arise from unfavorable real sector

conditions and inadequate domestic infrastructure.

Surveillance has revealed that corporate performance, in

general, are in decline, mainly in terms of profitability and

liquidity. In addition, leverage tended to rise in line with

declining capital due to a drop in profitability. Furthermore,

despite positive survey results in 2008 showing that the

household sector was relatively safe, the threat of lay-offs

at several companies has the potential to pinch households

in the future. Meanwhile, infrastructure over the past six

months has not shown any significant progress. Overall,

such inauspicious conditions in both the real sector and

infrastructure could spark additional pressures on financial

system stability, predominantly in terms of an increase in

NPL and a contraction in bank credit extension.

1.4. Financial innovation and structured prod-

ucts

As stated in the previous Financial Stability Review

(No. 11, September 2008), it is now mandatory for the

banking industry to adhere to strict risk management and

customer protection principles in the innovation of financial

products offered to the consumer, including structured

products. With the recent currency depreciation, several

countries have experienced difficulties due to losses from

structured products. This has ignited disputes between

banks and their customers. The losses suffered in Indonesia

were less than that in other countries; however, vigilance

is still required to avoid an increase in credit risk and

exchange rate risk. Additionally, reputational risks and legal

risks of banks have the potential to increase in relation to

structured products, in particular if the ongoing disputes

are not resolved quickly and amicably.

Also, the banking industry must also be more prudent

in taking roles as agents of offshore products. Such is

because excessive placements in these products represent

capital flight of domestic investors abroad, creates greater

bank exposure to reputational and legal risks, and increases

the potential of disputes with bank clients particularly if

consumer protection is not considered as priority by the

bank.

1.5. Segmentation in the inter-bank money

market

In general, liquidity pressure during the second

semester of 2008 was well mitigated and the banking

5

Overview

industry has become more liquid. However, interbank

money market (PUAB) segmentation remained a key issue,

with major banks preferring to transact with other major

banks, while the small and medium banks faced increasing

difficulty in obtaining funds. Moving forward, PUAB

segmentation will require urgent resolution to ease

pressure on banking stability, particularly liquidity.

1.6. The political climate and homeland security

The 2009 General Election will influence the future

political climate, homeland security, and in turn, financial

stability. However, as society becomes more familiar with

elections, such as for new governors and regents, which

occur year round throughout Indonesia, the upcoming

general election is expected to run safely and under control.

A successful general election will catalyze domestic

investment, by both local and international investors.

2. RISK MITIGATION

2.1. Improving risk management and good

governance

The best way to minimize financial sector instability

is by strengthening risk management and good governance

in financial institutions, both banks and non-banks.

Improved risk management will be extremely helpful in

taking the necessary mitigatory measures against the risk

of losses. Meanwhile, the implementation of good

governance will encourage financial institutions to pay

more attention to transparency, accountability and fairness

principles. This, in turn, will ensure adequate market

discipline and sufficient customer protection. Compared

to previous years, the implementation of risk management

and good governance in the banking sector has shown

greater encouraging progress. However, in order to

anticipate the pervasive impacts of the deteriorating global

economy, more efforts are required in terms of

strengthening risk management and improving good

governance implementation.

2.2. Intensifying surveillance

Risk mitigation in the financial sector can also be

achieved by intensifying surveillance. To this end, various

tools and methods have been developed, such as stress

tests, probability of default analysis, a financial stability

index as well as household surveys to support surveillance

at a macro-prudential level. Each of these approaches is

reviewed regularly and developed further to become a

capable early warning tool. At the micro-prudential level,

human resources were improved and various approaches

were applied in the implementation of risk-based

supervision to strengthen bank surveillance. In addition,

several new regulations to maintain financial system

stability were also issued.

2.3. Improving the Crisis Management Protocol

To mitigate risk in the financial sector from a wider

perspective, a crisis management protocol was formulated

and became an important aspect of the Financial System

Safety Net (JPSK). To mitigate risk from volatility in the

financial sector in October 2008, the government

promulgated three regulations in lieu of law (PERPPU) as

follows: (i) Raising the guarantee limit covered by the

Deposit Insurance Corporation (LPS) from Rp100 million

to Rp2 billion per customer; (ii) Amending the Law on Bank

Indonesia to facilitate the use of credit classified as current

as collateral for the short-term funding facility (FPJP) from

Bank Indonesia; and (iii) Implementing a Financial System

Safety Net (JPSK).

The issuance of these three PERPPU minimized bank

liquidity pressure and, consequently, the banking sector

remained stable. However, when liquidity pressure

intensified, one bank was handed over to LPS for immediate

6

Overview

recovery. The People»s Representative Council approved the

regulation regarding a change in guarantee limit covered

by LPS and the amendment to the Law on Bank Indonesia,

while the regulation legislating FPJP was not. At the time

of writing the Government had prepared a draft regulation

concerning the JPSK, which had been submitted to the

People»s Representative Council for further approval.

3. FINANCIAL SYSTEM STABILITY OUTLOOK

The prospects for the financial system is expected to

remain positive despite the onset of larger challenges

primarily from deteriorating economic conditions both

domestically and globally. As will be elaborated upon in

more detail in Chapter 4 there are various factors

underlying this projection. First, financial volatility has

reoccurred recently, principally caused by external factors,

however domestic banks are not suffering as severely as

overseas banks. Second, the banking sector and the

supervisory authority are more prepared to confront the

crisis when compared to conditions in 1997/98. Third,

financial sector infrastructure has been improved with the

addition of a reliable Deposit Insurance Corporation (LPS)

that provides assurance to consumers. Another important

factor that supports financial stability is the Financial System

Safety Net (FSSN), for which the law draft has been

submitted to the People»s Representative Council.

Amidst such optimism, vigilance must be intensified

as the current global crisis is seen as the most severe since

the Great Depression in 1929. The collective impacts on

the domestic economy of the downturn in global economic

growth will be difficult to avoid. Thus, it is vital to protect

the domestic financial sector by creating a broad safety

net and put prudential principles on the forefront of

business activities.

7

Chapter 1 Macroeconomic Conditions and the Real Sector

Chapter 1Macroeconomic Conditionsand the Real Sector

8



9


1. MACROECONOMIC CONDITIONS

International economic performance during semester

II 2008 was marred by the escalating global financial crisis

and its impact on the real sector. A lack of liquidity and

greater volatility in the money market undermined the

corporate sector (producers) as well as household sector

(consumers) confidence in the economy. A drop in the

Business Confidence Indicator, issued by the IMF, was clear

evidence of this.

Against this inauspicious backdrop, producers and

consumers took anticipatory measures, which manifested

in a slowdown in investment and consumption.

Consequently, such behavior contributed to a slump in

economic growth, particularly in developed countries.

During 2008, the global economy was projected to grow

Macroeconomic stability in Indonesia was well maintained during semester

II 2008 despite pressures from the ongoing global financial crisis. A loss of

market confidence compounded the financial crisis, which spilled over into

the real sector and triggered an economic slowdown in many countries

including Indonesia. Meanwhile, weaker purchasing power coupled with

tumbling commodity prices undermined profitability in the corporate sector.

As a result, business players improved their efficiency through lay offs and

by curtailing business expansion, which subsequently eroded household

income. If such conditions persist, domestic financial system stability could

be threatened.

by only 3.4%; compared to 5.2% in 2007. This downturn

is expected to persist in 2009, with growth of just 0.5%.

Growth is expected to rebound in 2010 to approximately

3.0%.

Figure 1.1Business Confidence Indicators

Source: World Economic Outlook-IMF November, 2008

Manufacturing PMls

(values greater than 50 indicate expansion)

2008

EmergingEconomies

UnitedStates

Euro Area

1985 1990 1995 2000 2005Oct

35

40

45

50

55

60

65

Macroeconomic Conditions andthe Real Sector

Chapter 1

10


Sluggish economic activity in developed countries

caused a subsequent drop in demand for commodities,

which brought down commodity prices on the global

market. In semester I 2008, US dollar depreciation and

money market volatility encouraged the flow of investment

funds to the commodity market, which precipitated a hike

in commodity prices. The global price of crude oil peaked

at nearly USD150 per barrel followed by a rise in other

commodity prices. Upon entering semester II 2008,

however, in line with the decline in demand due to a slump

in economic activity and a drop in speculative transactions

in the commodity market, the price of crude oil and other

key commodities plummeted. Compared to the end of

semester I 2008, the global price of oil plunged more than

50% to USD44.6 per barrel by the end of semester II 2008.

This dramatic drop in prices was also followed by a decline

in other global commodity prices.

Lower demand for goods and services, particularly

from developed countries such as the US and European

Union, who had staunchly remained the primary export

market for emerging market countries, coupled with falling

commodity prices on global markets weakened the export

Figure 1.2Price Index of several Commodities

Table 1.1Global Economic Indicators

World Output: 5.2 3.4 0.5 3.0Advanced Economies 2.7 1.0 (2.0) 1.1

United States 2.0 1.1 (1.6) 1.6Euro area 2.6 1.0 (2.0) 0.2

Emerging & Developing Countries 8.3 6.3 3.3 5.0

Consumer Price:Advanced Economies 2.1 3.5 0.3 0.8Emerging & Developing Countries1) 6.4 9.2 5.8 5.0

LIBOR2)

US Dollar Deposit 5.3 3.0 1.3 2.9Euro Deposit 4.3 4.6 2.2 2.7Yen Deposit 0.9 1.0 1.0 0.4

Oil Price (USD) - average3) 10.7 36.4 (48.5) 20.0

Category 2007 2008

(%)(%)(%)(%)(%)Projection

2009 2010

Source: World Economic Outlook - IMF January 2009

Figure 1.3GDP Growth of Industrial Countries

1990 = 100

Source: Bank Indonesia

0

100

200

300

400

500

600

2000 2001 2002 2003 2004 2005 2006 2007 20080

100

200

300

400

500

600Oil CopperTin GoldPalm Oil CoffeeRice RubberAluminium

Source: Bloomberg

%

(3.00)

(2.00)

(1.00)

-

1.00

2.00

3.00

4.00

5.00

6.00

2000 2001 2002 2003 2004 2005 2006 2007 2008

Q1 Q2 Q3 Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4

USA JapanGermany UKCanada

performance of emerging market countries including

Indonesia. The income of emerging market countries

generally depends on their exports, therefore the decline

in export performance instigated a slowdown in economic

growth.

It is important to note, however, that despite a

downturn in Indonesian economic growth during quarter

III 2008, taken holistically, growth in 2008 remained strong

at approximately 6.1%, exceeding that of other ASEAN

countries such as Singapore, South Korea and Thailand.

This was supported by robust private consumption growth,

in particular from nontradable sectors such as

transportation and communication, which offset the

decline in export-oriented sectors.

11


Figure 1.4GDP Growth of Several Emerging Market Countries

Source: Bloomberg

%

(9.00)

(6.00)

(3.00)

-

3.00

6.00

9.00

12.00

2000 2001 2002 2003 2004 2005 2006 2007 2008Q1 Q2 Q3 Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4 Q1 Q2 Q3Q4

IndonesiaThailandChina

SingaporeSouth KoreaIndia

Financially, the growing intensity of the global

financial crisis spurred investors to withdraw their

investment portfolio from emerging market countries for

two main reasons: flight to liquidity as well as flight to

quality. This has also affected Indonesia. Compared to the

end of semester I 2008, the JSX Composite Index (IHSG)

plummeted by 42.3% from 2,349 to 1,355 at the end of

semester II 2008. The reversal of foreign investment

precipitated a deficit in the Indonesian capital and financial

accounts in quarter IV 2008. In 2008, the Indonesian

balance of payments was projected to run a deficit of

USD2,302 million.

Increasing financial turbulence, particularly since the

beginning of semester II 2008, exacerbated rupiah

depreciation and intensified volatility. Compared to the

end of semester I 2008, the rupiah weakened by 20.5%

to Rp11,120 per US dollar by the end of semester II 2008.

The exchange rate remained weak but volatility dispersed.

Waning demand and lower commodity prices on the

international market prompted inflationary pressures,

which had been significant in mid 2008, to ease. The

momentum of this drop in inflation encouraged the central

banks of several countries to ease their monetary policy

by reducing their interest rates in order to stimulate

economic activity. In December 2008 the Fed Fund Rate

reached its nadir at 0.25%, meanwhile the interest rate

of the European Central Bank was reduced to 2.5%. The

BI Rate was cut to 9.25% in December 2008 with further

cuts in February 2009 to 8.25%. Despite the lower BI Rate

the investment climate in Indonesia is expected to remain

Figure 1.6Rupiah Exchange Rate against

the US Dollar

Source: Bloomberg

9,039 9,210

9,258 9,352

Monthly AverageSemester Average

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2007 2008

9,07

59,

077

9,17

29,

095

8,84

28,

981

9,06

79,

358

9,10

59,

102

9,26

79,

356

9,40

69,

180

9,17

89,

203

9,28

19,

288

9,15

99,

151

9,35

49,

990

11,3

1411

,803

1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 120

2,000

4,000

6,000

8,000

10,000

12,000

14,000

Figure 1.5Global Stock Price Index

Source: Bloomberg

0

5000

10000

15000

20000

25000

30000

35000

2006 2007 2008

Singapore

NYANew York

Dow Jones

IndonesiaNikkei

0

5000

10000

15000

20000

25000

30000

35000

Figure 1.7Inflation in ASEAN-5 and Vietnam

Source: CEIC

y.o.y %

(5)

0

5

10

Jan Apr Jul Oct Jan Apr Jul Oct

2007 2008

Philippine Singapore Thailand

Malaysia Indonesia Vietnam

12


This is reflected by the deteriorating financial performance

of nonfinancial public listed companies, which limited their

expansionary activities and sought lay offs. Consequently,

such conditions will undermine household purchasing

power.

Falling prices, waning export demand and weaker

public purchasing power due to the global crisis impinged

on the margin of the corporate sector, in particular

nonfinancial, public listed companies. This is evidenced by

the decline in business profitability (ROA and ROE) of such

companies in quarter III 2008 compared to that of the

same period in the previous year.

From a financing perspective, the corporate sector

suffered from limited capital. To fulfill its operational needs,

Figure 1.9ROA and ROE of

Nonfinancial Public Listed Companies

Source: Bursa Efek Indonesia

-200

-100

0

100

200

300

400

500

600

700

-150

-100

-50

0

50

100

150

200

250

300

350

2003 2004 2005 2006 2007 2008Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3

ROA (left)ROE (right)

Figure 1.10DER and TL/TA of

Nonfinancial Public Listed Companies

Source: Bursa Efek Indonesia

0.00

0.20

0.40

0.60

0.80

1.00

1.20

1.40

1.60

1.80

2003 2004 2005 2006 2007 2008Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3

DERDebt/TA

Figure 1.8Real Interest Rate in Indonesia, US and Singapore

Sources: Bloomberg and CEIC

%

IndonesiaUSASingapore

(8.0)

(6.0)

(4.0)

(2.0)

0.0

2.0

4.0

2003 2004 2005 2006 2007 2008Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec

attractive because the real interest rate remains higher than

those of several other ASEAN countries.

Looking forward, pressures emanating from the global

economic downturn are expected to continue to suffuse

the domestic economy. The drop in export demand due to

sluggish global economic activity may bring to bear

additional pressures on national economic growth.

Notwithstanding, domestic monetary and fiscal stimuli are

expected to hasten private consumption and offset pressures

from the external sector. Monetary stimuli include a drop

in the interest rate, whereas from the fiscal side the impetus

comes from a number of sources including a national

government program to strengthen public purchasing

power known as Pemberdayaan Masyarakat Mandiri; a drop

in fuel prices and transportation fees; a hike in the Regional

Minimum Wage expected to exceed 11%; and a rise in the

salaries of civil servants. Just as important is the General

Election as well as local and regional elections, which are

expected to catalyze private consumption; vital to offset

the pressures from the external sector.

2. REAL SECTOR CONDITIONS

A slump in exports due to the global financial crisis

has also affected the performance of the domestic real

sector, both the corporate sector and households alike.

13


businesses began to rely on deposits; from banks or

through the issuance of bonds and other securities. This

was demonstrated by the rise in debt-to-equity ratio (DER)

and ratio of total liabilities to total assets (TL/TA) in quarter

III 2008 compared to quarter III 2007.

Along with the decline in performance of

nonfinancial public listed companies, estimations to

measure the probability of default (PD) also demonstrated

an increase. The number of companies with a PD of greater

than 0.5 grew from 21 companies in September 2008 to

29 in September 2009. For banks, this is an early indicator

showing a potential increase in future credit risk.

to take into consideration risk potential due to exchange

rate fluctuations. Of the 46 major conglomerates regularly

monitored, stress testing indicated that capital in general

is well maintained and will only be effected to 100%

should the rupiah exchange rate exceed Rp 16.100 to

the USD.

Lower profitability due to weaker purchasing power

and falling prices forced business players, particularly those

in export oriented sectors, to economize by reducing their

workforce and limiting business expansion. This had the

potential to raise national unemployment. Based on the

latest data from 2008 and despite a declining trend,

unemployment in Indonesia, at 8.4%, was the highest of

all ASEAN member countries.Figure 1.11

Probability of Default (PD) ofNonfinancial Public Listed Companies

Probability of Default - September 2008

Total

215

5 4 3 2 0 1 1 019

250

200

150

100

50

00.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-0.10

Probability of Default - September 2009

Total

171

2114

6 90 4 1 1

23

0.0-0.1 0.1-0.2 0.2-0.3 0.3-0.4 0.4-0.5 0.5-0.6 0.6-0.7 0.7-0.8 0.8-0.9 0.9-0.10

180

160

140

120

100

80

60

40

20

0

In addition to the onset of increasing credit risk,

companies in the real sector, particularly the major

conglomerates in Indonesia, were also exposed to

pressures from exchange rate risk. Based on data for

September 2008, major conglomerates in Indonesia had

Figure 1.12Unemployment Rate in ASEAN

%

0.0

2.0

4.0

6.0

8.0

10.0

Indonesia Thailand Malaysia Singapore

2006 2007 2008*)

Source: CEICNote:*) : Data for Indonesia (August 2008), Thailand (November 2008), Malaysia and Singapore (September 2008)

Results of the household balance sheet survey

demonstrated that in 2008 Indonesian households

maintained their debt repayment capacity. This was

reflected by the small ratio of debt to total income as well

as to disposable income, namely between 6.31% and

28.62%. However, considering that 56% of total

household income originates from salaries and benefits,

lay offs could potentially lower household income. If this

was allowed to persist it could lower household repayment

capacity.

14


Based on asset composition, Indonesian households

have little exposure to financial assets. Indonesian

household assets are dominated by nonfinancial assets in

the form of houses, buildings and land with a 76.81%

share of total assets. In line with the small exposure of

household assets to the financial system, the direct effects

of financial market volatility on household assets are

expected to be relatively small. However, caution is still

required considering that the increase in value of such

assets is heavily influenced by the increase in property price

index, which has persisted since 2004. Amid the current

economic slowdown it is likely that demand for property

will diminish, thus precipitating a decline in property prices.

If property prices slump, the value of household assets

will clearly also drop. Such a decline in asset value and

household income would place additional pressure on the

repayment capacity of households.

In the future, challenges in the real sector are

expected to persist in line with the limited development of

domestic infrastructure. The impacts of the global financial

crisis are expected to continue affecting the domestic

economy. Anticipatory measures to mitigate significant

export pressure and promote growth in the nontradable

sector are required. In the short term, the monetary and

fiscal stimuli introduced are expected to expedite

consumption growth and boost real sector resilience. If

successful, the outlook for financial system stability is sound.

Figure 1.13Structure of Household Income Sources

PensionIncome

3%

Salary and Benefits56%

Others10%

Net Income31%

Source: Household Survey 2008

15


Indonesian Household Balance Sheet Survey 2008Box 1.1

Household balance sheets are key indicator to

analyze the potential credit risk for the household

sector. In June 2008, Bank Indonesia collaborated with

the Central Bureau of Statistics (BPS) in conducting a

survey to tabulate Indonesian household balance

sheets. The survey took place in 10 provinces, including

West Sumatera, South Sumatera, Jakarta, West Java,

Yogyakarta, East Java, Bali, South Kalimantan, East

Kalimantan and Gorontalo, with a total number 3,553

households as respondents.

Overview of Indonesian Household Balance

Sheet

Household Assets

As a common theme in developing countries,

household assets in Indonesia are dominated by

nonfinancial assets in the form of property such as

houses, buildings and land with a 76.81% share of

total assets, followed by other nonfinancial assets

(15.57%) and financial assets (7.62%).

Compared to 2007 survey results, the

composition of other nonfinancial assets (gold, cattle,

etc.) increased slightly. This was triggered by the mid-

2008 rise in the price of gold, which encouraged

households to divert some of their financial assets to

gold. Meanwhile, household financial assets were

dominated by bank placements (73%), followed by

placements in nonbank financial institutions (13%).

Household Source of Funds

Household»s primary source of funds came from

their net worth, namely 96.13% of total assets.

Funding from bank credit only represented 3.01% of

total assets, followed by funding from nonbank

financial institutions (0.47%) and other fund sources

(0.39%). The relatively high household net worth was

supported by their ability to save. This was reflected

by the ratio of total expenditure to total household

income and the ratio of consumption expenditure to

disposable income; both below 100%, more specifically

91.29% and 90.59% respectively.

However, the ability of households not in debt

to save tended to be larger, as reflected by ratio of

total expenditure to total household income and the

ratio of consumption expenditure to disposable income,

namely 83.64% and 83.39%. Meanwhile, the ability

of indebted households to save tended to be minimal,

therefore, such households were forced to borrow to

fund the additional purchase of assets. This is reflected

by the ratio of total expenditure to total income and

the ratio of consumption expenditure to disposable

income; both surpassing 100%, namely 102.61% and

103.12%.

Household Debt

The majority (approximately 65%) of respondents

acknowledged that they have cash set aside to mitigate

unforeseen circumstances. However, if the cost of such

unforeseen circumstances exceeds the reserve funds

then the household would be forced to borrow.

Figure Box 1.1.1Composition of Household Debt

Percentage of Total Debt

LKBB Debt12%

Other Debts10%

Bank Debts78%

16


Based on value, Indonesian household debt is

dominated by bank debt (78%), followed by debt to

nonbank financial institutions (12%) and other sources

excluding financial institutions (10%).

The purpose of the loan is 24% for working

capital, 16% to purchase a vehicle, 14% to build or

renovate a house and 13% for food consumption. The

average repayment period is approximately 20 months.

Liquidity Mismatch Ratio

This ratio illustrates the ability of household

income to cover household debt. Survey results

demonstrate that the ratio of household debt to total

or disposable income is less than 100%, namely

10.38% and 11.22% respectively. The household debt-

servicing ratio is also below 100%; just 6.31%. The

small magnitude of these ratios indicates that

households are able to manage their expenditure in a

way that their income is sufficient to repay outstanding

debt.

Although the ratio of debt to disposable income

and debt-servicing ratio of indebted households to

banks and nonbank financial institution (LKBB) is the

highest (72.11% and 33.08%), both ratios are below

100%. Therefore, such households are expected to

have a good repayment capacity.

Solvency Ratio

This ratio illustrates the ability of a household»s

assets to cover its debt in the case of default. Survey

results demonstrate that the ability of Indonesian

households» assets to cover debt is good, as reflected

by the very low household gearing ratio and ratio of

total debt to net worth; 3.87% and 4.03% respectively.

The low household-gearing ratio is one indicator that

evidences a household»s ability to obtain additional

bank funding.

By grouping households based on their source

of debt, it is found that indebted households to banks

and LKBB have the highest household-gearing ratio.

However, the ratio is below 100%. This shows that

indebted households also have a good repayment

capacity.

Potential Risk

Along with the small exposure of household

financial assets, it is projected that the direct impact of

financial market volatility on household assets is

relatively small. Risk against the financial system,

particularly transmitted through property price volatility,

will increase considering that the majority of household

assets are in the form of housing assets (property assets

such as houses, buildings and land). Meanwhile, the

risk of indebted households to the financial sector is

relatively low because their repayment capacity is

sound. Some salient analysis results using various

financial ratios are as follows:

Figure Box 1.1.2Purpose of Household Credit

To Open Business24%

Food13%

Education8%

Health3%

Building/RenovatingHouse14%

Buy Houseto occupy

2%

Buy House butnot occupying it

2%

Buying Vehicles16%

Electronic2%

Others16%

17


Corporate Sector Credit Risk: Credit Default Swaps (CDS)Box 1.2

The real sector covers two components, namely

households and the corporate sector. The latest

developments in the household sector were elaborated

in Box 1.1. Box 1.2 will cover one of the approaches

used to assess corporate sector credit risk, namely by

using Credit Default Swaps (CDS).

