FORMULATION OF CONVENTIONAL AND ISLAMIC FINANCIAL STABILITY INDICES UNDER DUAL FINANCIAL SYSTEM IN INDONESIA A s c a r y a and Diana Yumanita Center for Central Banking Education and Studies, Bank Indonesia Jl. M.H. Thamrin 2, Sjafruddin Prawiranegara Tower, 20th fl., Jakarta 10350, Indonesia Email:
[email protected]; Phone: +6221.381.7345; Fax: +6221.350.1912
ABSTRACT Indonesia has been implementing dual financial system since 1998 and it is moving towards full‐fledged dual financial system. Meanwhile, more frequent financial crisis with steeper and wider impact has made financial stability system of a country becomes more important, including that of dual financial system in Indonesia. It can be measured in terms of Financial Stability Index (FSI) that covers performance and volatility. Objective of this research is to formulate conventional and Islamic FSI under dual financial system that covers banking, money market, share market, and bond/sukuk market. Moreover, composite FSI is the addition of Performance Index (IK) and Volatility Index (IV). Methodologies used in this research include: 1) Kalman Filter, to measure the volatility of each FSI indicator; 2) Bordo, et al. (2001) Approach to calculate composite FSI; 3) Equal Variance, Cumulative Distribution Function and Factor Analysis methods to assign weight; and 4) VECM and Correlation methods to choose the best FSI . The results show that the best FSI model is using Factor Analysis weighing with addition of IK and IV. Conventional FSI has positive trend which is lower than that of Islamic FSI. The average and variance of conventional FSI are lower than those of Islamic FSI, but the differences are not significant, statistically. The only different between conventional FSI and Islamic FSI is on Volatility Index (VI). This is considered to be normal since total market share of Islamic finance is still around 1.7 percent, which is very small compared to that of conventional market share at 98.3 %. JEL Classification: E5, G10, G20, G28 Keywords: Financial System Stability, Islamic Financial System, Dual Financial System, Financial Stability Index
Paper presented in 10th IRSA Conference 2010 “Reintegrating Indonesian Economy in the Global Era,” UNAIR, Surabaya, July 28‐29, 2010.
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1. Introducttion
1.1 Baackground d In the p past few yeaars, international finan ncial system m has been w widely expaanded follow wing the develop pment of fin nancial institutions and d financial iinstrument innovation ns that creatted new risks. In ndonesia ass part of in nternationaal community is also involved in the develo opment. Islamic financial system s hass started to grow to ogether witth the conventional financial f he implementation of d dual financiial system in n 1998. Rap pid develop pment of institution since th Islamic banking fo ollowed byy other finaancial instittutions succh as Islam mic money market, Islamic capital maarket, Islamic bond or sukuk, Islaamic mutuaal funds, Isllamic multiifinance, or takaful, aas well as Isslamic micro o finance. Financial sysstem in Indo onesia is Islamic insurance o growingg towards fu full‐fledged dual financcial system, as illustrateed in Picturee 1.3. Financiaal system in i Indonesiia is mainlyy dominate ed by bankking, stock market, an nd bond market (see Picturre 1.1.), thus dynamicc movemen nt of the Isllamic financial system m can be best deescribed by these threee markets. U Until the en nd of 2008, the bankingg sector reccorded a stable growth g at 15.0‐17.0 1 percent per year, and seems s to be b unaffecteed by the on‐going o global ffinancial crisis at the time. On con ntrary, the b banking assset increaseed since Sep ptember 2008. C Capital markket also grew rapidly to o a record h high in whicch compositte stock pricce index reached d 2.745, witth capitalizaation value of Rp.1.988 8 trillion by December 2007. Howe ever the compossite index then fell and d reached tthe trough point at 1,241, wheree capitalizattion was valued at Rp.993 ttrillion in November 2 2008. The sh harp decreaase was staarted in Sep ptember 2008, to ogether witth sharp inccrease in baanking asse et. Meanwh hile, bond m market and banking seems rrelatively sttable. 5.3% 3.9%
3.6%
4.1%
2500 0
Pasarr Modal
Obligasi
2000 0
46.8% %
13.1% %
Perbankaan
1500 0 1000 0
23.2%
500 0
Pasarr Modal
Obligassi
Asurransi
PP
Lain2
0
SBI/S
2007.1 2 3 4 5 6 7 8 9 10 11 12 2008 1 2008.1 2 3 4 5 6 7 8 9 10 11 12 2009.1
Perb bankan
Figure 1.1 1 Mapping o of Financiall System in Indonesia Islamic financial syystem in Ind donesia wass firstly starrted in 1980 0 in the form of Baitutt Tamwil and coo operative. TThe first Islaamic bank, Bank Muam malat Indon nesia, was eestablished in 1992, followeed by the first Islamic insurance ((takaful) co ompany in 1 1994 (see P Picture 1.2).. Islamic financiaal industry officially staarted in 19 999 with the e issuance of Islamic B Banking Actt No. 10 Year 1998 1 and Act No.23 3 Year 199 99 concern ning Bank Indonesia,, marked by the establisshment of the second Islamic com mmercial baank and launch of seveeral Islamic banking units. Developmen D nt of Islamic banking industry is expected to t grow evven faster with w the issuance of Act No o. 19/2008 concerningg Islamic Go overnment Bond and Islamic Banking Act No. 21//2008, as well w as the issuance of o Government/Sovereeign Islamic Bond (Sukkuk) and Sovereign Retail Islamic Bond (Retail Sukkuk). 2
1980
1994
2000
2002
2004
2007
2009
▪ Baitut Tamwil Salman Bdg, Koperasi Ridho Gusti Jkt. ▪ 1st Islamic Bank Bank Muamalat Ind
Islamic banking
▪ 1st Islamic Branch Bank IFI Syh ▪ 2nd Islamic Bank Bank Syh Mandiri
▪ 1st Takaful Company Asuransi Takaful Keluarga
Takaful
▪ 3200 BMT
Money Market
▪ Office Channeling Islamic Banking Act
▪ 1st Takaful Branch ▪ 1st Islamic Re-insurance Assi Great Eastern ReINDO ▪ Islamic Money Market IMA Certificate (Mudharabah)
Capital Market
▪ Islamic Capital Market Jakarta Islamic Index ▪ ICM Master Plan (2005)
Sukuk
▪ 1st Corporate Sukuk Indosat (Mudharabah)
Monetary Management
▪ SWBI (Wadi’ah)
▪ Central Bank Act
1992
1999
▪ 1st Govt Sukuk (Ijarah) 1st Govt Retail Sukuk (Ijarah) ▪ Sukuk Guidelines ▪ Govt. Islamic Bond Act
2001
2003
2006
▪ SBIS (Ju’alah)
2008
Figure 1.2 Development of Islamic Financial System in Indonesia From the above stages of development, clearly, Indonesia is moving towards a full‐fledged dual financial system, although the market size is still relatively small. In general, main components of Indonesian financial system are as follows: 1) Banking, with market share of 47.0 percent (Islamic banking market share is currently 2.0 percent or Rp49.5 trillion); 2) Stock market, with market share of 23.0 percent (Islamic capital market share is 0.1 percent or Rp0.6 trillion); and 3) Bond market, with market share of 13.0 percent (Islamic bond market or Islamic corporation bond is 3.0 percent or Rp5.4 trillion). Other Islamic financial sector including that of retail Islamic bond is Rp5.6 trillion, Islamic bond is Rp4.7 trillion, Bank Indonesia Islamic certificate is Rp2.8 trillion, Baitul Mal wa Tamwil is Rp2.5 trillion, Islamic Mutual Funds Rp.1.8 trillion, Islamic insurance is Rp1.7 trillion, Islamic Rural Bank is Rp1.7 trillion, and Islamic pawnshop (rahn) Rp1.6 trillion. As a whole, total of Islamic financial markets share in Indonesia is about 1.7 percent or valued at Rp79.0 trillion, which is dominated by Islamic banking portion (Rp49.5 trillion or 63.0 percent) and Islamic bond both corporation and government (Rp15.7 trillion or 20.0 percent), see Picture 1.3.
