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Journal of Business & Policy Research Vol.4 No. 2 December 2009, Pp.189-211

189

Lending Structure and Bank Insolvency Risk: A Comparative Study Between Islamic and

Conventional Banks

Aisyah A. Rahman, Mansor Ibrahim and Ahamad Kameel

Mydin Meera,

This study investigates the impact of lending structure on the insolvency risk exposure. A comparative analysis between the insolvency risk behavior between the Islamic and conventional banks is made. Our findings show that real estate lending is positively related to the conventional banks’ risk, but inversely related to the Islamic banks’ risk exposure. Thus, the policy makers as well as the banks should react accordingly in the decision making process.

Field of Research: Banking, Risk Exposure.

1. Introduction The banking literature nowadays has focused on the insolvency risk exposures in ensuring banks safety and soundness. Undeniably, the interest in this subject is pronounced after the 1997 financial crisis. As most studies find that loan expansion is a significant factor to bank risk exposure, the structure of lending composition per se is of crucial importance. Hanson et al. (2008) suggests that if firm parameters come from different sectors, there will be further scope for risk diversification by changing the portfolio weights, even in the case of sufficiently large portfolio. In fact, exploratory studies in the after math of the 1997 financial crisis suggest that real estate lending is responsible for the banking crisis for the Asian countries. (Krugman,1998; Tan, 2000, Mera & Renaud, 2000; Quiley,2001; Collyns & Senhadji, 2002). Besides, Blasko and Sinkey Jr (2006) show that specialization in real estate lending challenges the ability of the banks to manage interest rate risk, especially during rising climate. _______________ A. Rahman, Aisyah, Faculty of Economics and Business, Universiti Kebangsaan, 43650, Bangi, Selangor, Malaysia. Email: [email protected] Ibrahim, Mansor. Faculty of Economics and Management Sciences, Universiti Putra Malaysia, 43400 UPM Serdang, Selangor. Malaysia. Mydin Meera, Ahamed Kameel. IIUM Institute of Islamic Banking and Finance (IIiBF), International Islamic University Malaysia, No. 205A, Jalan Damansara, Damansara Heights, 50480 Kuala Lumpur, Malaysia.

Rahman, Ibrahim & Meera

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Further, Madura et al. (1994) find that real estate lending could increase the implied risk exposure depository institutions, but such relationship disappears for the case of commercial banks. For the Malaysian context, Ahmad and Ariff (2003, 2004) reveal that loan on broad property sectors, consumer credit, and purchase of securities could only increase market risk exposure, but not total and unsystematic risk exposure. However, their study is for the depository institutions, which comprise the commercial banks, bank holding companies, merchant banks, and finance companies. Against this background, the result for the Malaysian commercial banks is still questionable. As Malaysia employs a dual banking system (conventional and Islamic banking system), studying the risk behavior for each is beneficial. Thus, the objective of this study is to examine the lending structure and bank insolvency risk exposure for the case of the Islamic and conventional banks in Malaysia. In doing so, we seek to answer at least three research questions: first, does lending structure affect the insolvency risk of the conventional banks?; second, does lending structure affect the insolvency risk of the Islamic banks? And finally, does the risk behavior between the conventional and Islamic banks differ? The remainder of this paper is organized as follows. Section 2 deliberates the literature reviews as well as the history of the Islamic and conventional banks in Malaysia. Section 3 outlines the data and methodology. Section 4 presents the research findings, and finally section 5 concludes. 2. Literature Review Studies on the determinants of bank risk exposures per se are very limited. So far, only Madura et al. (1994) and Ahmad and Ariff (2003) have explicitly examined the factors affecting financial institutions’ risk exposure. For the former, Madura et al. (1994) research on the determinants of the ex-ante risk for the deposit-taking institutions and commercial banks in the U.S.1 Their findings show that depository institutions and commercial banks have different determinants, inferring each entity should be studied separately.2 For the later, Ahmad and Ariff (2003) investigate the determinants of the CAPM risk measures using the single-factor CAPM approach. Unlike Madura et al. (1994), they only focus on the deposit-taking institutions in Malaysia.3 They analyze fourteen bank-specific variables (BSV) on the equity risk, market risk, total risk and unsystematic risk exposure.4 Their findings show that different types of risk exposures have different risk determinants.5 As the theoretical framework for risk exposure has not yet established, most studies include the BSV when investigating a specific issue relating to risk. Saunders et al. (1990) incorporate three BSV when studying