CDS is widely known as a credit derivative

instrument. Conceptually, CDS can be seen as an

insurance or protection from the default of credit or

bonds (Duffie and Singleton, 2003; Lando, 2004).

Lately, in accordance with the global financial market

slowdown, the development of CDS price and spread

has become more of a concern. Technically, credit risk

is reflected by CDS spread. However, CDS price also

needs to be considered because it can illustrate the

development of market pressure.

With the recent deterioration of the global

financial market, the developments in CDS price and

spread have increasingly received attention. CDS no

longer merely reflect corporate credit risk, but has

become an indicator of sovereign risk.

Shocks beset the financial market in semester II

2008 and triggered a rapid escalation in CDS spread

and price. This peaked on 28 October 2008, when the

Indonesian Stock Exchange was temporarily closed as

a result of the Jakarta Composite Index (IHSG) nose-

diving to 1,111.4, its lowest ebb since 2005. However,

after the government and Bank Indonesia instituted a

number of key policy responses, CDS price and spread

started to decline, albeit remaining higher than prior

to October 2008.

Compared to neighboring countries, CDS price

and spread in Indonesia remained the highest. This

indicates a strong market perception that corporate

credit risk in Indonesia is high.

Figure Box 1.2.1CDS Price in Indonesia

Figure Box 1.2.2CDS Spread in Indonesia

Source: Bloomberg

0

200

400

600

800

1000

1200

3Jul

2Aug

1Sep

1Oct

31Oct

30Nov

30Dec

29Jan

2008 2009

Thailand

Korea

Philippine

Indonesia

Source: Bloomberg2008 2009

-80

-30

20

70

120

170

220

270

320

370

3Jul

2Aug

1Sep

1Oct

31Oct

30Nov

30Dec

29Jan

IndonesiaPhilippine

KoreaThailand

Such perceptions tend not to depict the actual

condition because the excessively high CDS price and

spread was also attributable to a thin market.

For the purpose of financial system resilience

surveillance, however, data on CDS price and spread

can be used as an early warning tool.

References:

Lando, D. (2004), Credit Risk Modeling, Princeton

University Press, Princeton, New Jersey.

Duffie, D. dan Singleton, K.J. (2003), Credit Risk:

Pricing, Measurement, and Management, Princeton

University Press, Princeton, New Jersey.

18


Transition Matrices: The Risk Potential of Corporate Credit ofThree Sectors

Box 1.3

Transition matrices are one of the tools or

approaches to detect the risk potential in corporations»

credits, by calculating the probability of rating migration

or the changes in the company»s last credit quality. The

transition matrices also serve as fundamental input in

several risk management applications. In addition, the

calculation of capital requirements, as recommended

by the New Basel Accord (BIS, 2001), must take into

account, among others, rating migration.

Previously, another research (Credit Risk

Modelling: Rating Transition Matrices by Hadad et al.,

2007 as can be found in FSR No. 9 September 2007)

also utilizes the rating published by PT Pemeringkat Efek

Indonesia (Pefindo) from February 2001 to June 2006.

The research employs two methodologies, including the

Continuous Time method and Cohort method, and using

the assumption that the credit rating process follows

the Markov chain. In conclusion, the Continuous Time

method delivers more efficient results in comparison to

the Cohort method. In addition, the Continuous Time

method also allows the probability of migrations to

significantly different ratings (rating default).

To further the research of Hadad, et al. (2006), a

new examination is conducted to study the 2008»s

credit collectability migration in three sectors (property,

transportation and textile) using the SID quarterly data

comprising of 448,183 debtors. The preferred

methodology is the Continuous Time method, with

the consideration that it is more advanced than the

Cohort method.

The results of the estimation indicate that among

the three sectors, the debtors in the property sector

are relatively better than those of the other two sectors.

This can be seen in:

The potential of debtor migration with collectability

of 1 and 2 (Performing Loans/PL) to the collectability

of 3, 4 and 5 (Non Performing Loans/NPL) in the

property sector is lower than the other two.

The potential of debtor migration of NPL to PL in

the property sector is higher than the other two.

The potential of debtor migration of collectability

2 to 5 in the property sector is smaller than that of

the two other sectors.

Table Box 1.3.1Collectability of Debtor Migration of Three Sectors

1 89.7% 9.3% 0.3% 0.3% 0.4%2 64.4% 28.0% 1.7% 1.5% 4.4%3 37.7% 19.6% 5.8% 3.8% 33.1%4 23.4% 10.8% 1.5% 4.7% 59.5%5 0.0% 0.0% 0.0% 0.0% 100.0%

PropertyCollect 1 2 3 4 5

1 89.5% 8.0% 0.5% 0.4% 1.7%2 53.5% 28.7% 1.7% 2.0% 14.0%3 7.5% 3.5% 3.3% 1.7% 84.1%4 2.9% 1.1% 0.3% 3.0% 92.6%5 0.0% 0.0% 0.0% 0.0% 100.0%

TransportationCollect 1 2 3 4 5

1 94.0% 3.8% 0.6% 0.3% 1.4%2 77.9% 4.6% 1.0% 1.1% 15.3%3 27.0% 2.3% 0.7% 1.5% 68.6%4 0.0% 0.0% 0.0% 0.6% 99.4%5 0.0% 0.0% 0.0% 0.0% 100.0%

TextileCollect 1 2 3 4 5

19

Chapter 2 The Financial Sector

Chapter 2The Financial Sector

20



21


2.1. INDONESIAN FINANCIAL SYSTEM

STRUCTURE

Compared with conditions in the previous semester,

the structure of Indonesia»s financial system in semester II

2008 did not experience any major changes. The banking

industry, which consists of commercial banks and rural

banks, dominated the industry with an approximate 74%

share of total financial sector assets. Meanwhile, the

relative share of other players in the financial industry, such

as insurance companies, pension funds, finance

companies, securities and pawn brokers remained low.

In terms of the banking industry, the 15 major banks

account for the majority (70%) of total industry assets. In

semester II 2008, the total assets of commercial banks

grew by Rp2,69.7 trillion (13.2%) to Rp2,310.6 trillion.

Such growth serves as an indicator that the current global

crisis has not significantly affected the banking industry.

However, the crisis has lowered IHSG from 2.349,11 (June

During semester II 2008, Indonesia»s financial sector continued to grow amid

growing pressures from the global financial crisis. In general, financial system

stability was well maintained. The banking industry, the most dominant

industry in the financial sector, performed positively. Hitherto, the global

financial turmoil has not significantly affected the banking industry in

Indonesia; however it has intensified pressures on the capital market, reflected

by the declining Jakarta Composite Index (IHSG) and falling price of

government bonds (SUN).

2008) to 1.355,41 (December 2008); a 42.3% decline.

Meanwhile, the price of SUN dropped 2.3% during the

period from 30 June to 25 September 2008, yet

experienced a subsequent rebound of 8.6% from 25

September 2008 to 31 December 2008. Nevertheless, since

December 2008 to mid March 2009, the price of SUN was

beset by more pressures and dropped by approximately

5.62%.

Figure 2.1Assets of Financial Institutions

79.0%

1.1%8.0%

3.2%5.8% 2.7% 0.3% Commercial Banks

Rural Banks

Insurance Companies

Pension Fund

Finance Companies

Securities Companies

Pawnshops

The Financial SectorChapter 2

22


2.2. FINANCIAL STABILITY INDEX

Financial stability growth over time is reflected by

the Financial Stability Index (FSI).1 Impacted by the global

financial crisis, the domestic financial sector encountered

turbulence, thus, putting pressure on financial stability

during semester II 2008 (refer to Box 2.1). As a

consequence, the FSI increased sharply from 1.60 at the

end of June 2008 to 2.10 by the end of December 2008,

peaking in November 2008 at 2.43. Simultaneously, since

October 2007 the rupiah exchange rate has also faced

increasing pressures.

Thus, FSI during the final two months of 2008

exceeded the maximum indicative level of 2. The high

FSI was primarily attributable to the declining IHSG

and tumbling SUN price as impacts of the global

crisis.

Latest developments indicate that pressures from the

global financial crisis have eased slightly, which was

reflected by the improving IHSG and rising price

of SUN. Policy response by the government and

Bank Indonesia also softened the persistent financial

turbulence. Consequently, FSI declined to 2.06 as of

January 2009.

The declining FSI reflects that, in general, financial

stability was relatively well maintained. Moreover, up to

the end of June 2009 FSI is projected at approximately

1.77 √ 2.13; or using a moderate scenario at approximately

1.95, which is relatively lower than the position at the end

of December 2008. Consequently, the outlook for financial

system stability is expected to remain positive and well

preserved.

2.3. THE BANKING INDUSTRY

2.3.1. Funding and Liquidity Risk

Deposits Growth

At the outset of semester II 2008, deposits, as the

main source of funds for banks, experienced negative

growth, however, this turned around mid semester.

Significant growth of deposits since September 2008

ensured that during the reporting period, deposits

expanded by approximately 12.87% totaling Rp1,753.3

trillion. Such growth affected all components of deposits,

from demand deposits to savings and time deposits.

Growing deposits since mid semester II 2008 are

congruous to the high interest rate at the time, prior to

subsequent reductions at the end of 2008. The high

interest rate generated public interest in bank placements.

In addition, amid unstable economic conditions, many

investors saw investment in nonbank institutions as high

risk with an uncertain yield compared with saving funds

at the banks. Another important factor that also

contributed to the growth in deposits was government

policy, instituted through a government regulation in lieu

of a law (PERPPU). The PERPPU, issued in mid October

2008, increased the deposit insurance coverage by the

Deposit Insurance Corporation (LPS) from Rp100 million

to Rp2 billion per customer per bank. This policy was

effective in maintaining and even increasing public funds

held at banks.1 A detailed explanation on the methodology and approach applied to calculate the Financial

Stability Index can be found in the Financial Stability Review, No. 8 March 2007 and No.9 September 2007.

Figure 2.2Financial Stability Index

2.13

2.10

1.95

1.77

0

0.5

1

1.5

2

2.5

3

2003 2004 2005 2006 2007 2008 2009

FSI ProjectionsFSI

23


denomination, growth of deposits in a foreign currency

during the reporting period in fact decreased USD1.36

billion, mostly in terms of time deposits and demand

deposits, which declined USD0.98 billion and USD0.58

billion respectively.

Liquidity Adequacy

Slow growth of deposits at the beginning of semester

II 2008, which occurred concomitantly with decreasing

global liquidity, placed additional pressures on domestic

bank liquidity. Furthermore, relatively high loan growth

up to October 2008, which had principally been financed

by cashing secondary reserves, undermined bank liquidity.

Consequently, liquidity declined, with a further contraction

in August 2008 when excess liquidity reached its lowest

ebb2. Up to August 2008, excess liquidity declined by

approximately 30.18% (y-t-d), primarily causing holdings

of Bank Indonesia Certificates (SBI) to decrease.

Based on currency type, growth of deposits

denominated in a foreign currency was 18.94%, which

was slightly higher than growth of rupiah deposits at

18.85%. However, due to rupiah depreciation against the

US dollar, which was relatively significant during the

reporting period, when measured in foreign currency

Figure 2.4Performance of Foreign Exchange Deposits

18

21

24

27

30

Deposits in USD(left)

Deposits in Rp(right)

Trillion RpBillion USD

Dec Feb Apr Jun Aug Oct Dec

2007 2008

200

230

260

290

320

2 Excess liquidity consists of BI Certificates, other placements at Bank Indonesia and giroaccounts at BI (Fasbi/FTK) and securities.

3 Liquid instruments comprise of cash and placements at BI (BI giro, SBI, and otherplacements). NCD is assumed to comprise of 30% giro + 30% savings + 10 time depositsup to (3 month time deposits).

Figure 2.5Growth of Foreign Exchange Deposits vs

Rp Exchange Rate to US Dollar

Foreign ExchangeDeposits in USD

(left)

Rp Exchange Rateto USD(right)

RupiahBillion USD

18

21

24

27

30

2006 2007 2008Dec Apr Aug Dec Apr Aug Dec

8,500

9,300

10,100

10,900

11,700

12,500

Figure 2.6Excess of Bank Liquidity

SBI (left)

Fasbi/FTK (left)

SUN (right)

0

50

100

150

200

250

Dec Feb Apr Jun Aug Oct Dec270

275

280

285

290

2007 2008

Figure 2.3Performance of Deposits

Demand Deposits (left)

Savings (left)

Time Deposits (right)

Trillion Rp

350

400

450

500

550

0

150

300

450

600

750

900

Dec Feb Apr Jun Aug Oct Dec2007 2008

Other than reflected from the drop of excess liquidity,

the decrease of bank liquidity adequacy is also apparent

from the ratio of liquid instruments to non core deposits

(NCD)3 which continues to fall and reaching its lowest

point, 84.9%, in August 2008. In principle, this ratio shows

24


the bank»s ability to meet deposit withdrawals. Ratios of

less than 100% indicate that a bank is less than adequate

in its liquidity.

However, along with the significant increase in

deposits since the beginning of September 2008, liquidity

pressure eased. The growth in deposits, as described earlier,

was due to government policy to increase the deposit

insurance coverage by the Deposit Insurance Corporation

(LPS). Besides, Bank Indonesia also issued several policies

to alleviate liquidity pressures, including loosening the

rupiah and foreign currency minimum reserve requirement.

Consequently, liquidity in the banking industry improved

and liquidity stabilized. Such positive developments were

also reflected in continuously increasing ratios of liquid

instruments to NCD, which in December 2008 reached

109.1%. Such indicates that banking liquidity has

increasingly become under control.

Interbank Money Market (PUAB)

Along with increasing global liquidity pressure,

domestic banks tended to hold their liquidity and limit

interbank transactions, thus creating segmentation in the

interbank money market (PUAB). In addition, average daily

bank transaction volume in the domestic interbank money

market has shown a declining tendency, both in rupiah

and foreign currency.

To minimize the impact of PUAB segmentation, in

February 2008 Bank Indonesia enhanced its open market

operation. Bank Indonesia also activated the Fine Tune

Operation (FTO) facility and followed up with

improvements in its features. The FTO with expansionary

effects is known as FTE and is provided for banks

experiencing liquidity problems, while the FTO for

contractionary effects is known as FTK and is provided for

banks with excess liquidity. The improvements in features

cover the extension of FTE tenor from 14 days to a

maximum of three months and thus allowing banks to

have greater access to the central bank for liquidity. Bank

Indonesia also held repo transactions with longer tenors

(two to 14 days) to help banks experiencing liquidity

problems. These steps proved to be successful in

overcoming liquidity pressures in the banking industry.

Furthermore, in efforts to understand the banking

industry»s liquidity strength, particularly in the face of

sudden deposit withdrawals, a simulation exercise was

held. This exercise assumed that decreases in or

withdrawals of deposits will be funded by a bank»s excess

liquidity. Using end of December 2008 data, the exercise

results revealed that excess liquidity held by banks remains

adequate in withstanding up to 29.27% deposit

withdrawals. Liquidity risk stress tests were also held to

understand bank capital»s ability to absorb the costs of

securing liquidity from PUAB should the bank experience

funding problems. Stress test results revealed that in

general the banking industry»s capital level remains strong

in facing liquidity risk pressures.

2.3.2. Credit Growth and Credit Risk

Credit Growth

Strong credit growth stood out in 2008. In fact, the

symptoms of expansive credit growth began in 2007. At

that time, credit growth reached 25%, which exceeded

Figure 2.7Transaction Volume of PUAB

(daily average)

Million USDTrillion Rp

0

2

4

6

8

10

12

14

0

100

200

300

400

500

2008Jan Mar May Jul Sep Nov

Rupiah PUABForex PUAB

25


the target of 22%. According to the banks» business plans,

the 2008 target for credit growth was 24%. However,

before yearend, credit growth had far surpassed its target,

peaking at 37% y-o-y in October 2008.

Along with increasing pressure due to the

deteriorating economy, in November 2008 credit growth

began to slow, dropping to 29.5% by yearend. During

the reporting period the rupiah experienced significant

depreciation, therefore, when excluding the exchange rate

factor, credit growth in 2008 was actually lower at 25.7%.

Robust credit growth was stimulated by high demand

from domestic businesses for working capital and

investment credit, compounded by the constraints in

obtaining foreign funds due to the global crisis.

Maintaining relatively strong credit growth appeared to

be banks» strategy to maintain their profit level amid thinner

spreads between interest payments on deposits and

interest income earned on the inter-bank money market

and BI Certificates. Solid credit growth was also the result

of various policies previously taken by Bank Indonesia to

improve the bank intermediary function.

As described previously, during the reporting period

deposits grew by approximately 12.87%. As credit growth

exceeded that of deposits, the loan to deposit ratio (LDR)

increased from 76.6% in June 2008 to 77.2% in December

2008. In addition, LDR reached its highest point since the

1997/1998 Asian crisis, namely 81.6% in August 2008.

Figure 2.8Credit Growth (yoy)

Forex Credit (USD)

Data of Dec'08 is based on Commercial Bank Daily Report

0

5

10

15

20

25

30

35

40

45

50

%


Forex Credit (in Forex)

Total CreditTotal Credit (fixed exchange rate)Rupiah Credit

Figure 2.9Credit Growth during 2007-2008

Total Credit (Rp T)

Rupiah Credit (Rp T)

Forex Credit (Rp T)

Forex Credit (USD T)

(15) 25 65 105 145 185 225 2650

20082007

Figure 2.10Credit Growth by Bank Group (y-t-d)

State Owned Banks

National Private Banks

Regional Development Banks

Joint Venture Banks

Foreign Banks

Industry

32%

27%

36%

50%

46%

32%

%

20082007

0 10 20 30 40 50

By bank group, state-owned and private banks

continued to dominate the extension of credit. During the

reporting period, credit from state-owned banks expanded

significantly, principally to the manufacturing sector, others

(consumption) and trade sector. Despite persistently high

credit growth from private banks, it tended to be lower

than that of previous semesters. The trade and others

(consumption) sectors were the primary contributors to

the waning credit growth at private banks, whereas credit

to the manufacturing sector remained strong.

A favorable aspect of credit growth in semester II

2008 was the relatively expansive credit extension to the

productive sector. This was evidenced by the growth in

working capital credit and investment credit, which

contributed 49% and 27% respectively to total credit

growth. Working capital credit and investment credit

26


reporting period. With total credit amounting to Rp198.9

trillion, the share of property credit shrank slightly from

15.7% at end of June 2008 to 15.2% in December 2008.

experienced relatively high growth of 32% and 37%

respectively. Based on sector, however, robust credit growth

was found in the utilities sector (electricity, water and gas);

transportation and communications sector; construction

sector; business services sector; and the manufacturing

sector.

Figure 2.15Credit Growth by Its Initial Denomination

% Rp

(60)

(40)

(20)

-

20

40

60

2000 2001 2002 2003 2004 2005 2006 2007 20086,000

8,000

10,000

12,000

14,000

yoy Rp (%) yoy Va USD (%) Convention Value

Figure 2.11Credit Growth by Usage (y-t-d)

Figure 2.12Credit Growth by Economic Sector

Working Capital

Investment

Consumer

32%

37%

29%20082007

%0 10 20 30 40

Trade

Others

Manufacturing

Transportation

Construction

Agriculture

Business Services

Social Services

Mining

Electricity

%

20.7%

29.1%

37.9%

70.2%

42.8%

19.1%

39.6%

11.1%

25.9%

133.8%

0 20 40 60 80 100 120 140

20082007

Figure 2.13Growth of Housing loans, Credit Cards and Others

Figure 2.14Growth of Property Credit

29%

26%

29%

Housing Loan

Credit Card

Others

%0 5 10 15 20 25 30

20082007

Growth in 2007 (%) Growth in 2008 (% ytd)

Credit Delta 2007 (Rp M) Credit Delta 2008 (Rp M)

0 9 18 27 36 45

Construction

Real Estate

Housing Loan

Despite weaker growth compared to other types of

credit, consumption credit still gained Rp39 trillion during

semester II 2008. Increasing consumption credit are mostly

caused from automobile loans, uncollateralized loans and

others, amounting to Rp25.6 trillion, followed by housing

loans totaling Rp10.1 trillion. During 2008, growth of other

credit and housing loans exceeded credit card growth.

Meanwhile, of the three types of credit incorporated in

property credit (housing loans, real estate credit and

construction credit), housing loans contributed 54.6% of

the total, which reached Rp18.5 trillion during the

Rupiah credit continued to dominate bank loan

disbursements during the reporting period with an 80%

share of total credit growth. Meanwhile, credit

denominated in a foreign currency grew by Rp32.4 trillion,

which was affected by rupiah depreciation factors.

27


Expressed in US dollars, foreign currency credit actually

contracted by USD0.8 billion to USD23.1 billion. This was

in line with increasing risk due to exchange rate

fluctuations and inauspicious global economic conditions.

In terms of project location, credit disbursements

remain centralized on the island of Java, mostly for working

capital credit (share of 72.9%). Growth of investment credit

and consumption credit were more evenly spread, as

reflected in the share for Java at approximately 50% - 60%.

Meanwhile, credit on the islands of Sumatera, Kalimantan

and Sulawesi were more for investment credit.

During semester II 2008, micro, small and medium

(MSM) credit increased by Rp58.6 trillion; up 26.1% y-o-y,

falling short of total bank credit growth. As a consequence,

its share of total credit decreased slightly from 50.1% at

the end of June 2008 to 48.5% at the end of December

2008. In general, MSM credit remains dominated by

consumption credit with a 61.5% share of total MSM credit

growth. Productive credit in MSM credit was mainly

disbursed in the form of working capital credit for daily

operational needs, of which growth during the reporting

period reached Rp19 trillion (32.4% of total credit growth).

Meanwhile, the contribution of investment credit was

relatively small at approximately 6.1% of total MSM credit

growth. By sector, the others and trade sectors experienced

the largest credit growth.

Figure 2.16Credit Share by Usage

0 5 10 15 20 25 30 35%

Consumer LoanInvestment LoanWorking Capital Loan

West Java + Banten

DKI Jakarta

Central Java + DIY

East Java

Sumatera

Kalimantan

Sulawesi

Bali + NusTra

Maluku + Papua

Credit Risk

During semester II 2008, nominal NPL tended to

increase along with increasing pressure from the sluggish

economy. There was only a slight rise in nominal NPL of

Rp2.3 trillion to Rp50.9 trillion during the reporting

period. However, it should be noted that the small

increase was due in large part to the significant write

offs by one major bank. Therefore, the rise in nominal

NPL requires vigilance bearing the current economic

conditions in mind.

In terms of the NPL ratio, compared with the final

position in semester I 2008, gross NPL ratio declined to

3.76%. The low NPL ratio was influenced by high credit

growth that far exceeded the nominal NPL increase.

Meanwhile, the rise in nominal NPL was followed by

increasing loan loss provisions; rising Rp4.4 trillion to

Rp47.5 trillion during the reporting period. This caused

the net NPL ratio to decrease by 0.2% to 1.47%. The

expansion of loan loss provisions by banks, surpassing the

jump in nominal NPL, indicated that banks began to

anticipate the possibility of higher credit risk in the future.

By bank group, the increase in nominal NPL affected

private banks, foreign bank branches and joint-venture

banks during the reporting period. The nominal NPL of

state-owned banks declined by Rp3.1 trillion due to credit

write offs. Increasing nominal NPL at private and joint-

venture banks was followed by a rise in gross NPL, which

Figure 2.17Growth of MSM Credit

%

44

46

48

50

52

54

200

400

600

800

1000

1200

1400

2006 2007 2008 Dec

Total Credit Rp T (left)

MSM Rp T (left)% MSM/Credit

28


occurred from mid semester II 2008 onwards. The

increasing NPL ratio at foreign bank branches began at

the end of the semester. The increase in NPL at private

and joint-venture banks was primarily due to loans to the

manufacturing sector and business services sector, while

for foreign bank branches, credit to the others

(consumption) sector, particularly from credit cards, also

played a part.

The business services sector and manufacturing

dominated the increase in sectoral nominal NPL, to the

tune of Rp1 trillion and Rp0.7 trillion respectively, with a

gross NPL ratio of 2.12% and 5.41% respectively. Thus,

the manufacturing industry was plagued by relatively high

credit risk; despite a slight improvement by the end of the

reporting period in line with write offs by one major bank.