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Syariah
Konvensional
Financial System
Surplus Sector
Financial Market
Deficit Sector 5,3%
Money Market
Direct Financial Market
Indirect Financial Market
Insurance
46,8% Commercial Bank 97,9% Unit 2,1% Trusts 3,6% 13,1% 23,2% Finance Merchant Bank 97,0% 99,9% Bond Market 3,0% Equity Market 0,1% Company Capital Market
Ket:
Pasar
Syariah
Konvensional
Figure 1.3 Dual Financial System in Indonesia The success of a financial system can be seen from its performance in allocating economic resources at optimum level, and can be distinguished from the role of financial system stability in facing any financial or macroeconomic shocks. From its financial side, the shocks can be from its performance in allocating non‐performing loan‐NPL/non‐performing financing‐NPF. On the other hand, macroeconomic shocks are largely due to fluctuating exchange rates. The current global financial crisis phenomenon has raised number of questions and resulted in number of analysis about the financial system stability. The financial instability is due to issues such as bank failure that worsen the NPL, problem of liquidity after huge public withdrawals and high volatility of asset prices together with its uncertainty. Currently, Islamic economists believe that Islamic financial system is more stable compared to the conventional system that uses interest rate mechanism. This is based on the argument that profit‐sharing scheme used in the Islamic financial system appears to be shock absorber on various shocks. Ascarya et al. (2008a) suggests that empirically profit‐ sharing system stabilizes money demand more than that of interest rate system. Likewise, Ascarya et al. (2008b) also concludes that descriptive and deductive method of profit sharing is more efficient than interest rate in optimizing the output, fair distribution of social welfare, and improve justice. This research finding is in line with previous studies that are presented in Siddiqui (2008), which in general concludes the profit sharing system is inherently stable, thus it can be adopted as monetary instrument. Further, Ascarya (2009) also alludes that Islamic monetary system contributes smaller impact to inflation compared to that of conventional monetary system. Therefore, financial sector will not experience huge losses in the event of negative shock. Beside, Islamic financial system will not encounter negative spread unlike the conventional financial system. In the deeper analysis, some other arguments may be accommodated.
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On the other hand, empirical study that test Islamic financial system either in Indonesia or other countries is rare. The opinion that Islamic financial system is more stable is not based on comprehensive empirical study but only from conclusion of the preceding events. For instance, during financial crisis in 1997 – 1998, Bank Mualamat Indonesia managed to pass the crisis very well, when some conventional banks were liquidated and the rest experienced financial difficulty due to negative spread. However, the size of monetary crisis impact on Islamic financial system is still unknown. Based on that statement, further research is necessary to explain theoretically and empirically the importance of Islamic financial system in stabilizing financial system as a whole. As Islamic financial system serves as part of Indonesian financial system, which is at the moment dominated by conventional financial system, the Islamic financial system shall be compared with its conventional counterpart. Thus, the research will explore the impact of the economic shocks such as economic growth, inflation, exchange rate and interest rate towards the stability of the both systems. Hence, it will ascertain which financial system is more stable and how to optimize the dual financial system in Indonesia. 1.2 Problem Statements From various problems faced by financial system in Indonesia currently, the research is expected to respond to the following questions, as follows: 1. What are the indicators used as a reference to measure Islamic financial system and conventional system? 2. How financial stability index of both systems are formulated? 3. Empirically, Is Islamic financial system more stable than conventional financial system? 1.3 Research Objectives To answer the above problems, the objectives of the research are as follows: 1. To determine various indicators that can be used in the formulation of conventional and Islamic financial stability index (FSI). 2. To formulate conventional and Islamic FSI based on performance and volatility of the indicators. 3. To compare the stability of conventional FSI and Islamic FSI. 1.4 Data and Methodology Data used in this research refers to the data from period of January 2004 to March 2009. Sources of data are Indonesian Banking Statistics, Islamic Banking Statistics, Indonesia Capital Market, the Capital Market Supervisory Agency (Bapepam‐LK), and Census and Economic Information Center (CEIC). Monthly real GDP is derived from 3‐months GDP that is interpolated using the Cubic Spline method.
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Methodologies applied are: 1) Kalman Filter, to measure financial stability indicators on both Islamic and conventional systems; and 2) Bordo, et al. (2001) Approach, to formulate Financial Stability Index (FSI).
2. Literature Review
2.1 Theoretical Review 2.1.1 Islamic Financial System In general, financial system has a purpose as intermediary between surplus spending unit‐ SSU deficit spending unit‐DSU, which become partners in productive activities and improves welfare of the people. Financial system covers the following: 1) policies and legal aspects; 2) financial institution; 3) money market; 4) financial support; and 5) financial agent. Good financial system will lead to more efficient allocation of resources, reduce cost, and curb the risk. It also ensures smooth payment system, fairness of revenue distribution, and price stability. Eventually, condition of financial system will accelerate economic growth followed with improvement of welfare. There is no major difference from elements in conventional and Islamic financial system. The difference is in norms and values. Development of conventional economics and finance leads to activities that embrace more behavioral, environmental, ecological, and ethical values, which are embedded in Islamic economics and finance. In sum, principles of Islamic economic and financial system are as follows (Zabswari, 1984, data has been rearranged);
Allah is the owner of all Sovereignty (al‐Malikal Mulk) and He is the Satisfier of all needs as a place for people to rely on for everything (ash‐Shamad). Allah is the Absolute Ruler (al‐Maalik) (QS Ali Imran [3]:26, QS Ibrahim [14]:2, QS al‐Mulk [67]:1).
Man is created as khalifah/leader (vicegerent of Allah) on earth, but not the real owner (QS Al‐Baqarah [2]:30, QS Faathir [35]:39), with duties to obey Allah until all creatures feel the bounty and fairness of Allah.
Worldly things that human obtains are due to Allah, hence unfortunate fellow human beings have a right over his wealth in the form of zakat.
Decent life, not luxurious, and not mubadzir.
Wealth is not subject for own sake.
Wealth is to be circulated.
Economic exploitation in any form is prohibited, such as riba and maysir (speculative activities).
Eradication of economic differences that are attached to individual, by dividing his wealth among his heirs, will eliminate conflict of classes.
Ensure responsibility as obligatory or voluntary to individuals who are willing to help the poor in the society through zakat, infaq, shadaqah, and waqaf (ZISWaf).
From the above analysis, it is clear that the main pillars of Islamic economy and finance covers: 1) Fairness; 2) maslahah (advantages); 3) zakat obligation; 4) prohibition of interest; 6
5) prohibition of speculative activities (maysir); 6) prohibition of doubtful activities (gharar) and 7) division of risk (risk sharing, not risk transfer). Fairness covers rights and obligation, partnership relationship, (no exploiting and exploited party), put things in the right place, and uphold the truth. Injustice is happened when SSU and DSU interact at the same level of partnership in which one party has higher bargaining position. For example, it occurs when interest rate is determined at the beginning of the contract, while result of the cooperation between SSU and DSU (profit or loss) is only known at the end of the cooperation. Benefits include orientation on public needs (public needs above individual needs), orientation that fulfills basic need of the people (priority to fulfill needs and not wants), concern on environment, and investment in permissible business. Thus, fulfillment of macro objectives (such as balance of economy, accelaration of growth, and development of public amenities/infrastructure, are given priority to micro objectives (such as individual interest). Zakat obligation serves as social safety net, in which it motivates people to invest their assets and bridges relationship between the rich (muzakki or zakat payee) and the poor (mustahik or receiver of zakat), which consists of 8 groups. Zakat obligation is also meant to mobilize wealth, and not accumulating it in individual’s own pocket, rather to use it for investment activities. Riba (usury) is prohibited because it creates uncertainty and could cause one party to be exploited. Riba prohibition is also an attempt to maximize investment by offering investment alternatives that negate uncertainty. Rosly (2005) quotes Ibnu Arabi who states that economic transaction without ‘Iwad equals to riba. ‘Iwad can be translated as equivalent counter value in the form of work (Ghurmi), make effort (Kasb), and responsibility (Daman). Thus, riba is not only in usury term, but can exist in other economic activities such as money multiplier, credit card, fractional reserve banking system (added according to reference), and derivative products. Maysir or speculative activity is prohibited because it is a kind of zero‐sum game that does not add any economic value. For instance, gambling and stock trading activities with the purpose of earning capital gain in a short time are considered as maysir. The purpose of its is to minimize speculative activities that are not productive nor related to real sector development, instead it tries to motivate people to invest in real sectors with orientation of long‐term gain. Gharar is also rejected to avoid asymmetric Information. Each and every transaction has to be transparent to avoid agency problem, moral hazard, and adverse selection. Hence, prohibition of gharar will anticipate injustice and exploitation. Risk sharing has a purpose to remind partners who are involved in the business that risk embeds in the business; therefore any business requires good planning and management. The risk is not to be transferred to other party but to be shared equally. From the above financial pillars, Mirakhor and Krichene (1988) define Islamic financial system as a system that treats asset as non risk‐free asset and all transactions must be based on profit and loss sharing. Financial asset serves as contingent claims and not debt instrument with fixed or floating interest rate. Therefore, financial sector in Islam is actually a sector that deals with productive activities in real sectors. In other words, Islamic economic style is real sector activities supported by monetary sector. Instruments that 7
meet characteristics of real sector are profit sharing (mudharabah, musyarakah), trading (murabahah, istisna, salam), rent (ijarah), pawn (rahn), etc. The characteristics of Islamic financial activity and their implication to the economy are summarized on table 2.1. Basically, this implication mapping is a guidance for shariah compliance, both in real economic sector and financial application (operational or product engineering), which avoids possibility of instability to occur in financial/economic system as experienced by conventional sector. Table 2.1 Characteristics and Implication of Islamic Financial Activities Basic Characters
Intermediate Implication
Forbidden Financial sectors: • Riba (usury) • Maysir (speculative) • • Gharar (uncertainty)
• No/reduce money multiplier • No piling up or concentration of money • No segregation between financial and real sectors Permissible financial activity: • Profit Sharing • Fully support real sector • Trading • Mutual trust and understanding • Safety keeping and Services in business and partnership • Social (ZISWaf) • Motivates the poor to involve in economy Object of permissible and acceptable transactions shall be free from: • Eradicate social problems • Khamr (drugs) • Curb environmental degradation • Pork • Pornography • Environmental pollution
Implication (Macro) • Financial System Stability
• Create job opportunity • Economic growth • Poverty eradication
• Social stability • Environmental conservation
Another important elements in Islamic financial application are good governance and shariah compliance. Good governance acts as a guidance to manage and achieve micro targets of Islamic financial application that is especially conducted by Islamic financial institution. Meanwhile, shariah compliance acts as a guidance for regulation, supervisory and management in achieving macro targets of the economy.
