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Bank Indonesia, Financial Stability Review No. 12 March 2009

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Bank Indonesia, Financial Stability Review No. 12 March 2009 Powered By Docstoc
					Publisher : Bank Indonesia Jl. MH Thamrin No.2, Jakarta Indonesia

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∆. 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.

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 : BSSK@bi.go.id

Financial Stability Review
( No. 12, March 2009 )

ii

Table of Contents

Foreword Overview Chapter 1 Macroeconomic Conditions and the Real Sector Macroeconomic Conditions Real Sector Conditions Box 1.1. Indonesian Household Balance Sheet Survey 2008 Box 1.2. Corporate Sector Credit Risk: Credit Default Swaps (CDS) Box 1.3. Transition Matrices: The Risk Potential of Corporate Credit of Three Sectors Chapter 2 The Financial Sector

vi 3

Box 2.3. Segmentation in the Interbank Money Market (PUAB) Box 2.4. Structured Products and Offshore Products: Their Impact to the Stability of the Financial System 48 50 46

9 9 12

Box 2.5. The Impact of Foreign Debt to Financial System Stability Chapter 3 Financial Infrastructure and Risk Mitigation Payment System Performance Credit Bureau Financial System Safety Net Box 3.1. The Financial System Stability and PERPPU on the Amendments to the Law on Bank Indonesia Box 3.2. Best Practices of Systemic Impact Analysis towards the Financial System Chapter 4 Prospects of the Financial System in Indonesia Economic Prospects and Risk Perception Bank Risk Profile: Level and Direction Prospect of the Indonesian Financial System Articles Article 1 Impact of Contagion Risk on the Indonesian Capital Market Article 2 Corporate Balance Sheet Modelling: Determinants of Indonesian Corporate Debt 83 73 67 67 68 69 64 63 53 53 56 60

15 17 18 21 21 22 22 22 24 30 32 35 38 44 45

Indonesian Financial System Structure Financial Stability Index The Banking Industry Funding and Liquidity Risk Credit Growth and Credit Risk Market Risk Profitability and Capital Finance Companies Capital Market Box 2.1. Chronology of the 2008 Financial Sector Shocks and Policy Responses Box 2.2. Bank Century»s Takeover, Bank Indover»s Closure and Financial System Stability

Nonbank Financial Institutions and the Capital Market 35

iii

List of Tables and Figures

Tables
1.1 2.1 2.2 2.3 2.4 2.5 2.6 3.1 3.2 4.1 4.2 Global Economic Indicators Banking Profit and Loss Financing Growth of Finance Companies Financial Ratios of Finance Companies NPL of Finance Companies Price Index Perfomance of Several Stock Exchanges in the Region Sectoral Price Index Debtor Identification Number (DIN) Data (2006-2008) Financial Safety Net Framework Projection of Several Economic Indicators Risk Perception of Indonesia 58 61 67 68 39 40 10 33 36 36 37

Figures
1.1 1.2 1.3 1.4 1.5 1.6 1.7 1.8 1.9 1.10 1.11 1.12 Business Confidence Indicators Price Index of several Commodities GDP Growth of Industrial Countries GDP Growth of Several Emerging Market Countries Global Stock Price Index Rupiah Exchange Rate against the US Dollar Inflation in ASEAN-5 and Vietnam Real Interest Rate in Indonesia, US and Singapore ROA and ROE of Nonfinancial Public Listed Companies DER and TL/TA of Nonfinancial Public Listed Companies Probability of Default (PD) of Nonfinancial Public Listed Companies Unemployment Rate in ASEAN Structure of Household Income Sources Assets of Financial Institutions Financial Stability Index Performance of Deposits Performance of Foreign Exchange Deposits Growth of Foreign Exchange Deposits vs Rp Exchange Rate to US Dollar 46 46 50 2.6 2.7 2.8 2.9 2.10 2.11 2.12 2.13 2.14 2.15 Excess of Bank Liquidity Transaction Volume of PUAB (daily average) Credit Growth (yoy) Credit Growth during 2007-2008 Credit Growth by Bank Group (y-t-d) Credit Growth by Usage (y-t-d) Credit Growth by Economic Sector Growth of Housing loans, Credit Cards and Others Growth of Property Credit Credit Growth by Its Initial Denomination 26 26 26 23 23 24 25 25 25 26 26 1.13 18 2.1 2.2 2.3 44 44 2.4 2.5 13 13 14 21 22 23 23 12 12 12 11 11 11 11 9 10 10

Box Tables : 1.3.1 Collectability of Debtor Migration of Three Sectors 2.1.1 Chronology of Shocks to the Indonesian Financial Sector in 2008 2.1.2 Policy Response 2.3.1 Daily Average Transaction Volume of Rupiah PUAB from January to December 2008 2.3.2 Daily Average Transaction Volume of Domestic Foreign Exchange PUAB 2.5.1 Private Foreign Debt Maturing in 2009

iv

2.16 2.17 2.18 2.19 2.20 2.21 2.22 2.23 2.24 2.25 2.26 2.27 2.28 2.29 2.30 2.31 2.32 2.33 2.34 2.35 2.36 2.37 2.38 2.39 2.40 2.41 2.42 2.43 2.44 2.45 2.46 2.47 2.48 2.49 2.50

Credit Share by Usage Growth of MSM Credit Non Performing Loans Credit, NPL and Provisions Gross NPL Ratio by Bank Group Gross NPL Ratio by Economic Sector Gross NPL Ratio by Credit Usage Gross NPL Ratio of Consumer Credit Gross NPL Ratio of Property Credit Gross NPL Ratio of Credit in Rupiah and Foreign Exchange Gross NPL Ratio of MSM and Non MSM Credit Gross NPL Ratio of MSM Credit Rupiah Interest Rate and Exchange Rate Rupiah Maturity Profile Foreign Exchange Maturity Profile Net Open Positions SUN Portfolio of Banking Industry Performance of SUN Owned by Banks Bank Profitability Bank Interest Income ROA by Bank Groups Ratio of Interest Expense to Interest Income by Bank Group Capital, Risk-Weighted Assets and CAR Interbank Stress Test Business Activities of Finance Companies Finance Companies Source of Funds Composition of Financing by Finance Companies (Nov «08) NPL of Financing by Finance Companies Developments of NPL Value Cash Flow of Private Finance Companies Cash Flow of Joint Venture Finance Companies Bank Exposure The Decrease of NPL of Bank Subsidiary Finance Companies The Increase of NPL of Bank Subsidiary Finance Companies

27 27 28 28 28 28 28 29 29 29 30 30 31 31 31 31 32 32 33 33 33 34 34 35 35 35 36 36 36 37 37 37

2.51 2.52 2.53 2.54 2.55 2.56 2.57 2.58 2.59 2.60 2.61 2.62 2.63 2.64 2.65 2.66 2.67 2.68 3.1 3.2 3.3 3.4 3.5 3.6 4.1

Foreign Investment: SBI √ SUN √ Stocks Foreign Placements: SBI √ SUN √ Stocks SUN and SBI Ownership by Foreign Investors Institutions Performance of JCI, Global and Regional Index (Based on Index per 31 Dec 2005) Volatility of Asian Stock Indices (30 days) Stock Transaction Value of Domestic and Foreign Investors Capitalization and Issuance Value Stock Price Performance of Several Banks P/E Ratio of Bank Stocks Price Performance of Several FR Series Bonds Yield of 1 to 30 year SUN Government Bonds: Market Liquidity of Various Tenors Issuance and Position of Corporate Bonds Net Asset Value of Mutual Funds Mutual Fund: NAV-Participating Units

38 38 39 39 39 40 40 40 41 41 41 41 42 42 42 43

SUN Absorption by Domestic and Foreign Financial

Mutual Funds: Redemptions-Subscriptions-NAV 42 Performance of Fund Collection of Mutual Funds 43 Performance of BI-RTGS Transactions Performance of Bank Indonesia National Clearing System E-Money Transactions Role of Credit Bureau Credit Bureau Strategic Policy Bank Risk Profile and Outlook 54 54 57 58 69 Performance of Card Based Payment Instruments 54 53

Integrated Stress Test on CAR of 15 Major Banks 34

Figures included in Boxes: 1.1.1 1.1.2 Composition of Household Debt Percentage of Total Debt Purpose of Household Credit CDS Price in Indonesia CDS Spread in Indonesia 1.2.1 1.2.2 15 16 17 17

38 38

v

Foreword

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.

vi

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 importance 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

vii

viii

Overview

Overview

1

Overview

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Overview

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

3

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.

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 products
As stated in the previous Financial Stability Review (No. 11, September 2008), it is now mandatory for the

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.

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

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

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

4

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.

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

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.

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. 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

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

5

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.

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

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

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.

6

Chapter 1 Macroeconomic Conditions and the Real Sector

Chapter 1 Macroeconomic Conditions and the Real Sector

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Chapter 1 Macroeconomic Conditions and the Real Sector

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Chapter 1 Macroeconomic Conditions and the Real Sector

Chapter 1 Macroeconomic Conditions and the Real Sector

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.

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

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 1.1 Business Confidence Indicators
Manufacturing PMls

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

65 60 55 50 45 40 35

(values greater than 50 indicate expansion) Euro Area

Emerging Economies United States Oct 2008

1985

1990

1995

2000

2005

Source: World Economic Outlook-IMF November, 2008

9

Chapter 1 Macroeconomic Conditions and the Real Sector

Table 1.1 Global Economic Indicators
(%) Projection 2009 0.5 (2.0) (1.6) (2.0) 3.3 0.3 5.8 1.3 2.2 1.0 (48.5) 2010 3.0 1.1 1.6 0.2 5.0 0.8 5.0 2.9 2.7 0.4 20.0
600 500 400 300 200 100 0 2000

Figure 1.2 Price Index of several Commodities
1990 = 100
Oil Tin Palm Oil Rice Aluminium Copper Gold Coffee Rubber

Category World Output: Advanced Economies United States Euro area Emerging & Developing Countries Consumer Price: Advanced Economies Emerging & Developing Countries1) LIBOR2) US Dollar Deposit Euro Deposit Yen Deposit Oil Price (USD) - average3)

2007 5.2 2.7 2.0 2.6 8.3 2.1 6.4 5.3 4.3 0.9 10.7

2008 3.4 1.0 1.1 1.0 6.3 3.5 9.2 3.0 4.6 1.0 36.4

600 500 400 300 200 100 0

2001

2002

2003

2004

2005

2006

2007

2008

Source: Bank Indonesia

performance of emerging market countries including Indonesia. The income of emerging market countries generally depends on their exports, therefore the decline

Source: World Economic Outlook - IMF January 2009

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

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.