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ownership structure of the U.S. banking institutions. The BSV are financial leverage, operating leverage, and size. They find that the BSV affect seven different types of risk exposure differently. In another ownership study, Anderson and Fraser (2000) apply an additional BSV, which is ‘frequency’. While Saunders et al. (1990) and Anderson and Fraser (2000) analyze for the case of the U.S., Konishi and Yasuda (2004) examine the same issue for the Japanese banks. They find that size and capital buffer are negatively related market risk exposure. For the case of Spanish banks, Marco and Fernandez (2008) employ three BSV (size, profitability and types of business). Taken together, studies on ownership structure shows that size, credit quality, liquidity ratio, and capital buffer are the BSV. Focusing on the loan sales and risk, Hassan (1993) incorporates six BSV which comprise credit, interest rate, and business variables.6 Based on five market risk measures, he finds that lending specialization and loan expansion are positively related to all types of market risk measures.7 Unlike Hassan (1993), Cebenoyan and Strahan (2004) apply four BSV which consist capital, liquidity, and credit variables.8 Analyzing four accounting risk measures, he finds that all BSV are significant.9 Capital and liquidity variables are inversely related to risk, and vice versa for the credit variables. Examining the impact of derivative activities on Asia-Pacific banks’ interest rate and exchange rate risk exposure, Yong et al. (2009) employ seven BSV that reflects business, capital, liquidity, interest rate, and credit related variables.10 In summary, studies on off-balance sheet activities shows that credit, interest rate, liquidity, capital, and business variables are the BSV. Focusing on mutual fund investment, Gallo et al. (1996) incorporate five BSV which reflect credit, investment, capital, and business activities.11 Analyzing the market, industry, and unsystematic risk, they discover three interesting findings: 1) all BSV are not significant to the unsystematic risk; 2) loan expansion is negatively related to the market risk; and 3) loan expansion, investment, and mutual fund activities are inversely related to the industry risk. Looking into the effect of income structure on credit risk of European banks, Lepetit et al. (2008) employ five BSV that reflect business, credit, and capital variables.12 Studying five accounting risk measures (standard deviation of ROA, standard deviation of ROE, loan loss provisions to total loans, Zrisk index, and ZP-score) and three market risk measure using single-CAPM (total risk, unsystematic risk and market risk), they conclude that size is positively related while capital buffer is inversely related to all market and accounting risk measures.


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As this study examines the impact of lending structure on insolvency risk exposure for both the conventional and Islamic banks in Malaysia, it is important to know the brief history of the two banking systems. The establishment of the first full-fledge Islamic bank in Malaysia is in 1983. It was established to carry out commercial banking activities that are Syariah compliant and it is structured to operate side by side with the conventional banks. It was given a monopoly status of the Islamic banking business for the ten years to strengthen its position before the government allows other Islamic banks to emerge. In 1993, the central bank permitted the conventional banks to offer Islamic banking products via their Islamic banking windows. The Islamic windows operate using the facilities of conventional (parent) banks. The Islamic banks are governed by the Islamic Banking Act 1983 (IBA). As in April 2009, there are 11 local and 6 foreign Islamic banks, operating in Malaysia. The conventional banking system has come to Malaysia prior to its independence. It is governed by the BAFIA 1989. In 1990, there were 22 local and 16 foreign banks altogether. As financial liberalization takes place, the central bank launched a consolidation program in late 1990’s as a strategy to sustain the Malaysian banks in the future. As in April 2009, there are 10 local and 13 foreign conventional banks, operating in Malaysia. 3. Data and Methodology The following linear model is conducted using the generalized least squares (GLS) estimation to test the risk behavior of lending structure. Based on unbalanced panel data, three models are analyzed; namely, pooled effect (also known as none effect model), fixed effect, and random effect model. The best model is selected based on the Likelihood Ratio and Hausman test.13 We incorporates cross-section weight in the GLS estimation because our data is not normally distributed. GLS method helps to reduce the heteroskecasticity issue; hence it is most appropriate for this study as compared the ordinary least squares (OLS) estimation. Following Shahimi (2006) and Zakaria (2007), first order autocorrelation problem is tackled based on the Park’s model by incorporating AR(1) in the regression model. This study comprises of 14 and 23 bank sample for the Islamic and conventional banks, respectively for year 1994-2006. 14 out of 17 Islamic banks is included in this sample since the other 3 were newly established in 2005 and 2006. Meanwhile we include all 23 conventional banks since the data is complete from 1994 to 2006. We include the most available data that we can gather for each banking system to obtain rigorous results. As we regress each banking system separately, the different numbers of bank sample for Islamic and conventional banks does not create estimation problems. We cannot test whether there is any difference between foreign and local banks due to limited number of