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Direct Finance
Surplus Sector
Deficit Sector
PLS Sukuk Other Sukuk Stock
Islamic Investment Banks
Islamic Funds
Indirect Finance
Fund Units
Islamic Insurance Policies
Surplus Sector
Islamic Commercial Banks
Deficit Sector
PLS Investment Others
PLS Financing Others
Figure 2.1 Blueprint of Islamic Financial System There are similarities and differences between conventional and Islamic financial system as described by Obaidullah (2005) that portrays Islamic financial system blueprint as shown in Figure 2.1.1 From global overview, the differences between conventional and Islamic financial system are as described in Table 2.2. Table 2.2 Comparison between Conventional and Islamic Financial System Financial Markets Financial Institutions
Basis of Transaction
Conventional System Banking, Stock Market, Bond Market, Money Market Banking, Insurance, Non‐Banking Financial Institution, Finance Company Interest‐based
Management
Based on prudential ethics, market based
Production Factor
Capital has big role therefore deserves bigger portion Creditor vs. Debtor Standard of auditing, accounting, product design and universal players Guaranteed by government and institution Saving and Borrowing
Relationship Between Partners Common Standards
Savings Intermediary Money/credit creation Asset‐Liability Management Money Multiplier
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Create money/credit Mismatch and not liquid Subject to reserve ratio; Very high; Not limited with securitization
Islamic System Islamic Banking, Stock Market, Bond/Sukuk Market, Money Market Islamic Banking, Insurance, Non‐ Banking Financial Institution, Finance Company Non‐interest based, but equity or trading based Based on prudential ethics, market based, shariah and values based such as zakat obligation, prohibition of riba, maysir, and gharar Human Capital has important role Partnership No universal standards including auditing, accounting, permissible product recognition Treated as stock, and no guarantee on the value retention Stock and real asset owner from investment project Not to create money/credit No mismatch and liquid Subject to savings ratio; very low
Detailed explaination can be obtained from Obaidullah (2005), page 15‐19.
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Speculation Return
Social Impact Economic Growth Systemic Risk Monetary Policy
High speculation; Gambling; Trading of debt Level of interest rate is not related with real economy; high price distortion Social gap; inflation of tax; redistribution problem Very cyclical; booms and busts; uncertain; unexpected growth Mass bankruptcy; contagion; bailouts Interest rate policy; no stability of policy
No speculation; No trading of debt Level of profit is determined by real economy; no price distortion Social justice; no inflation of tax; no redistribution Stable economic growth; can be predicted No systemic bankruptcy; no bailouts No interest rate policy; use monetary aggregate; very stable policy
Source: Compiled from various sources.
2.1.2 Financial System Stability International Monetary Fund or IMF (2005) defines stable financial as stable system that can avoid the fall in financial system and disturbance in intermediary system. European Central Bank or ECB (200x) states that financial system is said to be stable if the system can encounter shocks occurred in the market or financial institution without creating significant negative impact. Meanwhile, Schinasi (2004) defines financial system as a condition when a financial system can facilitate and support economic process, manage risk and encounter shocks. Minsky (1986, 1992) states that financial instability embeds (endogenous) in the conventional financial system. Meanwhile, public consensus from international economists (Keynes, 1943; Triffin, 1959; Mundell, 2005; Ruef, 1964; etc.) state that financial instability that spreads over is due to unsustainable fiscal and monetary policies and beg‐thy‐neighbor trading policy. According to Mirakhor and Krichene (2009), conventional financial system with interest basis and has speculative possibility is inherently unstable. Stable financial system has two main advantages such as smooth economic activities and effective monetary policies. According to IMF, there are four elements of surveillance that can rescue financial system stability; they are financial markets surveillance, macro‐ prudential surveillance, macro‐financial linkages, and macroeconomic conditions surveillance that can be seem on picture 4.2.2 Financial markets surveillance is to measure risk due to shocks and combination of shocks that will interrupt financial sector. Models adopted for this surveillance such as Early Warning System (EWS), as implemented by Kaminsky and Reinhart (1999) and Berg and Patillo (1999). Indicators used include money market data, macroeconomics data and other variables. Macro‐prudential surveillance has a purpose to anticipate soundness and vulnerability of financial system towards any potential shock. The main analysis is Financial Soundness Indicators (FSIs) and stress test. The tools are used to translate non‐financial sector condition into financial sector vulnerability. Other analysis is qualitative in nature, such as to estimate quality of surveillance and resistance of financial infrastructure.
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Detailed explanation can be read on IMF (2005).
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Analysis of macro‐financial linkages has the main purpose to understand exposure that may lead to shocks due to macroeconomic conditions transmitted through financial system. This analysis is conducted by utilizing data, such as balance sheets of various economic sectors, and accessing indicators to source of financing by private sectors. Macroeconomic condition surveillance has a purpose to monitor impact of financial system towards macroeconomic condition as a whole, specifically towards the ability of liability settlement. Types of Surveillance Surveillance of Current Financial Market Conditions to Assess the Risk of Shocks
Types of Indicators Macroeconomic & Asset Price Shocks Conditions of Non-financial Sectors Corporate Real Estate Household Credit Linkages
Macroprudential Surveillance Framework
Financial Sector Vulnerabilities Credit Risk Market Risk Liquidity Risk
Surveillance of Macroeconomic Conditions
FSIs Monitoring Leverage Return on Equity Forex Exposure Real Estate Price Structural Information FSIs Monitoring Asset Quality Forex & Interest Rate Exposure (Access to) Liquidity Market Liquidity Information on Supervision, financial structure, market functioning, the safety net, and monetary operations
Accounting Linkages
Analysis of Macrofinancial Linkages
Financial Market Data Early Warning Indicators
Capital Adequacy (Capacity of the Financial Sector to Absorb Losses)
Capital Ratio FSIs Return on Equity FSIs
Examples of Macrofinancial Linkages Access to financing by private sector for investment Wealth effect from bank deposits at risk in a crisis Role of banking system in monetary policy transmission Effect on debt sustainability of banking sector holdings of government debt Government securities held by the financial sector
Interest Rates, Credit Spreads Credit to Private Sector (including BIS data) Sector Balance Sheet Data Monetary Data Other Macroeconomic Data Structure of Private and Government Debt
Impact on Macroeconomic Conditions Debt Sustainability
Cost of Capital Productivity and Wage Growth Real Exchange Rate Foreign Growth Macroeconomic Policies
Figure 2.2 Analytical Working Framework for Financial Stability System From classical ulama point of view, Islamic financial system stability exists when distribution of saving to investment works smoothly, or no idle fund that is not utilized. According to Mirakhor and Krichene (2009), Islamic financial system with the basis of zakat, production, interest‐free, and speculative‐free trading are inherently strong from instability. Furthermore, Mirakhor and Kirchene (2009) define stability of Islamic financial system as system that occurs when there is no more risk free assets and all financial infrastructure is based on profit sharing mechanism. All financial assets become contingent claim and
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interest based borrowing instrument will no longer exist. All liabilities are backed up by real asset that is owned directly by institutions. In details, stable financial system features from Mirakhor’s view are shown on table 2.3. Table 2.3 Features of Stable Islamic Financial System Component Financial Asset Model Credit Paradigm Liabilities Central Bank
Banking
Growth of Financial Activity
Risk
Features Contingent claims, no debt instrument with fixed or floating interest rate Non speculative equity shares, rate of return reflects return from real financial system is always at positive return position No credit chain creation Investment = saving AS = AD Say’s law Secured with tangible asset and directly owned Monopoly of money multipliers Interest rate cannot be used as reference or policy instrument No financing to the bank through re‐discount Activities of saving and payment that is secured with 100 percent reserve requirement Fee based system Investment activities are from long‐term deposits, and bank directly shares the risk, Short term deposits = short term financing Long term investment = long term deposits Return = portion of invested fund Typical operation : Mudharabah, Musharakah, Murabahah, Istisnaa, Ijarah (leasing), and installment of sales operations New cash flow = new savings Stable and real economic growth, non speculation and from money multipliers Money multiplier is 1,25 (currently conventional finance 10) Money multipliers is determined by real activities Slow inflation Resistant to shocks Individual and non systemic Credit risk, market risk, displaced commercial risk, operational risk and governance risk
Source : Mirakhor and Kirchene (2009)
Khan (1987) asserts that Islamic financial system will quickly make adjustment in the event of shock compared to conventional financial system. This can happen because Islamic financial system is based on equity, interest free, and no guarantee on saving, so any event of shocks will be quickly absorbed by changes in stock values (deposits/investment) itself. Thus, value of asset and liabilities from a bank will remain the same at all points of time.