Figure 1.3 GDP Growth of Industrial Countries
%

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

6.00 5.00 4.00 3.00 2.00 1.00 (1.00) (2.00) (3.00)
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 Germany Canada

Japan UK

2000
Source: Bloomberg

2001

2002

2003

2004

2005

2006

2007

2008

10

Chapter 1 Macroeconomic Conditions and the Real Sector

Figure 1.4 GDP Growth of Several Emerging Market Countries
% 14,000 12.00 9.00 6.00 3.00 6,000 (3.00) (6.00) (9.00) 4,000 12,000 10,000 8,000

Figure 1.6 Rupiah Exchange Rate against the US Dollar
14,000 Monthly Average Semester Average
9,258 9,039 9,210 9,352

12,000 10,000 8,000 6,000 4,000

9,075 9,077 9,172 9,095 8,842 8,981 9,067 9,358 9,105 9,102 9,267 9,356 9,406 9,180 9,178 9,203 9,281 9,288 9,159 9,151 9,354 9,990 11,803 11,314
1 2 3 4 5 6 7 8 9 10 11 12 1 2 3 4 5 6 7 8 9 10 11 12

Indonesia Thailand China 2000 2001 2002 2003 2004 2005 2006

Singapore South Korea India 2007 2008

2,000 0

2,000 0

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

2007
Source: Bloomberg

2008

Source: Bloomberg

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
Figure 1.5 Global Stock Price Index

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 1.7 Inflation in ASEAN-5 and Vietnam
y.o.y %

35000 30000 25000 20000 15000 10000 5000 0 2006
Source: Bloomberg Singapore NYA New York Dow Jones Indonesia Nikkei

35000 30000 25000 20000 5 15000 10000 5000 0 2007 2008 (5) 0
Philippine Malaysia Jan Apr Jul Oct Singapore Indonesia Jan Apr Thailand Vietnam Jul Oct

10

2007
Source: CEIC

2008

11

Chapter 1 Macroeconomic Conditions and the Real Sector

Figure 1.8 Real Interest Rate in Indonesia, US and Singapore
%

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

4.0 2.0 0.0 (2.0) (4.0) (6.0) (8.0)
Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Mar Jun Sep Dec Indonesia USA Singapore

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
2004 2005 2006 2007 2008

2003
Sources: Bloomberg and CEIC

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.

same period in the previous year. From a financing perspective, the corporate sector suffered from limited capital. To fulfill its operational needs,
Figure 1.9 ROA and ROE of Nonfinancial Public Listed Companies
700 600 500 400 300 200 100 0 -100 -200
Q1 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)

350 300 250 200 150 100 50 0 -50 -100 -150 2003 2004 2005 2006 2007 2008

Source: Bursa Efek Indonesia

Figure 1.10 DER and TL/TA of Nonfinancial Public Listed Companies
1.80 1.60 1.40 1.20 1.00 0.80 DER Debt/TA

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.

0.60 0.40 0.20 0.00
Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3 Q4 Q1 Q2 Q3

2003
Source: Bursa Efek Indonesia

2004

2005

2006

2007

2008

12

Chapter 1 Macroeconomic Conditions and the Real Sector

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.
Figure 1.11 Probability of Default (PD) of Nonfinancial Public Listed Companies
Total 250 215 200 150

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 1.12 Unemployment Rate in ASEAN
%
2006 2007 2008*)

10.0
100 50 5 0
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

8.0
19

6.0 4.0

4

3

2

0

1

1

0

Probability of Default - September 2008
Total 180 160 140 120 100 80 60 40 20 0
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 21 14 6 9 0 4 1 1 23 171

2.0 0.0 Indonesia Thailand Malaysia Singapore
Source: CEIC Note: *) : 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 In addition to the onset of increasing credit risk, 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.

Probability of Default - September 2009

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

13

Chapter 1 Macroeconomic Conditions and the Real Sector

Figure 1.13 Structure of Household Income Sources

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

Pension Income 3%

economic slowdown it is likely that demand for property
Others 10% Salary and Benefits 56%

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.

Net Income 31%

Source: Household Survey 2008

In the future, challenges in the real sector are expected to persist in line with the limited development of

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

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.

14

Chapter 1 Macroeconomic Conditions and the Real Sector

Box 1.1

Indonesian Household Balance Sheet Survey 2008

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.

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%.

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 mid2008 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 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 1.1.1 Composition of Household Debt Percentage of Total Debt

Other Debts 10%

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

LKBB Debt 12%

Bank Debts 78%

15

Chapter 1 Macroeconomic Conditions and the Real Sector

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.
Figure Box 1.1.2 Purpose of Household Credit
Others 16%

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 debtservicing 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.

Electronic 2% Buying Vehicles 16%

To Open Business 24%

Food 13% Buy House but not occupying it 2% Buy House to occupy 2% Education 8%

Building/Renovating House 14%

Health 3%

Solvency Ratio
This ratio illustrates the ability of a household»s assets to cover its debt in the case of default. Survey

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:

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.

16

Chapter 1 Macroeconomic Conditions and the Real Sector

Box 1.2

Corporate Sector Credit Risk: Credit Default Swaps (CDS)

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) nosediving 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.
370 320 270 220 170 120 70 20 -30 -80
3 Jul

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.
Figure Box 1.2.1 CDS Price in Indonesia
1200 1000 800 600 400 200 0
3 Jul Source: Bloomberg 2 Aug 1 Sep 1 Oct 31 Oct 30 Nov 30 Dec 29 Jan

Indonesia Philippine

Korea Thailand

2008

2009

Figure Box 1.2.2 CDS Spread in Indonesia
Indonesia Philippine Korea Thailand

2 Aug

1 Sep

1 Oct

31 Oct

30 Nov

30 Dec

29 Jan

2008
Source: Bloomberg

2009

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.

17

Chapter 1 Macroeconomic Conditions and the Real Sector

Box 1.3

Transition Matrices: The Risk Potential of Corporate Credit of Three Sectors
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.

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).

Table Box 1.3.1 Collectability of Debtor Migration of Three Sectors
Property Collect
1 2 3 4 5

1
89.7% 64.4% 37.7% 23.4% 0.0%

2
9.3% 28.0% 19.6% 10.8% 0.0%

3
0.3% 1.7% 5.8% 1.5% 0.0%

4
0.3% 1.5% 3.8% 4.7% 0.0%

5
0.4% 4.4% 33.1% 59.5% 100.0%

Transportation Collect
1 2 3 4 5

1
89.5% 53.5% 7.5% 2.9% 0.0%

2
8.0% 28.7% 3.5% 1.1% 0.0%

3
0.5% 1.7% 3.3% 0.3% 0.0%

4
0.4% 2.0% 1.7% 3.0% 0.0%

5
1.7% 14.0% 84.1% 92.6% 100.0%

Textile Collect
1 2 3 4 5

1
94.0% 77.9% 27.0% 0.0% 0.0%

2
3.8% 4.6% 2.3% 0.0% 0.0%

3
0.6% 1.0% 0.7% 0.0% 0.0%

4
0.3% 1.1% 1.5% 0.6% 0.0%

5
1.4% 15.3% 68.6% 99.4% 100.0%

18

Chapter 2 The Financial Sector

Chapter 2 The Financial Sector

19

Chapter 2 The Financial Sector

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20

Chapter 2 The Financial Sector

Chapter 2 The Financial Sector

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).

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.

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 2.1 Assets of Financial Institutions

3.2% 8.0% 1.1%

0.3% 5.8% 2.7%

Commercial Banks Rural Banks Insurance Companies Pension Fund

79.0%

Finance Companies Securities Companies Pawnshops

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

21

Chapter 2 The Financial Sector

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.
3 2.5 2 1.5 1 0.5 0 2003

Figure 2.2 Financial Stability Index
FSI Projections FSI

2.10 2.13 1.95 1.77

2004

2005

2006

2007

2008

2009

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

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.

effective in maintaining and even increasing public funds held at banks.

22

Chapter 2 The Financial Sector

Figure 2.3 Performance of Deposits
Trillion Rp 550
Time Deposits (right)

denomination, growth of deposits in a foreign currency during the reporting period in fact decreased USD1.36
900 750 600

billion, mostly in terms of time deposits and demand deposits, which declined USD0.98 billion and USD0.58 billion respectively.

500
Savings (left)

450
Demand Deposits (left)

450 300 150

400

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

350 Dec 2007

0 Feb Apr Jun 2008 Aug Oct Dec

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 2.4 Performance of Foreign Exchange Deposits
Billion USD 30 Deposits in USD (left) 27 290 Trillion Rp 320

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.
Figure 2.6 Excess of Bank Liquidity
250 200 290

24 Deposits in Rp (right) 21

260

285 SBI (left) SUN (right) 280

150

230
100

18 Dec 2007 Feb Apr Jun 2008 Aug Oct Dec

200
50 0 Fasbi/FTK (left)

275

270 Dec 2007 Feb Apr Jun 2008 Aug Oct Dec

Figure 2.5 Growth of Foreign Exchange Deposits vs Rp Exchange Rate to US Dollar
Billion USD 30 Rupiah 12,500 11,700 Foreign Exchange Deposits in USD (left)

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

27

10,900 10,100

24 Rp Exchange Rate to USD (right)

21

9,300 8,500
Dec Apr Aug Dec Apr Aug Dec

18 2006 2007 2008

2 Excess liquidity consists of BI Certificates, other placements at Bank Indonesia and giro accounts at BI (Fasbi/FTK) and securities. 3 Liquid instruments comprise of cash and placements at BI (BI giro, SBI, and other placements). NCD is assumed to comprise of 30% giro + 30% savings + 10 time deposits up to (3 month time deposits).

23

Chapter 2 The Financial Sector

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.