Figure 2.20Gross NPL Ratio by Bank Group

Figure 2.18Non Performing Loans

(%) (Trillion)

Gross NPL (left)

NPL Value (right)

NPL Net (left)

-

1

2

3

4

5

6

7

8

9

10

2006 Jun 2007 Jun 2008 Jun Dec30

35

40

45

50

55

60

65

70

75

Figure 2.19Credit, NPL and Provisions

NPL Value (left)

Credit (right)

30

35

40

45

50

55

60

65

70

75

2006 2007 2008 Dec200

400

600

800

1000

1200

1400

1600

Figure 2.22Gross NPL Ratio by Credit Usage

0

1

2

3

4

5

6

7

Working Capital Investment Consumer

Dec-07Jun-08

Dec-08

Figure 2.21Gross NPL Ratio by Economic Sector

Agriculture

Mining

Manufacturing

Construction

Trade

Transportation

Business Services

Others Dec-07Jun-08Dec-08

0.0 1.5 3.0 4.5 6.0 7.5

By credit usage type, increasing nominal NPL during

semester II 2008 only affected working capital credit; to

the amount of Rp1.7 trillion. Nominal NPL for investment

and consumption credit decreased. In spite of an increase

in terms of its nominal amount, the NPL ratio for working

capital credit decreased slightly (to 3.4%) compared to

that of the previous period. Furthermore, even though NPL

ratios were highest for investment credit, there was

significant decrease in credits not classified as current due

to write offs. Consequently, the gross NPL ratio of

0

1

2

3

4

5

6

7

State OwnedBanks

National PrivateBanks

RegionalDevelopment

Banks

Joint VentureBanks

ForeignBanks

Dec-07Jun-08Dec-08

29


Credit denominated in a foreign currency has become

the main source of bank nominal NPL. During semester II

2008, nominal NPL of credit in a foreign currency rose by

Rp1.9 trillion to Rp10.5 trillion due to the deteriorating

rupiah exchange rate. If calculated in USD, the nominal

NPL of credit in a foreign currency rose by just USD 29.7

million. Accordingly, gross NPL of credit in a foreign

currency also went up; to 4.14%. The largest increase in

nominal NPL of credit in a foreign currency affected state-

owned banks and amounted to Rp0.8 trillion, followed

by foreign bank branches with Rp0.7 trillion.

Conversely, the gross NPL ratio of credit in rupiah

declined to 2.98% in line with the Rp0.7 trillion drop in

nominal NPL. Decreasing nominal NPL for credit in rupiah

was mainly due to write offs by the state-owned bank

group amounting to Rp3.9 trillion. Looking forward, close

surveillance is needed considering lower exports and the

weak rupiah exchange rate could potentially affect debtor

repayment capacity, in particular their liabilities in a foreign

currency.

investment credit decreased from 4.6% at the end of June

2008 to 3.8% by the end of December 2008. Meanwhile,

in line with the declining nominal NPL for consumption

credit, the gross NPL ratio also decreased; from 2.9% to

2.5%.

The declining nominal NPL of consumption credit was

principally due to fewer housing loans (nominally), which

reduced the gross NPL ratio of housing loans to 2.26%.

Meanwhile, the gross NPL ratio of credit cards remained

relatively high at 10.8% by the end of December 2008.

This was after a modest decline compared to its position

in June 2008 of 11.6%. The majority (78.2%) of nominal

NPL for credit cards affected the group of foreign bank

branches. Despite the decline in nominal NPL for housing

loans, as a whole, property credit experienced an increase

of Rp0.3 trillion. This was due to increasing nominal NPL

of real estate credit which pushed NPL ratios up to 4.51%.

Figure 2.23Gross NPL Ratio of Consumer Credit

0

2

4

6

8

10

12

Housing Loan Credit Card Others

Dec-07

Jun-08

Dec-08

%

Figure 2.24Gross NPL Ratio of Property Credit

-

1

2

3

4

5

6

Construction Real Estate Housing Loan

Dec-07Jun-08Dec-08

Figure 2.25Gross NPL Ratio of Credit in Rupiah and

Foreign Exchange

0

1

2

3

4

5

Rupiah Foreign Exchange

Dec-07

Jun-08Dec-08

During the reporting period, the nominal NPL of MSM

credit slid Rp1 trillion to Rp18.8 trillion. Accordingly, the

gross NPL ratio of MSM credit also declined to 2.97%.

Based on usage type, the nominal NPL of all types of MSM

credit declined, mostly affecting working capital credit,

30


5.6%. However, stress test results on 15 major banks,

applying pessimistic scenarios (i.e., gross NPL ratio

increasing to 5.6%, which is the highest projection made

for 2009) indicated that in general, banks can handle the

potential losses, therefore, bank CAR should not drop to

below 8%.

2.3.3. Market Risk

The domestic economic performance during the early

part of semester II 2008 was marked by high inflation as a

result of fuel price hikes and soaring commodity prices.

Relatively strong economic growth at that time also

generated inflationary pressures. In response to such

conditions, Bank Indonesia raised its policy rate (BI Rate)

to alleviate inflationary pressures. From July to October,

the BI Rate was raised repeatedly in increments of 25 bps,

reaching 9.5% in October 2008.

However, global economic conditions rapidly

deteriorated, which began to spill over into the domestic

economy, primarily affecting the financial market. This

precipitated a decline in the stock market, lower SUN prices

and significant rupiah depreciation. Additionally, the global

economic downturn undermined Indonesian export

growth dramatically, thus, exacerbating conditions in the

domestic economy. Against this unfavorable backdrop,

Bank Indonesia maintained its BI Rate at 9.5% in

November. By end of 2008, however, BI had begun to

reduce its BI Rate, initially by 25 bps to 9.25% in order to

catalyze economic activities. Such measures were necessary

as the prospect of future domestic economic recovery was

considered deeply protracted.

The lower BI Rate at the end of 2008 did not

simultaneously propagate a corresponding decline in bank

interest rates. Indeed, bank interest rates continued to rise,

albeit slowly. During the reporting period, the interest rate

of 1-month time deposits increased 356 bps to 10.75%,

totaling Rp0.5 trillion. By sector, the drop in nominal NPL

was evident in almost all sectors, except the manufacturing

sector. The most significant decline affected the trade

sector; to the amount of Rp1 trillion. Notwithstanding,

the nominal NPL of MSM credit in the manufacturing sector

increased by Rp0.6 trillion, which raised the gross NPL to

7.5%. This shows that credit risk in the manufacturing

sector does not only stem from major corporations (non

MSM), but also from small and medium enterprises.

Figure 2.26Gross NPL Ratio of MSM and Non MSM Credit

Figure 2.27Gross NPL Ratio of MSM Credit

0

1

2

3

4

5

MSM Non MSM

Dec-07Jun-08

Nov-08

0.0

1.0

2.0

3.0

4.0 Dec-07 Jun-08Dec-08

Micro Small Medium

As elaborated in Chapter 1, potential increase in

credit risk was also evidenced by the results of the

Probability of Default (PD) analysis, which indicated that

credit risk from the real sector (corporate) will tend to rise

in the future. Based on PD and econometric model

analyses, it is projected that by the end of 2009, the gross

NPL ratio of banks will increase to approximately 4.9%-

31


liquidity. Oppositely, for assets/liabilities in a foreign

currency, the short position tended to be lower in

accordance with increasing risk due to rapid rupiah

depreciation.

The increase in short-term short position had the

potential to aggravate bank market risk due to the rising

interest rates, moreover with narrower spread. During

semester II 2008, net interest revenue earned by banks

exceeded that earned in the first semester as result of

relatively expansive credit growth; however, this had the

potential to reduce profitability. Stress test results indicated

that if interest rates rise 1%, bank CAR would not drop to

below 8%.

whereas the lending rate increased at a lower level.

Relatively high bank interest rates, particularly time

deposits, were the result of an interest rate war among

the banks to attract consumer funds to generate bank

liquidity. The interest rates of working capital credit,

investment credit and consumption credit rose respectively

by 222 bps, 139 bps and 27 bps, thus narrowing interest

rate spread.

Figure 2.28Rupiah Interest Rate and Exchange Rate

Figure 2.29Rupiah Maturity Profile

1-month Time Deposits(left)

Investment Credit (left)

Consumer Credit (left)

Exchange Rate (right)

% Rp

Working Capital Credit (left)

6

8

10

12

14

16

18

20

2006 2007 2008 Dec7500

8500

9500

10500

11500

12500

Trillion Rp

(500)

(400)

(300)

(200)

(100)

0

100

200

300

400

500

sd 1 month 1 - 3 months 3 - 6 months 6 - 12 months > 12 months

Dec07 Mar08 Jun08

Sep08 Dec08

The bank maturity profile, both rupiah and foreign

currency, which tended to be short in the short term and

long in the long term, meant that an increase in the interest

rates was unfavorable because it reduced profit or

increased loss. In the reporting period, the short position

of rupiah assets/liabilities in the very short term (up to 1

month) tended to increase in line with considerable efforts

taken by the banks to accumulate public money to boost

Figure 2.30Foreign Exchange Maturity Profile

Billion USD

(15)

(10)

(5)

0

5

10

sd 1 month 1 - 3 months 3 - 6 months 6 - 12 months > 12 months

Dec07 Mar08 Jun08

Sep08 Dec08

Figure 2.31Net Open Positions

Global financial market turbulence also intensified

pressures on the rupiah exchange rate. The rupiah

depreciated to Rp12,150 per USD in November 2008.

Joint Venture BanksNational Private Banks Regional Development Banks

State Owned Banks All BanksForeign Banks

%

0

1

2

3

4

5

6

7

8

9

Dec07 Mar08 Jun08 Sep08 Dec08

32


(HTM). This alleviated much of the pressure on the banks»

balance sheets and profit and loss statements; as indicated

by the net unrealized loss on the balance sheet and net

loss on the profit and loss statement, both of which

decreased in December 2008 after spiking in October 2008.

Figure 2.32SUN Portfolio of Banking Industry

Figure 2.33Performance of SUN Owned by Banks

(Trillion Rp)

%

0

10

20

30

40

50

60

HTM AFS Trading

Dec07Jun08Dec08

2007 2008

130.

628

.212

6.8

Dec Jun Jul Aug Sep Oct Nov Dec

AFS Trading HTM

156.

416

.910

1.4

0

50

100

150

200

250

300

Consequently, the average exchange rate of the rupiah

against the US dollar in semester II 2008 was Rp10,138

per USD, compared to Rp9,235 in semester I. However,

the relatively low NOP of banks (6.2%) limited bank

exposure to exchange rate risk. Stress test results indicated

that if the rupiah reached Rp5,000 per USD, the capital

adequacy ratio (CAR) of banks would remain above 8%.

However, the impact of exchange rate fluctuations on

banks should be carefully monitored as it could undermine

debtor repayment capacity.

Pressures on the stock market and domestic bonds

market during semester II of 2008 were more intense due

to the deteriorating global financial market turmoil. One

impact of the crisis was a significant slide in the prices of

government bonds in October. Nevertheless, by the end

of 2008 bond prices had begun to rebound. Such

developments dramatically affected the banks» balance

sheets as well as profit and loss statements because most

banks used SUN in their portfolio of earning assets.

To curtail higher loss, on 9 October 2008, Bank

Indonesia, the government (through the Capital Market

and Financial Institution Supervisory Agency or BAPEPAM-

LK) and Indonesian Accountant Association issued a joint

decree allowing banks to postpone the implementation of

marking-to-market in setting a fair SUN value. In addition,

banks were also permitted to shift SUN ownership from

Trading and Available for Sale (AFS) to Hold to Maturity

The tumbling SUN price encouraged banks to shift

SUN ownership from AFS to HTM in order to reduce loss.

Consequently, during semester II 2008, AFS ownership of

SUN declined by 10.8% to 36.9%, whereas the share of

HTM jumped 11.3% to 56.9%. The share of trading SUN,

which was relatively low, and the postponement of

marking-to-market reduced banks exposure to the drop

in SUN prices. Stress test results indicated that even if the

SUN price slid by 20%, no banks would experience a drop

in CAR to below the 8% minimum

2.3.4. Profitability and Capital

Profitability

Amid greater pressure on the economy, the banking

industry maintained its profitability, despite a slight decline

when compared to that of the previous year. Net interest

income (NII), as an indicator of profitability, increased from

Rp53.2 trillion (Semester I 2008) to Rp59.9 trillion

(Semester II 2008). Such an increase was due to expansive

credit growth since the beginning of the year, however, it

33


began to slow in November 2008. Therefore, the rise in

NII was supported by credit interest rate income.

2008) to Rp12.2 trillion (December 2008). After tax, profits

in semester II 2008 dropped 33.9% from Rp18.4 trillion

to Rp12.2 trillion.

It is important to note that a decline in profits during

the second half of 2008 is an annual phenomenon that

also occurred in 2007. Nevertheless, increasing pressure

on banks in 2008 exacerbated this annual phenomenon

and thus causing profits in 2007 to exceed those in 2008

(Rp35.0 trillion and Rp30.6 respectively). Meanwhile, in

the same period, total bank assets increased, which

precipitated a corresponding decline in ROA.

Less operational profit in 2008 was also due in part

to a drop in efficiency. Lower efficiency was reflected by

the increasing ratio of operational costs to operational

revenue (BOPO). Consequently, a priority on the banking

industry»s agenda will be to boost efficiency.

Recent data indicated that inefficiency has primarily

been observed occurring more in the group of small banks

compared to other groups of bank. Therefore, one effort

Figure 2.34Bank Profitability

Trillion Rp

0

5

10

15

20

25

Nov2007

Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec2008

Interest IncomeInterest ExpenseNII

Figure 2.35Bank Interest Income

0

50

100

150

200

250OthersCredit

SecuritiesPlacement in BI

Nov Dec Nov Dec2007 2008

However, profitability garnered from interest income

cannot fully become net profit of banks. This is because

banks anticipate unfavorable credit quality in association

with upcoming sluggish economic growth by raising their

loan loss provisions. Consequently, operational profit

declined by 30.6%, namely from Rp17.6 trillion (June

Operational P/L 18.07 16.97 35.04 17.63 12.23 29.86

Non Operational P/L 7.10 7.72 14.82 7.23 11.01 18.24

Before Tax P/L 25.17 24.69 49.86 24.86 23.24 48.10

After Tax P/L 18.38 16.63 35.02 18.39 12.16 30.55

Table 2.1Banking Profit and Loss

2007 2008

Semester I Semester II Total Semester I Semester II Total

Trillion RpTrillion RpTrillion RpTrillion RpTrillion Rp

Figure 2.36ROA by Bank Groups

%

-

1

2

3

4

15 MajorBanks

MiddleBanks

SmallBanks

Regional Dev.Banks

Joint VentureBanks

ForeignBanks

Industry

ROA Dec'07 ROA Dec'08

34


Bank resilience to pressures from various risks was

estimated using integrated stress testing, which included

credit risk, interest rate risk, exchange rate risk and SUN

price risk. Stress tests were conducted on 15 major banks

which make approximately 70% of the banking industry»s

total assets. By applying a scenario of 5.6% in gross NPL

(the most pessimistic NPL ratio projected for 2009), SUN

prices falling by 20%, interest rates dropping by 1% and

the rupiah depreciating by Rp5,000/USD, stress test results

showed that no bank»s CAR would drop to below the

minimum 8%. For the test, it is also assumed that shortfalls

in liquidity are covered through the interbank money

market.

to raise efficiency could be to expand the size or business

scale of the smaller banks. This can be achieved by bank

consolidation through mergers and acquisitions.

Capital

In general, the capital adequacy ratio (CAR) of the

banking industry by the end of semester II 2008 was relatively

high at 16.2%. However, there was a slight decline when

compared to the final position of the previous semester,

namely 16.4%. This was caused by high credit growth

coupled with a concomitant slowdown in bank profit. If

future bank credit expands in the range of 15%-18%, bank

CAR by the end of 2009 is projected to drop to 14.3%.

The tier-1 capital to risk weighted assets ratio

remained high at 14.4%. Consequently, bank capital is

sufficient to absorb risk and provide adequate room to

grow and expand credit.

Figure 2.38Capital, Risk-Weighted Assets and CAR

Figure 2.39Integrated Stress Test on CAR of 15 Major Banks

0%

5%

10%

15%

20%

25%

30%

A B C D E F G H I J K L M N O

OLD CAR

NEW CAR

Figure 2.37Ratio of Interest Expense to Interest Income

by Bank Group

Currently, several banks are facing potential losses

due to structured products, therefore, a stress test was

conducted to assess the capital resilience of said banks.

The results indicated that, in general, bank capital is

relatively strong. However, the several foreign bank

branches active in structured products must be prepared

to promptly raise their capital should the loss potential

increase.

However, when observed individually several banks

still maintain a minimum tier-1 capital of less than Rp100

billion. Regulations are in place that legislate a minimum

tier-1 capital of Rp100 billion by the end of 2010, therefore,

banks that have yet to reach Rp100 billion are required to

%

-

20

40

60

80

100

120BOPO Dec'07 BOPO Dec'08

15 MajorBanks

MiddleBanks

SmallBanks

Regional Dev.Banks

Joint VentureBanks

ForeignBanks

Industry

%Trillion Rp

-

400

800

1,200

1,600

2,000

0

5

10

15

20

25CapitalRisk -Weighted AssetsCAR (right)


35


the performance of finance companies improved

dramatically, as reflected by the increase in total assets

and capital by 23.8% and 2.01% respectively.

Furthermore, financing activities expanded by 16.58%.

The rapidly growing business of finance companies

was supported by greater funding, predominantly

originating from bank credit, for which the share increased

from 24.42% to 42% of total funding. The global financial

crisis, which tightened liquidity, pushed up the costs of

shares and bonds» issuers. Consequently, finance

companies became more dependent on bank credit.

take preparatory steps to meet this legislation. One possible

solution would be bank consolidation through mergers

and acquisitions.

To gain insight on banks» resilience in facing

macroeconomic shocks, a macroeconomic stress test was

held for the 15 major banks. The stress test results show

that NPL ratios of the banks are significantly affected by

GDP growth, exchange rate, inflation and the Jakarta

Composite Index (IHSG). The stress test results also show

that by the end of 2009, with the projected slowdown in

growth, NPL levels of the 15 banks will on average increase,

but only to levels within the 5% range.

Moreover, interbank stress tests were also held to

understand the contagion effects of a bank failure to other

banks (contagion risk). The stress test shows that in a case

where eleven trigger banks fail (i.e., eleven single failures),

there will be 14 banks which capital are impacted.

Meanwhile, its second round effects will cause another

24 banks (multiple failures) to have their capital impacted.

Figure 2.40Interbank Stress Test

Impacted Banks

Trig

ger B

anks A

BCDEFGHIJKL

F M N O P Q R S T U V J W K

Figure 2.41Business Activities of Finance Companies

0.00

20.00

40.00

60.00

80.00

100.00

120.00

140.00

160.00

180.00

200.0023.80%

16.58%28.19%

2.01%

Assets Financing Funding Capital

Jun 07Dec'07Jun'08Nov'08

2.4. NONBANK FINANCIAL INSTITUTIONS AND

THE CAPITAL MARKET

2.4.1. Finance Companies

Finance companies (FC) are one type of nonbank

financial institution that provides finance through various

means, such as consumer financing, leasing, factoring and

credit cards. During semester II 2008 (up to November),

Figure 2.42Finance Companies Source of Funds

*Total of Funds: Securities, Subordination Borrowed and Total Domestic and Foreign Borrowed

-7.33%

24.42%

28.19%

Billion Rp

Credit fromDomestic Banks

Securities Total of Funds*

Jun'07

Dec'07

Jun'08

Nov'08

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

In terms of the finance provided, the share of

consumer financing contracted and tended to diversify to

leasing. The lower concentration of consumer financing was

mostly due to the decline at joint-venture finance companies

from 52.40% (June 2008) to 47.96% (November 2008).

36


in 2008 also increased, reaching 6.22 million units, far

exceeding sales in 2007 of 4.69 million units.

Figure 2.43Composition of Financing by Finance Companies

(Nov «08)

Financing Receivables 141,179 46,257 93,795

Leasing 53,480 5,759 46,634

Factoring 2,222 1,182 1,001

Credit Card 1,178 2 1,175

Consumer Financing 84,299 39,314 44,985

Financing (in Billion Rp)

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

160,000

Total National Private Joint Venture

The profit of finance companies increased

significantly from Rp2.85 trillion to Rp5.96 trillion. Stronger

profits helped improve ROA and ROE. Business efficiency

was also well maintained with an Operational Cost to

Operational Revenue (BOPO) ratio of 77%.

The performance of finance companies was

buttressed by robust growth in the automobile market

during the reporting period. Based on data from the

Indonesian Automobile Industry (Gaikindo), during 2008,

automobile sales in Indonesia surged 40%, peaking at a

record high 607.15 units despite a slight downward trend

in November and December. Meanwhile, based on the

Indonesian Motorcycle Association (AISI), motorcycle sales

Figure 2.45Developments of NPL Value

Billion Rp

0

500,000,000

1,000,000,000

1,500,000,000

2,000,000,000

2,500,000,000

3,000,000,000

Jun Dec Jun Nov

2007 2008

SGUAPKK

PKTotal

Asset 116,000,000,000 127,000,000,000 140,649,000,000 174,124,731,707

Debt 81,524,052,728 90,319,642,214 100,183,895,911 128,423,157,567

Liabilities 95,241,046,752 102,466,196,738 113,722,737,895 143,568,726,785

Equity 20,758,953,248 24,533,803,262 26,926,262,105 30,556,004,922

Profit Before Tax 2,978,914,227 5,763,866,446 4,134,560,328 8,078,856,892

Profit After Tax 2,244,670,921 4,379,780,690 3,114,695,467 5,961,654,328

ROA 0.03 0.05 0.03 0.05

ROE 0.14 0.23 0.15 0.26

Ops Expense to

Ops Income 0.81 0.83 0.77 0.77

Debt/Equity 3.93 3.68 3.72 4.2

Liabilities/Equity 4.59 4.18 4.22 4.7

Table 2.3Financial Ratios of Finance Companies

Dec-06 May-07 Dec-07 May-07

Leasing 33.34% 11.31% 44.85%Factoring 1.82% 3.10% 1.05%Credit Card 1.07% 0.01% 1.69%Consumer Financing 63.76% 85.59% 52.40%

Table 2.2Financing Growth of Finance Companies

Jun»08 Total National Private Joint Venture

Nov»08 Total National Private Joint Venture

Leasing 37.88% 12.45% 49.72%Factoring 1.57% 2.56% 1.07%Credit Card 0.83% 0.01% 1.25%Consumer Financing 59.71% 84.99% 47.96%

Figure 2.44NPL of Financing by Finance Companies

NPL (%)

Jun'07 2.67% 14.14% 4.28% 1.55%

Dec'07 2.28% 11.59% 3.66% 1.68%

Jun'07 1.90% 11.32% 2.79% 1.70%

Nov'08 1.67% 9.04% 3.09% 1.66%

0.00

2.00

4.00

6.00

8.00

10.00

12.00

14.00

16.00

Leasing Factoring Credit Card ConsumerFinancing

37


Figure 2.46Cash Flow of Private Finance Companies

Net cash flow ofoperating activities 792 1,184

-

1,312

-

1,772

Net cash flow ofinvesting activities -45 -162 -177 -322

Net cash flow offinancing activities -903 -811 1,721 3,109

Billion Rp

Jun 07 Dec 07 Jun 08 Nov 08-3,000

-2,000

-1,000

0

1,000

2,000

3,000

4,000

Relatively high lending rates during the reporting

period heightened potential risk exposure to finance

companies. In addition, a decline in customer income as

an impact of the global crisis also had the potential to

exacerbate NPL. In 2008, the NPL ratio of finance

companies decreased, however, when expressed nominally

NPL actually increased, particularly consumer financing and

leasing.

Liquidity risk also had the potential to rise, largely

due to an increasing liquidity mismatch. Liquidity inflow

from funding was relatively high but still could not offset

the outgoing cash flow due to vast operational activities.

Figure 2.47Cash Flow of Joint Venture Finance Companies

Net cash flow ofoperating activities 3,528 7,133 5,221 9,786

Net cash flow ofinvesting activities 174 494 944 724

Net cash flow offinancing activities 4,790 7,513 4,480 11,222

Billion Rp

-15,000

-10,000

-5,000

0

5,000

10,000

15,000

Jun-07 Dec-07 Jun-08 Nov-08

Figure 2.48Bank Exposure

Billion Rp

0

5,000

10,000

15,000

20,000

25,000

30,000

35,000

40,000

45,000

50,000Channelling

Joint Financing

Jun Dec Jun Nov2007 2008

Higher financing risk and liquidity risk could

subsequently disrupt the performance of or intensify risks

to banks as the main source of funds for finance

companies. Therefore, banks with subsidiary finance

companies would be exposed to greater risk. Meanwhile,

increasing channeling and joint financing activities between

banks and finance companies had the potential to increase

risk to banks. During semester II 2008, channeling

increased 23.74% to Rp9.33 trillion, whereas joint

financing increased 9.8% to Rp49.61 trillion.

Based on the surveillance of 21 finance companies

affiliated with banks, it was shown that 10 finance

companies had NPLs with six of them showing a tendency

to increase. The significant rise in nominal NPL mostly

affected finance companies with a larger portion of leasing.