2.2 Previous Studies From time to time, financial crisis occurs more frequently with higher intensity. After the fall of Bretton Woods agreement in 1971, there have been more than 96 financial crisis and 176 monetary crisis taken place (Caprio and Klingebiel, 1996). From latest database on financial crisis that occurred between 1970‐2007 , which can be seen from Laeven and Valencia (2008), it publishes 395 episodes of financial crises (banking crisis, currency crisis, and government debt crisis), including 42 twin crises and 10 triple crises. According to Lietaer et
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al. (2008), crisis strike several countries not due to cyclical or managerial failures, but due to structural failure, regardless of regulatory system and stages of development of the countries. Further, Lietaer et al. states that solution is taken only to mitigate symptoms but not to get to the bottom of the systemic cause. This means understanding the instability of financial system has becomes even more important. 60 50 40 30 20 10 0 1970
1974
1978
1982
1986
1990
1994
1998
2002
2006
Figure 2.1 Frequency of World Financial Crisis There are a number of studies that record instability of financial system. Lai (2002) identifies three main matters that are summarized from several literatures on instability of financial system, such as instability, source of instability, trigger of crisis, and design of financial system infrastructure that is adopted to avoid and mitigate impact of financial crisis. De Bandt, et al. (2007) states that another tool to measure financial system stability is stress testing. One of the methods is Financial Sector Assessment Program Macro Stress Testing. The purpose of the stress testing is to discover impact of shock, contagion risk, and feedback from financial and real sector. There are three frameworks (working frameworks) that can be adopted to analyze financial stability, namely: contingent‐claims‐analysis (CCA) framework, semi‐structural framework (SSF), and structural financial stability model (SFSM). Further, de Bandt et al. states that factors contributed the most to financial system instability are sudden changes in interest rate and certain less liquid markets. From the working framework of CCA, payoff from financial asset depends on other asset values. The CCA working framework is very beneficial to monitor and quantify default risk. Nevertheless, there are some weaknesses of this working framework, such as it does not explicitly model behavior of economic players and impact of policy instrument. In relation with credit risk, the CCA working framework is similar with Merton model. This working framework is then further developed by Gray et al. (2008), to accommodate balance sheet that has been adjusted according to the risk at national level. In the working framework, financial sector is depicted as linkage between asset, liabilities, and security. Souissi (2007) adopts this framework to analyze Canadian mortgage portfolio. He calculates probability of default for different value of borrowings and combine it with mortgage distribution based on loan to value (LTV) with the purpose to estimate overall rate of default. Allenspach and Monnin (2007) adopt CCA working framework to identify impact of global economic integration towards the sensitivity of bank due to shocks occurred between period of 1993‐2006 and its impact on systemic risk at global banking. Lehar (2005) analyzes 13
linkage between ratio of bank’s asset to debt and evolution of systemic risk index. He concludes that: i) there is no significant trend in the size of systemic risk; and ii) common exposures that are not measured from inter‐bank correlation cannot be relied to determine systemic risk. Semi‐structural working framework integrates several potential contagion channels among the financial institutions. First channel is through direct inter‐relation with balance sheet of the financial institutions. For instance, distress in one bank will influence distress in other bank due to mutual exposures. Second channel is from impact of auction sales of financial institutions assets that on the marked‐to‐market values in their balance sheet and other institutions that hold similar assets. Semi‐structural working framework accommodates effect of contagion. However, limitation of this working framework is that it is not based on the specification of clear micro foundation and perhaps it does not quantify response impact between financial and real sectors. One of the purposes of this working framework is to integrate several potential contagion channels that exist between financial institutions. Allessandri in de Bandt, et al. (2007) integrates the above two channels in one quantitative framework to measure systemic risk. The framework quantifies impact of macro credit risk, interest income risk and market risk towards banks’ balance sheet. SSF working framework is employed by Cifuentes, et al. (2005) to identify banks’ failures in fulfillment of capital requirement through sales of assets. In the sales of assets, banks prefer to process liquid asset than non‐liquid assets. If the banks’ assets are not liquid, their prices would decline inconsistently. This would complicate the banks in fulfilling their capital requirement and diminishing the banks’ assets. Weaknesses of CCA and SSF working framework are solved by structural financial stability model (SFSM). This working framework is employed by Goodhart et al. (2004) and Aspachs et al. (2006). In his study, Goodhart et al. (2004) applies stability model that bases on micro foundation, such as modeling endogenous default and heterogeneous agents, besides accommodating policy instrument. This model analyses the creation of contagion that occurs in the financial crisis. The finding of his study, among others, shows the trade‐off between efficiency and financial stability. This model accommodates fluctuation of real business cycle without estimating parameter. The stressing main factor is a strong micro economic foundation, especially in the forward looking context in order to optimize the resources. Meanwhile, Laidler (2006) stresses on the failure of coordination and role of money in the business cycle and macroeconomic condition. In this matter, he includes role of central bank as lender of the last resort into the model, by employing non trivial monetary/financial sector as proxy. Aspachs et al. (2006) measures overall banking stability using two factors, such as probability to default and profitability. Banking system stability is closely related with credit risk, which is many cases can be rated with probability to default (correlation with profitability or value of equity). In this case, impact of financial stability towards GPD quantified by adopting vector auto regression (VAR) approach. Variables used in the model are probability to default, real GDP growth, banking equity index growth, and level of inflation. Besides, this study also analyses other variables such as price of property and short term interest rate. The finding of this study shows that default risk and the decline of banking level of profitability give negative impact to growth of GDP. In this case, the
14
feedback effect is also estimated, that is the impact of shocks that occurs on the variables towards the financial stability. Bardsen, et al. (2006) supports the SFSM working framework. He asserts that the model to estimate financial stability must possess several structural characteristics, such as contagion, default, missing financial market, role of money and bank, heterogeneous agent, macroeconomic condition, and structural micro‐foundation. This model must be traceable randomly and empirically and can be used for forecasting and policy analysis, and test financial system condition. One important step in financial stability study is to formulate financial stability index (FSI). Van den End (2006) formulates Financial Condition Stability Index (FCSI) based on variables that are derived from market information and balance sheet from financial institution and other economic players. This index then is also applied in the case of Netherlands (van den End and Tabbae, 2005) and in the cases of six OECD countries (van den End, 2006). On the other hand, Illing and Liu (2003) sets up an index to measure financial stress in Canada. This index is formulated by using two main indicators to raise uncertainty and changes in expectation of losses, both in the market or financial institution. The methods adopted to formulate this index are factor analysis, econometric benchmarking and generalized autoregressive conditional heteroscedasticity (GARCH) modeling. For the case of Indonesia, study on financial soundness index (FSI) is, among others, conducted by Hadad, et al. (2007). In this study, FSI is established based on four blocks of economic activities such as stock market block, bond market block, banking block, real sector block, that are estimated using simultaneous equation. Irawan (2006) employs FSI to identify impact of monetary policy towards price stability and financial stability. In this case, FSI is formulated by combining NPL variables and Rupiah exchange rate. In addition, estimation is conducted to assess impact of monetary policy towards price stability and financial stability by employing VAR method. Variables used are real GDP, inflation, SBI rate, volume of banking credit, and financial stability index. In details, previous studies that are related to formulation of financial system stability index are summarized in the following table 2.4. Table 2.4 Summary of Previous Studies No. 1
2
3
Writer
Description
Method
Lai (2002)
“Modeling Financial Instability: A Survey of the Literature” Survey literature Bardsen, et al. “Evaluation of (2006) Macroeconomic Models for Financial Stability Analysis” This research is to identify structural characteristics that must be possessed by a model that is used in estimating financial stability. de Bandt, et al. “Developing a Framework to (2007) Assess Financial Stability: Conference Highlights and
15
Indicators/Variables
Descriptive
‐‐
Descriptive
‐‐
Descriptive
‐‐
4
5
Lessons” FSAP (Financial Sector Assessment Program) Macro Stress Testing Goodhart, et al. ”A Model to Analyze Financial (2004) Fragility: Applications” Framework: Theoretical study with Structural simulation Aspachs, et al. “Searching for a Metric for (2006) Financial Stability” Framework: This research is to measure Structural banking system stability with simulation of shock To try setting up single measurement for financial stability. The empirical result is not robust hence it returns to double measurement.