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

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.
Figure 2.7 Transaction Volume of PUAB (daily average)
Trillion Rp 14 12 400 10 8 6 4 2 0
Jan

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
Million USD 500

in facing liquidity risk pressures.

300

2.3.2. Credit Growth and Credit Risk

Credit Growth
200

Strong credit growth stood out in 2008. In fact, the
Rupiah PUAB Forex PUAB
Mar May Jul Sep Nov

100

symptoms of expansive credit growth began in 2007. At that time, credit growth reached 25%, which exceeded

0

2008

24

Chapter 2 The Financial Sector

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
Figure 2.8 Credit Growth (yoy)
% 50 45 40 35 30 25 20 15 10 5 0 Dec 2007
Forex Credit (USD) Total Credit Total Credit (fixed exchange rate) Rupiah Credit Forex Credit (in Forex)

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 2.10 Credit Growth by Bank Group (y-t-d)
32% 46%

Industry Foreign Banks Joint Venture Banks Regional Development Banks National Private Banks State Owned Banks 0 10 20 30
27%

50% 36%

32%

2008 2007

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

Feb

Apr

Jun 2008

Aug

Oct

Dec

than that of previous semesters. The trade and others (consumption) sectors were the primary contributors to

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

Figure 2.9 Credit Growth during 2007-2008
Forex Credit (USD T) 2008 2007

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

Forex Credit (Rp T)

productive sector. This was evidenced by the growth in
Rupiah Credit (Rp T)

working capital credit and investment credit, which contributed 49% and 27% respectively to total credit growth. Working capital credit and investment credit
25 65 105 145 185 225 265

Total Credit (Rp T) (15) 0

25

Chapter 2 The Financial Sector

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.

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.
Figure 2.13 Growth of Housing loans, Credit Cards and Others
29% 2008 2007 26%

Others

Figure 2.11 Credit Growth by Usage (y-t-d)
Credit Card
2008 2007

Consumer

29%

29%

Housing Loan
37%

Investment

0

5

10

15

20

25

30 %

Working Capital

32%

Figure 2.14 Growth of Property Credit
40 %

0

10

20

30

Housing Loan

Figure 2.12 Credit Growth by Economic Sector
Electricity Mining Social Services Business Services Agriculture Construction Transportation Manufacturing Others Trade 0 20
20.7% 37.9% 29.1% 2008 2007 19.1% 42.8% 70.2% 11.1% 39.6% 25.9% 133.8%

Growth in 2007 (%)

Growth in 2008 (% ytd) Credit Delta 2008 (Rp M)

Real Estate

Credit Delta 2007 (Rp M)

Construction

0

9

18

27

36

45

Rupiah credit continued to dominate bank loan disbursements during the reporting period with an 80%
140 %

40

60

80

100

120

share of total credit growth. Meanwhile, credit denominated in a foreign currency grew by Rp32.4 trillion, which was affected by rupiah depreciation factors.
Figure 2.15 Credit Growth by Its Initial Denomination
% Rp 14,000

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

60 40

12,000 20 (20) 8,000 (40)
yoy Rp (%) yoy Va USD (%) Convention Value

10,000

(60) 2000

6,000 2001 2002 2003 2004 2005 2006 2007 2008

26

Chapter 2 The Financial Sector

Expressed in US dollars, foreign currency credit actually contracted by USD0.8 billion to USD23.1 billion. This was
1400

Figure 2.17 Growth of MSM Credit
% 54

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.

1200 1000 800
Total Credit Rp T (left) MSM Rp T (left) % MSM/Credit

52

50 48

600 400 200 2006 2007 2008 Dec 46

44

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

Figure 2.16 Credit Share by Usage

expansion of loan loss provisions by banks, surpassing the jump in nominal NPL, indicated that banks began to

Maluku + Papua Bali + NusTra Sulawesi Kalimantan Sumatera East Java Central Java + DIY DKI Jakarta West Java + Banten 0 5 10 15 20

Consumer Loan Investment Loan Working Capital Loan

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-

25

30

35 %

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

27

Chapter 2 The Financial Sector

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.
Figure 2.18 Non Performing Loans
(%) 10 9 8 7 6 5 4 3 2 1 2006 Jun 2007 Jun 2008 Jun Dec NPL Net (left) NPL Value (right) Gross NPL (left) (Trillion) 75 70 65 60 55 50 45 40 35 30

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 2.21 Gross NPL Ratio by Economic Sector

Others Business Services Transportation Trade Construction Manufacturing Mining Agriculture 0.0 1.5 3.0 4.5 6.0

Dec-07 Jun-08 Dec-08

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

Figure 2.19 Credit, NPL and Provisions
75 70 65 60 55 50 45 40 35 30 2006 2007 2008 Dec 400 200 NPL Value (left) 1400 Credit (right) 1200 1000 800 600 1600

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
Figure 2.22 Gross NPL Ratio by Credit Usage
7 6 5
Dec-07 Jun-08 Dec-08

Figure 2.20 Gross NPL Ratio by Bank Group
7 6 5 4
4
Dec-07 Jun-08 Dec-08

3
3

2
2

1
1

0 State Owned National Private Regional Joint Venture Banks Banks Development Banks Banks Foreign Banks
0 Working Capital Investment Consumer

28

Chapter 2 The Financial Sector

Figure 2.23 Gross NPL Ratio of Consumer Credit
% 12 10 8 6 4 2 0 Housing Loan Credit Card Others
Dec-07 Jun-08 Dec-08

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 stateowned banks and amounted to Rp0.8 trillion, followed

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

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.
Figure 2.25 Gross NPL Ratio of Credit in Rupiah and Foreign Exchange

5 4
Dec-07 Dec-08 Jun-08

of Rp0.3 trillion. This was due to increasing nominal NPL of real estate credit which pushed NPL ratios up to 4.51%.
Figure 2.24 Gross NPL Ratio of Property Credit
6 5 4 3 2 1 Construction Real Estate Housing Loan
Dec-07 Jun-08 Dec-08

3 2 1 0 Rupiah Foreign Exchange

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,

29

Chapter 2 The Financial Sector

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 2.26 Gross NPL Ratio of MSM and Non MSM Credit
5 4 3 2 1 0 MSM Non MSM
Dec-07 Jun-08 Nov-08

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

Figure 2.27 Gross NPL Ratio of MSM Credit
4.0 Dec-07 Dec-08 Jun-08

3.0

2.0

1.0

November. By end of 2008, however, BI had begun to
0.0 Micro Small Medium

reduce its BI Rate, initially by 25 bps to 9.25% in order to catalyze economic activities. Such measures were necessary

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%-

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%,

30

Chapter 2 The Financial Sector

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 2.28 Rupiah Interest Rate and Exchange Rate
% 20 Consumer Credit (left) 18 16 14 Investment Credit (left) 12 10 8 Exchange Rate (right) 6 2006 2007 1-month Time Deposits (left) 8500 7500 2008 Dec 9500 10500 Working Capital Credit (left) 11500 Rp 12500

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%.
Figure 2.30 Foreign Exchange Maturity Profile
Billion USD 10 5

0

Figure 2.29 Rupiah Maturity Profile
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 Sep08 Mar08 Dec08 Jun08

(5)

(10) (15) sd 1 month 1 - 3 months 3 - 6 months

Dec07 Sep08

Mar08 Dec08

Jun08

6 - 12 months

> 12 months

Figure 2.31 Net Open Positions
% 9 8 7 6 5

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

4 3 2 1 0 Dec07 Mar08 Jun08 Sep08 Dec08 Foreign Banks All Banks National Private Banks Joint Venture Banks Regional Development Banks State Owned Banks

Global financial market turbulence also intensified pressures on the rupiah exchange rate. The rupiah depreciated to Rp12,150 per USD in November 2008.

31

Chapter 2 The Financial Sector

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 BAPEPAMLK) 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

(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 2.33 Performance of SUN Owned by Banks (Trillion Rp)
300 250 200 150 100 50 0
126.8 156.4 130.6 101.4

AFS

Trading

HTM

Dec 2007

28.2

Jun

Jul

Aug Sep 2008

Oct

Nov

Dec

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
Figure 2.32 SUN Portfolio of Banking Industry
% 60 50 40 30 20 10 0
Dec07 Jun08 Dec08

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

HTM

AFS

Trading

32

16.9

Chapter 2 The Financial Sector

began to slow in November 2008. Therefore, the rise in NII was supported by credit interest rate income.
Figure 2.34 Bank Profitability
Trillion Rp 25 20
Interest Income Interest Expense NII

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.

15 10 5 0

Nov Dec Jan 2007

Feb Mar Apr May Jun Jul 2008

Aug Sep Oct

Nov Dec

Less operational profit in 2008 was also due in part to a drop in efficiency. Lower efficiency was reflected by

Figure 2.35 Bank Interest Income
250 200 150 100 Others Credit Securities Placement in BI

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

50 0 Nov 2007 Dec Nov 2008 Dec
% 4
ROA Dec'07 ROA Dec'08

Figure 2.36 ROA by Bank Groups

However, profitability garnered from interest income cannot fully become net profit of banks. This is because banks anticipate unfavorable credit quality in association

3

2

with upcoming sluggish economic growth by raising their loan loss provisions. Consequently, operational profit declined by 30.6%, namely from Rp17.6 trillion (June
1

15 Major Banks Middle Banks Small Banks Regional Dev. Joint Venture Banks Banks Foreign Banks Industry

Table 2.1 Banking Profit and Loss
Trillion Rp

2007 Semester I Semester II Operational P/L Non Operational P/L Before Tax P/L After Tax P/L 18.07 7.10 25.17 18.38 16.97 7.72 24.69 16.63 Total 35.04 14.82 49.86 35.02

2008 Semester I Semester II 17.63 7.23 24.86 18.39 12.23 11.01 23.24 12.16 Total 29.86 18.24 48.10 30.55

33

Chapter 2 The Financial Sector

Figure 2.37 Ratio of Interest Expense to Interest Income by Bank Group
% 120
BOPO Dec'07 BOPO Dec'08

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

100 80 60 40 20 -

15 Major Banks

Middle Banks

Small Banks

Regional Dev. Joint Venture Banks Banks

Foreign Banks

Industry

the rupiah depreciating by Rp5,000/USD, stress test results showed that no bank»s CAR would drop to below the

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.

minimum 8%. For the test, it is also assumed that shortfalls in liquidity are covered through the interbank money market.
Figure 2.39 Integrated Stress Test on CAR of 15 Major Banks
30%
OLD CAR

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 2.38 Capital, Risk-Weighted Assets and CAR
Trillion Rp 2,000
Capital Risk -Weighted Assets CAR (right)

25% 20% 15% 10% 5% 0% A B C D E F G H I J K L M

NEW CAR

N

O

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

% 25

to promptly raise their capital should the loss potential increase. However, when observed individually several banks

1,600

20

1,200

15

still maintain a minimum tier-1 capital of less than Rp100
800 10

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

400

5

-

Dec 2007

Feb

Apr

Jun 2008

Aug

Oct

Dec

0

34

Chapter 2 The Financial Sector

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 2.40 Interbank Stress Test
Impacted Banks F M N O

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.
Figure 2.41 Business Activities of Finance Companies
200.00 180.00 160.00 140.00 120.00 100.00 80.00 60.00 40.00 20.00 0.00 Assets Financing Funding 2.01% 23.80% 16.58% 28.19% Jun 07 Dec'07 Jun'08 Nov'08

Capital

Figure 2.42 Finance Companies Source of Funds
P Q R S T U V J W K 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0 -7.33% 24.42% Jun'07 Dec'07 Jun'08 Nov'08 Billion Rp 28.19%

Trigger Banks

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

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),

Credit from Domestic Banks

Securities

Total of Funds*

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

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).