1 0.54% 0.37% - - - -3,558,4162 32.63% 53.28% 93,069,554 413,988 - 42,591,7523 0.37% 0.37% - - - - -990,6234 1.03% 0.00% - - - -26,578,3285 1.20% 1.07% - - - -5,406,0966 0.00% 0.06% 799,020 - - -7 0.20% 0.44% - - - 1,370,6088 0.79% 0.58% 142,127 - - -9 0.00% 0.66% -540,907 - - -

10 0.02% 0.03% - - - 279,805

Table 2.4NPL of Finance Companies

%Changeof NPL

FinanceCompanies

Jun»08 Nov»08

∆ Change in NPL Value

Jun»08 -Nov»08

Leasing FactoringCreditCard

ConsumerFinancing

38


Meanwhile, the nominal NPL of consumer financing tended

to decrease.

2.4.2. Capital Market

Foreign Investment Portfolio

In semester II 2008, foreign investors tended to realize

their gains. As a consequence, there were outflows of

foreign investment from rupiah financial assets totaling

Rp20.4 trillion, despite inflows during the previous

semester of Rp18.5 trillion. The outflows were evidenced

by less foreign ownership in SBI and SUN of Rp25.2 trillion

and Rp6.7 trillion respectively.

Negative sentiment after the collapse of international

financial institutions such as Lehman Brothers in the U.S.

and several investment banks in Europe as well as the

failure of AIG Insurance, and volatile share prices have

encouraged profit taking by foreign investors. On the

domestic stock market, profit-taking behavior by foreign

investors triggered net stock buying totaling Rp11.5 trillion.

Figure 2.50The Increase of NPL of Bank Subsidiary

Finance Companies

0

50000000

10000000

15000000

20000000

25000000

30000000

0

500000

1000000

1500000

2000000

2500000

3000000

3500000

4000000

4500000

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

2008

236

7910

Figure 2.49The Decrease of NPL of Bank Subsidiary

Finance Companies

0

2000000

4000000

6000000

8000000

10000000

12000000

0

20000000

40000000

60000000

80000000

10000000

12000000

1 8 4 5

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

2008

Figure 2.51Foreign Investment: SBI √ SUN √ Stocks

Trillion Rp

-35

-25

-15

-5

5

15

25

35SBI SUN Stocks

Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2007 2008

Figure 2.52Foreign Placements: SBI √ SUN √ Stocks

Trillion Rp

-35

-25

-15

-5

5

15

25

35

Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2007 2008

Profit taking by foreign investors could undermine

financial system stability as it has the potential to trigger a

sudden reversal. Vulnerability mainly stemmed from SUN

portfolio held by foreign investors to the tune of Rp87.4

trillion at the end of December 2008, most being the

portfolio of foreign investment managers. In addition to a

potential sudden reversal, the release of SUN by foreign

investors could be detrimental to the rupiah exchange rate

and SUN price.

Vulnerabilities were exaggerated by the herd behavior

of SUN investors. This was clearly evidenced during the

reporting period when the release of SUN by foreign

investors totaling Rp4.7 trillion was immediately followed

39


by similar action by domestic investors (particularly financial

institutions) totaling approximately Rp10.1 trillion. As a

consequence, the weakening of the SUN market deepened

further and market recovery became very slow.

Furthermore, as large SUN portfolios continued to be held

by domestic financial institutions, such as banks (Rp253.9

trillion), insurance companies (Rp53.2 trillion), pension

funds (Rp32.3 trillion) and mutual funds (Rp31.9 trillion),

the above situation caused SUN market weakening to

disrupt the performance of domestic financial institutions.

This requires close surveillance.

increasing reports of losses posted by international financial

institutions. The Dow Jones plummeted 23% reaching its

lowest level of 7,552.2 (mid November 2008). The prospect

of a deteriorating global economy and the expectations

of a recession in the U.S. as well as several countries in

Europe have seriously undermined the performance of

Asian regional markets. Against this unpropitious

backdrop, the IHSG nose-dived 42.3% to 1,355.41

(December 2008), reaching its lowest ebb of 1,111.39 on

28 October 2008. With such poor performance, the

average IHSG during semester II 2008 was approximately

1,723.06; much lower than the average for the previous

semester of 2,485.47.Figure 2.53

SUN and SBI Ownership by Foreign Investor

0

20

40

60

80

100

120SBI SUN

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2008

Trillion Rp

Figure 2.54SUN Absorption by Domestic and Foreign

Financial Institutions

-50

-40

-30

-20

-10

0

10

20

2008Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Trillion Rp

Foreign Financial InstitutionDomestic Financial Institution

Stock Market

During the reporting period, the global stock market

was corrected downwards due to negative sentiment

surrounding the bankruptcy of top investment banks and

Table 2.5Price Index Perfomance of Several Stock Exchanges

in the Region

Figure 2.55Performance of JCI, Global and Regional Index

(Based on Index per 31 Dec 2005)

0.20

0.70

1.20

1.70

2.20

Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2007 2008

IHSG FSSTI SET KLCIPCOMP NKY Hang Seng KOSPIFTSE NYA DJIA

JCI 2,139.28 2,745.83 2,349.11 1,832.51 1,355.41 28.35 (42.30) (21.99)STI 3,475.89 3,465.63 2,947.54 2358.91 1,761.56 (0.30) (40.24) (19.97)SET 776.79 858.10 768.59 596.54 449.96 10.47 (41.46) (22.39)KLCI 1,354.38 1,445.03 1,186.57 1,018.68 876.75 6.69 (26.11) (14.15)PCOMP 3,660.86 3,621.60 2,459.98 2,569.65 1,872.85 (1.07) (23.87) 4.46NIKKEI 18,138.36 15,307.78 13,484.38 11,259.86 8,859.56 (15.61) (34,28) (16.48)HSCI 21,772.73 27,812.65 22,102.01 18,016.21 14,387.48 27.74 (34.90) (18.49)KOSPI 1,743.60 1,897.13 1,674.92 1,448.06 1,124.47 8.81 (32.86) (13.54)FTSE 9,873.02 9,740.32 8,660.48 7,532.80 5,757.05 (1.34) (33.53) (13.02)UKX 6,607.90 6,456.90 5,625.90 4,902.45 4,434.17 (2.29) (21.18) (12.86)DJIA 13408.62 13264.82 11350.01 10850.66 8776.39 (1.07) (22.68) (4.40)

Sem II 07 Sem II 08 Jun-Sep 08

Growth (%)Jun 07 Dec 07 Jun 08 Sep 08 Dec 08

40


Overall, sectoral indices have weakened; most

severely affected were the farming sector and mining

sector, which spiraled 70% and 74% respectively. Sectors

vulnerable to exchange rate fluctuations also witnessed

severe declines in their respective indices, for example the

trade sector and the mixed industry sector, which

experienced declines of 58% and 40% respectively.

Sharp declines in the stock market index were also

followed by fewer transactions caused by end-of-year

festivities. During semester II 2008, stock market

transactions declined by 64% to Rp34.88 trillion. The stock

market transactions of foreign investors decreased, yet the

persistently high interest of investors spurred net purchases

of Rp7.77 trillion. Tumbling prices followed by less trade

sparked 45.86% lower market capitalization (Rp1.076

trillion). In addition, market liquidity remained low, as

reflected by share issuances, which only increased 6.94%

to Rp407.46 trillion. The number of issuers increased by

just 17 companies to 485 companies.JCI 2,139.28 2,745.83 2,349.11 1,832.51 1,355.41 28.35 (42.30) (21.99)

Financial Sector Index 223.14 260.57 203.74 203.37 176.33 16.78 (13.45) (0.18)

Agriculture Sector Index 1,680.12 2,754.76 3,061.06 1,489.57 918.77 63.96 (69.99) (51.34)

Basic Industry Sector Index 196.10 238.05 200.05 162.93 134.99 21.39 (32.52) (18.55)

Consumer Sector Index 437.01 436.04 398.29 381.36 326.84 (0.22) (17.94) (4.25)

Property Sector Index 211.72 251.82 168.53 142.42 103.49 18.94 (38.59) (15.49)

Mining Sector Index 1,647.04 3,270.09 3,415.96 1,833.24 877.68 98.54 (74.31) (46.33)

Infrastructure Sector Index 750.43 874.07 652.81 570.91 490.35 16.47 (24.89) (12.55)

Trade Sector Index 387.38 392.24 356.76 261.33 148.33 1.26 (58.42) (26.75)

Miscellaneous Sector Index 324.96 477.35 360.65 326.15 214.94 46.89 (40.40) (9.57)

Table 2.6Sectoral Price Index

Sem II 07 Sem II 08 Jun-Sep 08

Growth (%)Jun 07 Dec 07 Jun 08 Sep 08 Dec 08

The easing of inflationary pressures and the lower

interest rate by the end of 2008 successfully slowed any

further declines in the financial sector index, which dropped

just 0.18%. Nevertheless, global financial market turbulence

created additional domestic stock market volatility from

September √ November 2008. However, on average,

domestic stock market volatility remained rather moderate

and thus investor interest in short-term profit taking persisted.

Figure 2.56Volatility of Asian Stock Indices

(30 days)%

0

20

40

60

80

100

120


2007 2008

Indonesia JapanThailand MalaysiaSingapore Hongkong

Figure 2.58Capitalization and Issuance Value

Figure 2.57Stock Transaction Value of Domestic and

Foreign Investors

Trillion Rp

0

20

40

60

80

100

120

140

160


Total Indonesia Foreign

Trillion Rp

0

500

1000

1500

2000

2500

3000

3500


0

50

100

150

200

250

300

350

400

450

Cap. Value (BEI)Cap. Value (BEJ)Cap. Value (BES)JCI (RHS)Issuance Value

During the reporting period, the prices of most banks»

shares slid significantly, however, approaching the end of

semester a rebound looked imminent.

41


Figure 2.60P/E Ratio of Bank Stocks

%

0

10

20

30

40

50

60

70

80

90Jun 07 Dec 07

Jun 08 Dec 08

Danamon BCA BRI Mandiri BNI BII CIMBNiaga

Figure 2.59Stock Price Performance of Several Banks

-

1,000.00

2,000.00

3,000.00

4,000.00

5,000.00

6,000.00

7,000.00

8,000.00

9,000.00

-

200.00

400.00

600.00

800.00

1,000.00

1,200.00


2007 2008

BCA (left) BRI (left) Mandiri (left)Danamon (left) BNI (left) BII (right)CIMB Niaga (right)

Bonds Market

The soaring interest rate from the beginning to mid

semester II 2008 undermined bond market performance.

The SUN price decreased, as indicated by the 11% decline

in the IDMA index to 88.21. Moreover, the IDMA index

reached its lowest point of 67.11 on 29 October 2008. To

reduce potential losses to investors due to the falling SUN

price, a loosening policy was applied to the regulation

regarding marking-to-market for SUN investors. Along with

the BI Rate cuts since early November 2008, the market

began to rebound, as indicated by the decreasing yield of

rupiah investments of various tenors.

In terms of liquidity, the absence of SUN auctions in

Q4 (since 14 October 2008) offset market liquidity where

selling was dominant. In addition, the position of SUN in

the reporting period dropped from Rp515.0 trillion to

Figure 2.62Yield of 1 to 30 year SUN

Figure 2.61Price Performance of Several FR Series Bonds

2008

20

40

60

80

100

120

140


FR02 FR49 FR27FR48 FR47 FR45

%

4

6

8

10

12

14

16

18

20


2007 2008

1 year 3 years 5 years

10 years 15 years 30 years

Rp511.0 trillion. From a tenor perspective, SUN market

liquidity remained concentrated in short-term and medium-

term SUN, and thus resulting in less developments in

transactions for long-term SUN tenors. The lack of a proper

yield reference for long-term (more than 10 years) rupiah

investments also hindered the development of long-term

SUN transactions.

Figure 2.63Government Bonds: Market Liquidity of

Various TenorsTrillion Rp

0

5

10

15

20

25

30

35

40

45FR VR ORI Zero Coupon SPN

2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2037 2038

42


A depressed bond market reduced the interest of

issuers to issue bonds. In 2008, financing through

corporate bond issuances was low, with issuer value only

increasing by approximately 9% to Rp145.9 trillion with 3

additional issuers, making a total of 178. Corporate bond

issuances did not affect liquidity on the corporate bonds

market as most issuers refinanced. In general, corporate

bonds by the end of December 2008 recorded Rp73 trillion;

down 13.7% from the end of December 2007.

addition, the share of protected mutual funds by the end

of December 2008 was the largest at 36%. In December

2007 the share was only 17%. The higher NAV of protected

mutual funds succeeded in alleviating some of the

redemption pressures. During 2008, subscriptions

exceeded redemptions, at Rp83.8 trillion and Rp81.6 trillion

respectively.

Figure 2.64Issuance and Position of Corporate Bonds

0

20

40

60

80

100

120

140

160

170

171

172

173

174

175

176

177

178

179


2008

(Issuance & Position Trl Rp) (Issuer)

Issuance Position Issuer

Mutual funds

A weak financial market was detrimental to the

performance of mutual funds. Net Asset Value (NAV) in

the reporting period (up to October 2008) dropped 25%

to Rp68.9 trillion. With such negative growth, the NAV of

mutual funds decreased by approximately 27% in 2008.

The unfavorable stock market, which was followed by

increasing IHSG volatility, triggered a 53% decline in the

NAV of equity funds to Rp16.6 trillion, whereas discretionary

funds dropped 38% to Rp8.7 trillion. Furthermore,

congruous to the weaker bonds market, the NAV of fixed-

income funds decreased 15% to Rp14.0 trillion.

Meanwhile, the introduction of a Bapepam-LK

regulation to prohibit the redemption of protected mutual

funds before its maturity has caused the NAV of protected

mutual funds to increase by 21% to Rp24.9 trillion. In

Figure 2.65Net Asset Value of Mutual Funds

Fixed Income Equity Mixed Money Market Protected

0

5

10

15

20

25

30

35

40

2008Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct

2007

Trillion Rp

Figure 2.66Mutual Funds: Redemptions-Subscriptions-NAV

0

2

4

6

8

10

12

14

2007 2008Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct

Rdmp, trl Rp, left Subscr, trl Rp, left NAV, trl Rp, right

0

20

40

60

80

100

120

Figure 2.67Mutual Fund: NAV-Participating Units

0

20

40

60

80

100

120NAV, trl Rp, left Participating Units,

bil unit, leftNAV/Unit, right

Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct2007 2008

0

200

400

600

800

1000

1200

1400

1600

1800

2000

43


However, there were signs of waning investor interest

in mutual funds; indicated by the subscription units. Even

though there was a 17% increase in subscription units

during 2008, since September 2008 the number of

subscription units dropped to approximately 62.5 billion.

Nonetheless, the increase of funds generated in 2008 (up

to September) was considered small at only 2% to Rp135.5

trillion, whereas the number of mutual funds increased

dramatically by 16% to 549.

Figure 2.68Performance of Fund Collection of Mutual Funds

0

20

40

60

80

100

120

140

160

2007 2008Dec Jan Feb Mar Apr May Jun Jul Aug Sep

420

440

460

480

500

520

540

560Number of ParticipatingUnits, left

Total Funds,Trl Rp, left

Number of MutualFunds, right

44


Chronology of the 2008 Financial Sector Shocks and PolicyResponsesBox 2.1

The financial sector experienced many shocks

in 2008, and especially in the second half of the year.

As explained previously, these shocks caused the

Financial Stability Index (FSI) to increase sharply in the

reporting period, even surpassing the maximum

indicative level of 2 in the months of November and

December 2008. Meanwhile, the rupiah also came

under pressure. In the latest developments, the FSI

has shown a slight decline in line with improvements

in the Jakarta Composite Index (IHSG) and the price

of government bonds (SUN), although the rupiah

exchange rate has not yet returned to its level before

October 2008, although its volatility has increasingly

lessened.

The following is a chronological summary of the

financial shocks in Indonesia in the second half of 2008

and the policy responses taken to safeguard the

stability of the financial system.

16 September 2008

23 September 2008

13 October 2008

15 October 2008

24 October 2008

30 October 2008

13 November 2008

14 November 2008

18 November 2008

16 December 2008

Table Box 2.1.2Policy Response

Date Event

BI lowers the O/N repo rate from the BI rate plus 300 bps to the BI rate plus 100 bps.

BI adjusts the FASBI rate from the BI rate minus 200 bps to the BI rate minus 100 bps.

BI lengthens the time span for Fine Tune Operations (FTO) from 1 -14 days to 1 day - 3 months (BI Regulation No.10/14/PBI/2008).

Issuance of PERPPU No.2 Year 2008 on changes in Regulations concerning Bank Indonesia, which allowed current credits to become

collateral to receive the short term liquidity facility (FPJP).

Issuance of PERPPU No.3 Year 2008 which regulates the increase in the value of a depositor»s funds guaranteed by LPS from Rp100

million to Rp2 billion.

BI lengthens the tenor on the foreign exchange swap from a maximum of 7 days to 1 month (BI RegulationNo.10/21/PBI/2008).

BI committs to supply foreign exchange to domestic corporations through banks (BI Regulation No.10/22/PBI/2008).

Issuance of PERPPU No.4 Year 2008 concerning the Financial System Safety Net (JPSK).

BI amends BI Regulation No.10/19/PBI/2008 to improve the calculation of Rupiah GWM, i.e. primary GWM being 5% of rupiah

deposits and secondary GWM being 2.5% of rupiah deposits (BI Regulation No.10/25/PBI/2008).

BI issues a regulation concerning the short term liquidity facility for commercial banks (FPJP) (BI Regulation No.10/26/PBI/2008).

BI issues a regulation which limits speculative foreign currency transactions to the rupiah by requiring an underlying transaction for

each foreign currency purchase in excess of USD100,000 (BI Regulation No.10/28/PBI/2008).

BI issues an amendment in regard to PBI No.10/26/PBI/2008 concerning the Short Term Liquidity Facility (FPJP) for Public Banks

(BI Regulation No. 10/30/PBI/2008).

BI issues a regulation concerning an Emergency Funding Facility (FPD) (BI Regulation No.10/31/PBI/2008).

BI forbids derivative transactions of structured products in relation to foreign currency transactions (BI Regulation No.10/38/PBI/2008).

8-10 October 200828 October 200829 October 2008

20 November 2008

24 November 2008

Table Box 2.1.1Chronology of Shocks to the Indonesian

Financial Sector in 2008

Date Event

Indonesia Stock Exchange is temporarily closed.IHSG: 1,111.4, lowest level since December 2005.IDMA: 67.11, lowest level since first SUN issuance inJanuary 2005.LPS takes over one bank which is said to have beensystematically hit (Bank Century).The rupiah/USD exchange rate is at Rp12,650/USD,its lowest level since the 1997/1998 crisis.

45


Bank Century’s Takeover, Bank Indover’s Closure andFinancial System StabilityBox 2.2

In semester II 2008, two major predicaments

in the banking industry are highlighted. The first is

the takeover of Bank Century by LPS and the second

is the closure of Bank Indover. The question remains,

do these problems interfere with the stability»s of

Indonesia»s financial system?

Bank Century is a merger between Bank CIC,

Bank Pikko and Bank Danpac in December 2004.

When the drought of global liquidity hit this country,

in July 2008, Bank CIC experienced liquidity

problems and on several occasions, violated the

minimum reserve requirement. Afterwards, the

performance of the bank chronically deteriorated,

until it was listed into Bank Indonesia»s special

surveillance watch. The condition of the bank

continued to worsen and on 20 November 2008,

was considered a failed bank. Afterwards, the bank

was regarded to have systemic impact and was

seized by LPS to be restored.

In reality, the takeover of Bank Century by LPS

did not generate a significant shock within the

banking industry. Both the customers and the bank

institution remained calm and the problem did not

cause pressure to the stability of the financial system.

The undisruptive takeover also marks the strong

coordination amongst all related stakeholders in

Indonesia»s financial system and the existence of crisis

management protocol and mechanism that has been

collectively agreed upon.

In addition, De Indonesische Overzeese Bank or

Bank Indover is Bank Indonesia»s subsidiary based in

Amsterdam, Netherlands. Bank Indover, used to have

good performance but experienced liquidity problems,

due to a drastic drop in the money market line available

to it as repercussions of the global financial turmoil,

particularly those taken place in Europe. Later, the

Dutch court ordered to freeze the bank on 6 October

2008.

One of the potential pressures in the financial

stability is the investment of domestic banks to Bank

Indover. Data show that more than 14 local banks

had placements in Bank Indover before the bank was

shut down. Taking into account that the exposure of

the local banks in Bank Indover was only IDR 1.6 trillion

or approximately 0.07% from the total assets of the

banking industry as per October 2008, the closure of

Bank Indover did not generate significant impact to

the resilience of Indonesia»s financial system.

Moreover, its impact to Capital Adequacy Ratio

(CAR) was also trivial. The closure of Bank Indover

only instigated a slight decline in CAR from 16.18%

to 16.09%. The results of interbank stress test also

illustrate that banks which declined in CAR due to

the closure of Bank Indover are not banks with

systemic impacts. From a liquidity point of view, no

significant damage occurs as banks only experience a

slight decrease in liquidity ranging from 0.01% to

7.28% of banks» secondary reserves.

46


Segmentation in the Interbank Money Market (PUAB)Box 2.3

Segmentation in the interbank money market

(PUAB) is a situation in which interbank transactions

tend to be limited and only occurring between certain

bank groups. As the PUAB is segmented, banks with

liquidity become increasingly careful in placing or

managing their liquidity. Meanwhile, banks in need of

liquidity become even more careful in borrowing funds

in the PUAB not only considering the limited level of

liquidity, but also the bank»s reputation.

Segmentation in the PUAB is indicated by the

decrease in average daily PUAB transactions. For the

rupiah PUAB, the decrease in average transaction

volume occured from September 2008, while for the

domestic foreign exchange PUAB the decrease started

only a month later, i.e. October 2008.

The following table divides 2008 into two time

periods. Period I represents the period prior to liquidity

pressures (January to August for the rupiah PUAB or

January to September for the domestic foreign

exchange PUAB), while Period II represents the period

in which liquidity pressures occurred (October to

December for the rupiah PUAB and October to

December for the domestic foreign exchange PUAB).

By comparing the two periods, it can be observed

that in Period II almost all bank groups limit their

transactions, both in placing or taking funds. Also

noted is that even if transactions do occur, they will

only take place within certain group of banks. The

bigger banks can be observed as only willing to

transact amongst them, while smaller and medium

Table Box 2.3.1Daily Average Transaction Volume of Rupiah PUAB from January to December 2008

Period I 266,184 260,786 99,627 8,628 706,069 143,799 1,485,0931,485,0931,485,0931,485,0931,485,093Period II 17,690 30,547 4,762 0 112,154 3,962 169,115169,115169,115169,115169,115Change -93.4% -88.3% -95.2% -100.0% -84.1% -97.2% -88.6%Period I 456,839 239,003 119,152 69,866 592,022 188,310 1,665,1921,665,1921,665,1921,665,1921,665,192Period II 121,980 372,240 173,638 20,184 367,196 143,939 1,199,1771,199,1771,199,1771,199,1771,199,177Change -73.3% 55.7% 45.7% -71.1% -38.0% -23.6% -28.0%Period I 49,585 62,317 36,332 50,926 81,815 17,459 298,434298,434298,434298,434298,434Period II 51,991 100,921 126,384 31,345 90,521 33,659 434,819434,819434,819434,819434,819Change 4.9% 61.9% 247.9% -38.5% 10.6% 92.8% 45.7%Period I 9,382 53,515 63,656 36,223 7,424 22,954 193,155193,155193,155193,155193,155Period II 4,963 37,090 45,772 15,076 1,594 12,730 117,226117,226117,226117,226117,226Change -47.1% -30.7% -28.1% -58.4% -78.5% -44.5% -39.3%Period I 10,229 4,897 2,377 1,411 252,279 0 271,193271,193271,193271,193271,193Period II 2,500 11,701 2,778 0 318,728 0 335,707335,707335,707335,707335,707Change -75.6% 139.0% 16.9% -100.0% 26.3% - 23.8%Period I 873,565 695,964 197,388 71,858 97,870 917,118 2,853,7632,853,7632,853,7632,853,7632,853,763Period II 225,304 614,915 355,914 15,469 51,586 1,090,923 2,354,1122,354,1122,354,1122,354,1122,354,112Change -74.2% -11.6% 80.3% -78.5% -47.3% 19.0% -17.5%Period I 1,665,7831,665,7831,665,7831,665,7831,665,783 1,316,4821,316,4821,316,4821,316,4821,316,482 518,532518,532518,532518,532518,532 238,913238,913238,913238,913238,913 1,737,4801,737,4801,737,4801,737,4801,737,480 1,289,6401,289,6401,289,6401,289,6401,289,640 6,766,8296,766,8296,766,8296,766,8296,766,829Period II 424,429424,429424,429424,429424,429 1,167,4151,167,4151,167,4151,167,4151,167,415 709,247709,247709,247709,247709,247 82,07482,07482,07482,07482,074 941,778941,778941,778941,778941,778 1,285,2131,285,2131,285,2131,285,2131,285,213 4,610,1574,610,1574,610,1574,610,1574,610,157Change -74.5% -11.3% 36.8% -65.6% -45.8% -0.3% -31.9%

PLACING BANKS

Bank Group

Million RpMillion RpMillion RpMillion RpMillion Rp

TAK

ING

BA

NK

S

Total4 StateOwned Banks

Major Banks(non-state

owned)

MediumPrivate Banks

SmallPrivate Banks

RegionalDev»t Banks

(BPD)

Joint Ventures& Foreign

Bank Offices

4 StateOwned Banks


owned)

MediumPrivate Banks

SmallPrivate Banks

Regional Dev»tBanks(BPD)

Joint Ventures& Foreign Bank

Offices

TOTALTOTALTOTALTOTALTOTAL

47


banks find it relatively difficult to obtain these

interbank funds.