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observations, especially for the Islamic banks. Year 1994 is chosen as the starting date since 1993 was the initial year when the central bank allowed the Islamic windows to operate in Malaysia where their first annual reports were in 1994). We stop at year 2006 since studies on global financial crisis suggests that the U.S subprime crisis started to become pronounce in 2007 onwards.

+ ? + - + + - +/- - +/- Risk = f (TL, LS, PLL, TE, GAP, INTEXP, INV, LTA, NONII, MGT) Where the four alternate lending structure (LS) variables are:

• three alternate real estate lending variables (BPS, RE, RISKY) • Specialization index (SPEC) • Lending Composition Change (LCC) • Variance of Traditionality Index (VART)

and where: Insolvency Risk = the Zrisk index TL = Ratio of Total loan to total asset PLL = Ratio of provision for Loan Loss to total asset TE = Ratio of total equity to total asset GAP = Ratio of GAP to total asset14 INTEXP = Ratio of cost of fund to total asset15 INV = Ratio of short term investment securities to total asset LTA = Logarithm of total asset NONII = Ratio of non-interest income to total asset16 MGT = Ratio of total earning asset to total asset TL, LS, PLL, TE, GAP, INTEXP, INV, LTA, NONII, and MGT are proxies for the loan expansion, lending structure, loan quality, capital buffer, GAP analysis, the cost of fund, liquid asset, size, non-financing activities, and management efficiency. The expected sign of this model is follows the justification from previous empirical studies. With regards to loan expansion, Hassan (1993), Madura et al (1994), Gallo et al. (1996) suggest that illiquidity and default issues are the underlying reason for a positive relationship between TL and risk. With regards to loan default, earlier studies hypothesize that PLL represents the probability of future default; thus, it is expected to be positively related to risk. For the case of capital, total equity is perceived to provide buffer against loss; hence, increasing TE can reduce the insolvency risk exposure. For the GAP analysis, a positive GAP indicates that a particular bank is an asset sensitive bank while a negative GAP