35

Chapter 2 The Financial Sector

Figure 2.43 Composition of Financing by Finance Companies (Nov «08)
Financing (in Billion Rp) 160,000 140,000 120,000 100,000 80,000 60,000 40,000 20,000 0
Financing Receivables Leasing Factoring Credit Card Consumer Financing

in 2008 also increased, reaching 6.22 million units, far exceeding sales in 2007 of 4.69 million units.
Table 2.3 Financial Ratios of Finance Companies
Dec-06
Asset Debt Liabilities Equity Profit Before Tax 116,000,000,000 81,524,052,728 95,241,046,752 20,758,953,248 2,978,914,227 2,244,670,921 0.03 0.14 0.81 3.93 4.59

May-07
127,000,000,000 90,319,642,214 102,466,196,738 24,533,803,262 5,763,866,446 4,379,780,690 0.05 0.23 0.83 3.68 4.18

Dec-07
140,649,000,000 100,183,895,911 113,722,737,895 26,926,262,105 4,134,560,328 3,114,695,467 0.03 0.15 0.77 3.72 4.22

May-07
174,124,731,707 128,423,157,567 143,568,726,785 30,556,004,922 8,078,856,892 5,961,654,328 0.05 0.26 0.77 4.2 4.7

Total
141,179 53,480 2,222 1,178 84,299

National Private
46,257 5,759 1,182 2 39,314

Joint Venture
93,795 46,634 1,001 1,175 44,985

Profit After Tax ROA ROE Ops Expense to Ops Income Debt/Equity Liabilities/Equity

Table 2.2 Financing Growth of Finance Companies
Jun»08 Leasing Factoring Credit Card Consumer Financing Nov»08 Leasing Factoring Credit Card Consumer Financing Total 33.34% 1.82% 1.07% 63.76% Total 37.88% 1.57% 0.83% 59.71% National Private 11.31% 3.10% 0.01% 85.59% National Private 12.45% 2.56% 0.01% 84.99% Joint Venture 44.85% 1.05% 1.69% 52.40% Joint Venture 49.72% 1.07% 1.25% 47.96%

Figure 2.44 NPL of Financing by Finance Companies
NPL (%) 16.00 14.00 12.00 10.00 8.00 6.00 4.00 2.00 0.00 Leasing 2.67% 2.28% 1.90% 1.67% Factoring 14.14% 11.59% 11.32% 9.04% Credit Card 4.28% 3.66% 2.79% 3.09% Consumer Financing 1.55% 1.68% 1.70% 1.66%

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,

Jun'07 Dec'07 Jun'07 Nov'08

Figure 2.45 Developments of NPL Value
Billion Rp 3,000,000,000 2,500,000,000 2,000,000,000 1,500,000,000

SGU AP KK

PK Total

automobile sales in Indonesia surged 40%, peaking at a
1,000,000,000

record high 607.15 units despite a slight downward trend
500,000,000

in November and December. Meanwhile, based on the Indonesian Motorcycle Association (AISI), motorcycle sales

0

Jun 2007

Dec

Jun 2008

Nov

36

Chapter 2 The Financial Sector

Relatively high lending rates during the reporting period heightened potential risk exposure to finance
Billion Rp

Figure 2.48 Bank Exposure
50,000 45,000 Channelling Joint Financing

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 2.46 Cash Flow of Private Finance Companies
Billion Rp 4,000 3,000 2,000 1,000 0 -1,000 -2,000 -3,000 Jun 07 Net cash flow of operating activities Net cash flow of investing activities Net cash flow of financing activities 792 -45 -903 Dec 07 1,184 -162 -811 Jun 08 1,312 -177 1,721 Nov 08 1,772 -322 3,109

40,000 35,000 30,000 25,000 20,000 15,000 10,000 5,000 0 Jun 2007 Dec Jun 2008 Nov

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.

Figure 2.47 Cash Flow of Joint Venture Finance Companies
Billion Rp 15,000 10,000 5,000 0 -5,000 -10,000 -15,000 Net cash flow of operating activities Net cash flow of investing activities Net cash flow of financing activities

Table 2.4 NPL of Finance Companies
%Change of NPL Finance Jun»08 Companies
1 2 3 4 5 6 7 8 9 10

∆ Change in NPL Value Leasing Factoring
93,069,554 413,988 799,020 142,127 -540,907 -

Nov»08

Jun»08 Nov»08

Credit Consumer Card Financing
-3,558,416 42,591,752 -990,623 -26,578,328 -5,406,096 1,370,608 279,805

Jun-07 3,528 174 4,790

Dec-07 7,133 494 7,513

Jun-08 5,221 944 4,480

Nov-08 9,786 724 11,222

0.54% 0.37% 32.63% 53.28% 0.37% 0.37% 1.03% 0.00% 1.20% 1.07% 0.00% 0.06% 0.20% 0.44% 0.79% 0.58% 0.00% 0.66% 0.02% 0.03%

-

37

Chapter 2 The Financial Sector

Figure 2.49 The Decrease of NPL of Bank Subsidiary Finance Companies
12000000 10000000 8000000 6000000 4000000 2000000
1 8 Mar Apr 4 May 5

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 2.51 Foreign Investment: SBI √ SUN √ Stocks
Trillion Rp 35
SBI SUN Stocks

12000000 10000000 80000000 60000000 40000000

25
20000000 0
Jan Feb Jun Jul Aug Sep Oct Nov

15 5 -5 -15 -25 -35
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

0

2008

Figure 2.50 The Increase of NPL of Bank Subsidiary Finance Companies
30000000 25000000 20000000 15000000 10000000 50000000 0
2 3 6 7 9 10

4500000 4000000 3500000 3000000 2500000 2000000 1500000 1000000 500000
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov

2007

2008

Figure 2.52 Foreign Placements: SBI √ SUN √ Stocks
Trillion Rp 35 25 15 5 -5 -15

0

2008

Meanwhile, the nominal NPL of consumer financing tended to decrease.

-25 -35
Sep Oct Nov Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2007

2008

2.4.2. Capital Market

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

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

38

Chapter 2 The Financial Sector

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.
Figure 2.53 SUN and SBI Ownership by Foreign Investor
Trillion Rp 120
SBI SUN

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.
Table 2.5 Price Index Perfomance of Several Stock Exchanges in the Region
Growth (%)

100 80 60 40 20 0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Jun 07 Dec 07 Jun 08 Sep 08 Dec 08
JCI STI SET KLCI PCOMP NIKKEI HSCI KOSPI FTSE UKX DJIA 2,139.28 3,475.89 776.79 1,354.38 3,660.86 18,138.36 21,772.73 1,743.60 9,873.02 6,607.90 13408.62 2,745.83 3,465.63 858.10 1,445.03 3,621.60 15,307.78 27,812.65 1,897.13 9,740.32 6,456.90 13264.82 2,349.11 2,947.54 768.59 1,186.57 2,459.98 13,484.38 22,102.01 1,674.92 8,660.48 5,625.90 11350.01 1,832.51 2358.91 596.54 1,018.68 2,569.65 11,259.86 18,016.21 1,448.06 7,532.80 4,902.45 10850.66 1,355.41 1,761.56 449.96 876.75 1,872.85 8,859.56 14,387.48 1,124.47 5,757.05 4,434.17 8776.39

Sem II 07 Sem II 08 Jun-Sep 08
28.35 (0.30) 10.47 6.69 (1.07) (15.61) 27.74 8.81 (1.34) (2.29) (1.07) (42.30) (40.24) (41.46) (26.11) (23.87) (34,28) (34.90) (32.86) (33.53) (21.18) (22.68) (21.99) (19.97) (22.39) (14.15) 4.46 (16.48) (18.49) (13.54) (13.02) (12.86) (4.40)

2008

Figure 2.54 SUN Absorption by Domestic and Foreign Financial Institutions
Trillion Rp 20 10 0 -10 -20 -30

Figure 2.55 Performance of JCI, Global and Regional Index (Based on Index per 31 Dec 2005)

2.20

-40 Domestic Financial Institution -50
Jan Feb Mar Apr May Jun Jul

Foreign Financial Institution
1.70
Aug Sep Oct Nov Dec

2008
1.20

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

0.70

0.20
Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2007
IHSG PCOMP FTSE FSSTI NKY NYA

2008
SET Hang Seng DJIA KLCI KOSPI

39

Chapter 2 The Financial Sector

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.
Table 2.6 Sectoral Price Index
Growth Jun 07 Dec 07 Jun 08 Sep 08 Dec 08
JCI Financial Sector Index Agriculture Sector Index Basic Industry Sector Index Consumer Sector Index Property Sector Index Mining Sector Index Infrastructure Sector Index Trade Sector Index Miscellaneous Sector Index 2,139.28 223.14 1,680.12 196.10 437.01 211.72 1,647.04 750.43 387.38 324.96 2,745.83 2,349.11 1,832.51 260.57 203.74 203.37 2,754.76 3,061.06 1,489.57 238.05 200.05 162.93 436.04 398.29 381.36 251.82 168.53 142.42 3,270.09 3,415.96 1,833.24 874.07 652.81 570.91 392.24 356.76 261.33 477.35 360.65 326.15 1,355.41 176.33 918.77 134.99 326.84 103.49 877.68 490.35 148.33 214.94