The most recent developments show that,

commencing the end of 2008, along with the

improvements in domestic liquidity attributed to a

series of policies taken by Bank Indonesia and the

government, both the rupiah PUAB and foreign

exchange PUAB showed increases in their average

daily transaction volume. As such, it is expected that,

looking forward, the segmentation issue in the PUAB

will be completely resolved and thus not putting

pressure on financial stability.

Table Box 2.3.2Daily Average Transaction Volume of Domestic Foreign Exchange PUAB

Period I 8,623 14,935 5,980 759 1,337 2,873 34,508Period II 4,455 16,072 4,481 894 174 4,418 30,494Change -48.3% 7.6% -25.1% 17.6% -87.0% 53.8% -11.6%Period I 10,481 9,057 6,014 1,109 52 6,561 33,274Period II 2,209 7,193 4,530 1,065 50 2,121 17,168Change -78.9% -20.6% -24.7% -4.0% -4.5% -67.7% -48.4%Period I 2,504 2,837 670 1,525 24 330 7,889Period II 1,170 1,568 648 1,212 8 376 4,982Change -53.3% -44.7% -3.3% -20.5% -65.0% 14.1% -36.8%Period I 0 3 78 53 0 45 179Period II 0 0 0 18 0 25 43Change - -100.0% -100.0% -66.9% - -44.7% -76.2%Period I 32 14 0 0 0 94 139Period II 0 700 19 19 0 0 737Change -100.0% 5037.9% - -100.0% 429.7%Period I 45,668 60,364 24,368 5,943 144 81,611 218,098Period II 2,585 41,127 13,445 6,093 0 71,763 135,014Change -94.3% -31.9% -44.8% 2.5% -100.0% -12.1% -38.1%Period I 67,307 87,209 37,110 9,390 1,558 91,513 294,087Period II 10,419 66,660 23,122 9,300 232 78,703 188,437Change -84.5% -23.6% -37.7% -1.0% -85.1% -14.0% -35.9%

PLACING BANKS

Bank Group

Thousand US$Thousand US$Thousand US$Thousand US$Thousand US$

TAK

ING

BA

NK

S

Total

4 StateOwned Banks


owned)

MediumPrivate Banks

SmallPrivate Banks

Regional Dev»tBanks(BPD)

Joint Ventures& Foreign Bank

Offices

TOTALTOTALTOTALTOTALTOTAL

4 StateOwned Banks


owned)

MediumPrivate Banks

SmallPrivate Banks

RegionalDev»t Banks

(BPD)

Joint Ventures& Foreign

Bank Offices

48


Structured Products and Offshore Products: Their Impact tothe Stability of the Financial SystemBox 2.4

Structured Products

A number of banks, especially branches of

foreign banks, have recently been active in offering

investment products which are known in Indonesia as

structured products. In general, structured products

can be viewed as derivative of a conventional financial

product with an asset structure which is expected to

provide optimum returns or give yield enhancement

to clients, based on specific assumptions from general

indicators in the financial markets, such as interest

rates, the exchange rate and stock indices.

The structured products which have been

developed in Indonesia are generally derivatives from

time deposit options or hedging (usually forward)

options. Data shows that developments in options

transactions have been very brisk, that is increasing

by 251% in 2007 and by 134% in 2008. Meanwhile,

forward transactions have also experienced an

increase, up by 24% in 2007 and by 46% in 2008.

Meanwhile, the weakening of the global

economy has put pressure on Indonesia»s balance of

payments. This subsequently resulted in negative

sentiment on the rupiah, causing it to depreciate. In

2008, the value of the rupiah relative to the US dollar

weakened by around 18.5% and as such the exchange

rate by the end of December was around Rp11,120/

USD. The weakening of the rupiah had an adverse

impact on the performance of structured products

which generally had not anticipated the significant

depreciation in the value of the rupiah.

In further developments, the weaker

performance of structured products resulted in losses

for investors, while investors still had to supply funds

to conserve the value of their savings. And customers

of certain structured products, like exporters, are even

facing problems at the present time with overseas

importers unilaterally canceling contracts as a

consequence of the global economic downturn. As a

result, these clients do not have sufficient funds to

preserve the value of their savings, and, moreover,

they also face difficulties in canceling structured

product transactions due to the high cost of

unwinding these transactions. Meanwhile, because

banks still have obligations with other banks in relation

to the structured product transactions of clients, banks

often paid for its clients» unpaid maturing»obligations.

Nonetheless, this practice will increase the bank»s

exposure to credit risk, and can become a source of

dispute with the customer. As such, structured product

transactions have already created new problems in

the banking sector and if this problem cannot be

resolved in a wise manner, then it will increase the

risk of instability in the financial system.

A valuable lesson which can be drawn from the

current problems in regard to structured products is

the importance of banks adopting prudential principles

and transparent in their marketing of such products,

including in explaining risk mitigation aspects and

consumer protection. If the problem of structured

products cannot be resolved satisfactorily, then risks

relating to the reputation of a bank will increase as

the legal risks.

Offshore Products

Meanwhile, the prevalence of mutual funds

transactions has encouraged banks to undertake the

role of agent for mutual funds. As a result, the agency

role undertaken by banks is no longer limited to

onshore mutual funds, that is mutual funds which

are issued by domestic investment managers, but also

involves offering offshore financial products, including

both structured funds and structured notes. In

49


principal, structured funds are a type of mutual fund

issued by an overseas investment manager, while

structured notes are a type of structured financial

product which is issued by investment banks abroad.

There are a number of main reasons why banks

offer offshore products to their customers: (i) there is

demand for such products from the bank»s prime

customers; (ii) to maintain good relationships with the

customer so they do not switch to another bank; and

(iii) to face competition given the increasing number

of overseas financial products offered by banks and

investment managers abroad which is done by directly

visiting prospective investors in Indonesia.

With this background, offices of foreign banks

(KCBA) are the most active among all banks in

undertaking the role of agents for offshore financial

products, especially through their private banking units

or wealth management divisions. In some banks, the

wealth management unit in Indonesia is directly

connected and is a part of the wealth management

unit at the bank»s global office overseas. Another

factor which causes offices of foreign banks to be fairly

active in offering offshore financial products is because

similar activities are already being carried out at the

bank»s branch offices in other countries.

Based on reports from a number of banks acting

agents for offshore financial products it is known that

the offering of overseas financial products in the

semester II 2008 fell 14% to around Rp32 trillion.

This decline is attributable to the weakening in the

global financial markets which reduced investor

appetite for structured investment products.

Nonetheless, the number of banks competing in this

field is increasing. This is due, among other things,

the increasing level of takeover of more domestic

banks by foreign banks.

Besides banking industry, the offering of offshore

financial products has also been done by domestic

investment managers. Based on provisional data up

to November 2008, the offering of offshore financial

products by domestic investment managers is much

smaller, only around Rp2.5 trillion. And in the semester

II 2008 (data up to November), the amount had even

declined by around 6% to around Rp2 trillion.

Nonetheless, overall, the amount of offshore financial

products offered by banks and domestic investment

managers is relatively small, that is its share is on

average only around 29% of onshore mutual funds.

In general, the offering of offshore financial

products as being done by banks is still rather limited

and only directed at prospective investors who already

have an adequate understanding of the risks involved

in investing in offshore financial products. Although

still limited, caution needs to be increased given that

the activities of being an agent for offshore financial

products means the bank may be more exposed to

reputational and legal risks, besides increasing the

dangers of having a misunderstanding with investors,

especially if the problem of transparency and customer

protection are not given due attention. Another

important consequence that needs to be given

attention is the danger of excessive investment in

offshore financial products, which potentially may lead

domestic investor funds to run overseas.

50


The Impact of Foreign Debt to Financial System StabilityBox 2.5

The experience of the 1997/1998 crisis showed

that banking and corporate foreign debt can trigger a

crisis, particularly if the exchange rate experiences

significant weakening. Learning from the past, banks

today are prudent in maintaining their Net Open

Positions (NOP). This is indicated by the banking

industry»s low NOP levels, i.e. 6.2% when the allowable

maximum is 20% from capital. However, considering

that the banking industry»s foreign exchange maturity

profile shows relatively high short-term (tenors up to

one month) short positions, prudence must be

increased.

It is predicted that repayment of 2009 foreign

debt, in general, remains to be manageable. Also under

prediction is that USD27.5 billion of foreign debt, both

government and private, will be repaid during 2009.

The principal and interest of banking private

foreign debt maturing in 2009 is only USD3.1 billion,

while non-banking private foreign debt is approximately

USD14.2 (not including foreign debt that is standstill).

Foreign debt obligations for the banking industry are

predicted to be under control considering that 60%

of total foreign debt maturing in 2009 are in the form

of banker»s acceptance. Meanwhile, the amount of

non-banking private foreign debt remains relatively low

compared to foreign reserves. As such, pressure on

foreign exchange from private foreign debt, including

from banks, is predicted to be insignificant.

Table Box 2.5.1Private Foreign Debt Maturing in 2009

Loan Agreement 4,208.97 2,191.40 1,919.57 3,190.13 11,510.06Securities 1,614.73 750.35 223.03 93.36 2,681.46Trade Credits 755.45 154.43 87.03 87.55 1,084.47Other Loan 32.94 10.76 3.23 57.55 104.46TotalTotalTotalTotalTotal 6,612.086,612.086,612.086,612.086,612.08 3,106.943,106.943,106.943,106.943,106.94 2,232.862,232.862,232.862,232.862,232.86 3,428.583,428.583,428.583,428.583,428.58 15,380.4615,380.4615,380.4615,380.4615,380.46

LOAN TYPE QI-09 QII-09 QIII-09 QIV-09 Million USD

Loan Agreement 271.47 555.55 238.14 699.22 1,764.38Securities 54.60 66.11 49.06 62.66 232.44TotalTotalTotalTotalTotal 326.07326.07326.07326.07326.07 621.67621.67621.67621.67621.67 287.21287.21287.21287.21287.21 761.88761.88761.88761.88761.88 1,996.821,996.821,996.821,996.821,996.82Grand TotalGrand TotalGrand TotalGrand TotalGrand Total 6,938.146,938.146,938.146,938.146,938.14 3,728.613,728.613,728.613,728.613,728.61 2,520.062,520.062,520.062,520.062,520.06 4,190.464,190.464,190.464,190.464,190.46 17,377.2817,377.2817,377.2817,377.2817,377.28

Bank 3,140.50Non Bank 14,236.70Private Foreign Debt* 17,377.20

INTEREST

LOAN TYPE QI-09 QII-09 QIII-09 QIV-09 Million USD

* Not including domestic securities held by foreign parties (USD 1,308 million).

PRINCIPAL

51

Chapter 3 Financial Infrastructure and Risk Mitigation

Chapter 3Financial Infrastructureand Risk Mitigation

52



53


3.1. PAYMENT SYSTEM PERFORMANCE

Generally the role of the Bank Indonesia-Real Time

Gross Settlement (BI-RTGS) in the payment system is

increasingly important as, in terms of transaction value,

93% of payments use this system. However, in terms of

volume transactions, card-based payments instruments

(credit cards, debit cards, and ATM cards) dominate as they

make a 97% share of total payments.

Transaction value of the BI-RTGS system experienced

14.68% growth, equivalent to Rp3.1 thousand trillion, to

a total of Rp23.9 thousand trillion (y-o-y). Transaction

volume grew by 710 thousand transactions (14.9%) to

5.45 million compared to that of the previous period. Such

a rise in value and volume was primarily attributable to

the burgeoning number of transactions between

consumers and the government through BI-RTGS.

Conversely, settlements processed through the

clearing system have shown a different pattern to that of

BI-RTGS. Over the last two years up to the end of Semester-

II 2008, the value and volume of payments processed

through the Bank Indonesia-National Clearing System

(SKN-BI) increased, however, during semester II 2008 a

downward trend was reported. Specifically, when

compared to semester II 2007, retail transfers through SKN-

BI declined by Rp105.35 trillion (14.31%) to Rp631 trillion.

Furthermore, transaction volume also declined by some

The dependability of the financial infrastructure during semester II 2008 was

well maintained and thus provided significant support to the financial system

and the economy. Improvements continued to be made on the payment system

while use of information provided by the Credit Bureau has seen an increase.

The introduction of a Financial System Safety Net (JPSK), for which the draft

Law is currently awaiting parliamentary approval, will further reinforce financial

system stability.

Figure 3.1Performance of BI-RTGS Transactions

-

2.0

4.0

6.0

8.0

10.0

12.0

-

10,000

20,000

30,000

40,000

50,000

2004 2005 2006 2007 2008

Volume (millions)

Nominal Value (trillions)

Financial Infrastructure and Risk MitigationChapter 3

54


19.35 million transactions (47.96%) to 21 million

transactions.

3.1.1. Risk Assessment and Risk Mitigation

To mitigate credit risk in the payment system and in

an effort to anticipate the impacts of the ongoing global

crisis, which has the potential to threaten liquidity in the

payment system, Bank Indonesia improved the Intra-day

Liquidity Facility (FLI) and short-term funding facility (FPJP),

as well as issued new regulations for an emergency funding

facility (FPD).

Meanwhile, to mitigate settlement risk in the

implementation of the SKN-BI, Bank Indonesia introduced

prefunds as a failure-to-settle (FtS) mechanism as regulated

by Bank Indonesia Regulation No. 7/18/2005 regarding

the Bank Indonesia-National Clearing System. Prefunds are

mandatory for all participating banks in the national

clearing system in order to provide initial funds in the form

of cash (cash prefund) as well as securities (collateral

prefund) in demand deposit accounts and collateral held

Figure 3.2Performance of Bank Indonesia National

Clearing SystemVolume Value (million Rp)

6

5

4

3

2

1

-1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

2007 2008

20

-

40

60

80

100

120Volume (millions)

Value (trillions)

The use of card-based payment instruments was

high, dominated by ATM/Debit cards (89%), with credit

card usage at just 11%. Based on transaction value, ATM/

Debit cards were also most prevalent with a 95% share,

compared to credit cards with just 5%.

E-money transactions in semester II 2008 experienced

significant growth compared to semester I 2008. Based

on value, e-money grew by Rp0.05 trillion (398.44%).

Moreover, based on volume, semester II witnessed an

additional 1.15 million transactions (163.75%). Such

increase was due to the emergence of new e-money

issuers, At the end of 2008 there were a total of eight e-

money issuers.

Figure 3.3Performance of Card Based Payment Instruments

95%

5%

Based Card (ATM and ATM + Debit) Credit Card Based Card (ATM and ATM + Debit) Credit Card

89%

11%

Figure 3.4E-Money Transactions

0.00

100.00

200.00

300.00

400.00

500.00

0.00

5.00

10.00

15.00

20.00Volume (thousands)

Value (billions)


55


at Bank Indonesia to complete debit clearing activity. With

mandatory prefunds, the risk of a liquidity shortfall for

debit clearing settlement can be minimized. Failure to meet

the prefund within a specified time can lead to the

participating bank being excluded from debit clearing on

that day.

To mitigate default risks of an interbank debit

payment transaction clearing settlement, in 2008 Bank

Indonesia issued a policy imposing the «no money no game»

principle for debit clearing. Through such implementation

of interbank debit payment transaction clearing settlement,

the default risk in debit clearing settlement is mitigated

and thus protecting Bank Indonesia as a clearing operator

from default risks of banks participating in the debit

clearing process. The implementation of the «no money

no game» policy through the use of the pre-fund instrument

entails that all debit payment transactions will be cancelled

by the clearing operator should the amount of pre-fund

to cover obligations to complete debit clearing is found

insufficient.

In relation with efforts to mitigate risk in terms of

money remittance, Bank Indonesia promulgated Bank

Indonesia Circular No. 10/49/DASP on 24 December 2008

regarding Money Remittance Permits, which superseded

the previous regulation (BI Circular No. 3/32/DASP dated

20 December 2006 regarding Money Remittance

Registration). With the new circular, the transition period

of two years provided to money remitters to register their

remittance business has expired and all remitters are

required to obtain a permit from Bank Indonesia. With

this new regulation it is expected that money remittance

implementation can be better monitored and adhere to

standards in accordance with international best practices.

Bank Indonesia also strives to improve card-based

payment instrument regulations and supervision in order

to ensure that card issuers can manage potential risks. To

tighten security and mitigate the potential misuse of

payment cards and/or credit card fraud, including Electronic

Data Capture hardware, Bank Indonesia instituted a policy

that stipulates the mandatory use of chip technology on

credit cards no later than 31 December 2009.

Meanwhile, as a follow-up to a progress and security

assessment results of chip implementation for credit cards

held in semester I of 2008, it can be reported that 46

findings or 58% of all 80 findings have been settled by

the end of semester II 2008. Further on, issuers and

acquirers have been required to submit progress reports

regarding the implementation of the chip and follow-ups

of the security assessments on a quarterly basis.

To mitigate potential risk in the Indonesian interbank

payment system, a Payment-Versus-Payment (PVP)

Settlement mechanism will be developed for the BI-RTGS

system. This is aimed at mitigating payment failure risk in

settling inter-bank foreign exchange trades (FX settlement

risk) in Indonesia. Using PVP settlement, the payment of

domestic and foreign currency through interbank foreign

exchange trade in Indonesia will be performed

simultaneously. Therefore, the final transfer of one currency

occurs if, and only if, a final transfer of the other currency

or currencies takes place.

The PVP settlement mechanism to be developed for

the BI-RTGS system is specifically designed for US Dollar

trading against the Rupiah (USD/IDR). This is because USD/

IDR trading represents the largest share of interbank foreign

exchange trade in Indonesia. The PVP mechanism, known

as USD/IDR PVP, will be developed by building a USD/IDR

PVP link that connects the BI-RTGS system (for Indonesian

rupiah payment settlement) with the USD-CHATS1 System

in Hong Kong (for US dollars payment settlement). To this

end, Bank Indonesia and the Hong Kong Monetary

Authority signed a Memorandum of Understanding on

24 October 2008.

56


3.1.2. Business Continuity Plan (BCP) for the BI-

RTGS System

Payment system failure could disrupt financial system

stability. Therefore, it is imperative that the payment system

performs well, is reliable and risk is properly mitigated.

This requires competent human resources and reliable

infrastructure (applications, hardware and the network)

particularly in facing emergencies, for both, the

administrator and participants.

To maintain the continuity of the BI-RTGS system on

the administrator»s side, Bank Indonesia regularly tests the

backup system by simulating multiple scenarios. To ensure

the continuity of the backup system on the participants»

side, Bank Indonesia provides a facility to test their

connection. Also, Bank Indonesia also provides an

alternative transaction settlement mechanism that can be

used in an emergency in the form of a Guest Bank facility

(the use of hardware and software in Bank Indonesia) and

the use of Bank Indonesia Cheques and Giro Biljet.

To maintain the reliability of BI-RTGS system

infrastructure in an emergency situation, Bank Indonesia

will continue to hold tests and analyses in order to minimize

the recovery time objective (RTO). RTO is a time target set

for operation activity recovery to ensure the continuity of

operational activity should a disaster strike. RTO setting is

both an iterative and negotiation process performed by

taking into account the cost and risk to bear. Considering

that BI-RTGS is a large-value transaction settlement system

and part of the systemically important payment system

(SIPS), RTO should be as low as possible. In this regard,

efforts to improve recovery time are ongoing through

technical analysis and the implementation of periodic

disaster recovery plan (DRP) tests.

3.1.3. Effort to Fulfill CP-SIPS

Bank Indonesia endeavors to meet international

standards in hosting its systemic payment system such as

the core principles of a systemically important payment

system (CP-SIPS) issued by the Bank for International

Settlements (BIS) regarding the implementation of the BI-

RTGS system. Measures taken include the improvement

of good corporate governance through the reorganization

of a BI-RTGS work unit and the formation of a BI-RTGS

working group comprising of several participants which

serves as communication forum between Bank Indonesia

as an operator and banks as participants.

At the end of 2008, Bank Indonesia issued internal

regulation No 10/86/Intern dated 23 December 2008

regarding the Reorganization of the Accounting and

Payment System Directorate (DASP) as one of the measures

to ensure an effective, responsible and transparent

payment system. DASP reorganization included the

introduction of good governance principles for the SIPS

administrator through the separation of the reporting line

for the work unit that handles payment system oversight

and the work unit responsible for the operational BI-RTGS

system.

In addition, Bank Indonesia collaborated with several

participants of the BI-RTGS system to form a working group

in order to improve transparency between the

administrator and the participants by involving the

participants in BI-RTGS system development. This approach

is expected to improve the efficiency and reliability of the

existing system.

3.2. CREDIT BUREAU

The Credit Bureau (CB), established in June 2006,

represents one of Bank Indonesia»s efforts to further

bolster»the Indonesian banking system infrastructure and

the financial system. This is a realization of the Indonesian4 CHATS is the Clearing House Automated Transfer System, one of the RTGS systems inHong Kong.

57


Banking Architecture (API), in particular Pillar V, namely

infrastructure improvement to establish sound, strong and

efficient banks.

The primary role of CB is to collate, store and

distribute credit data as debtor information in support of

financial intermediation. CIB is expected to minimize

asymmetric information between fund providers and

beneficiaries.

To support task achievement, CB operates and

manages a Debtor Information System (SID). This system

has undergone continuous improvements and has been

web-based since 2005. Consequently, data reporting can

be submitted online and debtor information can be

requested online in real time. Credit data to be used as

SID input is collated from all fund providers, including

commercial banks, rural banks (BPR) and non-bank

financial institutions (LKBB) including non-bank credit card

issuers (PKKSB).

Currently there are two types of SID participation:

obligatory and voluntary. Reporting is obligatory for

commercial banks, rural banks with total assets of over 10

billion rupiah for six consecutive months, and non-bank

credit card issuers. Voluntary reporting incorporates rural

banks with total assets of less than 10 billion rupiah or

total assets of 10 billion rupiah for less than six consecutive

months, non-bank financial institutions and savings/loans

cooperatives.

Statistically, CB implementation has yielded

satisfactory results. Two years since its establishment,

significant improvements have occurred regarding the

number of reporters, debtors, credit facilities and access

to debtor information. However, it is important to note

that SID reporting from non-bank financial institutions,

especially finance companies (PP) remains negligible. This

is partly because participation is voluntary, but also because

of the wide gap between the data structure used by non-

bank financial institutions and the data structure required

by SID.

Based on the utilization of SID output, requests for

debtor information in 2008 experienced a 55% rise

compared to that of 2007. The largest share of debtor

information was util ized by commercial banks.

Conversely, use of SID output by rural banks remains very

low.

To further develop CB as well as overcome existing

constraints, Bank Indonesia introduced several strategic

policies, including aspects of data quality improvement,

system and application improvement, expanding the

ECONOMYGROWTH

REAL SECTOR GROWTH

FINANCIALSECTOR

BA

NK

NO

NB

AN

K

INFOR-MATION

INFOR-MATION

POOL AND PROVISION FUNDS

CREDIT INFORMATION BUREAUTO SMOOTHEN

INTERMEDIATIONFUNCTION

TO MINIMIZEGAPS BETWEEN INFORMATION

AND RISK

SOCIETY

TO SHORTENTHE DECISION

MAKING PROCESS

TO REDUCECOSTS

INST

ITU

TIO

N

IND

IVID

UA

L

NON FINANCIALSECTOR

PUBLIC UTILITYCOMPANY

TRANSPARENCYGOVERNMENT / REGULATOR

MARKET DISCIPLINE

Figure 3.5Role of Credit Information Bureau

58


coverage of reporters and users, improving regulations,

developing the products and services, and educating the

public.

removal of redundant data, providing feedback on errors

in reporting, and verification of reporters to boost their

awareness of prevailing regulations and the importance

of accurate reporting. Training has also been provided to

reporting clerks to improve their knowledge as well as

enhance the quality of the reports. In addition, the service

provided by the SID help-desk has also been improved.