6
Bordo, et al. (2001) Framework:
7
Illing dan Liu (2003) Framework:
8
van den End (2006) Framework: CCA
Performance indicators: ‐ aggregate banking profitability ‐ aggregate default: bank and household (non pecuniary) General Performance indicators: equilibrium Model, ‐ banking profitability adopting model in ‐ banking default rate Goodhart, et al. (probability to default) (2004) It is not Performance indicators: mentioned, the ‐ banking profitability (% change conclusion is of equity value of banking derived from sector) descriptive ‐ banking default rate statistics. (probability to default of banking sector) To analyze impact of financial VAR ‐ probability to default fragility towards economic ‐ banking equity (profitability) welfare ‐ GDP ‐ inflation Performance indicators: “Aggregate Price Shocks and Indexing in the ‐ household and firm Financial Stability: The United way of : Kingdom 1796 – 1999” t 0.5 x t x t x bankruptcy rate (bankruptcy per capita) This research is to establish ‐ excess return on housing financial conditions index (housing price – market yield of (FCI) with 5 categories: 1) consoles) financial distress; 2) moderate distress; 3) normal; 4) financial expansion; and 5) financial euphoria. To analyze impact of Dynamic Probit ‐ financial condition index aggregate price shock model ‐ price level towards the index ‐ GDP ‐ M0 ‐ terms of trade ”An Index of Financial Stress Generalized Performance and volatility for Canada” Autoregressive indicators: This research is to establish Conditional ‐ banking sector model that measures financial Heteroscedastic ‐ foreign exchange market stress index (FSI). Extreme (GARCH) model ‐ equity market values of FSI referred as ‐ debt market financial crisis. Can adopt performance/volatility indicator only, or combination of the two, based on its performance in measuring the crisis. “Indicator and Boundaries of Reduced form Performance indicator: Financial Stability” aggregate demand ‐ real interest rate This research is formulate function ‐ real effective exchange rate Financial Condition Stability and VAR (to ‐ house price Index (FCSI) for Netherlands determine value of ‐ stock price ‐ solvency buffer (capital ratio each indicators)
16
General Equilibrium Model
9
Hadad, et al. (2007) Framework:
”Macroeconomic Model to Measure the Financial Stability Index: Indonesian Case Study” This research is measure Financial Soundness Indicator (FSI)
Two‐Stage Least Square
index from bank and funding ratio of insurance company or pension fund) Volatility Indicator: ‐ volatility of the stock price index Performance indicators: ‐ NPL ‐ IHSG (composite index) ‐ Bond yield spread
3. Methodology
3.1 Conceptual Framework of Discussion This discussion follows the scheme of conceptual framework as illustrated in Figure 2.1. Specifically, the steps conducted in this research are: first, formulate FSI in dual financial system starting from variable identification, calculation of volatility and formulation of Islamic and conventional FSI. Second, conducting shock on conventional and Islamic FSI using macroeconomic variables that influence both (common shocks) or specifically influence these indexes. Third, formulate policy implications from the first and second steps to produce strategies in order to optimize the stability of dual financial system in Indonesia both in the short term and long term. Identification of Conventional FSI Variables
1
PM, PO, PU, Bank ‐ Konvensional
Formulation of Conventional FSI
Identification of Islamic FSI Variables PM, PO, PU, Bank ‐ Islam
2
Formulation of Islamic FSI
Dual Financial System FSI
3
Note: PM = Stock Market; PO = Bond Market; PU = Money Market; NPL = Non Performing Loan; NPF = Non Performing Financing.
Figure 3.1 Conceptual Framework of Discussion
3.2 Conceptual Framework of Empirical Study
17
As noted earlier, since the inception of Law No. 10 year 1998, financial system in Indonesia has been operated under dual financial system namely Islamic and conventional system. The distinction of characteristics between the two systems can be seen in their philosophical differences, schemes or instruments used and regulations abide by each system. From theories that have been discussed earlier, financial instability is basically characterized by two aspects, i.e. deterioration in performance of financial sector that is attributed by worsening of some indexes and high uncertainty of markets shown on high volatility in indexes. Thus, in conducting empirical test on stability system of Islamic and conventional finance, both dimensions are to be considered. Indicators that are used for this research are: a. NPF/NPL and ROE of Islamic and conventional banks. This indicator used as proxy for stability of banking sector3. The higher the NPF/NPL and the lower the ROE indicates less stable financial sector. Volatility of both indicators can also be referred as instability of financial system. b. Stock price indices, namely Jakarta Islamic Index, Composite Stock Price Index and bond index (Islamic bond or sukuk and conventional bond). These indicators are used as proxy for capital market stability. The weak and fluctuating stock price and bond price index indicates deteriorating capital market stability in the country. Specifically, indicators for financial system stability are shown on the following table: Table 3.1 Indicators of Financial System Instability Indicator
Performance
Volatility
Banking NPF and NPL ↑ ↑ SROE and KROE ↓ ↑ Capital Market JII dan CSPI ↓ ↑ Bond Market Sukuk Price and Bond index ↓ ↑ PUAS and PUAB ↑ ↑ Note: The arrow direction indicates changes in indicators that cause financial system instability
To ascertain the impact of aggregate indicators towards stability of a dual financial system, it is necessary to establish an index such as Financial Stability Index (FSI) for each system. Thus, it is important to establish scientific procedures to formulate composite index.
3.3 Data This research generates monthly data from January 2004 to March 2009. Sources of data are Islamic Banking Statistics, Indonesian Banking Statistics, Indonesia Stock Index, The Capital Market Supervisory Agency (Bapepam‐LK) and CEIC. Monthly GDP is obtained by interpolating the three‐month GDP data using cubic spline method. The indicators of financial system stability of banking sector, money market, stock market, bond market and data of macroeconomic variables can be seen as follows: 3
This research only uses banking data as the share of LKBB considering indirect financing in Indonesia is still insignificant
18
80
Conventional ROE
%
Islamic ROE 10
%
Conventional NPL
Islamic NPF
8
60
6 40 4 20
2
0
0 Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Figure 3.1 Banking Indicator Data In banking sector, ROE (return on equity) of Islamic banks tends to increase while ROE of conventional banks remains relatively the same. During March 2009, ROE of conventional banks reached 19.1% while ROE for Islamic banks was 62.5% or three times higher than ROE of conventional banks. Meanwhile, NPL (non performing loan) of conventional banks tend to decrease and in contrast, NPF (non performing financing) of Islamic banks tend to increase. In March 2009 NPL of conventional banks was 3.9% while NPF of Islamic banks was 5.1% (see figure 3.1). Conventional PUAB 25
Islamic PUAS
BOND Yield
20
%
20
SUKUK Yield
%
15
15 10 10 5
5
0
0
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Figure 3.2 Data Indicator of Money Market and Bonds Market In money market, conventional inter‐bank market interest rate (PUAB) fluctuates aggressively during the period of 2005‐2008, and in general it tends to decrease slightly. As for the revenue sharing among Islamic inter‐bank money market (PUAS), it did not really fluctuate but tends to increase to some extent. Starting from mid 2008, PUAB interest rate and revenue sharing of PUAS tend to move towards convergence. In bond market, during the period of 2004‐2009, sukuk yield tends to increase compared to that of conventional yield. Starting from middle of 2008, Islamic and conventional yield in both markets tend to move towards the convergence (see graph 3.3). Conventional IHSG
Islamic JII
600
0.2 %
2500
500
0.1
2000
400
0
3000 %
1500
300
1000
200
500
100
0
0 Jul‐04 Jan‐05
Jul‐05 Jan‐06
Jul‐06 Jan‐07
Jul‐07
Jan‐08
Jul‐08 Jan‐09
Conventional RetIHSG
Islamic RetJII
0.2 0.1
0 Jul‐04
Jan‐05
Jul‐05
Jan‐06
Jul‐06
Jan‐07
Jul‐07
Jan‐08
Jul‐08
Jan‐09
‐0.1
‐0.1
‐0.2
‐0.2
‐0.3
‐0.3
‐0.4
‐0.4
Figure 3.3 Data of Stock Market Indicator
19
In stock market, the movement of Islamic stock price index JII (Jakarta Islamic Index) is similar to movement of conventional Composite Stock Price Index (IHSG) and applies the same on both returns.
3.4 Variables and Operational Definitions Variables used in this research comprise of banking indicator, money market, stock market, bond market and macroeconomic variables such as growth, inflation, exchange rate and interest rate/revenue sharing. a. Banking Indicators NPF (Non Performing Financing): default payment ratio (collectability categories of 3 – 5) out of total financing in Islamic banks. The data used is monthly data obtained from Islamic Banking Statistics, Bank Indonesia. NPL (Non Performing Loans) is default credit ratio (collectability categories 3‐5) out of total credit of conventional banks. The monthly data is derived from Indonesian Banking Statistics, Bank Indonesia. SRO (Islamic Return on Equity) is profit ratio before tax within last 12 months out of average total capital at Islamic banks. The monthly data is derived from Islamic Banking Statistics, Bank Indonesia. KROE (Conventional Return on Equity) is profit ratio before tax within the last 12‐months out of average total capital of conventional banks. The monthly data is derived from Indonesian Banking Statistics, Bank Indonesia. b. Money Market Indicators PUAS is weighted average of profit sharing rate for Mudharabah Interbank Investment Certificate (SIMA) in interbank money market according to Islamic principles. The monthly data is derived from monthly ending position of daily PUAS and taken from Directorate of Monetary Management, Bank Indonesia. PUAB is weighted average of interest rate for conventional interbank money market. The monthly data is derived from monthly ending position of daily PUAB and taken from Directorate of Monetary Management Bank Indonesia. c. Stock Market Indicators Jakarta Islamic Index is stock price index that fits the criteria of JII. The monthly data is derived from monthly ending position and taken from the Capital Market Supervisory Agency (Bapepam‐LK). RETJII (JII Return/change of price index velocity) is calculated using equation . Composite Stock Price Index (CSPI/IHSG). The monthly data is derived from monthly ending position of daily data taken from Bapepem‐LK. RETIHSG (return CSPI) is calculated using . equation d. Bond Market Indicators
20
SUKUKYIELD is average return from traded corporate sukuk. The data derived from Indonesian Stock Exchange (previously known as Surabaya Stock Exchange). The monthly data is taken from geometric mean of existing data transaction of the respective month. BONDYIELD is a yield from government bond, in which the serial number is mentioned. The monthly data is derived from monthly ending position of daily government yield bond taken from Indonesian Stock Exchange (previously known as Surabaya Stock Exchange).