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%

(%)

Sem II 07 Sem II 08 Jun-Sep 08 28.35 16.78 63.96 21.39 (0.22) 18.94 98.54 16.47 1.26 46.89 (42.30) (13.45) (69.99) (32.52) (17.94) (38.59) (74.31) (24.89) (58.42) (40.40) (21.99) (0.18) (51.34) (18.55) (4.25) (15.49) (46.33) (12.55) (26.75) (9.57)

to Rp407.46 trillion. The number of issuers increased by just 17 companies to 485 companies.
Figure 2.57 Stock Transaction Value of Domestic and Foreign Investors
Trillion Rp 160
Total Indonesia Foreign

140 120 100

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 2.56 Volatility of Asian Stock Indices (30 days)
% 120 100 80 60 40 20 0
Indonesia Thailand Singapore Japan Malaysia Hongkong

80 60 40 20 0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

2008

Figure 2.58 Capitalization and Issuance Value
Trillion Rp 3500 3000 2500 2000 1500 1000 500 0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Cap. Value (BEI) Cap. Value (BEJ) Cap. Value (BES) JCI (RHS) Issuance Value

450 400 350 300 250 200 150 100 50 0

2008

During the reporting period, the prices of most banks» shares slid significantly, however, approaching the end of
Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

semester a rebound looked imminent.

2007

2008

40

Chapter 2 The Financial Sector

Figure 2.59 Stock Price Performance of Several Banks
9,000.00 8,000.00 7,000.00 6,000.00 5,000.00 600.00 4,000.00 3,000.00 2,000.00 1,000.00 Dec Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Figure 2.61 Price Performance of Several FR Series Bonds
1,200.00 1,000.00 800.00
140 120 100 80 60 40 20

400.00 200.00

FR02 FR48 Jan Feb Mar Apr May Jun

FR49 FR47 Jul Aug

FR27 FR45 Sep Oct Nov Dec

2007 BCA (left) Danamon (left) CIMB Niaga (right)

2008 BRI (left) BNI (left) Mandiri (left) BII (right)

2008

Figure 2.62 Yield of 1 to 30 year SUN
% 20 18

Figure 2.60 P/E Ratio of Bank Stocks
% 90 80 70 60 50 40 30 20 10 0 Danamon BCA BRI Mandiri BNI BII CIMB Niaga
Jun 07 Jun 08 Dec 07 Dec 08

16 14 12 10 8 6 4
Dec Jan Feb 1 year 10 years Mar Apr May 3 years 15 years Jun Jul Aug 5 years 30 years Sep Oct Nov Dec

2007

2008

Rp511.0 trillion. From a tenor perspective, SUN market liquidity remained concentrated in short-term and mediumterm 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 2.63 Government Bonds: Market Liquidity of Various Tenors
Trillion Rp 45 40 35
FR VR ORI Zero Coupon SPN

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

30 25 20 15 10 5 0
2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2037 2038

41

Chapter 2 The Financial Sector

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 2.65 Net Asset Value of Mutual Funds
Trillion Rp 40 35 30

Figure 2.64 Issuance and Position of Corporate Bonds
(Issuance & Position Trl Rp) 160 140 120 100 80 60 40 20 0
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

(Issuer) Issuer 179 178 177 176 175 174 173 172 171 170

25 20 15 10 5 0 Dec 2007 Jan Feb Mar Equity Apr May Jun 2008 Mixed Jul Aug Sep Oct

Issuance

Position

Fixed Income

Money Market

Protected

2008

Figure 2.66 Mutual Funds: Redemptions-Subscriptions-NAV
14 120 Rdmp, trl Rp, left 12 10 8 60 6 4 40 20 0 Subscr, trl Rp, left NAV, trl Rp, right 100 80

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 fixedincome 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

2 0 Dec 2007 Jan Feb Mar Apr May Jun 2008 Jul Aug Sep Oct

Figure 2.67 Mutual Fund: NAV-Participating Units
120 100 80 60 40 20 0 NAV, trl Rp, left Participating Units, bil unit, left NAV/Unit, right 2000 1800 1600 1400 1200 1000 800 600 400 200 Dec 2007 Jan Feb Mar Apr May Jun 2008 Jul Aug Sep Oct 0

42

Chapter 2 The Financial Sector

Figure 2.68 Performance of Fund Collection of Mutual Funds
160 140 120 100 80 480 60 40 20 0 Dec 2007 Jan Feb Mar Apr May 2008 Jun Jul Aug Sep 460 440 420
Number of Participating Units, left Total Funds, Trl Rp, left Number of Mutual Funds, right

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.

560 540 520 500

43

Chapter 2 The Financial Sector

Box 2.1

Chronology of the 2008 Financial Sector Shocks and Policy Responses

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.
Table Box 2.1.1 Chronology of Shocks to the Indonesian Financial Sector in 2008
Date Event

8-10 October 2008 Indonesia Stock Exchange is temporarily closed. IHSG: 1,111.4, lowest level since December 2005. 28 October 2008 IDMA: 67.11, lowest level since first SUN issuance in 29 October 2008 January 2005. 20 November 2008 LPS takes over one bank which is said to have been systematically hit (Bank Century). 24 November 2008 The rupiah/USD exchange rate is at Rp12,650/USD, its lowest level since the 1997/1998 crisis.

Table Box 2.1.2 Policy Response
Date 16 September 2008 23 September 2008 13 October 2008 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. 15 October 2008 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). 24 October 2008 30 October 2008 13 November 2008 14 November 2008 18 November 2008 16 December 2008 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).

44

Chapter 2 The Financial Sector

Box 2.2

Bank Century’s Takeover, Bank Indover’s Closure and Financial System Stability

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.

45

Chapter 2 The Financial Sector

Box 2.3

Segmentation in the Interbank Money Market (PUAB)

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 2.3.1 Daily Average Transaction Volume of Rupiah PUAB from January to December 2008
Million Rp PLACING BANKS Bank Group 4 State Owned Banks Period I Period II Change Period I Period II Change Period I Period II Change Period I Period II Change Period I Period II Change Period I Period II Change Period I Period II Change 266,184 17,690 -93.4% 456,839 121,980 -73.3% 49,585 51,991 4.9% 9,382 4,963 -47.1% 10,229 2,500 -75.6% 873,565 225,304 -74.2% 1,665,783 424,429 -74.5% Major Banks Regional Joint Ventures Medium Small (non-state Dev»t Banks & Foreign Private Banks Private Banks owned) (BPD) Bank Offices 260,786 30,547 -88.3% 239,003 372,240 55.7% 62,317 100,921 61.9% 53,515 37,090 -30.7% 4,897 11,701 139.0% 695,964 614,915 -11.6% 1,316,482 1,167,415 -11.3% 99,627 4,762 -95.2% 119,152 173,638 45.7% 36,332 126,384 247.9% 63,656 45,772 -28.1% 2,377 2,778 16.9% 197,388 355,914 80.3% 518,532 709,247 36.8% 8,628 0 -100.0% 69,866 20,184 -71.1% 50,926 31,345 -38.5% 36,223 15,076 -58.4% 1,411 0 -100.0% 71,858 15,469 -78.5% 238,913 82,074 -65.6% 706,069 112,154 -84.1% 592,022 367,196 -38.0% 81,815 90,521 10.6% 7,424 1,594 -78.5% 252,279 318,728 26.3% 97,870 51,586 -47.3% 1,737,480 941,778 -45.8% 143,799 3,962 -97.2% 188,310 143,939 -23.6% 17,459 33,659 92.8% 22,954 12,730 -44.5% 0 0 917,118 1,090,923 19.0% 1,289,640 1,285,213 -0.3% Total

4 State Owned Banks Major Banks (non-state owned) Medium Private Banks Small Private Banks Regional Dev»t Banks (BPD) Joint Ventures & Foreign Bank Offices TOTAL

1,485,093 169,115 -88.6% 1,665,192 1,199,177 -28.0% 298,434 434,819 45.7% 193,155 117,226 -39.3% 271,193 335,707 23.8% 2,853,763 2,354,112 -17.5% 6,766,829 4,610,157 -31.9%

46

TAKING BANKS

Chapter 2 The Financial Sector

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 2.3.2 Daily Average Transaction Volume of Domestic Foreign Exchange PUAB
Thousand US$ PLACING BANKS Bank Group 4 State Owned Banks Period I Period II Change Period I Period II Change Period I Period II Change Period I Period II Change Period I Period II Change Period I Period II Change Period I Period II Change 8,623 4,455 -48.3% 10,481 2,209 -78.9% 2,504 1,170 -53.3% 0 0 32 0 -100.0% 45,668 2,585 -94.3% 67,307 10,419 -84.5% Major Banks Regional Joint Ventures Medium Small (non-state Dev»t Banks & Foreign Private Banks Private Banks owned) (BPD) Bank Offices 14,935 16,072 7.6% 9,057 7,193 -20.6% 2,837 1,568 -44.7% 3 0 -100.0% 14 700 5037.9% 60,364 41,127 -31.9% 87,209 66,660 -23.6% 5,980 4,481 -25.1% 6,014 4,530 -24.7% 670 648 -3.3% 78 0 -100.0% 0 19 24,368 13,445 -44.8% 37,110 23,122 -37.7% 759 894 17.6% 1,109 1,065 -4.0% 1,525 1,212 -20.5% 53 18 -66.9% 0 19 5,943 6,093 2.5% 9,390 9,300 -1.0% 1,337 174 -87.0% 52 50 -4.5% 24 8 -65.0% 0 0 0 0 144 0 -100.0% 1,558 232 -85.1% 2,873 4,418 53.8% 6,561 2,121 -67.7% 330 376 14.1% 45 25 -44.7% 94 0 -100.0% 81,611 71,763 -12.1% 91,513 78,703 -14.0% Total

4 State Owned Banks Major Banks (non-state owned) Medium Private Banks Small Private Banks Regional Dev»t Banks (BPD) Joint Ventures & Foreign Bank Offices TOTAL

34,508 30,494 -11.6% 33,274 17,168 -48.4% 7,889 4,982 -36.8% 179 43 -76.2% 139 737 429.7% 218,098 135,014 -38.1% 294,087 188,437 -35.9%

TAKING BANKS

47

Chapter 2 The Financial Sector

Box 2.4

Structured Products and Offshore Products: Their Impact to the Stability of the Financial System

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

48

Chapter 2 The Financial Sector

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.