3.2.2. Refining the System and Application

Improving the SID»s system and its application is a

continuous process, which begins with an evaluation of

the existing system and application performed by Bank

Indonesia internally with reporter involvement. Not only

are DIS applications evaluated so are other related

applications. The results of such evaluations are

subsequently used as a basis for improvements as well as

inputs when in formulating the future CB improvement

plan review.

Figure 3.6Credit Bureau Strategic Policy

DATAQUALITY

PRODUCTS &SERVICES

SYSTEM &APPLICATION

RULES &REGULATION

REPORTERS &USERS

SOCIETYEDUCATION

CREDIT BUREAU

Number of Reporters (Institution) 486 751 777Commercial Banks 130 130 127Rural Banks 355 618 646Finance Companies 1 3 4

Number of Reporters (Branch Offices) 3,374 3,788 4,054Commercial Banks 2,548 2,788 2,790Rural Banks 825 2,633 1,260Finance Companies 1 3 4

Number of Debtors (Based on DIN) 20,359,850 28,187,986 35,900,857Commercial Banks 19,535,979 26,312,078 33,070,536Rural Banks 822,849 1,780,534 2,521,748Finance Companies 1,022 95,374 308,573

Number of Credit Facilities*) 21,689,062 29,479,139 57,782,495Commercial Banks 20,863,200 27,640,264 53,573,464Rural Banks 824,839 1,697,186 3,813,657Finance Companies 1,023 141,689 395,374

Number of Debtor Information Demand**) 782,626 1,178,957 2,050,957Commercial Banks 751,769 1,147,096 1,833,158Rural Banks 30,857 30,192 206,255Finance Companies 0 1,669 10,915

Table 3.1Debtor Identification Number (DIN) Data, 2006-2008

December December December2006 2007 2008

Notes:*) For December 2006 period, the number of credit facility is only for active accounts. Meanwhile for December 2007 and 2008 periods, the number of

credit facility includes active and passive accounts.**) The number of demands in that months.

3.2.1. Improving Data Quality

To improve the quality of data and information

produced by SID, Bank Indonesia implemented a policy of

periodic attendance to ensure report punctuality, the

59


The CB improvement plan review includes a SID

improvement plan for the short, medium and long term.

Initially, SID improvement will focus on improving data

accuracy and system performance. In the subsequent

phase, the submission of debtor reports will be changed

to become more effective and efficient. Implementation

of this review will begin in 2009 and continue for the

ensuing two years.

3.2.3. Expanding Reporter and User Coverage

The reliability of debtor information produced by CB

is partially determined by the breadth of its data sources.

The small number of non-bank financial institutions that

currently report to SID demonstrates a clear potential for

data yet to be utilized. To this end, Bank Indonesia, in

collaboration with the Indonesian Capital Market and

Financial Institution Supervisory Agency (Bapepam LK), is

encouraging non-bank financial institution participation

in SID through a Memorandum of Understanding signed

in September 2007. As a follow up, a socialization activity

plan for Bapepam LK staff has been compiled and periodic

workshops organized for the Finance Company Association

of Indonesia (APPI) and non-bank financial institution SID

reporting candidates. Furthermore, a standard operating

procedure (SOP) has been issued for the joint procedure

of non-bank financial institutions to commence SID

reporting in 2009.

Adhering to international standards for credit

bureaus, SID data sources are to be expanded in order to

cover the customer data of public utility companies, such

as Telkom, PLN and PDAM. This is legislated through the

Financial Sector Policy Package (PKSK) 2008, with the

goal of ≈incorporating public utility company data in SID∆.

To this end, a review was conducted on database

integration of public utility companies. Based on the

review, a number of constraints, including legal

constraints, were identified. Therefore, the harmonization

of related regulations is necessary, which can be achieved

through the provision of relevant data by public utility

companies to SID. Meanwhile, to increase the amount

of debtor information utilized by rural banks, SID

socialization and training has been provided to the

relevant staff members.

3.2.4. Regulation Improvements

To ensure the smooth operation of CIB, in 2007-2008

Bank Indonesia Regulations pertaining to SID were

improved and a Bank Indonesia Circular was issued. The

SID regulations legislate the parties that qualify as reporters;

the reporters» obligations; coverage and procedures for

debtor report submissions; parties eligible to request debtor

information and its usage limitations; Bank Indonesia

supervision of reporters; and penalties for infringements.

With this regulation, the rights and obligations of reporters

and debtors are much clearer.

The introduction of such policy has accommodated

the requirements of the credit industry through the

involvement of SID reporter representatives, comprising

of government banks, foreign banks, rural banks and non-

bank financial institutions, all under the SID Working

Group. The active contribution of the SID Working Group

has enriched the relevant regulations and proved invaluable

as an input to the CB application and development

improvement plan.

3.2.5. Products and Services

The improvement of CB»s products and services is

aimed at meeting the international standards of credit

bureaus. CB currently offers debtor information, known

as BI Checking, to the banking community. Debtor

information covers positive information (i.e., information

of loans not experiencing failure in repayments) and

60


negative information (i.e., information of loans failing to

be repaid) for all fund provisions of more than Rp1, and

also includes information on debtor credit history in the

last 24 months. Consequently, debtor information can

illustrate the credit exposure, performance and quality of

credit of the related debtor.

Other products developed include the provision of

consumer reports or debtor information that are also

available to the debtor from Bank Indonesia»s Information

Booth or a SID reporting financial institution. The provision

of such consumer reports is one form of reporter

transparency to the debtor, as well as a crosscheck platform

for the debtor on the report submitted. The consumer

report provision service is currently being expanded to Bank

Indonesia branch offices and credit information counters

set up at special events such as the MSME Bazaar and

Sharia Economic Festival.

For credit bureaus adhering to international

standards, the products offered are not merely a basic

report but also include value-added services, which are

the result of data development and the technology used.

Value-added services include credit scoring, fraud alert/

detection, credit risk management, consultation, etc. Based

on the data source, the data collated by international credit

bureaus covers data from public utility companies,

cooperatives and court verdicts. As part of the efforts to

bring CB in line with international standards, the provision

of value-added services, in particular credit scoring and

expanding the data source to include public utility

companies, is the immediate target of CB product

expansion.

3.2.5. Public Education

Establishing a healthy and efficient credit system not

only depends on the awareness of fund providers when

submitting reports, it also requires public awareness on

the importance of maintaining a good credit history. By

understanding that one»s credit history is kept at CB and

can be accessed by all fund providers that report to SID, it

is expected that debtor awareness to maintain a good

credit history will improve.

A number of measures have been taken to raise

public awareness of CB»s presence, such as through

seminars and public education advertisements in the

national mass media. The result has been a rise in the

number of consumer reports submitted to Bank Indonesia»s

Information Booth by the public. This is a positive sign for

future CB development. Frequent public access to SID

outputs will increase the pressure on CB to improve debtor

data and information quality.

3.3. FINANCIAL SYSTEM SAFETY NET

Another aspect of financial infrastructure deemed

crucial to financial system stability in a country is a Financial

System Safety Net (JPSK). Conceptually, a JPSK is extremely

beneficial in mitigating systemic risk. In a JPSK, crisis

management protocol is regulated as part of a coordination

mechanism among the relevant institutions when the

financial sector experiences pressures. In greater detail,

the benefits of a JPSK are as follows:

JPSK provides a strong legal foundation for the

prevention and resolution of a crisis;

JPSK allows greater transparency and accountability

in the decision-making process in order to prevent

and manage a crisis;

JPSK is a coordination mechanism between the

relevant institutions when confronting disruptions that

could potentially threaten national financial system

stability, without impinging upon the independence

of each individual authority;

JPSK helps identify and resolve problems at financial

institutions with systemic impact; and

61


JPSK ensures a clear source of funds to prevent and

resolve crises by adhering to the rules and budget

constraints of the People»s Representative Council

(DPR).

During Semester-II 2008 Indonesia experienced

protracted periods of intense pressure on the financial sector,

marked by rupiah and foreign exchange liquidity shortfalls

coupled with significant rupiah depreciation. In response,

the Government issued several regulations in lieu of a law

(PERPPU) in October 2008, one of which was in relation to

the JPSK (PERPPU No. 4 2008 dated 15 October 2008).

Based on PERPPU No. 4 2008, the JPSK is a financial

system security mechanism to protect against a crisis threat

that includes crisis prevention and resolution. Prevention

and resolution includes: (i) the resolution of liquidity

shortfalls and/or solvency of banks with systemic effects;

and (ii) liquidity management and/or solvency of non-bank

financial institution solvency with systemic effect. To

achieve the goal of JPSK, the Financial System Stability

Committee (KSSK) was established whose members

include the Minister of Finance (as its Chairman) and the

Governor of Bank Indonesia. KSSK is authorized to set

policy and roll out measures to prevent and resolve a crisis

in the financial sector and coordinate with all relevant

authorities in its implementation.

PERPPU No. 4 2008 did not garner the approval of

DPR. Consequently, the bill was redrafted and resubmitted

to DPR for approval. Currently, the draft JPSK bill has been

compiled and awaits DPR approval.

The scope of the draft JPSK bill includes crisis

prevention and crisis management, which in itself comprises

of measures to overcome liquidity shortfalls as well as issues

with bank and non-bank financial institution solvency with

systemic effect. Crisis prevention includes overcoming the

problems of: (i) liquidity problems at banks with systemic

effect; (ii) insolvent banks or failure to repay an Emergency

Funding Facility (FPD) with systemic effect; and (iii) insolvent

non-bank financial institutions with liquidity problems and

systemic effect. Meanwhile, crisis resolution includes

overcoming: (i) insolvent banks with liquidity problems,

which individually have a systemic effect, (ii) banks that

individually do not have a systemic effect under normal

circumstances but do have a systemic effect under crisis

conditions; and (iii) several non-bank financial institutions

that suffer liquidity problems and/or solvency issues with

systemic effect. The proposed framework is as follows:

Crisis PreventionCrisis PreventionCrisis PreventionCrisis PreventionCrisis Prevention

Table 3.2Financial Safety Net Framework

Objectives/Coverage

DecisionMaking Process

DecisionTools/

MechanismSource of

Funds

1. Bank Liquidity

2. Bank Solvability

3. Bank Liquidity and/or Solvability

KSSK is responsible for:a. Evaluating the problemb. Identifying the problemc. Deciding on problem

solving steps

1. Liquidity bail out

2.a. Temporary Placementfor Systemic Banks

2. b. Problem solving forNon-systemic banks

3. Providing lendingfacility or Placementsfor Non Bank FinancialInstitution

Emergency FundingFacility from BI which isguaranteed by government2.a. Temporary placement

by LPS2.b. Bank closure and

insurance payment byLPS

3. Providing lending facilityor equity placement bygovernment

Government financescrisis prevention andresolution fromgovernment incomeand expenditurebudget throughgovernmentsecurities issuance orcashBI is permitted tobuy governmentsecurities in theprimary market

62


Table 3.2Financial Safety Net Framework (cont.)

The use ofgovernment incomeand expenditurebudget have to beapproved byparliament

Crisis ResolutionCrisis ResolutionCrisis ResolutionCrisis ResolutionCrisis Resolution

1. Bank Liquidity and/or Solvability

2. Non Bank Liquidity

KSSK is responsible for:a. Evaluating the problemb. Identifying the problemc. Deciding on problem

solving steps

1.a. Liquidity bail out1.b. Temporary placement

2. Liquidity bail out/temporary placement

1.a. Emergency FundingFacility from BI

1.b. Temporary placementby LPS or governmentor certain institutions

2. Lending facility/Temporary placement byLPS or government orcertain institutions

Objectives/Coverage

DecisionMaking Process

DecisionTools/

MechanismSource of

Funds

63


The Financial System Stability and PERPPU on theAmendments to the Law on Bank IndonesiaBox 3.1

One of the principal policies undertaken by the

Government in mid 2008 is the issuance of PERPPU

(government regulation in lieu of a Law) Article 2 in

2008 concerning the Second Amendment of Law 23

1999 on Bank Indonesia. This PERPPU is essential to

the stability of the financial system, as it provides a legal

ground for Bank Indonesia to provide a wider access

of short-term financing facility (FPJP) to banks in need.

The wider access for the banks is based on the

amendments in Article 11 of the Law on Bank Indonesia.

Before the amendments, Article 11 regulates that

Bank Indonesia can only provide credit or financing

using the sharia principle for a period of not more than

90 days to the banks in efforts to solve the banks»

short-term financing issues. The credit or financing

under sharia principles must be secured with high

quality collateral with a minimum value of the received

amount of credit or financing. What is referred as high

quality and liquid collateral include securities and/or

bonds issued by the Government or other legal entities

with high ratings, based on the valuation results of

competent rating institutions and at any time, can be

easily sold to the market in return for cash.

The changes stipulated by PERPPU assert that

what is referred as high quality and liquid collateral

not only include securities and/or bonds issued by the

Government or other legal entities with high ratings,

based on the valuation results of competent rating

institutions and at any time, can be easily sold to the

market in return for cash, but also credit assets with

high collectability. In other words, objects which can

be used as collateral for banks to obtain FPJP have

more varieties and hence, widen the access for banks

to utilize FPJP. In addition to providing wider FPJP access

for banks with liquidity issues, this regulation can also

serve as background for Bank Indonesia to offer its

emergency funding facility (FPD) to banks with financial

difficulties that might potentially create systemic

impact and later on, a crisis that can jeopardize the

financial system.

In the crisis prevention and management, a

strong legal ground and a clear operating mechanism

are vital to support the important decision-making

processes to prevent any crisis or to save the economy

from crisis. The changes in the Law of Bank

Indonesia, exhibited in the above PERPPU, is an

example of the Government»s anticipative

movements from the legal stand point to ensure the

stability of the financial system in confronting the

global crisis.

64


Best Practices of Systemic Impact Analysis towardsthe Financial SystemBoks 3.2

Conceptually, the systemic impact towards the

financial system occurs only when the problems of a

financial institution, both individually and collectively,

due to the size of the financial institution and the

potential of contagion effect that might occur, can

cripple the entire financial system.

Based on international best practices, the criteria

of systemic impact are not determined explicitly ex ante

in the law»s stipulations, due to two main reasons.

Firstly, an ex ante stipulation might create moral hazard.

With explicit criteria, banks and nonbank financial

institutions might be encouraged to conduct excessive

risk taking, because they are certain that the

Government will bail them out when troubled.

Secondly, the systemic impact stipulations tend

to be situational. The triggers to systemic crisis are

varied depending on situations, can be both internally

or externally, for instance, the global financial crisis,

terrorist»s attacks and natural disasters. For those

reasons, it is difficult to decide on systemic impacts

up-front. A financial institution can be considered to

have systemic impact on one occasion, but might not

on another circumstance. Therefore, the stipulations

on systemic impact requires professional judgment.

One of the widely used references on the

systemic impact analysis is the document of

Memorandum of Understanding on Cooperation

between the Financial Supervisory Authorities, Central

Banks and Finance Ministries of the European Union

on Cross Border Financial Stability (Annex 2 Template

for Systemic Assessment Framework). On one of the

statements, it recommends the analysis of systemic

impact to be based on the impact of failure or impact

of issues faced by the banks to: (i) the other financial

institutions overall, (ii) the financial market, (iii) the

payment system and (iv) the psychology of the market.

In addition, the analysis should also cover the

estimation of interference within the real sector by

examining the role or contributions of the related bank

towards the particular sector.

65

Chapter 4 Prospects of the Financial System in Indonesia

Chapter 4Prospects of the FinancialSystem in Indonesia

66



67


4.1. ECONOMIC PROSPECTS AND RISK

PERCEPTION

The Indonesian economic growth is expected to slow

down to approximately 4-5% in 2009 as the global

economic downturn continuous. Slower economic growth

will reduce demand-side pressures thus making inflation

easier to control. Furthermore, several other contributing

factors will ease inflationary pressures, including: lower

commodity prices on the global market, which will bring

down the prices of domestic commodities; a falling oil

price at the beginning of 2009; and a domestically

produced rice surplus, which will persist in 2009.

Consequently, in quarter II 2009 inflation is projected to

drop to single digits, namely from 11.1% at the end of

2008 to 8%. However, the global economic slowdown

and sliding commodity prices on the global market have

the potential to trigger a decrease in export value, which

will undermine the trade account in 2009.

In general, the prospects of the Indonesian financial system are predicted to

remain positive, despite pressures from instability in the global and domestic

economies. The banking sector»s positive prospect is supported by, among

others, its relatively high capital levels. Additionally, coordination between

authorities of the banking industry, capital market and non-bank financial

institutions will continue to be enhanced. Such coordination is a vital element

of the Financial System Safety Net (JPSK) and will bolster financial sector

resilience.

GDP (% yoy) 6.3 6.4 6.4 5.2 4.5 4.3 4.4 4.7

Inflation (% yoy) 7.6 11.0 12.0 11.1 10.2 8.0 5.8 5.8

Balance of Trade (Billions of USD) 7.5 5.3 5.8 8* 6.7 7.1 6.8 7.7

Table 4.1Projection of Several Economic Indicators

2008 2009*

Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4

* Asia Pacific Concensus Forecast

The impact of the global financial crisis on the

domestic financial sector has increased risk perception

concerning Indonesia. This is clearly reflected by the

increasing yield spread. Greater perception of risk will

impede the flow of investment into the country. Moreover,

overseas investors are also confronted by liquidity

difficulties as an impact of the ongoing global turmoil.

Notwithstanding the higher perception of risk will

encourage banks to become more selective when

extending credit. Sluggish credit growth since November

Prospects of the Financial System in IndonesiaChapter 4

68


2008 and lackluster economic growth in 2009 could trigger

a credit crunch.

In addition, low investment in the country will

pressurize economic growth and subsequently disrupt the

real sector, both the corporate and household sectors

which could intensify bank credit risks. Also, low

investment into the country could also aggravate the

exchange rate. As a result, banks with a short position on

foreign currencies will suffer losses due to exchange rate

risk. These inauspicious circumstances need to be

anticipated in order to maintain a sound banking and

financial system.

stress tests, a CAR of 16% is sufficient to absorb various

risks including market risk (interest rate risk, exchange rate

risk and a decline in SUN prices), liquidity risk, credit risk,

as well as risks from losses caused by structured products.

Market risk was deemed moderate despite

experiencing a significant rise in semester II 2008 primarily

due to declining SUN prices and persistent rupiah

depreciation, as well as the climbing interest rate trend.

Approaching yearend 2008, however, the risk attributable

to falling SUN prices eased as a result of a new Bank

Indonesia Circular which allowed banks to postpone their

marking-to-market obligations.

Exchange rate risk remained relatively steady as

demonstrated by the relatively low Net Open Position (NOP)

held by banks (6.2%) and most banks held a long position

on foreign currencies. Furthermore, interest rate risk

declined in accordance with the periodic reductions in the

BI Rate from December 2008 to a level of 8.25% in

February 2009.

However, in the coming years banks will remain to

be highly exposed to market risk given that the global

financial crisis remains a long way from being fully resolved.

In addition, in line with rupiah depreciation, several banks

were found to face losses as a result of structured products.

As evident from stress test results, the potential losses can

be absorbed by bank capital. However, banks will need to

be more cautious of similar products and derivative

transactions in general, including offshore products.

Liquidity risk tended to increase at the beginning of

semester II 2008, especially in August, in line with the

decline in excess liquidity due to slow growth in deposits

and expansive credit extension. At that time, affected by

the global financial crisis, the condition of the interbank

money market (PUAB) was tight and market segmentation

appeared, which limited bank access (particularly medium

and small banks) to PUAB. However, by loosening the

4.2. BANK RISK PROFILE: LEVEL AND DIRECTION

The key challenges threatening financial system

stability in semester I 2008 continued and grew in semester

II 2008. As elaborated in previous chapters, global financial

volatility and the global economic slowdown, which have

compounded conditions in the domestic financial market

and economy as a whole, placed additional pressures on

the Indonesian financial sector. This was indicated by a

decline in the Jakarta Composite Index (IHSG) as well as

falling government bond (SUN) prices. However, taken

holistically the financial sector remains sound.

Meanwhile the banking industry, which dominates

the domestic financial sector, maintained a relatively good

position as reflected by a satisfactorily high Capital

Adequacy Ratio (CAR) of 16.2%. Based on the results of

Indo 49 Ba3 (Moody's) 11.70 997.47 1015.41

Indo 48 Ba3 (Moody's) 11.86 932.46 965.17

Indo 45 Ba3 (Moody's) 11.95 918.30 925.92

Table 4.2Risk Perception of Indonesia

Bonds Rating Y-t-m (%)Yield Spread (bp)

September Desember2008 2008

Source: Bloomberg

69


minimum reserve requirement (GWM) and raising the limit

of deposits guaranteed by the Deposit Insurance

Corporation (LPS), bank liquidity continued to improve.

Moreover, since November 2008, with burgeoning deposits

and a contraction in credit extension, bank placements in

liquid assets such as Bank Indonesia Certificates (SBI)

experienced a significant rise. Despite relatively stable

liquidity risk, caution is still required regarding pressures

from global liquidity, which remains sub-optimal, and PUAB

segmentation.

Bank exposure to credit risk is at a moderate level

and is relatively stable due to a declining NPL ratio.

However, increasing credit risk due to deteriorating

economic conditions needs to be anticipated. As

mentioned in Chapter I, potential credit risk is also

evidenced by estimation results for the Probability of

Default (PD), using financial data from non-financial public

listed corporations on the Indonesian Stock Exchange. In

addition, an increase in credit risk can also emanate from

debtors suffering from losses caused by rupiah exchange

depreciation, which will eventually affect their repayment

capacity.

Operational risk is also significant. In general, there

remain various challenges confronting banks in terms of

operational risk, particularly those related with the capacity

and integrity of human resources to minimize human error

and fraud, including supporting infrastructure such as

adequate information technology and good governance.

Meanwhile, pressures permeating from the global crisis

also need to be considered, in particular their impact on

the capability of banks to evaluate their operational risk.

To improve the preparedness of the banking industry during

this global crisis the implementation of Basel II, which is

marked by mandatory capital charges for operational risk,

has been postponed from 2009 until 2010. The

postponement of Basel II implementation is expected to

boost the awareness of banks with reference to aspects

of operational risk, including strengthening internal control.

4.3. PROSPECT OF THE INDONESIAN FINANCIAL

SYSTEM

The prospect of the Indonesian financial system is

expected to remain positive amid the ongoing global and

domestic economic slowdown. Such expectations are

based on a number of contributing factors. First, the recent

financial turmoil was principally triggered by external

factors; therefore, domestic banks are not beset with the

same significant difficulties as banks overseas. This situation

Figure 4.1Bank Risk Profile and Outlook

Risk Control System (RCS)

Sem II-2008

Outlook

Sem II-2008

Outlook

Sem II-2008

Outlook

High

Mod

erat

eLo

w

Liquidity Risk Credit RiskMarket Risk

StrongAcceptableWeak StrongAcceptableWeak StrongAcceptableWeak

Inhe

rent

Risk

InterestRate

ExchangeRate

GovernmentBond Price

70


is very dissimilar to the 1997/98 Asian Crisis that was

dominated by a multitude of weaknesses in domestic banks

such as high NPL, as well as countless Legal Lending Limit

and Net Open Position violations. Consequently, the impact

of the ongoing global crisis on the domestic financial sector

is expected to be limited.

Second, banks are far more prepared to confront the

crisis when compared to conditions in 1997/98. Superior

bank preparedness is the result of improved risk

management and the implementation of good governance.

Compared to a decade ago, the criteria to become a

member of bank top management or shareholder are more

stringent through the use of Fit and Proper Tests. It is

envisaged that enhanced good governance will enable

banks to be more resilient to financial volatility.

Third, the bank supervisory authority is also more

prepared to overcome a crisis when compared to 1997/

98. At that time, bank supervision was compliance-based

oriented, as opposed to risk-based. At present, bank

supervisors are required to graduate from a certification

program and are given broader opportunities to participate

in capacity building training. In the future, to improve the

quality of supervision, an expert panel will discuss

surveillance and investigation results.

Financial stability is also bolstered by greater trust in

LPS by the wider community. LPS»s presence is becoming

more visible through the closure of several rural banks and

the takeover of one commercial bank deemed as systemic

in November 2008. In fact, the closure and takeover of

these banks did not trigger any volatility in the banking

sector. Efforts to strengthen financial infrastructure will

be redoubled if the People»s Representative Council

approves the draft regulations pertaining to the Financial

Sector Safety Net (JPSK).

Overall, the positive outlook for financial stability is

reflected by the Financial Stability Index (FSI), which after

experiencing a sharp increase during semester II 2008,

began to decline in the past few months. As described in

Chapter 2, at the end of June 2009, FSI is predicted to be

within the range of 1.77-2.13, or using moderate scenario

at 1.95. This is comparatively lower than the position at

the end of December 2008, namely 2.10. A lower FSI is

predicted to garner optimism that the financial sector in

future can be well maintained.