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indicates that it is a liability sensitive bank. A positive GAP bank (or an asset sensitive bank) is exposed to risk that interest rate will fall whereas a negative GAP bank (or a liability bank) is exposed to risk that interest rate will increase. Thus, the greater the absolute value of GAP, the more the bank is exposed to changes in interest rate. Despite the GAP analysis, Madura et al (1994) argue that bank risk also depends on the proportion of funds obtained in the deposit account, which is not captured in the GAP analysis (The proportion of funds in the deposit account can be measured by total interest expense). They underline that the higher the deposit, the higher the interest expense, the higher the volatility of net interest income, thus the riskier is the bank. For the case of INV, risk is linked to it from the perspective of deposit withdrawal. Having cash idle is an opportunity cost to banks, but banks hold short term investment securities to standby the need for extraordinary deposit withdrawal. With regards to size, Saunders et al. (1990), Hassan (1993) argue that the greater the size, the greater will be the potential to diversify business risk and adjust unexpected liquidity and capital shortfall, thus reducing the bank risk exposure. However, Anderson and Fraser (2000) suggest the impact of size on risk is depending on the lending structure. If the loan composition is the same, bigger banks should have lower risk as compared to smaller banks. Nonetheless, if the loan structure is different, a bigger bank can have higher risk exposure than the smaller one due to its tendency to embark into riskier lending sector that can give higher return. Similarly, Gonzales (2004) mention that with the existence of the economy of scale, increase market power, and the ‘too big to fail’ policy for big banks, a bigger bank tends to enter into risky activities either through lending strategy or off-balance sheet activities. Against this background, size can be positively or inversely related to risk exposure. For the case of non-financing activities, Madura et al. (1994) offer evidence that diversifying from the traditional role banking (lending) can reduce bank risk. Finally, with regards to management efficiency, Angbazo (1997) and Norhayati and M. Ariff (2003, 2004) believe that the increasing efficiency of the management reduces the bank risk exposure. 3.1 The Insolvency Risk Measure From a regulatory discipline, Zrisk Index is a comprehensive measure of bank insolvency risk exposure because it captures the impact of capital. It was developed by Hannan & Hanweck (1988) and has been employed by Liang & Savage (1990), Eisenbeis & Kwast (1991), Sinkey & Nash (1993), Nash & Sinkey (1997), Blasko & Sinkey Jr. (2006), and Ahmad et al. (2005). Theoretically, Hannan and Hanweck (1988) hypothesize that insolvency occurs when current losses exhaust capital, thus, the probability of insolvency (Zrisk) can be is expressed as follows:


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Where E(ROA) is the expected return on asset, CAP is the ratio of total equity to total asset, and σROA is the standard deviation of ROA. CAP is often used as an indicator for risk in banks because high levels of capital provide protection against large decline in income. Hence, better capitalized banks will, other things equal, incur less risk of insolvency because of loan losses, lower revenues, higher cost of funds, etc. Thus, a lower Zrisk index implies a riskier bank while a higher Zrisk implies a safer bank.17 3.2 The Lending Structure Measure In analyzing the impact of lending structure on risks, four measurements are developed; 1) the real estate lending, 2) change in lending composition (LCC), 3) variance of traditionality index (VART), and 4) concentration or diversification index (SPEC).18 The detail explanations of each are as follows: a) Real Estate Lending Previous studies adopt slightly different definition of real estate sector. In the U.S., Madura et al (1994) and Blasko and Sinkey Jr (2005) use the narrow definition of real estate sector (RE), defined as summation of residential properties, non-residential properties, and real estate. In Malaysia, Roza Hazli (2007) employs the broad property sector (BPS) to measure real estate lending while Norhayati and M. Ariff (2003, 2004) investigate the risky sector lending (RISKY) on bank risk exposure. They define the risky lending as a summation of BPS, purchased of securities and consumption credit. To be comparable to previous studies, we employ three measures for real estate lending; namely, RE; BPS; and RISK. b) Lending Composition Change (LCC) LCC is used to measure the stability of lending composition in the short run. Based on the classification by the central bank of Malaysia (BNM), twelve lending sectors are adopted to construct lending indices representing characteristics of lending composition change.19 The LCC is computed as follows:


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where sit is the share of broad property sectors i in total lending in year t.20 For example, if lending shares of all 12 sectors remain exactly the same, LCC will have a value of 1. On the other hand, LCC equals 0 if a bank lending sector, none of which were loaned in the previous year. c) Specialized Index (SPEC) SPEC is employed to measure the specialization in the lending portfolio composition. It is constructed as follows:

where, sit is the lending share of industry i in total lending at year t. A score approaching 1 suggest a high degree of concentration while a score approaching 0 indicates a high degree of diversification. d) Variance of Traditionality Index (VART) VART is used to measure the stability of lending portfolio composition in an intermediate term. It is calculated using five-year intervals for each sector. For example, the traditionality index for the year 1995 is computed using lending data from 1993 to 1997; for 1996, using data from 1994-1998, and so on. The TI formula is as follows: whereby, the cumulative lending experience (Cit) for each industry is calculated as:

where t0 and t1 are initial and terminal periods of the data and eit is lending of industry i in year t. Since VART is a variance of TI across sectors, a high variance indicates an episode of divergent pattern of lending during the 5 year period. Meanwhile a low variance suggests a stability of lending composition. 4. Research Findings Panel (a) and (b) of table 2 illustrate the trend for the focal variables for the Islamic and conventional banks, respectively. For the case of Islamic


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banks, the Zrisk Index achieves its peak in 1996, inferring the lowest point of the insolvency risk exposure for the Islamic banks. The index however plummets and reaches its lowest point in 1999. In contrast, the Zrisk index for the conventional banks shows an uptrend despite a number of hick-ups before it breaks its highest record in 2002. Afterwards, the index tumbles down throughout the study period. A dramatic increase of Zrisk index, especially after the year 1999 infers that the conventional bank recovers very well from the financial crisis. Meanwhile, for the Islamic banks, the recovery process is rather slow. For the trend of the real estate lending (BPS, RE, RISKY), the trend of both banks start to diverge after 1996. While the Islamic banks show a declining pattern, the conventional banks still maintain an upward trend. With regards to LCC, both banks show instability patterns across the study period. For SPEC, the low value in 1995 for the case of the Islamic banks shows that they start with a diversified loan portfolio, followed by a specialized lending strategy in 1997, but then revert back to diversifying trend for the rest of the period. Interestingly, the trend for conventional banks appears to be the opposite. Since 1996 it shows a diversified lending portfolio for a quite sometimes before starts becoming specialized in 2004 onwards. Finally, for VART, the years from 1996 to 2003 show a divergent pattern of lending structure for both banks. This infers a flexible behavior of the Islamic banks as well as the conventional banks to react against their risk-return profile and customers’ demand as a strategy in maintaining a sound banking system. To investigate the multicollinearity problem, table 3 (a) and (b) show the Pearson’s correlation results for the insolvency risk exposure for the Islamic and conventional banks, respectively. Following Gujarati (2003) with the cut-off point of 0.8, the result shows no severe correlation for the case of Islamic bank; thus all independent variables are included in the regression model. However for the conventional bank, the result shows that INTEXP is severely correlated with NONII with a correlation value of 0.897751. As the VIF values in table 3 (c) for INTEXP in all models are greater than NONII, it is excluded in the regression model for the conventional banks.21 Against this background, we proceed with the panel regression analysis. The fixed effect estimation technique appears to be the best model based on the Likelihood Ratio and Hausman test. It also has the highest adjusted R2 values and the lowest standard error of regressions.


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Table 2: Trends of Lending structure and Zrisk Index Panel (a): Islamic Banks Panel (b): Conventional Banks Figure 1: Trend of Zrisk Index

0

4

8

12

16

20

24

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean of ZRISK

12

14

16

18

20

22

24

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean of ZRISK

Figure 2: Share of Broad Property Sector, Real Estate and Risky Sectors to Total Lending.

.1

.2

.3

.4

.5

.6

.7

.8

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean BPSMean REMean RISKY

.0

.1

.2

.3

.4

.5

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean BPSMean REMean RISKY

Figure 3: Change in Lending Composition

0.68

0.72

0.76

0.80

0.84

0.88

0.92

0.96

1.00

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean of LCC

.72

.76

.80

.84

.88

.92

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean of LCC

Figure 4: Degree of Specialization of Lending

.2

.3

.4

.5

.6

.7

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean of SPEC

.12

.16

.20

.24

.28

.32

.36

.40

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean of SPEC


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Figure 5: Variance of Traditionality index across lending sectors