49

Chapter 2 The Financial Sector

Box 2.5

The Impact of Foreign Debt to Financial System Stability
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.

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

Table Box 2.5.1 Private Foreign Debt Maturing in 2009
PRINCIPAL LOAN TYPE Loan Agreement Securities Trade Credits Other Loan Total INTEREST LOAN TYPE Loan Agreement Securities Total Grand Total QI-09 271.47 54.60 326.07 6,938.14 QII-09 555.55 66.11 621.67 3,728.61 QIII-09 238.14 49.06 287.21 2,520.06 QIV-09 699.22 62.66 761.88 4,190.46 Bank Non Bank Private Foreign Debt*
* Not including domestic securities held by foreign parties (USD 1,308 million).

QI-09 4,208.97 1,614.73 755.45 32.94 6,612.08

QII-09 2,191.40 750.35 154.43 10.76 3,106.94

QIII-09 1,919.57 223.03 87.03 3.23 2,232.86

QIV-09 3,190.13 93.36 87.55 57.55 3,428.58

Million USD 11,510.06 2,681.46 1,084.47 104.46 15,380.46

Million USD 1,764.38 232.44 1,996.82 17,377.28 3,140.50 14,236.70 17,377.20

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Chapter 3 Financial Infrastructure and Risk Mitigation

Chapter 3 Financial Infrastructure and Risk Mitigation

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Chapter 3 Financial Infrastructure and Risk Mitigation

Chapter 3 Financial Infrastructure and Risk Mitigation

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.

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
12.0

Figure 3.1 Performance of BI-RTGS Transactions
50,000 Volume (millions) 10.0 8.0 30,000 6.0 20,000 4.0 2.0 2004 2005 2006 2007 2008 10,000 Nominal Value (trillions) 40,000

BI-RTGS. Over the last two years up to the end of SemesterII 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 SKNBI declined by Rp105.35 trillion (14.31%) to Rp631 trillion. Furthermore, transaction volume also declined by some

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Chapter 3 Financial Infrastructure and Risk Mitigation

19.35 million transactions (47.96%) to 21 million transactions.
500.00

Figure 3.4 E-Money Transactions
20.00
Volume (thousands) Value (billions)

Figure 3.2 Performance of Bank Indonesia National Clearing System
Volume 6
Volume (millions)

400.00

15.00

300.00 10.00 200.00 5.00

Value (million Rp) 120

5 4 3 2 1 1 2

Value (trillions)

100 80 60 40 20 -

100.00 0.00

0.00
Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

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

3

4

5

6 7

8 9 10 11 12 1

2

3

4

5

6 7

8 9 10 11 12

2007

2008

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 emoney issuers.

Figure 3.3 Performance of Card Based Payment Instruments

5%

11%

95%
Based Card (ATM and ATM + Debit) Credit Card

89%
Based Card (ATM and ATM + Debit) Credit Card

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Chapter 3 Financial Infrastructure and Risk Mitigation

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.

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Chapter 3 Financial Infrastructure and Risk Mitigation

3.1.2. Business Continuity Plan (BCP) for the BIRTGS 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 BIRTGS 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

4 CHATS is the Clearing House Automated Transfer System, one of the RTGS systems in Hong Kong.

the financial system. This is a realization of the Indonesian

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Chapter 3 Financial Infrastructure and Risk Mitigation

Figure 3.5 Role of Credit Information Bureau

ECONOMY GROWTH REAL SECTOR GROWTH
FINANCIAL SECTOR
BANK NON BANK

CREDIT INFORMATION BUREAU
TO SMOOTHEN INTERMEDIATION FUNCTION TO MINIMIZE INFOR- GAPS BETWEEN INFORMATION INFORAND RISK MATION MATION TO SHORTEN THE DECISION MAKING PROCESS TO REDUCE COSTS

SOCIETY
INSTITUTION INDIVIDUAL

NON FINANCIAL SECTOR PUBLIC UTILITY COMPANY

POOL AND PROVISION FUNDS TRANSPARENCY MARKET DISCIPLINE GOVERNMENT / REGULATOR

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 nonbank 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 utilized 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

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Chapter 3 Financial Infrastructure and Risk Mitigation

Table 3.1 Debtor Identification Number (DIN) Data, 2006-2008
December 2006 Number of Reporters (Institution) Commercial Banks Rural Banks Finance Companies Number of Reporters (Branch Offices) Commercial Banks Rural Banks Finance Companies Number of Debtors (Based on DIN) Commercial Banks Rural Banks Finance Companies Number of Credit Facilities*) Commercial Banks Rural Banks Finance Companies Number of Debtor Information Demand**) Commercial Banks Rural Banks Finance Companies 486 130 355 1 3,374 2,548 825 1 20,359,850 19,535,979 822,849 1,022 21,689,062 20,863,200 824,839 1,023 782,626 751,769 30,857 0 December 2007 751 130 618 3 3,788 2,788 2,633 3 28,187,986 26,312,078 1,780,534 95,374 29,479,139 27,640,264 1,697,186 141,689 1,178,957 1,147,096 30,192 1,669 December 2008 777 127 646 4 4,054 2,790 1,260 4 35,900,857 33,070,536 2,521,748 308,573 57,782,495 53,573,464 3,813,657 395,374 2,050,957 1,833,158 206,255 10,915

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.

coverage of reporters and users, improving regulations, developing the products and services, and educating the public.
Figure 3.6 Credit Bureau Strategic Policy

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

RULES & REGULATION SYSTEM & APPLICATION CREDIT BUREAU

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

3.2.2. Refining the System and Application
Improving the SID»s system and its application is a

DATA QUALITY

PRODUCTS & SERVICES

SOCIETY EDUCATION

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

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

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.

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Chapter 3 Financial Infrastructure and Risk Mitigation

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.

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 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 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 nonbank 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

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Chapter 3 Financial Infrastructure and Risk Mitigation

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.

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

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

stability, without impinging upon the independence of each individual authority; JPSK helps identify and resolve problems at financial institutions with systemic impact; and

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Chapter 3 Financial Infrastructure and Risk Mitigation

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:

Table 3.2 Financial Safety Net Framework
Objectives/ Coverage Crisis Prevention 1. Bank Liquidity KSSK is responsible for: a. Evaluating the problem b. Identifying the problem c. Deciding on problem solving steps 1. Liquidity bail out Emergency Funding Facility from BI which is guaranteed by government 2.a. Temporary placement by LPS 2.b. Bank closure and insurance payment by LPS 3. Providing lending facility or equity placement by government Government finances crisis prevention and resolution from government income and expenditure budget through government securities issuance or cash BI is permitted to buy government securities in the primary market Decision Making Process Decision Tools/ Mechanism Source of Funds

2. Bank Solvability

2.a. Temporary Placement for Systemic Banks 2. b. Problem solving for Non-systemic banks 3. Providing lending facility or Placements for Non Bank Financial Institution

3. Bank Liquidity and/ or Solvability

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Chapter 3 Financial Infrastructure and Risk Mitigation

Table 3.2 Financial Safety Net Framework (cont.)
Objectives/ Coverage Crisis Resolution 1. Bank Liquidity and/ or Solvability KSSK is responsible for: a. Evaluating the problem b. Identifying the problem c. Deciding on problem solving steps 1.a. Liquidity bail out 1.b. Temporary placement 1.a. Emergency Funding Facility from BI 1.b. Temporary placement by LPS or government or certain institutions 2. Lending facility/ Temporary placement by LPS or government or certain institutions Decision Making Process Decision Tools/ Mechanism Source of Funds The use of government income and expenditure budget have to be approved by parliament

2. Non Bank Liquidity

2. Liquidity bail out/ temporary placement

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Chapter 3 Financial Infrastructure and Risk Mitigation

Box 3.1

The Financial System Stability and PERPPU on the Amendments to the Law on Bank Indonesia

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.

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Chapter 3 Financial Infrastructure and Risk Mitigation

Boks 3.2

Best Practices of Systemic Impact Analysis towards the Financial System

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.

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Chapter 4 Prospects of the Financial System in Indonesia

Chapter 4 Prospects of the Financial System in Indonesia

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Chapter 4 Prospects of the Financial System in Indonesia

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Chapter 4 Prospects of the Financial System in Indonesia

Chapter 4 Prospects of the Financial System in Indonesia

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.

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.

Table 4.1 Projection of Several Economic Indicators
2008 Q1 Q2 Q3 Q4 GDP (% yoy) Inflation (% yoy) Balance of Trade (Billions of USD)
* Asia Pacific Concensus Forecast

2009* Q1 Q2 Q3 Q4

6.3 6.4 6.4

5.2 4.5 4.3 4.4 4.7

7.6 11.0 12.0 11.1 10.2 8.0 5.8 5.8 7.5 5.3 5.8 8* 6.7 7.1 6.8 7.7

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

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Chapter 4 Prospects of the Financial System in Indonesia

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.
Table 4.2 Risk Perception of Indonesia
Yield Spread (bp) Bonds Rating Y-t-m (%) September 2008 997.47 932.46 918.30 Desember 2008 1015.41 965.17 925.92

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

Indo 49 Indo 48 Indo 45
Source: Bloomberg

Ba3 (Moody's) Ba3 (Moody's) Ba3 (Moody's)

11.70 11.86 11.95

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

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

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Chapter 4 Prospects of the Financial System in Indonesia

Figure 4.1 Bank Risk Profile and Outlook

Market Risk

Liquidity Risk

Credit Risk

Inherent Risk

Moderate

High

Sem II-2008 Outlook

Sem II-2008 Outlook

Sem II-2008 Outlook

Exchange Rate

Government Bond Price Interest Rate

Low

Weak

Acceptable

Strong

Weak

Acceptable Risk Control System (RCS)

Strong

Weak

Acceptable

Strong

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

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Chapter 4 Prospects of the Financial System in Indonesia

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.