71

Artikel I - Impact of Contagion Risk on the Indonesian Capital Market

Ar t icle

72

Article I - Impact of Contagion Risk on the Indonesian Capital Market


73


Article I

Impact of Contagion Risk on the Indonesian Capital Market

Wimboh Santoso1, Bagus Santoso2, Ita Rulina3, Elis Deriantino4

This article aims to assess the presence of contagion risk in the Indonesian capital market. The approaches

used are the Multivariate GARCHI Dynamic Conditional Correlation (DCC) and Markov Regime Switching. The

data applied is daily composite index data from 15 countries, namely Indonesia, Australia, United States (Dow

Jones and Nasdaq), United Kingdom, Germany, Japan, Korea, Hong Kong, China, Taiwan, India, the Philippines,

Thailand, Singapore and Malaysia; 3-month T-Bill data; RP/USD exchange rate; PUAB interest rate and global oil

price. All data covers the period from 2 January 1995 to 13 November 2008. Using Indonesia as the reference

point, the daily data is divided into four distinct periods. Estimation results showed that contagion exists between

Indonesia and the other sample countries used in this research. Furthermore, the research indicated that Indonesia

is a shock absorber rather than shock transmitter, particularly with regard to developed countries (Japan, Australia,

Germany, United Kingdom and the US).

Keywords: Financial Aspect of Economic Integration, International Financial Market, Time Series Model

JEL classification: F36, G15, C22

1 Head of the Financial System Stability Bureau, Directorate of Banking Research andRegulation, Bank Indonesia; email [email protected]

2 Researcher, Gadjah Mada University, email: [email protected] Senior Researcher, Financial System Stability Bureau, Directorate of Banking Research

and Regulation, Bank Indonesia, email: [email protected] Junior Researcher, Financial System Stability Bureau, Directorate of Banking Research

and Regulation, Bank Indonesia, email: [email protected]

BACKGROUND

Financial globalization, with its inherent trend

towards financial sector integration to the global financial

market, has left many countries exposed to contagion risk.

Consequently, a crisis in one country can spread and affect

other countries. Exchange rate devaluation, defaults

against sovereign obligation in one country will impact

another country. For example, the 1997 crisis that began

in Thailand due to baht devaluation followed by the floating

exchange rate regime policy taken, rapidly spread to

Indonesia, Malaysia, Korea and the Philippines, triggering

severe depreciation in these countries by an average of

about 75%. In 1998, bankruptcy of the domestic bonds

market in Russia and the fall of LTCM affected Hong Kong,

Brazil, Mexico and other emerging markets. The most

recent example is the 2007 US subprime mortgage debacle

that has severely disrupted financial markets primarily in

the Euro Zone but which rapidly spread to other countries

of the world.

74


Philippines, India, Hong Kong, Taiwan, Korea, Japan,

China, UK, Germany, Australia, Dow Jones and

Nasdaq).

Tbil3m is the US Treasury bill √ 3 month. Multivariate

conditional variance is written as follows:

Ht = D t R t D t

Dt is the diagonal matrix nxn with an element of time-

varying standard deviation from the univariate model

with the ith diagonal and Rt nxn time-varying

correlation matrix.

In the DCC model, time-varying covariance matrix is

written as follows:

Qt = (1-a-β)Q = aut-1u»t-1 + βQt-1

Qt=(qij,t) time-varying covariance matrix from ut with

magnitude nxn, Q = E[ut ut»], matrix unconditional

variance ut with magnitude nxn, and a, β non-

negative scale. The correlation matrix can then be

written as follows:

Rt = (diag(Qt ))1/2 Qt (diag(Qt ))

1/2

Where: (diag(Qt ))1/2 = diag(1/ q1,t,...1/ qn,t).

The DCC model is then estimated using the log

likelihood function as follows:

2. Markov Regime Switching

The GARCH Multivariate method has its weaknesses,

however, in detecting contagion. Bekaert et al. (2005)

propose one weakness, claiming that the GARCH

model fails to take into account asymmetric volatility,

which could affect correlations estimated during a

crisis period. Therefore, the regime-switching method

is used to detect contagion.

Markov-switching is a method to measure the change

in stochastic time series data by modelling data using

several equations. The strength of the switching

regime method compared to the GARCH model in

The current growth in hot money flow is a blemish

on conditions in Indonesia. Deteriorating conditions and

the ongoing recession in the US has forced The Fed to

slash its Fed Fund rate, consequently broadening the

interest rate spread between Indonesia and US. As a result,

Indonesian assets offer a higher return than that of the

US, thus further encouraging the surge in hot money flow.

Global negative sentiment could trigger a sudden

reversal so significant it would spark downward pressure

on Indonesian asset prices. This would lower the return

on such assets and could initiate panic among domestic

investors, encouraging them to sell following foreign

investors. This would compound the decline in asset prices

and have other implications such as weakening the rupiah

exchange rate. Therefore, the detection of contagion is

critical, including identifying the source of such contagion.

The purpose of this research is to find out whether

contagion risk is present in the Indonesian capital market

as illustrated by the following analytical framework:

RESEARCH METHODOLOGY

This research employs several methodologies to test

the presence of contagion in the Indonesian capital market:

1. Multivariate GARCH/Dynamic Conditional

Correlations (DCC)

The Multivariate GARCH model proposed by Engle

(2002) can be used to estimate dynamic conditional

correlation (DCC). This research uses GARCH (1.1)

multivariate model using an equation of mean

constant AR(1) and tbill3m as the world common

factor:

rt = a0 + ai rt-1 + a2 tbill3m + et

Where:

rt = (r1,t, r2,t,..., rn,t), ai = (a1,i, a2,i,..., an,i), et = (e1,t, e2,t,...,

en,t), dan et l Σt-1 ~ N(0, Ht)

rt is the composite index return of each country with

n=16 (Indonesia, Singapore, Thailand, Malaysia,

lt (Σ,f ) = - (nlog(2p) + log l Dt l2 + e»tDt

2et) +12Σ»

- 12Σ»t-1

t-1(log l Rt l

+ u»tRt ut - u»tut)-1

75


estimating is its ability to estimate data with extreme

values, which is the indication of an extreme event.

This method is able to give a crisis period, which is

endogenously defined in the equation system.

Therefore, the switching regime model is able to

overcome the initial problem of contagion testing,

namely that it requires a crisis and tranquil period to

be defined before testing can begin.

For example, if the return on shares in the market

has two states, namely a tranquil state (St=1) and a

volatile state (St=2), to illustrate the transition from

(St=1) to (St=2), the Markov chain principle is used:

Pr (St = j l St-1 = i, St-2 = k,..., yt-1, yt-2,...) = Pr (St = j l

St-1 = i ) = pij

With first order Markov-switching, the transition

probability can be written as follows:

Where p11+p12=p21+p22=1 and

In case St cannot be directly observed, information is

required about St stochastic behaviour. A parameter

estimation is calculated using the maximum likelihood

method.

DATA

The data used in this study consists of the daily

composite index data (based on five working days) of 15

countries: Indonesia, Australia, United States (Dow Jones

and Nasdaq), United Kingdom, Germany, Japan, Korea,

Hong Kong, China, Taiwan, India, the Philippines,

Thailand, Singapore and Malaysia; 3-month T-Bill data;

RP/USD exchange rate; PUAB interest rate and global oil

price.

Daily data is from 2 January 1995 to 13 November

2008. Using Indonesia as the reference point, the daily

data is divided into four distinct periods. The periods are

as follows:

1. FirstFirstFirstFirstFirst period, known as Pre-CrisisPre-CrisisPre-CrisisPre-CrisisPre-Crisis. This period is from

2 January 1995 to 15 July 1997.

2. SecondSecondSecondSecondSecond period, known as Crisis ICrisis ICrisis ICrisis ICrisis I. This period is from

16 July 1997 to 29 December 2000.

3. ThirdThirdThirdThirdThird period, known as Post CrisisPost CrisisPost CrisisPost CrisisPost Crisis. This period is from

1 January 2001 to 14 August 2007.

4. FourthFourthFourthFourthFourth period, known as Crisis IICrisis IICrisis IICrisis IICrisis II. This period is from

15 August 2007 to 13 November 2008.

One of the constraints found in this study is the

determination of the break between daily and monthly

data. In determining the break, Indonesia is the reference

point or the relationship hub among the observed countries

in this study, except in determining the break for the

Markov switching estimation. The estimation methods

used to determine the presence of a break in the daily

data is as follows:

The Second Period is set as Crisis ICrisis ICrisis ICrisis ICrisis I (16 July 1996 √

29 December 2000) based on the crisis in Indonesia. The

break in this period was chosen because of the high

volatility in the return on shares in Indonesia.

The break in Crisis IICrisis IICrisis IICrisis IICrisis II (15 August 2007 √ 13 November

2008) is based on the global crisis. During that period, the

Dow Jones and Nasdaq composite index plummeted.

ESTIMATION RESULTS OF CONTAGION

DETECTION

1. Multivariate GARCH/Dynamic Conditional

Correlations (DCC)

Figure A1.1 shows the correlation between

Indonesia and Southeast Asian countries. It is evident that

the correlation between Indonesia and Thailand is lower

P =P11 P12

P21 P22

P11 - Pr [St - 1lSt -1- 1]

P12 - Pr [St - 2lSt -1- 1]

P21 - Pr [St - 1lSt -1- 2]

P22 - Pr [St - 2lSt -1- 2]

76


compared to the correlation with other countries in this

region in mid 1997. However, the correlation

subsequently escalates significantly, peaking during the

Asian Crisis in 1998. This shows that during the Asian

crisis, there was contagion between Indonesia and

Thailand. During 2007-2008, there was significantly

correlated growth between Indonesia and Singapore as

well as Indonesia and Malaysia. This indicates that

contagion existed between Indonesia and Singapore and

Malaysia during the Crisis II Period.

with Hong Kong, which indicates the presence of

contagion between Indonesia and Hong Kong. The Crisis

II Period shows a significant rise in correlation between

Indonesia and all countries on the graph, except China,

with significant correlation between Indonesia and Hong

Kong.

Figure A1.3 illustrates the correlation between

Indonesia and developed countries. It can be seen that

Indonesia does not have significant correlation with the

countries on the graph. During the Crisis I Period, Indonesia

experienced negative correlation with the US (both the

Dow Jones and Nasdaq indices). However, during the Crisis

II Period, correlation between Indonesia and Australia

increased dramatically. This indicates the presence of

contagion between Indonesia and Australia.

Figure A1.1Dynamic Conditional Correlation (DCC) of Indonesia

with several countries in Southeast Asia

Idn-sin

Idn-thai

Idn-mly

Idn-phl

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

01995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

Figure A1.2Dynamic Conditional Correlations (DCC) of Indonesia

with several countries in Asia (excluding Southeast Asia)

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

R_IND_HK

R_IND_TWN

R_IND_CHN

R_IND_INA

R_IND_JPN

R_IND_KOR

0.8

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

-0.1

Figure A1.3Dynamic Conditional Correlations (DCC) of Indonesia

with Developed Countries

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008-0.2

0.7

0.6

0.5

0.4

0.3

0.2

0.1

0

-0.1

R_IND_UK

R_IND_GER

R_IND_DOW

R_IND_NASDAQ

R_IND_AUS

Figure A1.2 shows the correlation between Indonesia

and countries in Asia (excluding countries in Southeast

Asia). It is evidenced that Indonesia has relatively low

correlation with countries on the graph until just before

the Crisis II Period, excluding Hong Kong. Indonesia

experienced increased correlation during the Crisis I Period To see whether the rise in correlation is significant,

the DCC obtained from Model 4 was divided into four

periods, namely the Pre Crisis Period, Crisis I, Post Crisis

and Crisis II. From the correlation series obtained, the

average correlation value was calculated for all four

periods. The results were then tested using the Fisher

Test. An increase in correlation on this test indicated

the presence of contagion between Indonesia and other

countries. The null hypothesis in this research represents

the absence of a difference in correlation between low

volatility periods and high volatility periods (ii < ih).

77


Meanwhile, the alternative hypothesis in this research

is that the correlation during high volatility periods is

higher compared to correlation during low volatility

periods (ii < ih).

Results of the Fisher Test in Table A1.1 indicate the

absence of a significant increase in the correlation between

Indonesian share returns and the returns in other countries

during Period 2. This implies that during the Asian Crisis,

there was no contagion between Indonesia and the other

countries sampled in this research.

For Period 3, estimation results using DCC show a

significant increase in correlation between Indonesia and

Japan and India with a significance level of 5%, and

between Indonesia and Korea and Taiwan with a

significance level of 1%. This suggests the presence of

contagion in Indonesia stems from these countries. For

Period 4, a significant correlation increase occurs in almost

all countries, except between Indonesia and the Philippines,

Germany, Dow Jones and Nasdaq.

2. Markov-Switching Regime Estimation Method

In this research, the switching regime method is

estimated using GARCH (1.1) estimation with the mean

and variance equations written as follows:

ridn,t = ac,St + a1,St

ridn,t-1 + a2,St idn_exe_dlog + a3,St

idn_int +

a4,St tbill3m + a5,St

oil_dlog + a6,St rm,t + eSt,t

eSt,t ~ N(0, s2

St,t)

s2St,t

= VASt + VBSt

e2St,t-1 + VCSt

s2St,t-1

Lengend:

ridn,t: return on shares in Indonesia

rm,t: return on shares in other countries

idn_exe_dlog: rate of rupiah depreciation

idn_int: Indonesian interest rate (Inter-bank money market)

tbill3m: 3-month T-Bill

oil_dlog: change in oil price

e: error

s2: variance

St: regime 1 (non-crisis) and regime 2 (crisis)

IDN - CHN 0.02885 0.01949 0.06040 0.21785 0.18315 -0.69170 -2.77582 ***

IDN - HK 0.38930 0.36340 0.35565 0.62349 0.59024 0.85513 -4.60957 ***

IDN - JPN 0.20369 0.21825 0.27484 0.44552 -0.29805 -1.65232 ** -3.92898 ***

IDN - KOR 0.14951 0.16979 0.29427 0.47997 -0.40707 -3.33905 *** -5.36774 ***

IDN - TWN 0.13661 0.16031 0.26543 0.44685 -0.47405 -2.94255 *** -4.94937 ***

IDN - PHIL 0.35927 0.32417 0.25437 0.37696 0.77732 2.53743 -0.29511

IDN - SIN 0.42429 0.40541 0.37972 0.54769 0.44598 1.16358 -2.33810 ***

IDN - MLY 0.33618 0.26867 0.27986 0.52233 1.45414 1.36211 -3.31258 ***

IDN - THAI 0.32858 0.33702 0.30109 0.44945 -0.18549 0.66773 -2.05835 **

IDN - AUS 0.28818 0.27669 0.30222 0.55227 0.24430 -0.33648 -4.68655 ***

IDN - UK 0.17723 0.15270 0.16984 0.31572 0.49313 0.16669 -2.13033 **

IDN - GER 0.18563 0.18358 0.15692 0.26901 0.04146 0.64712 -1.26861

IDN - INA 0.17594 0.13356 0.27282 0.47507 0.84920 -2.23446 ** -4.88472 ***

IDN - DOW 0.07272 0.03421 0.05611 0.12253 0.75542 0.36499 -0.72521

IDN - NASDAQ 0.05999 0.00406 0.04681 0.11701 1.09522 0.28916 -0.82877

Table A1.1Contagion Detection using Dynamic Conditional Correlation (DCC) √ Daily Composite Share Return

P1 P2 P3 P4 Z-Stat P2 Z-Stat P3 Z-Stat P4

Notes: P1 : 1st Period Correlation (January 1995 - July 1997)P2 : 2nd Period Correlation (August 1997 - December 2000)P3 : 3rd Period Correlation (1 January 2001 - 14 August 2007)P4 : 4th Period Correlation (15 August - 13 November 2008)

*** : Significant for α = 1%** : Significant for a = 5%* : Significant for a = 10%

78


The Markov-switching equation above assumes that

the direction of contagion is from the countries sampled

to Indonesia. The independent variable in the mean

equation is calculated based on estimation results using

Autoregressive Distributed Lag (ADL) to determine which

financial and economic variables affect Indonesia»s

composite return index.

In this research, calculations were performed by

dividing the state into two regimes, namely Crisis and Non-

Crisis Regime based on persistence and unconditional

variance obtained from conditional variance. Lower

persistence between the two states is categorized as

Regime 1 (non-crisis), whereas higher persistence is

categorized as Regime 2 (crisis). Contagion is said to occur

between Indonesia and another country should there be

a significant increase in the return coefficient of another

country (a6) from Regime 1 to Regime 2. Table A1.2 shows

that the a6 coefficient value is significant and experiences

growth during Regime 2 for all countries, except United

States (both Dow Jones and Nasdaq).

Based on the analysis using the Markov switching

equation, it can be concluded that contagion occurs

between Indonesia and nearly all countries sampled,

excluding the United States (both Dow Jones and Nasdaq).

IDN - CHN regime 1 0.004 *** 0.010 -0.142 *** -0.014 *** -0.019 0.005 0.011

regime 2 -0.006 *** 0.714 *** -0.696 *** 0.035 *** 0.017 0.054 * 0.152 ***

IDN - HK regime 1 0.003 *** 0.053 ** -0.068 *** -0.009 *** -0.027 ** 0.001 0.174 ***

regime 2 -0.001 0.159 *** -0.518 *** 0.005 0.035 0.055 ** 0.616 ***

IDN - JPN regime 1 0.003 *** 0.002 -0.132 *** -0.010 *** 0.015 0.003 0.137 ***

regime 2 -0.002 0.658 *** -0.622 *** -0.002 0.036 0.046 0.393 ***

IDN - KOR regime 1 0.003 *** -0.004 -0.120 *** 0.014 *** -0.015 -0.001 0.090 ***

regime 2 -0.004 ** 0.484 *** -0.614 *** 0.026 *** 0.016 0.050 * 0.334 ***

IDN - TWN regime 1 0.003 *** 0.006 -0.128 *** -0.014 *** -0.013 0.006 0.059 ***

regime 2 -0.004 ** 0.538 *** -0.640 *** 0.029 *** -0.001 0.046 0.451 ***

IDN - PHIL regime 1 0.004 *** -0.010 -0.116 *** -0.013 *** -0.020 0.003 0.119 ***

regime 2 -0.005 *** 0.442 *** -0.562 *** 0.021 *** 0.033 0.021 0.549 ***

IDN - SIN regime 1 0.003 *** -0.002 -0.040 ** -0.012 *** -0.015 -0.006 0.194 ***

regime 2 -0.001 0.243 *** -0.415 *** 0.008 0.007 0.045 *** 0.686 ***

IDN - MLY regime 1 0.003 *** -0.015 -0.104 *** -0.014 *** -0.018 -0.008 0.112 ***

regime 2 -0.002 * 0.297 *** -0.500 *** 0.015 ** 0.020 0.051 ** 0.672 ***

IDN - THAI regime 1 0.003 *** 0.009 -0.127 *** -0.011 *** -0.019 -0.002 0.085 ***

regime 2 -0.004 ** 0.357 *** -0.474 *** 0.008 0.065 * 0.047 * 0.603 ***

IDN - AUS regime 1 0.003 *** 0.009 -0.127 *** -0.011 *** -0.019 -0.002 0.085 ***

regime 2 -0.004 ** 0.357 *** -0.474 *** 0.008 0.065 * 0.047 * 0.603 ***

IDN - UK regime 1 0.003 *** 0.014 -0.135 *** -0.016 *** -0.004 0.015 0.050 ***

regime 2 -0.002 0.522 *** -0.608 *** 0.045 *** -0.080 ** -0.018 0.754 ***

IDN - GER regime 1 0.003 *** 0.014 -0.135 *** -0.016 *** -0.004 0.015 0.050 ***

regime 2 -0.002 0.522 *** -0.608 *** 0.045 *** -0.080 ** -0.018 0.754 ***

IDN - INA regime 1 0.003 *** 0.004 -0.122 *** -0.015 *** -0.015 0.001 0.056 ***

regime 2 -0.004 ** 0.387 *** -0.582 *** 0.029 *** 0.008 0.053 ** 0.437 ***

IDN - DOW regime 1 0.003 *** 0.000 -0.142 *** -0.012 *** -0.020 * 0.006 0.042 **

regime 2 -0.004 ** 0.796 *** -0.650 *** 0.007 0.042 0.042 0.051

IDN - NASDAQ regime 1 0.003 *** 0.000 -0.141 *** -0.012 *** -0.020 0.006 0.034 ***

regime 2 -0.004 ** 0.800 *** -0.651 *** 0.007 0.043 0.043 0.020

Table A1.2Markov Switching Mean Equation

a0 a1 a2 a3 a4 a5 a6

79


CONCLUSION

Table A1.3 summarizes the estimation results using

the DCC-MG method (Dynamic Conditional Correlation √

Multivariate GARCH) and Markov-Switching. Both

experiments were contagion experiments that disregarded

the country initiating the crisis. Markov-switching tested

contagion without periodising the crisis. This method was

used to overcome the constraints of contagion testing

that requires the arbitrary setting of crisis and non-crisis

points.

IDN-MLY √*** √***

IDN-SIN √*** √***

IDN-THA √*** √***

IDN-PHI √***

IDN-JPN √*** √*** √***

IDN-TWN √*** √*** √***

IDN-HK √*** √***

IDN-CHN √*** √***

IDN-KOR √*** √*** √***

IDN-INA √*** √*** √***

IDN-AUS √*** √***

IDN-GER √***

IDN-UK √*** √***

IDN-US(DJ)

IDN-US(NQ)

Table A1.3Results of Contagion (1) Detection

DCC/Multivariate GARCHMarkov-Regime Switching

P2 P3 P4

Notes: *** : Significant for α = 1% (critical value: -2.32)** : Significant for α = 5% (critical value: -1.64)* : Significant for α = 10% (critical value: -1.28)

Sign √ indicates that there is a contagion effect between two countries.

The table evidences contagion between Indonesia

and the other countries in this research. Contagion

primarily occurred between Indonesia and East Asian

countries, such as Japan, Taiwan and Korea. There was

also contagion between Indonesia and India. In addition,

the behaviour of stock market players in Indonesia differed

little from stock market players in India. This is possibly

due to the large number of foreign investors in India and

Indonesia. Indonesia and India have similar fundamental

and social conditions, so investors use India as a signal of

Indonesia market conditions, and vice versa. This indicates

the presence of wake-up call hypothesis.

Estimation results also showed that there is no

contagion between Indonesia and the United States, both

using the Dow Jones index and Nasdaq index. Therefore,

if Indonesia is affected by the current global crisis, with its

roots in the US subprime mortgage crisis, it is not a direct

effect from the US market but rather indirect effects from

capital markets in Asia that share a direct relationship with

the US capital market.

Table A1.4 shows that Indonesia has a contagion

relationship with several countries in Asia, such as Japan,

Taiwan, Korea, Hong Kong and India. The relationship is

two way, which means that Indonesia also affects other

countries and other countries affect Indonesia. However,

based on error detection tests, evidently Indonesia is more

of a shock absorber than a shock transmitter, especially

for the developed countries (Japan, Australia, Germany,

United Kingdom and US).

IDN-MLY √ ^^^ √ ^ √ ^

IDN-SIN √ ^^^ √ ^

IDN-THA √ ^^^ √ ^ √ ^ √ ^ √ ^

IDN-PHI √ ^^^ √ ^

Table A1.4Conclusions of Test on Contagion Detection

CountryDailyData

MonthlyData

Daily Data Monthly Data

P2 P3 P4 P2 P3*

80


IDN-JPN √ ^^^ √ ^ √ ^^^ √ ^ √ ^^^ √ ^^^

IDN-TWN √ ^^^ √ ^ √ ^^^ √ ^^^ √ ^^^

IDN-HK √ ^^^ √ ^^^

IDN-CHN √ ^ √ ^^^ √ ^^^ √ ^^^ √ ^^^

IDN-KOR √ ^^^ √ ^ √ ^^^ √ ^^^ √ ^^^ √ ^^^

IDN-INA √ ^^^ √ ^ √ ^^^ √ ^^^ √ ^^^

IDN-AUS √ ^ √ ^

IDN-GER √ ^ √ ^ √ ^^

IDN-UK √ ^ √ ^

IDN-US(DJ) √ ^ √ ^

IDN-US-(NQ) √ ^

Table A1.4Conclusions of Test on Contagion Detection (cont.)