3.5 Research Methodology As previously discussed, this study is an empirical study consists of three phases and aims to achieve the following objectives: a. To measure the volatility of FSI indicators in conventional and Islamic financial systems by employing Kalman Filter method; b. To construct an accurate composite index of dual financial system stability which reflect stability of conventional and Islamic financial systems, such as Financial Stability Index (FSI). To construct FSI, this study uses Bordo, et al. (2001) approach; and c. In the process of formulating composite FSI, each market of financial system should be weigh appropriately to reflect their real contribution to financial system stability/instability. Several weighing methods will be applied, namely equal variance, cumulative distribution function and factor analysis. Moreover, composite FSI could be a multiplication or addition of performance index (IK) and volatility index (IV). d. Several alternative FSIs will be formulated using different weighing methods as well as different composition methods. To analyze and determine the best FSI among several alternatives, VECM and correlation methods will be used to choose the best FSI. Meanwhile, indicators of financial system stability which will be covered in this research include the following sectors: a. Banking, with NPF/NPL and SROE/KROE indicators; b. Money market, with PUAS/PUAB indicators; c. Stock market, with JII/CSPI indicators; and d. Bond market, with SUKUKYIELD/BONDYIELD indicators. 3.5.1 Kalman Filter Financial data has a tendency to have huge variability. For certain period, volatility can be large while for other period, volatility can be very small. Data with no constant error variance condition creates the problem of heteroscedasticity. Gujarati (2004) reveals that the problem of heteroscedasticity is a problem of cross‐section data at the first stage and not time series data. For example, income and consumption level of household. For household with high income, variability consumption will tend to be bigger than household with low income. Therefore, in regressing cross‐section data, the value of error will tend to be bigger in line with higher income level of the household, or in other words, the assumption does not fit the criteria of homocedasticity. In general, this condition does not occur in time series data, aggregate consumption level and where fixed aggregate income has constant variance error. In the next process, the problem of heteroscedasticity appears in time series data, especially for financial data. With the
21
increasing growth of money market, the financial data tends to denote the value of variance error not constant from one period to other period. The tabulation of time series data that contains the problem of heteroscedasticity due to employing estimation technique of Ordinary Least Square still results in unbiased estimation, but using standard error and narrow confidence interval would result in faulty sense of precision.4 In this research, the measurement of financial volatility value is conducted using Kalman Filter method. The chosen technique of this estimation is inherent in the advantage of using this analysis, as mentioned by Brooks, et al. (1998). Brooks, et al. estimate the variation across the time from financial data by comparing three types of estimation technique which is generally used to estimate the variation across the time, namely Kalman Filter, GARCH, and Schwert and Seguin. Brooks et al. find the estimation technique of Kalman Filter is primary estimation if compared to other estimation technique. Therefore, measurement of financial data volatility in this research will be conducted by employing Kalman Filter method. Kalman Filter technique is a technique developed to describe the variation system across the time. This technique uses a recursive algorithm and part of state space framework model. This model contains dynamic equation with two other equations, namely observations/measurement equation and state equation (unobserved dynamic process). In some econometrics literatures, state space model has been applied for uncontrolled variables such as rational expectation, error measurement, missing observations, permanent income, trend, cycles and NAIRU. The framework understanding of state space model is certainly required to understand the framework of Kalman Filter. The general form of this state space model is: zt Hxt vt
Observation Equation:
(1)
State Equation:
xt Axt 1 But 1 wt 1
(2)
With zt is a variable that analyzes the period of t; H, and B is matrix parameter (using H as observation vector (mxn); A is matrix transition (m x m), and B (n x 1) as matrix control input); xt is a vector state ( m x 1), ut‐1 is a vector that denotes state disturbances with 0 average. Meanwhile, each vt and wt is considered as error of observation and error of state equation which is assumed to follow normal distribution with 0 average as follows: ~
0,
~
and
0,
(3)
At this equation, R is error covariance from observation equation and Q is error covariance from state equation. Commandeur and Koopman (2007) explain the component of state in state space model which is estimated through three different ways, i.e., by using: 1) smoothed state, or an estimation using all data and observations; 2) filtered state, is state vector estimation based on previous and future observation, and 3) predicted state, which is state vector estimation conducted using previous value only.
4
Robert Engle, “GARCH 101: The Use of ARCH/GARCH models in Applied Econometrics”, Journal of Economic Perspective, vol.5 no.4, 2001.
22
State component estimation results in hyper‐parameters such as irregular variance and state disturbance. This can be resolved by using two ways: 1. Forward pass, from t=1, ...n, using recursive algorithm which is known as Kalman Filter applied for observed time series data. 2. Backward pass from t=n, ...., 1, using recursive algorithm which is known as state and disturbance smoothers. The combination of forward pass through with Kalman (1960), is used to estimate the relevancy of predicted or filtered state. The objective of Kalman Filter is to obtain the optimum value of state at time t by considering observation {y1, y2, …, yt‐1}. Kalman Filter Algorithm Estimation of Kalman Filter through the form of feedback, which is Kalman Filter will estimate the process of state at certain time to obtain the feedback in the form of noisy measurement (Welch and Bishop, 2001). The equation of Kalman Filter is divided into two groups: 1. Time update equations or predictor equation It is an equation that projects the future based on value of existing state vector and estimate towards error covariance to derive the value of a priori estimation. 2. Measurement update equations or corrector equation It is an equation conducted for feedback process using new measurement into previous estimation (a priori estimation). Final result, among predictor equations will be similar to that of corrector. Illustration on estimation process of Kalman Filter can be examined by the following Figure, Time Update (“predict”)
Measurement Update (“correct”)
Project the state ahead t
xˆ Axˆt 1 But 1 Project the error covariance ahead
Pt APt 1 A Q
xˆ t xˆ t K t ( z t Hxˆ t )
(6)
(7)
(8)
Update the error covariance
P t ( I K t H ) Pt
K t Pt H T ( HPt H T R ) 1
Update estimate with measurement zk
T
Compute the Kalman Gain
Initial estimation from
xˆt 1danPt
1
Source: Welch and Bishop (2001), notation is adjusted
Figure 3.3 Process of Kalman Picture Estimation 23
Time update equation is projected for state and error covariance at time t by considering the previous value of t. Notation Q denotes first condition, notation A and B denote state vectors from time t‐1 until t. Meanwhile, the equation of measurement update to obtain the value of Kalman (Kt)xt, and lastly to obtain an estimation of a posteriori error covariance (Pt) by integrating equation (2). After comparing time update equation with measurement update, the process of estimation will be repeated by utilizing estimation result of a posteriori to predict the new value of a priori. There are six variations of state space or Kalman Filter model: 1. Deterministic Level; 2. Stochastic Level; 3. Deterministic Level and Slope; 4. Stochastic Level and Slope; 5. Deterministic Level and Stochastic Slope; and 6. Stochastic Level and Deterministic Slope. The most suitable model of variable can be seen from value of AIC, SIC or least HQ. Meanwhile, volatility figures are calculated from (original data – predicted data/filtered).2 3.5.2 Formulation of Financial Stability Index As mentioned earlier, one of the main objectives of this financial stability study is to set up a single index. This single index reflects the condition of dual financial system stability and can be used to analyze related factors with instability. To set up single index of FSI, estimation is conducted by following the approach developed by Bordo, et al. (2001) using banking indicators, stock market, bonds market and money market. The first step is calculating composite index to obtain performance index (IK) and volatility index (IV) from both financial systems (conventional and Islamic), as seen on the equation (9). The score of each indicator () is based on its role in economy. All indicators will be first standardized and then added to form composite index to follow the formula below: I xit x i t it i 1 a ,b
(9)
Is composite index IK and IV: i is score for each indicator and xi is indicator i, x i is median value during the period of analysis. In this research, composite index obtained from two elements, namely performance composite index ( ikt ) and volatility composite index ( ivt ). Performance composite index will be obtained from calculation using variables in the form of nominal size from financial indicator, while volatility composite index obtained from calculation using volatility value of financial indicator. In calculating this composite index, variable standardization conducted by measuring deviation from its median. Hence, the result will be divided by standard value of deviation ( a,b ) of each indicators, which is obtained by way of calculating the distance of each observation in a variable with median value from these variables during the period of observation. The value of median is used in this research because the observed distribution data is not symmetry. Therefore, distance calculation for each observation estimated separately between data that is below median value and data above median value. a is
24
standard deviation for all values of X that above median value, while b is standard deviation for all values of X that are below median (see Figure 3.6). Calculation of standard deviation by this median value conducted by general formula: a ,b
(x
i
Mea ,b ) 2 n
(10)
Where xi is time series data to i, Me is Media and n is number of data series
Source: Illing and Liu (2003)
Figure 3.4 Hypothetical Visualization of “Standardization” using Bordo, et al. (2000) After conducting standardization, calculation of composite index is continued by summing the result of standardization of each variable to form composite index. The sum of each variables conducted by weighing method. Illing and Liu (2003) reveal that there are several weighing methods that can be used to calculate FSI, as follows: 1) Weighing using factor analysis; 2) Weighing using aggregate credit basis; 3) Weighing using equal variance technique; and 4) Transformation using sample of cumulative distribution function (CDF). Based on factor analysis method, the weighing is obtained by integrating linear weighing combinations from several variables. For weighing with basis of aggregate credit, the score is obtained by using credit share value from each market towards the total economy. By using equal variance technique, weighing is conducted by using variance value of each variable, while using sample cumulative distribution, weighing is conducted by transforming variables to become basis of percentile. Illing and Liu (2003) conduct evaluation towards the four weighing methods and they find that the weighing with aggregate credit basis shows the best performance.5 However, since Illing and Liu adopting Canada as data in the analysis, output of this research cannot be fully applied to another country. For Indonesia, the use of aggregate credit basis as weighing of framework would not be able to provide fair description towards contribution of each market over stability of Indonesian financial system. Indeed, banking credit share has the 5
This evaluation is based on the degree of fault type I and fault type II in each of weighing method and it is obtained that the weighing with aggregate credit basis is able to give the smallest level of the fault type I and type II.