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

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

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

1 Head of the Financial System Stability Bureau, Directorate of Banking Research and Regulation, Bank Indonesia; email wimboh@bi.go.id 2 Researcher, Gadjah Mada University, email: bagussantoso@ugm.ac.id 3 Senior Researcher, Financial System Stability Bureau, Directorate of Banking Research and Regulation, Bank Indonesia, email: rulina@bi.go.id 4 Junior Researcher, Financial System Stability Bureau, Directorate of Banking Research and Regulation, Bank Indonesia, email: elis_derianto@bi.go.id

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.

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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:

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 timevarying standard deviation from the univariate model with the i th 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 nonnegative 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).

RESEARCH METHODOLOGY
This research employs several methodologies to test the presence of contagion in the Indonesian capital market: 1. Multivariate GARCH/Dynamic Conditional

The DCC model is then estimated using the log likelihood function as follows: » lt (Σ,f ) = - 1 Σ (nlog(2p) + log l Dt l2 + e»tDt2et) + 2 t-1 -1 » - 1 Σ (log l Rt l + u»tRt ut - u»tut) 2 t-1 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

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,

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

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

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. Pre-Crisis. First period, known as Pre-Crisis This period is from 2 January 1995 to 15 July 1997. 2. I. Second period, known as Crisis I This period is from 16 July 1997 to 29 December 2000. 3. Crisis. Third period, known as Post Crisis This period is from 1 January 2001 to 14 August 2007. 4. II. Fourth period, known as Crisis 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 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 II (15 August 2007 √ 13 November 2008) is based on the global crisis. During that period, the Dow Jones and Nasdaq composite index plummeted.

St-1 = i ) = pij
With first order Markov-switching, the transition probability can be written as follows:

P=

P11 P12 P21 P22

Where p11+p12=p21+p22=1 and

P11 - Pr [St - 1lSt -1- 1] P12 - Pr [St - 2lSt -1- 1] P21 - Pr [St - 1lSt -1- 2] P22 - Pr [St - 2lSt -1- 2]
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;

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

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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.
Figure A1.1 Dynamic Conditional Correlation (DCC) of Indonesia with several countries in Southeast Asia
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 Idn-sin Idn-thai Idn-mly Idn-phl

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 A1.3 Dynamic Conditional Correlations (DCC) of Indonesia with Developed Countries
0.7 0.6 0.5 0.4 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
Figure A1.2 Dynamic Conditional Correlations (DCC) of Indonesia with several countries in Asia (excluding Southeast Asia)
0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 -0.1 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

0.3 0.2 0.1 0 -0.1 -0.2 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

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).

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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, s2St,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)

Table A1.1 Contagion Detection using Dynamic Conditional Correlation (DCC) √ Daily Composite Share Return
P1 IDN - CHN IDN - HK IDN - JPN IDN - KOR IDN - TWN IDN - PHIL IDN - SIN IDN - MLY IDN - THAI IDN - AUS IDN - UK IDN - GER IDN - INA IDN - DOW IDN - NASDAQ
Notes: P1 : P2 : P3 : P4 : *** : ** : *:

P2 0.01949 0.36340 0.21825 0.16979 0.16031 0.32417 0.40541 0.26867 0.33702 0.27669 0.15270 0.18358 0.13356 0.03421 0.00406

P3 0.06040 0.35565 0.27484 0.29427 0.26543 0.25437 0.37972 0.27986 0.30109 0.30222 0.16984 0.15692 0.27282 0.05611 0.04681

P4 0.21785 0.62349 0.44552 0.47997 0.44685 0.37696 0.54769 0.52233 0.44945 0.55227 0.31572 0.26901 0.47507 0.12253 0.11701

Z-Stat P2 0.18315 0.59024 -0.29805 -0.40707 -0.47405 0.77732 0.44598 1.45414 -0.18549 0.24430 0.49313 0.04146 0.84920 0.75542 1.09522

Z-Stat P3 -0.69170 0.85513 -1.65232 ** -3.33905 *** -2.94255 *** 2.53743 1.16358 1.36211 0.66773 -0.33648 0.16669 0.64712 -2.23446 ** 0.36499 0.28916

Z-Stat P4 -2.77582 -4.60957 -3.92898 -5.36774 -4.94937 -0.29511 -2.33810 -3.31258 -2.05835 -4.68655 -2.13033 -1.26861 -4.88472 -0.72521 -0.82877 *** *** *** *** *** *** *** ** *** ** ***

0.02885 0.38930 0.20369 0.14951 0.13661 0.35927 0.42429 0.33618 0.32858 0.28818 0.17723 0.18563 0.17594 0.07272 0.05999

1st Period Correlation (January 1995 - July 1997) 2nd Period Correlation (August 1997 - December 2000) 3rd Period Correlation (1 January 2001 - 14 August 2007) 4th Period Correlation (15 August - 13 November 2008) Significant for α = 1% Significant for a = 5% Significant for a = 10%

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Table A1.2 Markov Switching Mean Equation a0
IDN - CHN IDN - HK IDN - JPN IDN - KOR IDN - TWN IDN - PHIL IDN - SIN IDN - MLY IDN - THAI IDN - AUS IDN - UK IDN - GER IDN - INA IDN - DOW IDN - NASDAQ regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 regime 1 regime 2 0.004 *** -0.006 *** 0.003 *** -0.001 0.003 *** -0.002 0.003 *** -0.004 ** 0.003 *** -0.004 ** 0.004 *** -0.005 *** 0.003 *** -0.001 0.003 *** -0.002 * 0.003 *** -0.004 ** 0.003 *** -0.004 ** 0.003 *** -0.002 0.003 *** -0.002 0.003 *** -0.004 ** 0.003 *** -0.004 ** 0.003 *** -0.004 **

a1
0.010 0.714 *** 0.053 ** 0.159 *** 0.002 0.658 *** -0.004 0.484 *** 0.006 0.538 *** -0.010 0.442 *** -0.002 0.243 *** -0.015 0.297 *** 0.009 0.357 *** 0.009 0.357 *** 0.014 0.522 *** 0.014 0.522 *** 0.004 0.387 *** 0.000 0.796 *** 0.000 0.800 ***

a2
-0.142 *** -0.696 *** -0.068 *** -0.518 *** -0.132 *** -0.622 *** -0.120 *** -0.614 *** -0.128 *** -0.640 *** -0.116 *** -0.562 *** -0.040 ** -0.415 *** -0.104 *** -0.500 *** -0.127 *** -0.474 *** -0.127 *** -0.474 *** -0.135 *** -0.608 *** -0.135 *** -0.608 *** -0.122 *** -0.582 *** -0.142 *** -0.650 *** -0.141 *** -0.651 ***

a3
-0.014 *** 0.035 *** -0.009 *** 0.005 -0.010 *** -0.002 0.014 *** 0.026 *** -0.014 *** 0.029 *** -0.013 *** 0.021 *** -0.012 *** 0.008 -0.014 *** 0.015 ** -0.011 *** 0.008 -0.011 *** 0.008 -0.016 *** 0.045 *** -0.016 *** 0.045 *** -0.015 *** 0.029 *** -0.012 *** 0.007 -0.012 *** 0.007

a4
-0.019 0.017 -0.027 ** 0.035 0.015 0.036 -0.015 0.016 -0.013 -0.001 -0.020 0.033 -0.015 0.007 -0.018 0.020 -0.019 0.065 -0.019 0.065 -0.004 -0.080 ** -0.004 -0.080 ** -0.015 0.008 -0.020 0.042 -0.020 0.043 * * *

a5
0.005 0.054 0.001 0.055 ** 0.003 0.046 -0.001 0.050 0.006 0.046 0.003 0.021 -0.006 0.045 *** -0.008 0.051 ** -0.002 0.047 -0.002 0.047 0.015 -0.018 0.015 -0.018 0.001 0.053 ** 0.006 0.042 0.006 0.043 * * * *

a6
0.011 0.152 *** 0.174 *** 0.616 *** 0.137 *** 0.393 *** 0.090 *** 0.334 *** 0.059 *** 0.451 *** 0.119 *** 0.549 *** 0.194 *** 0.686 *** 0.112 *** 0.672 *** 0.085 *** 0.603 *** 0.085 *** 0.603 *** 0.050 *** 0.754 *** 0.050 *** 0.754 *** 0.056 *** 0.437 *** 0.042 ** 0.051 0.034 *** 0.020

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 NonCrisis 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).

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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.
Table A1.3 Results of Contagion (1) Detection DCC/Multivariate GARCH P2
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) √*** √*** √*** √*** √*** √*** √*** √*** √*** √*** √*** √***

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
P3 P4
√*** √*** √***

Markov-Regime Switching
√*** √*** √*** √*** √*** √*** √*** √*** √*** √*** √*** √*** √***

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

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.

for the developed countries (Japan, Australia, Germany, United Kingdom and US).