CountryDailyData

MonthlyData

Daily Data Monthly Data

P2 P3 P4 P2 P3*

Keterangan: P2 : 2nd Period (daily data: 16 July 1997 - 29 December 2000; monthly data August 1997-December 2000)P3 : 3rd Period (daily data: 1 January 2001 - 14 August 2007; monthly data January 2000-September 2008)P4 : 4th Period (15 August - 13 November 2008)^^^ : Causality relationship^^ : Contagion relationship with Indonesia as the source of the shock^ : Contagion relationship with other countries as the source of the shockSign √ indicates that there is a contagion effect between two countriesLevel of significance is 5% and 1%

81


Agenor, Aizenman, dan Hoffmaister. 2008. ≈External

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83

Article II - Corporate Balance Sheet Modelling: Determinants of Indonesian Corporate Debt

Article II

Corporate Balance Sheet Modelling:Determinants of Indonesian Corporate Debt1

Wimboh Santoso, Viverita, Ardiansyah, Reska Prasetya, Heny Sulistyaningsih

This study attempts to investigate the determinants of Indonesian corporate debt which in turn will affect

the financing and investment decisions of companies. The model employed is developed by extending the model

by Gibbard and Stevens (2006) and combining it with the traditional trade-off theory and pecking order theory

of capital structure. Based on these theories, this study also models corporate leverage by combining debt,

equity issuance as well as investment models and applies the Generalized Moment of Method (GMM) to a panel

data of 128 Indonesian listed corporations. It is found that the level of corporate debt is determined by default

probability effect and thus careful consideration in financing decisions is required. The result also imply that the

pecking order theory contributes significantly to Indonesian corporations balance sheet model.

Keywords :Corporate debt; balance sheet;capital structure;speed of adjustment

JEL Classification: C51;C33;N65

1 The author gratefully acknowledge support from the Directorate of Research andBanking Regulation Bank Indonesia. I acknowledge with thank participants at the Researchon Stability of Financial System and Outlook Seminar, the Central Bank of Indonesia,Solo 17-19 December 2007.

1. INTRODUCTION

The increase of volatility in the commodity and

international financial markets coupled with the world

economic slow down has decelerated national economic

growth. Indirectly, such condition potentially puts

pressures on the corporate sector»s performance. Lower

consumer purchasing power will cause corporation sales

to decrease and hence pulling down corporate earnings.

A decrease in earnings not coupled by a decrease of

operational and production costs will cause companies»

need for financing from third parties, banking institutions

or nonbanking institutions. The more debt a corporate

has, the greater it is exposed to the financial system.

Furthermore, if increases in debt are coupled by decreases

in earnings, corporate repayment capacities will suffer.

Moreover, the drop in corporate earnings potentially

causes its debt repayment capacity to third parties to

decrease and thus can become a source of financial

system instability.

84


as Stiglitz (1972) suggest that the difference between

personal income tax applied to capital gain and regular

income diminish theoretical beliefs of the tax benefits of

using debt which results in the refusal to use debt as source

of capital. Meanwhile, literature on credit rationing gives

insight on creditors and the banking sector»s view as to

why corporations limit the amount of their borrowings.

Jaffe (1971) described the manager»s unwillingness to

obtain more debt in order to maintain their position and

stabilize their wealth. Other factors considered affecting

the use of debt is bankruptcy costs or cost of financial

distress (Warner, 1976 and Robichek and Myers, 1966).

According to theory, a firm will consider an

investment opportunity when it has cash. Therefore, the

decision of choosing internal or external financing sources

does not only depend on time of investment, but also the

availability of investment opportunities. Furthermore, the

decision to not issue stocks and therefore not take any

investment opportunities will cause misallocations, which

in turn will decrease the firm»s value. Such is known as

financing trap (Myers and Majluf, 1984). Based on this

phenomenon, firms tend to use debt as external source of

financing when existing shareholders are passive investors.

As a result, corporations with large enough financing slack

tend to take all available investment opportunities. Jensen

(1986) suggests that firms which prefer issuing and using

debt as sources of financing will benefit not only managers

in the form of delaying future dividends, but also give right

to the owners to take legal action in the case of default.

Increasing use of debt will increase the firm»s leverage as

well as agency and bankruptcy costs.

There are two main theories generally used to explain

the corporate debt structure, i.e. the trade-off theory and

the pecking order theory. The trade-off theory of capital

structure states that the level of corporate debt can be

explained by the balance between costs and benefits of

using debt as a source of financing, with cost of bankruptcy

There are a variety of reasons to issue debt as a source

of firm»s financing. For example, Jensen (1986) found that

debt is an efficient way of reducing costs related to issuing

shares, while Klaus and Litzenberger opine that debt will

optimize corporate capital structure through its tax

benefits. In addition, Ross (2008) and Leland and Pyle

(1977) suggest that debt is a critical indicator of to a firm»s

value. Raviv (1991) found that leverage increases as debts,

non-debt tax shields, investment opportunities and firm

size increases. In contrast, leverage decreases as volatility,

advertising expenditure, default probability and the

uniqueness of the products increase. Therefore, optimal

debt ratio is determined by the trade-off between benefits

and costs of issuing debts (Frydenberg, 2004).

Until June 2008, the banking sector»s contribution

in financing corporates through extending working capital

and investment credit made approximately 71 per cent of

total financing by banks. This is an indication of the banking

and financial sector»s significant exposure to Indonesian

corporations. Therefore, the need to model factors which

determine the Indonesian corporate balance sheet

becomes critical. This is done by investigating the role of

optimal debt in financing and investment decisions. The

study aims to construct a corporate balance sheet model

to investigate its debt structure as well as factors affecting

firm»s optimal level of debt.

2. LITERATURE REVIEW

Many literature on corporate capital structure have

been published in finance journals. Among them were

Modigliani and Miller (1963), who proposed that a

corporation tends to maintain its reserve borrowing

capacity in an ideal market and therefore, incremental

benefits of borrowing decreases as amount of debt

increases. As such, credit facilities received by a corporation

will decrease with the increase in debt extended to the

corporation. In addition, Farrar and Selwyn (1967) as well

85


as the cost of debt and tax deductions as the benefits of

using debt. This explains the trade-off between tax benefits

and the cost of financial distress. Accordingly, it focuses

on the balance between the benefits cost of using debts

and opportunities of financial distress. This theory, then,

explains the effect of connectivity between debt and

default risk as well as debt and growth opportunities.

The pecking order theory tells the hierarchy of the

firm»s long-term financing strategy and the preferred use

of internal sources of financing. In general, this theory

explains the firm»s priority in choosing internal sources of

financing over debt financing. However, if external

financing is in great need, debt is preferred to equity (Myers

and Majluf, 1984). Therefore, this theory focuses on how

to manage firms to achieve the best balance between its

economic needs and financial stability. The rules of this

theory can be explained as (1) internal financing (retained

earnings) is used as it is considered safer than debts as it

carries with it default risks, (2) issuing debt is the safest

means of external financing in cases where the use of

external financing cannot be avoided. Furthermore,

according to this theory, issuing stocks as sources of

financing is not the best financing decision as it will in

turn need other sources of financing. Such situation creates

a gap between corporate expenses and free cash flow

which need to be financed by debt (gap financing). Based

on this theory, a change in debt must be equal to financing

gap. Other studies done by Shyam-Sunder and Myers

(1999) used debt ratio as a proxy for optimal levels of debt

assuming constant target level of debts.

Based on those studies, Gibbard and Stevens (2006)

explored the determinants of corporate debt of firms in

the UK, US, French and Germany. They explain the role of

corporate debt by estimating its investment and equity

issuance. Using embedded equation, it is found that

pecking order variables, particularly simultaneous cash

flows and acquisition have significant impacts on the

corporate debt level. This study also found that debt is

positively correlated with corporate financing needed,

while the optimal level of corporate debt is negatively

correlated to market-to-book ratios. In addition, the

procyclicality of debt is an effect of procyclicality of

financing gap. Other findings show that growth of

corporate debt in the bullish economy cannot be explained

by the increase in optimal debt but by increases in financing

gap, while Welch (2002) suggests that the corporate capital

structure is determined by the lag of the stock returns (i.e.

equity value, predict current equity value, dan debt equity

ratio). Therefore, the main determinant of the capital

structure is the external influence rather than internal

capital structure decision. On the other hand, Welch (2004)

found that 40 per cent change in the corporate debt

structure most likely are due to stock returns, while issuing

long-term debt only explains 30 per cent of change in

debt level.

Fama and French (2002) conducted a two-step

regression to determine the optimal level of debt

combining the trade-off and pecking order theories, and

found four factors considered as the main determinants,

i.e. (1) profitability, (2) investment opportunity, (3) firm size

(proxies by total assets),and (4) target dividend payout.

This finding shows different and contradictory results when

applying the two theories. For example, applying the trade-

off theory, it is found that corporations with higher

investment have lower debt. In contrast, using pecking

order theory, they found negative association between

expected investment and book leverage. In addition, there

is a positive association between leverage and corporate

size as well as between dividend payout and size. It

indicates that big corporation earnings has significant

influence to capital structure.

Tsiplakov (2007) used a dynamic model of optimal

capital structure, and found a strong association between

company»s stock returns and change in debt level. This

86


finding supports Welch (2004). In addition, Drobetz and

Wanzenried (2002) examine the influence of firm specific

factors and macroeconomic factors to speed of adjustment

to corporate target leverage of 90 Swiss companies. They

found that firms with higher growth rates and those further

from the optimal debt level adjusted faster to their target

leverage. In addition, it was also found that tangibility and

size have a positive relation with leverage. In contrast,

profitability is negatively related to leverage. Furthermore,

high growth (market to book ratio) firms have lower

leverage compared to firms with lower market to book

ratio. Historical market to book value is used by Hovakimian

(2003) to investigate the effects of this factor to investment

and financing decisions, and found its significant effect to

investment and financing decisions. This means that

current market to book value of debt failed to reflect the

firm»s growth opportunity.

3. METHODOLOGY AND MODEL DEVELOPMENT

This study combined the trade-off theory and the

pecking order theory to construct a corporate balance sheet

of Indonesian corporation. While the pecking order theory

suggests of using internal sources as a primary financing

sources compared to issuing stocks. It urges that issuing

stocks will be received by investors as bad news.

Furthermore, the trade-off theory proposed the concept

of optimum level of debt proxies by the level of debt by

comparing the marginal benefit and marginal cost of using

debt. Based on the purpose of this study, an Indonesian

corporate balance sheet will be developed by adapting

models from previous empirical studies on capital structure,

primarily from the work of Gibbard and Stevens (2006).

There are different views in the way we look at the

change in debt position based on the two theories. The

pecking order theory suggests that change in debt position

is dependent on financing gap i.e. the gap between

corporate spending (for investments and acquisitions) and

cash as sources of financing. To cope with shortages in

cash, the companies will use debt as primary sources of

external financing. Meanwhile, the trade-off theory

suggests that change in debt position is the different

between the optimal level of debt and the actual debt.

Figure A2.1 shows the conceptual framework of

Indonesian corporate balance sheet debt model.

Figure A2.1Conceptual Framework of

Corporate Balance Sheet Model

DEBTEQUITYCASH

Use to financeINVESTMENTACQUISITION

A study by Gibbard and Stevens (2006) combined

the two theories of the capital structure. This gives us the

possibility to watch the cyclical movements of corporate

debt and quantifying how far debt movements trigger

financing needs and speed of adjustment of debt levels,

as describe below:

Dit = αG

it + βD

it

* + (1-β) Di,t-1

(1)

where Dit is the corporate debt at time t; G

i = Financing

Gap; where the main observed variables are Cash Flows,

Investment expenditure dan Acquisitions. Dit

* is the Optimal

Debt (Implied by trade off theory) and can be determined

by one of three methods: (1) Market to book value (Gibbard

and Stevens 2006, Welch 2002); (2) Average of debt ratios

over the observation period (Sunder and Myers 1999); and

(3) determine the level of optimum debt by regressing

possible factors that affect the corporate target debt ratio

(See also, Fama and French, 1999). Therefore, the fitted

values from the regression results will present the corporate

optimal debt.

Some empirical work have been done to model

corporate capital structure using various models such as

debt model, equity issuance model, and investment model,

as follows:

87


3.1. Debt Model

This model explains factors that affect level of

corporate debt. According to the pecking order theory,

investment (I) and acquisition (A) variables are positively

associated with the level of corporate debt, meanwhile,

cash (C) is negatively associated with the level of debt. In

addition, the level of optimum debt (M) is expected to

have negative associationss with the actual level of

corporate debt. Another study by Welch (2002) gives a

different conclusion, in which market to book value of

debt is negatively associated with level of corporate debt.

However, this association is not statistically significant.

These ambiguos results can be explained by two

approaches: the growth opportunities effect (Myers, 1977)

and default probabilities effect (Welch, 2002) as follows:

Dit = α + α

1D

i,t-1 + α

2I

it + α

3I

i,t-1 + α

4A

it + α

5A

i,t-1 + α

6C

it +

α7C

i,t-1 + α

8M

it + α

9M

i,t-1 + η

1 + ε

it(2)

η1 in equation (2) represents firm specific effect, which will

cause inconsistency in the regression coefficients, but can

be solved using differencing techniques. However,

differencing endogeneous variables may cause a correlation

between differenced of error term and differenced lag of

endogenous term. This problem can be resolved by

applying more than one lags to the variable levels. For

example, Arellano and Bond (1991) used the Generalized

Methods of Moment (GMM) to produce efficient

estimators.

3.2. Equity Issuance Model

Equation (3) presents a model of equity issuance that

is affected by financing gap as well as the optimum level

of corporate debt, based on an empirical model developed

by Benito and Young (2002). This model supports the debt

model in equation (2) and is used to determine the level

of Indonesian corporate debt level. This model expects that

capital expenditures (A and I) are positively associated with

increasing in number of stock issuing. Meanwhile, cash

(C) position is expected to be negatively associated with.

In addition, the level of optimum debt has an ambiguous

association with increasing numbers of stocks issued while

the level of optimum debt may be positively or negatiely

associated with the number of stocks issued. The equity

issuance model is shown in equation (3) below:

Eit = α + α

1D

i,t-1 + α

2I

it + α

3A

it + α

4C

it + α

5M

it + η

1 + ε

it(3)

3.3. Investment Model

Equation (4) models the level of corporate

investment. It expects that cash (C) is positively associated

with the value of corporate investment. First, the Q variable

(a variable based on an empirical evidence of Blundell,

at.al.,1992) is included in the equation. This variable is

expected to positively affect corporate investments. The

model can be written as follows:

Iit = α + α

1D

i,t-1 + α

3I

i,t-1 + α

4A

it + α

6C

it + α

7C

i,t-1 + α

8Q

it +

α9Q

i,t-1 + η

1 + ε

it(4)

3.4. Model Specification

Based on those previous models, a model is

constructed for this study particularly by extending the

model proposed by Gibbard and Stevens (2006). In

addition, referring to Welch (2002) this study includes

stock returns (R) as one of the variables affecting the

Indonesian corporate debt level. The inclusion of stock

returns aims to test the inertia of using debt in the capital

structure. This behavioral approach implies that negative

stock returns will deliver negative signals and therefore

will increase the corporate debt level. The model is

presented as follows:

Dit = α + α

1D

i,t-1 + α

2I

it + α

3I

i,t-1 + α

4A

it + α

5A

i,t-1 + α

6C

it +

α7C

i,t-1 + α

8M

it + α

9M

i,t-1 + α

10R

i,t,t-1 + η

1 + ε

it(5)

where Di,t-1

is debt at time t-1; I is the investment at time

t; Ait denotes as acquisition at time t; C

it is the corporation

cash flows at time t; Mit is target debt ratio at time t; and

Ri,t,t-1

represents the corporation»s stock returns at time t.

88


3.4.1. Determining Corporate Debt Optimum

Level

Based on Fama and French (2002), the ratio of

corporate target leverage is determined by the fitted values

of the equation 6. The corporation leverage target ratio

(M), will then model the Indonesian corporate balance

sheet.

Mt = b

0 + b

1MV

t-1 + b

2EBIT

t-1 + b

3DP

t-1 + b

4RD

t-1 +

b5 ln(A

t-1) + b

6FA

t-1 + b

7MI

t + b

8M

t-1 + e

t+1(6)

It is assumed that firm with high earning assets (EBIT)

could operate with high or low levels of leverage. In

addition, low leverage could occur in corporations with

high retained earnings or when the firm limits its leverage

to protect its franchises producing these high earnings.

Higher leverage might reflect the firm»s ability to meet debt

payments out of its relatively high cash flow. Furthermore,

high market to book ratio (MV) in general reflects better

growth in the future. In this case, high growth of firms

tend to be protected by limiting its leverage.

Depreciation (DP) is a proportion of total assets. Firms

with high depreciation will have more interest deductions

with the use of leverage as its source of financing. In

addition, firms with higher value of assets, tend to use

more debt compared to those with lower assets. This occurs

as these firms tend to be more transparent or have greater

access to public debt markets. Firms with high tangible

assets (FA) tend to have higher debt capacity, while firms

with more intangble assets in the form of R&D prefer equity

as their financing source. Furthermore, the firm»s lagged

industry median debt ratio (MI) is used to control industrial

characteristics which cannot be represented by other

independent variables.

4. ANALYSIS

This study uses unbalanced panel data of 218

Indonesian listed corporations covering eight sectors, i.e.

consumption, infrastructure, mining, property, basic

industries, agriculture, trading and miscellaneous industries

from 2004 to 2007. Data sources include Bloomberg and

the Indonesian Stock Exchange.

Table A2.1 shows that the average value of total debt

of Indonesian corporations were more than 400 times its

total assets. However, the standard deviation of these

variables were also much higher that their average. To

estimate factors that determine the corporate level of debt,

this study applies the generalised methods of moments

estimator (GMM-SYS) following Arrelano and Bover

(1995). This method is used to reduce the effect of firm-

specific effects of the corporations in the sample as they

come from various industrial sectors.

Following Fama & French (2002) and Hovakimian et.

al. (2003), this study found that the level of optimum debt

of Indonesian listed corporations are negatively and

significantly affected by levels of profit. This is inline with

the expected relations. This indicates that in general,

Total Asset (Trillion IDR) 6,133.65 55,821.59Current Asset (Trillion IDR) 872.02 1,891.74Fixed Asset (Trillion IDR) 1,118.58 4,157.16Tangible assets (Trillion IDR) 6,024.73 55,745.80Intangible Asset (Trillion IDR) 90.11 558.83Total Debt (Trillion IDR) 25,471.62 437,384.39Net Sales (Trillion IDR) 1,935.43 5,641.06Net income (Trillion IDR) 246.87 1,812.31Depretiation (Trillion IDR) 644.08 3,079.11Amortization (Trillion IDR) 46.55 473.89Capital expenditure (Trillion IDR) 167.08 992.40EBIT 327.52 1,658.52Cash per total asset 0.33 0.34Depreciation Expense per tangible asset 0.27 0.24R & D Expense per total asset 0.05 1.11Size (Log of Total Asset) 27.14 1.85Fixed asset per total asset 0.38 0.24Debt per total asset 0.78 1.04Investment (Capex per total asset) 0.04 0.10acquisition (Acquisition per total asset) 0.03 0.08

Table A2.1Descriptive Statistics of Indonesian Corporation,

2004-2007

VariableAll

Mean Standard Deviation

89


Indonesian corporations prefer to use their profits (internal

sources) rather than debt as source of financing. In contrast,

it is found that firm size is negatively and significantly

related to the use of external source of financing.

Table A2.2 presents the debt equation of the

Indonesian corporation. The table verifies that the pecking

order theory can significantly explain corporate debt. This

also shown by significant coefficients and signs of two

financing gap factors, i.e. investment and cash flows.

Meanwhile, another component, namely acquisition, has

an unexpected (negative sign) and is not significant.

are in agreement with the pecking-order theory,

in which the higher the firm profits, the more it

uses debts (excess cash is used for other matters

such as divident payout).

ACTA : The regression results indicate a negative

relationship in the use of corporate debt even if

the results found it to be insignificant. Empirical

studies find that different coefficients. The

negative relationship between acquisitions

indicate the priority for other funding sources to

fund acquisition activities.

The estimation results, as shown by Table A2.2 also

imply that the Fitted Values of Debt (OD-1) as a proxy of

optimum level of the corporate debt shows a negative

and significant association to the level of actual debt. The

negative sign of the coefficient supports default probability

effects (Myers, 1977 and Jensen, 1986). This implies that

the decision to take debt as a source of financing is crucial

and needs to be carefully considered by the corporations.

In addition, a company»s stock returns variable as a proxy

for market expectation shows that stock returns negatively

and significantly affects the level of optimum debt. This

means that the market poses high and positive expectation

to the corporate»s future performance and as such, the

corporation needs less debt financing. This implies that

corporate stock returns affect the variability of optimum

debt level. The result is inline with Welch (2004).

5. CONCLUSIONS

This study aims to model the Indonesian corporate

balance sheet and investigate the determinants of the

optimum level of corporate debt, by combining the well-

known capital structure theories i.e. the trade-off theory

and pecking order theory. Using the generalised methods

of moment estimator (GMM-SYS), this study also captures

the dynamics of the corporate debt level in adjusting to

the optimum level.

INVTA : The coefficient of investments to the ratio of

corporate debt is positive (+) and significant at a

5% level. This indicates that the more a firm

invests, the more it uses debt. This agrees with

the pecking-order theory.

CASH : The coefficient of investments to the ratio of

corporate debt is positive (+). The two variables

NotesDEBT : Total debt per total assetsACTA : Total Acquisition per total assetsCASH : Total Cash per total assetsINVTA : Total Investment per total assetsRETURN : Return of share per year

DEBT(-1) 0.5740 13.0137 0.0000ACTA -0.1456 -1.2976 0.1971ACTA(-1) 0.0692 0.4581 0.6478CASH 0.0574 0.6196 0.5368CASH(-1) 0.0359 0.6204 0.5362INVTA 0.1816 2.0067 0.0472INVTA(-1) 0.1186 1.3022 0.1956OD 0.0054 0.5342 0.5943OD(-1) -0.5757 -41.0524 0.0000RETURN -0.0341 -3.0392 0.0030RETURN(-1) -0.0158 -4.1306 0.0001Cross-section fixed (first differences)R-squared 0.983631P-value (Chi square) 0.00000SSE 0.07645N (Firms) 201

Table A2.2Determinants of Corporate Debt

DEBT EQUATION (GMM Sys)Dependent Variable :DEBT

Variable Coefficient t-Statistic Prob.

90


Results of the estimation show that the level of

Indonesian corporate debt is determined significantly by

its level of investment and cash flows. This finding also

indicates that the pecking order theory contributes

significantly to Indonesian corporations balance sheet

model. In addition, the optimum level of debt provides

support for the default probability effect as explained by

Myers (1977) and Jensen (1986). As it is found that the

model of the Indonesian corporate balance sheet in general

is affected by the pecking order theory, it indicates that

investment and acquisition activities will influence their

level of debt.

Based on GMM-SYS estimation, it found that the

Indonesian corporate debt adjusts itself to a level slightly

lower than its optimum level. The implied adjustment rate

is 0.43. This means that corporations take into account all

factors affecting their level of debt. They carefully pay

attention to adjustments in cost as the level of debt is

determined by default probability effect.

In summary, the findings of this study shed some

light on the determinants of corporate debt as corporates

adjust their level of debts to the optimum level.

Furthermore, the findings can be used to monitor firm»s

debts for investment and acquisition activities, which in

turn can be used to calculate its default risk potential. As

such, lenders and regulators need to hold rigorious

assessments to reduce the negative impacts of excessive

use of debts. Good management of debt levels will move

towards achieving optimum levels of debt and in turn

promote high value of the firm.

91


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DIRECTOR

Halim Alamsyah Wimboh Santoso Suhaedi

COORDINATOR & EDITOR

Agusman

WRITERS

Ardiansyah, Linda Maulidina, Ratih A. Sekaryuni, Anto Prabowo, Tirta Segara, Wini

Purwanti, Endang Kurnia Saputra, Ita Rulina, Boyke Suadi, Ida Rumondang, Azka Subhan,

Pipih Dewi Purusitawati, Noviati, Rosita Dewi, Erma Kusumawati, Darmawan Tohap B,

Sagita Rachmanira, Reska Prasetya, Elis Deriantino, Hero Wonida, Mestika Widantri,

Heny Sulistyaningsih, Primitiva Febriarti, Adidoyo Prakoso

COMPILATORS, LAYOUT & PRODUCTION

Boyke Suadi Primitiva Febriarti

CONTRIBUTORS

Directorate of Banking Supervision 1



Directorate of Sharia Banking

Directorate of Credit, Rural Bank Supervision and SMEs

Directorate of Bank Licensing and Banking Information

Directorate of Banking Investigation and Mediation

Directorate of Accounting and Payment Systems

Directorate of Economic Research and Monetary Policy

Directorate of Monetary Management

Directorate of Reserve Management

DATA SUPPORT

Suharso I Made Yogi

Financial Stability ReviewNo. 12, March 2009