25
biggest proportion if compared to stock and bond market, although shocks frequently occur in the stock and bond markets “disturbing” the stability of the Indonesian financial system like what happened in the late 2008. Realizing this condition, the research attempts to apply weighing concept that is able to reflect contribution of each market towards stability of the Indonesian financial system. After calculating performance composite index and volatility composite index by following the above calculation mechanism, the next step is to combine both types of indices, which is a crucial step in arranging FSI stability financial index. One alternative approach that can be used is by multiplying performance index and volatility index. This can be formulated as follows:
t ikt . ivt or t ikt ivt
(11)
where,
t : Financial Stability Index; ikt : Performance Index; and ivt : Volatility Index More appalling performance and more volatile of a financial system would lead to more unstable financial system. For that analysis, the value of FSI can be normalized within the value range of 0 – 100. The temporary output is described as follows: Table 2.3 Indicators of FSI Indicator
Islamic FS Conventional FS Performance Volatility Performance Volatility
NPF and NPL (x1)
x1 t
h x1t
x1 t
h x1t
ROE (x2)
x 2t
h x2 t
x 2t
h x2 t
JII and IHSG (x3)
x 3t
h x3 t
x 3t
h x3 t
Sukuk and Bond Price Indices (x4)
x4t
h x4 t
x 4t
h x4 t
PUAS and PUAB
x5t
h x5t
x5t
h x5t
Performance and Volatility Indices
ikt
ivt
ikt
ivt
Financial Stability Index
Islamic t
Conventional t
3.5.3 Vector Auto Regression (VAR) / Vector Error Correction Model (VECM) In simultaneous or structural equation, some variables will be treated as endogenous variable and some other variables will be treated as predetermined variables6. Estimation of the model equation assumes that predetermined variable exist in several equations. To construct this model, it requires theoretical economic framework. Although in some cases, theoretical economic framework is not sufficient to establish rigorous and accurate model specification towards dynamic relationship among variables. In responding to this condition, Christopher Sims (1980) develops technique analysis of Vector Autoregressive (VAR) as way out from this problem since this technique is theory‐free method in estimating the relationship among economic variables. 6
Lag from exogenous variable or endogenous variable
26
VAR is an econom metrics mod del used to respond to o the progrress and inter‐depend dence of time series data an nd considerred as generral form of A AR model. TThis model consists of number of endo ogenous varriables as linear function from previous values. All variaables in VAR R model are treaated as sym mmetry by integratingg lag from dependent d variables aand lag from m other variablees into each h equations as describeed by the fo ollowing equ uation:
(12) (
(13) (
Or, in general form m can be written as:
nt (interceptt), Ai is k xxk matrix (fo or each of vvalue i=1,2...p), p is Where, c is vectorr of constan nd is vecctor of k x 1 from error term that ffulfills the condition: numberr of lags, an , which h means every error teerm has 0 avverage value
(13a) (
, whicch means co ovariance m matrix is con ntemporaneeous from itts error term m Ω (deffinitely posiitive matrix n x x) (13b) ( , wh hich means for every in ncreasing vaalue of k no ot equal to zzero, no error term corrrelation acrross the time (13c) ( When itt is formulaated in matrrix form, equation (13) can be writtten as:
(14) (
Where, k is the number of endogenous vvariables in the equatio on system. When data d is not stationary at its level, VAR is exxercised at first f differeence that meets m its stationaary. As a result, r information on n long‐term m relationship cannott be examined. To overcom me this pro oblem, Vecttor Error Co orrection Model (VECM M) can be u used with co ondition that thee data has cco‐integratiion relation nship. That ccan be done by integraating first e equation into levvel of new eequation succh as the fo ollowing: k 1
y t 1 t y t 1 i y t i t
(15) (
i 1
Where, Π and Γ arre functionss of A. Matrix Π can be e divided in nto two maatrices λ and d β with T (nxr) diimension. Π Π = λβ , where w λ callled adjustm ment matrixx and β caalled co‐inte egration vector. Meanwhilee, r is co‐inteegration ran nk. The maain function of VAR/VEECM is to trace currentt and futuree response from each variable as a result of change or shock of certain variabless with Impulse Response Functio on (IRF). VAR/VEECM can alsso predict contribution n of variance e percentagge from eacch variable towards changes of certain variables through Foreecast Error Decomposiition of Variance (FEDV V). In this rresearch, th he three equ uation systeems (models) that will be tested aare: 1. Convention nal model w with conventional variables; FSI_KO ONV, ER, GDPR, INFLASSI, SBI; 2. Islamic mod del with varriables; FSI__SYH, ER, GDPR, INFLASI, SBIS;
27
3. Dual model with variables; FSI_KONV, FSI_SYH, ER, GDPR, INFLASI, SBI, SBIS. Data of FSI, ER and GDP are firstly transformed into natural logarithm (ln) format to maintain uniformity of data with other data which are already in the form of percentage (growth).