Table A1.4 Conclusions of Test on Contagion Detection
Daily Data √ √ √ √ ^^^ ^^^ ^^^ ^^^ Monthly Data √ √ √ √ ^ ^ ^ ^ Daily Data P2 P3 √ √ ^ √ P4 ^ ^ √ ^ Monthly Data P2 P3*

Country IDN-MLY IDN-SIN IDN-THA IDN-PHI

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

Table A1.4 Conclusions of Test on Contagion Detection (cont.)
Daily Data √ √ √ √ √ √ √ √ √ √ √ ^^^ ^^^ ^^^ ^ ^^^ ^^^ ^ ^ ^ ^ ^ Monthly Data √ √ √ ^ ^ ^^^ √ √ √ √ √ ^ ^ ^ ^ ^ Daily Data P2 √ √ √ √ √ √ P3 ^^^ ^^^ ^^^ ^^^ ^^^ ^ √ √ √ √ √ P4 ^ ^^^ ^^^ ^^^ ^^^ √ Monthly Data P2 ^^^ √ √ √ √ √ √ P3* ^^^ ^^^ ^^^ ^^^ ^^^ ^^

Country IDN-JPN IDN-TWN IDN-HK IDN-CHN IDN-KOR IDN-INA IDN-AUS IDN-GER IDN-UK IDN-US(DJ) IDN-US-(NQ)
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 shock Sign √ indicates that there is a contagion effect between two countries Level of significance is 5% and 1%

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

References

Agenor, Aizenman, dan Hoffmaister. 2008. ≈External Shocks, Bank Lending Spreads, External Shocks,Bank Lending Spreads, and Output Fluctuations∆, Review

Collins dan Gavron, 2004. ≈Channels of Financial Market Contagion∆, Applied Economics, 36:21, 2461- 2469. Collins dan Gavron. 2005. ≈Measuring Equity Market Contagion in Multiple Financial Events∆, Applied

of International Economics,16:1, 1-20.
Arestis, et al. 2005. ≈Testing for Financial Contagion between Developed and Emerging Markets during the 1997 East Asian Crisis∆, International Journal of

Financial Economics, 15:8, 531-538.
Dornbusch, Park, dan Claessens. 2000. ≈Contagion: How It Spreads and How It Can be Stopped∆, Forthcoming

Finance and Economics, 10, 359-367.
Caporale, Cipollini, dan Spagnolo. 2005. ≈Testing for Contagion: a Conditional Correlation Analysis∆,

World Bank Research Observer.
Duggar dan Mitra. 2007. ≈External Linkages and Contagion Risk in Irish Bank∆, IMF Working Paper. Engle, G. 2000. ≈Dynamic Conditional Correlation √ A Simple Class of Multivariate GARCH Models∆, UCSD

Journal of Empirical Finance, 12, 476-489.
Caramazza, Ricci, dan Salgado. 2004. ≈International Financial Contagion in Currency Crisis∆, Journal of

Economics Discussion Paper, 2000-9.
Essaadi, Jouini, dan Khallouli. 2007. ≈The Asian Crisis Contagion: A Dynamic Correlation Approach Analysis∆, Documents De Travail-Working Papers, 0725. Forbes dan Rigobon. 2000. ≈Contagion in Latin America: Definition, Measurement and Policy Implication∆,

International Money and Finance, 23, 51-70.
Cartapanis, Dropsy, dan Mametz. 2002. ≈The Asian Currency Crises: Vulnerability, Contagion, or Unsustainability∆, Review∆of International Economics, 10(1), 79-91. Castiglionesi. 2007. ≈Financial Contagion and the Role of the Central Bank∆, Journal of Banking and Finance, 31, 81-101. Chiang, Bang Nam Jeon, dan Huimin Li. 2007. ≈Dynamic Correlation Analysis Of Financial Contagion: Evidence From Asian Markets∆, Journal of International Money

NBER Working Paper Series, 7885.
Hatemi-J dan Hacker. 2005. ternative Method to Test for Contagion with an Application to the Asian Financial Crisis∆, Applied Financial Economics Letters, 1:6, 343347. Horta, Mendes, dan Vieira. 2008. Contagion Effects of the U.S Subprime Crisis on Developed Countries∆,

and Finance, 26, 1206-1228.
Chu-Sheng Tai. 2004. ≈Contagion: Evidence from International Banking Industry∆, Journal of

CEFAGE-UE Working Paper, 08.
Luo dan Tang. 2007. ≈Capital Openness and Financial Crises: A Financial Contagion Model with Multiple Equilibria∆, Journal of Economic Policy Reform, 10:4, 283-296.

Multinational Financial Management, 14, 353-368.
Cifuentes, Ferrucci, dan Shin. 2005. ≈Liquidity Risk and Contagion∆, Journal of the European Economic

Association, 3(2-3), 556-566.

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Marais dan Bates. 2006. ≈An Empirical Study to Identify Shift Contagion during the Asian Crisis∆, International

Suliman. 2005. ≈Interest Rate Volatility, Exchange Rates, and External Contagion∆, Applied Financial

Financial Markets Institutions and Money, 16, 468479. Marongiu. 2005. ≈Towards a New Set of Leading Indicators of Currency Crisis for Developing Countries: an Application to Argentina∆. Rodriguez. 2007. ≈Measuring Financial Contagion: A Copula Approach∆, Journal of Empirical Finance,14, 401-423. Sojli. 2007. ≈Contagion in Emerging Markets: the Russian Crisis∆, Applied Financial Economics, 17:3, 197-213. Sriananthakumar dan Silvapulle. 2008. ≈Multivariate Conditional Heteroscedasticity Models with Dynamic Correlations for Testing Contagion∆, Applied Financial

Economics, 15:12, 883-894.
Van Horen, Jager, dan Klaassen. 2006. ≈Foreign Exchange Market Contagion in the Asian Crisis: A RegressionBased Approach∆. Walti. 2003. ≈Contagion and Interdependence among Central European Economies: the Impact of≈Common External Shocks∆, HEI Working Paper, 02. Yang dan Lim. 2004. ≈Crisis, Contagion, and East Asian Stock Markets∆, Review of Pacific Basin Financial

Markets and Policies, 7:1, 119-151.
Yoon. 2005. ≈Correlation Coefficients, Heteroskedasticity and Contagion of Financial Crises∆, The Manchester

Economics, 18:4, 267-273.

School, 73:1, 92-100.

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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. 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

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

decrease and thus can become a source of financial system instability.

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

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.

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

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

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

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

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 tradeoff 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

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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.

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 A2.1 Conceptual Framework of Corporate Balance Sheet Model

DEBT EQUITY CASH

Use to finance

INVESTMENT ACQUISITION

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

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

financing needs and speed of adjustment of debt levels, as describe below: Dit = αGit + βDit* + (1-β) Di,t-1 (1)

where Dit is the corporate debt at time t; Gi = 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:

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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 + α1Di,t-1 + α2Iit + α3Ii,t-1 + α4Ait + α5Ai,t-1 + α6Cit +

(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 + α1Di,t-1 + α2Iit + α3Ait + α4Cit + α5Mit + η1 + εit (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:

α7Ci,t-1 + α8Mit + α9Mi,t-1 + η1 + εit

(2)

Iit = α + α1Di,t-1 + α3Ii,t-1 + α4Ait + α6Cit + α7Ci,t-1 + α8Qit +

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

α9Qi,t-1 + η1 + εit

(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

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

stock returns will deliver negative signals and therefore will increase the corporate debt level. The model is presented as follows: Dit = α + α1Di,t-1 + α2Iit + α3Ii,t-1 + α4Ait + α5Ai,t-1 + α6Cit +

α7Ci,t-1 + α8Mit + α9Mi,t-1 + α10Ri,t,t-1 + η1 + εit

(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; Cit 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.

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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 = b0 + b1MVt-1 + b2EBITt-1 + b3DPt-1 + b4RDt-1 + b5 ln(At-1) + b6FAt-1 + b7MIt + b8Mt-1 + et+1 (6)

industries, agriculture, trading and miscellaneous industries from 2004 to 2007. Data sources include Bloomberg and the Indonesian Stock Exchange.
Table A2.1 Descriptive Statistics of Indonesian Corporation, 2004-2007
All Variable Total Asset (Trillion IDR) Current Asset (Trillion IDR) Fixed Asset (Trillion IDR) Tangible assets (Trillion IDR) Intangible Asset (Trillion IDR) Total Debt (Trillion IDR) Net Sales (Trillion IDR) Net income (Trillion IDR) Depretiation (Trillion IDR) Amortization (Trillion IDR) Capital expenditure (Trillion IDR) EBIT Cash per total asset Depreciation Expense per tangible asset R & D Expense per total asset Size (Log of Total Asset) Fixed asset per total asset Debt per total asset Investment (Capex per total asset) acquisition (Acquisition per total asset) Mean 6,133.65 872.02 1,118.58 6,024.73 90.11 25,471.62 1,935.43 246.87 644.08 46.55 167.08 327.52 0.33 0.27 0.05 27.14 0.38 0.78 0.04 0.03 Standard Deviation 55,821.59 1,891.74 4,157.16 55,745.80 558.83 437,384.39 5,641.06 1,812.31 3,079.11 473.89 992.40 1,658.52 0.34 0.24 1.11 1.85 0.24 1.04 0.10 0.08

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.

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 firmspecific effects of the corporations in the sample as they come from various industrial sectors. Following Fama & French (2002) and Hovakimian et.

4. ANALYSIS
This study uses unbalanced panel data of 218 Indonesian listed corporations covering eight sectors, i.e. consumption, infrastructure, mining, property, basic

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,

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

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.
Table A2.2 Determinants of Corporate Debt
DEBT EQUATION (GMM Sys) Dependent Variable :DEBT Variable Coefficient t-Statistic 13.0137 -1.2976 0.4581 0.6196 0.6204 2.0067 1.3022 0.5342 -41.0524 -3.0392 -4.1306 Prob. 0.0000 0.1971 0.6478 0.5368 0.5362 0.0472 0.1956 0.5943 0.0000 0.0030 0.0001

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).

DEBT(-1) 0.5740 ACTA -0.1456 ACTA(-1) 0.0692 CASH 0.0574 CASH(-1) 0.0359 INVTA 0.1816 INVTA(-1) 0.1186 OD 0.0054 OD(-1) -0.5757 RETURN -0.0341 RETURN(-1) -0.0158 Cross-section fixed (first differences) R-squared 0.983631 P-value (Chi square) 0.00000 SSE 0.07645 N (Firms) 201
Notes DEBT ACTA CASH INVTA RETURN : Total debt per total assets : Total Acquisition per total assets : Total Cash per total assets : Total Investment per total assets : Return of share per year

5. CONCLUSIONS
This study aims to model the Indonesian corporate

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

balance sheet and investigate the determinants of the optimum level of corporate debt, by combining the wellknown 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.

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

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.

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

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92

Financial Stability Review
No. 12, March 2009
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 Banking Supervision 2 Directorate of Banking Supervision 3 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


				
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Description: 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. ___________________________________________________ !!!!!!!!!!!!!!!!!!!!!!!!!!! Advertising is the life of trade !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! This is just the reality of business do not misapprehend !!!!!!!!!!!!!!!!!!!!!!!!!!!!!! Advertising is the life of trade !!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!!! pro-aktif.blogspot.com _________________________________________________