4. Analysis and Discussion
4.1 Results of Kalman Filter Ready monthly data of FSI performance indicators is analyzed using Kalman Filter to obtain the volatility of each indicator. The results show that stochastic level model is the best model (significant) for all FSI indicators. The result can be seen in table 4.1. Table 4.1 Results of Kalman Filter Deterministic Level NPL NPF KROE SROE PUAB PUAS RETIHSG RETJII BONDYIELD SUKUKYIELD NPL NPF KROE SROE PUAB PUAS RETIHSG RETJII BONDYIELD SUKUKYIELD
Stochastic Level AIC
AIC
HQ
1.60E+16 3.374109 5.505742 5.65E+142 5.779888 4.373167 ‐1.806488 ‐1.642457 4.519175 3.867028
1.60E+16 3.387947 5.519579 5.65E+142 5.793726 4.387004 ‐1.792651 ‐1.628619 4.533013 3.880866
1.596733 1.624408 1.587079 1.614754 4.606466 4.634141 4.938755 4.96643 5.737762 5.765438 3.21359 3.241266 ‐1.823074 ‐1.7954 ‐1.665346 ‐1.63767 3.453444 3.48112 3.719346 3.747021 Deterministic Level and Stochastic Level and Slope Stochastic Slope AIC HQ AIC HQ
1.47E+17 1.47E+17 3.346947 3.360785 5.653863 5.667701 5.448776 3.45E+142 6.176899 6.190737 3.707444 3.721281 ‐1.389233 ‐1.375396 ‐1.227714 ‐1.213876 4.923757 4.937594 4.033264 4.047101 Stochastic Level and Deterministic Slope AIC HQ
1.952634 1.946312 4.937227 5.161934 6.116837 3.555526 ‐0.193354 ‐1.163326 3.79582 4.096741
1.994147 1.987825 4.97874 5.203447 6.15835 3.597039 ‐0.151841 ‐1.121813 3.837333 4.138254
1.923984 1.911829 4.913732 5.127451 6.082354 3.521042 2.64E+19 ‐1.193231 3.761354 4.062258
2.39232 2.11082 4.910045 5.448776 6.119176 3.713679 2.64E+19 ‐1.193231 3.887107 4.063395
HQ
Deterministic Level and Slope AIC HQ
2.419995 2.138496 4.93772 5.476451 6.146851 3.741354 2.64E+19 ‐1.165556 3.914782 4.09107
1.951659 1.939505 4.941408 5.155126 6.11003 3.548717 2.64E+19 ‐1.165556 3.789029 4.089933
4.2 Standardization for Performance and SSK Variables Volatility After obtaining all volatility values of FSI variables, the next step is standardizing these variables into the following formula:
28
x xi xit it a ,b
dan
a ,b
(x
i
Mea ,b ) 2 n
The result of variables standardization can be seen on Figure 4.1 to 4.5. On the left side is performance standardization; while on the right side is volatility standardization. Blue color is conventional variable while red color is Islamic variables. 20
SKROE
SSROE
25
15
svsroe
20
10
15
5
10
0 ‐5 Jun‐04
svkroe
Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
5
‐10
0
‐15 ‐20
‐5
‐25
‐10
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Figure 4.1 Performance and Volatility Standardization of Banks ROE 20
SNPL
SNPF
25
15
svnpf
20
10
15
5
10
0 ‐5 Jun‐04
svnpl
Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
5
‐10
0
‐15 ‐20
‐5
‐25
‐10
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Figure 4.2 Performance and Volatility Standardization of Banks NPL/NPF 20
SPUAB
SPUAS
15 10
30
svpuab_lp
svpuas
20
5 0 ‐5 Jun‐04
Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
10
‐10 ‐15 ‐20 ‐25
0 Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
‐10
Figure 4.3 Performance and Volatility Standardization of Money Markets PUAB/PUAS
29
20
SRETIHSG
SRETJII
15
svretjii
20
10
15
5
10
0 ‐5
svretihsg
25
Jun‐04 Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
5
‐10
0
‐15 ‐20
‐5
‐25
‐10
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Figure 4.4 Performance and Volatility Standardization of Share Markets IHSG/JII 20
SBONDYIELD
SSUKUKYIELD
15
svsukukyield
20
10
15
5
10
0 ‐5
svbondyield
25
Jun‐04 Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
5
‐10
0
‐15 ‐20
‐5
‐25
‐10
Jun‐04 Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
Figure 4.5 Performance and Volatility Standardization of Bond Markets BOND/SUKUK
4.3 Weighing Composite Index of FSI Performance and Volatility Variables After conducting standardization of performance value and all SSK variables volatility, the next step is calculating performance composite index ( ikt ) and volatility composite index ( ivt ) using three alternative weighing namely variance‐equal, cumulative distribution function (CDF) and factor analysis. The formula of composite index is as follows: I x it x i t it i 1 a ,b
(9)
where, composite index IK and IV; i is the weight for each indicator ; and xi is indicator of i, x i is median value during the period of analysis from indicator xi , and a,b is standard deviation of each indicator during the period of analysis. The result of calculation ikt and ivt with three weighing methods can be seen in figures 4.6, 4.7 and 4.8. Performance index is on the left side, while volatility index in on the right side. Blue color is conventional index, while red color is Islamic index.
30
100
100
80
80
60
60 40
40
20
20
0 0 Jun‐04 Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
Figure 4.6 Performance Composite Index and Volatility With Variance‐Equal Weighing 100
100
80
80
60
60
40
40
20
20 0
0
Jun‐04 Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
Jun‐04 Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
Figure 4.7 Performance Composite Index and Volatility With CDF Weighing IK KONVENTIONAL
100
IK SYARIAH
IV KONVENSIONAL
IV SYARIAH
100
80
80
60
60
40
40
20
20
0
0 Jun‐04 Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
Jun‐04 Dec‐04 Jun‐05 Dec‐05 Jun‐06 Dec‐06 Jun‐07 Dec‐07 Jun‐08 Dec‐08
Figure 4.8 Performance and Volatility Composite Indices using Factor Analysis Weighing Performance index and volatility index are used to calculate Islamic and conventional financial stability index (FSI) using the following formula:
t ikt . ivt or t ikt ivt
(11)
where,
t : Financial Stability Index; ikt : Performance Index; and ivt : Volatility Index. The results of t multiplication and t addition (each for Islamic and conventional FSIs) can be seen at figures 4.9, 4.10 and 4.11. t multiplication is on the left side, while t addition is on the right side. Blue color is conventional FSI, while red color is Islamic FSI.
31
100
100
80
80
60
60
40
40
20
20
0
0 Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Figure 4.9 FSI Multiplication and Addition using Variance‐Equal Weighing 100
100
80
80
60
60
40
40
20
20
0
0 Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Figure 4.10 FSI Multiplication and FSI Addition using CDF Weighing 100
Conventional FSI
Islamic FSI
100
80
80
60
60
40
40
20
20
0
FSI KONVENSIONAL
FSI SYARIAH
0 Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Jul‐04 Jan‐05 Jul‐05 Jan‐06 Jul‐06 Jan‐07 Jul‐07 Jan‐08 Jul‐08 Jan‐09
Figure 4.11 FSI Multiplication and FSI Addition using Factor Analysis Weighing From the above results of FSI variations, the multiplication versus addition and three alternative weighing cannot determine which model is the most suitable model for Indonesian case.
4.4 Choosing The Best FSI In order to choose or determine the best FSI, it is necessary to conduct two tests. First test is to determine the suitability of VECM model especially the sign of error correction term (ECT). The sign of ECT should be negative for the system to be able to return to its stability after some period of time. Table 4.2 shows the results of ECT from VECM model for each alternative FSI. It can be concluded that factor analysis weighing (addition and
32
multiplication) and variance‐equal weighing (multiplication) are the plausible models which have negative ECT. Table 4.2 Results of ECT using VECM Model Weighing Method
Composite Formation Method
ECT Value
Conventional Islamic Conventional Islamic Conventional Islamic Conventional Islamic Conventional Islamic Conventional Islamic
Addition Factor Analysis Multiplication Addition Variance‐Equal Multiplication CDF Transformation Function
System
Addition Multiplication
‐0.098712 ‐0.024967 ‐0.245930 ‐0.069678 0.055798 0.021667 ‐0.053573 ‐0.090150 ‐0.043197 0.048217 ‐0.077116 0.008992
Second test is to determine the correlations between FSI and macroeconomic variables which influence the movement of FSI. The higher the correlation is the better. Table 4.3 shows the results of correlations between six SFI models with five macroeconomic variables. Table 4.3 Correlation Results of Multiplication and Addition VARIABLE GDP EXCHANGE RATE INFLATION SBI SBIS
Factor Analysis (x)
Variance‐Equal (x)
CDF (x)
Conv FSI Islamic FSI
Conv FSI Islamic FSI
Conv FSI Islamic FSI
0.190 0.154 0.248 0.061 0.252 0.056 ‐0.029 0.832 0.063 0.638
0.184 0.168 0.230 0.082 0.327 0.012 ‐0.027 0.843 0.075 0.578
0.139 0.299 ‐0.019 0.885 ‐0.043 0.751 ‐0.312 0.017 ‐0.296 0.024
‐0.204 0.125 ‐0.026 0.848 0.439 0.001 ‐0.015 0.913 ‐0.024 0.860
‐0.355 0.006 ‐0.257 0.052 0.234 0.077 0.255 0.053 0.260 0.049
0.592 0.000 0.709 0.000 0.456 0.000 ‐0.265 0.044 ‐0.003 0.984
VARIABLE GDP EXCHANGE RATE INFLATION SBI SBIS
Factor Analysis (+) Conv FSI Islamic FSI 0.299 0.022 0.392 0.002 0.615 0.000 0.652 0.000 0.334
0.803 0.000 0.335 0.010 ‐0.218 0.099 ‐0.004 0.977 0.778
Variance‐Equal (+) Conv FSI
Islamic FSI
‐0.322 0.014 0.217 0.102 0.388 0.003 0.456 0.000 ‐0.210
0.686 0.000 0.400 0.002 ‐0.240 0.069 0.051 0.703 0.726
33
CDF (+) Conv FSI Islamic FSI ‐0.294 0.025 0.249 0.059 0.379 0.003 0.412 0.001 ‐0.227
0.643 0.000 0.391 0.002 ‐0.236 0.074 0.050 0.708 0.711
0.010
0.000
0.113
0.000
0.087
0.000
The correlation results show that CDF multiplication and Factor Analysis addition are the most suitable candidates for the best FSI. Therefore, based on the above two tests, it can be concluded that FSI using Factor Analysis addition is the best FSI.
4.5 Islamic and Conventional FSI From previous discussion, the best FSI using Factor Analysis (addition) can be seen on figure 4.12. FSI KONVENSIONAL
100
FSI SYARIAH
80 60 40 20 0 Jul‐04
Jan‐05
Jul‐05
Jan‐06
Jul‐06
Jan‐07
Jul‐07
Jan‐08
Jul‐08
Jan‐09
Figure 4.12 Conventional and Islamic FSI Based on figure 4.12, it can be seen that Islamic FSI has positive trend which is larger than that of conventional FSI. The results show that the value of conventional FSI has average of 25.57 and variance of 347.59, while the value of Islamic FSI has average of 51.59 and variance of 480.24. ANOVA result shows that the difference between conventional and Islamic FSI is not significant (1.3816