.00

.01

.02

.03

.04

.05

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean of VART

.00

.01

.02

.03

.04

.05

94 95 96 97 98 99 00 01 02 03 04 05 06

Mean of VART

Journal of Business & Policy Research Vol.4 No. 2 December 2009, Pp.189-211

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Table 3(a): The Results of Correlation Matrix of the Independent Variables for the Islamic Banks

Table 3(b): The Results of Correlation Matrix of the Independent Variables of Conventional Banks

TL PLL TE GAP INTEXP INV LTA NONII MGT

TL 1

PLL 0.42707 1

TE 0.198728 -0.01949 1

GAP 0.225239 0.039181 0.154484 1

INTEXP 0.281476 0.16713 -0.10523 0.013656 1

INV -0.47923 -0.13074 0.103644 -0.09596 -0.16105 1

LTA 0.079058 0.231115 -0.37404 -0.20711 -0.06285 -0.34689 1

NONII -0.17649 -0.07626 0.294738 -0.1736 0.033058 0.376613 -0.18556 1

MGT 0.350318 0.172557 0.141076 0.15328 0.234798 0.108347 -0.10431 0.061665 1

TL PLL TE GAP INTEXP INV LTA NONII MGT

TL 1

PLL 0.487902 1

TE -0.16783 -0.12088 1

GAP 0.050352 0.021812 0.234337 1

INTEXP 0.192818 0.147355 0.674893 0.332868 1

INV 0.319533 0.181785 -0.36129 -0.09166 -0.0984 1

LTA 0.362305 0.02057 -0.58614 -0.08523 -0.21887 0.273876 1

NONII 0.007666 0.028418 0.725366 0.304157 0.897751 -0.22496 -0.3078 1

MGT 0.625776 0.298239 -0.4005 -0.02342 0.02427 0.335122 0.424731 -0.0919 1


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Table 3(c): Result of Variance Inflation Factor (VIF): The Case of Conventional Banks

Collinearity Statistics (VIF) Model

1 (a) Model 1(b)

Model 1(c)

Model 2

Model 3

Model 4

BPS 1.911 RE 1.562 RISKY 2.000 LCC 1.197 SPEC 1.643 VART 2.178 TL 2.138 2.162 2.142 2.286 2.150 2.605 PLL 1.397 1.410 1.396 1.475 1.404 1.487 TE 3.059 3.061 3.051 3.635 3.071 5.003 GAP 1.617 1.559 1.598 1.480 1.533 1.488 INTEXP 5.646 5.673 5.650 6.093 5.887 8.635 INV 1.329 1.264 1.301 1.233 1.272 1.343 LTA 2.582 2.507 2.623 2.367 2.287 2.387 NONII 4.774 4.781 4.777 5.010 4.768 7.062 MGT 4.610 4.536 4.634 4.983 5.338 6.226

Thus, table 4(a) and table 4(b) report the GLS regression results of the fixed effect model for the Islamic and conventional bank, respectively. Majority of the lending structure variables are significant for both cases. Interestingly, the sign of direction for the Islamic banks contradicts the conventional banks. With regards to the Islamic banks, real estate lending (RE, BPS, and RISKY) is positively related to Zrisk index. Recall that the Zrisk index is a safety index. A lower value means ‘lower safety’ or ‘higher insolvency risk exposure’. Thus, the positive association with the Zrisk index implies that real estate lending is inversely related to the Islamic bank insolvency risk exposure. Surprisingly, this observation indicates that real estate lending, that are acknowledged as ‘risky sector’ by previous studies, are not applicable for the case of Islamic bank. Also, the specialization index (SPEC) shows that the increasing specialization reduces the insolvency risk exposure. This finding supports the results of BPS, RE, and RISKY. As real estate lending reduces the insolvency risk, the more an Islamic bank concentrates its lending into that sector, the more it reduces its insolvency risk. Two potential factors can be the reason. First, it could be that the Islamic banks have properly approved real estate loans to customers that are less likely to default. Second, it could be that the fixed rate of real estate financing is affordable and beneficial especially during the 1997 financial crisis when most borrowers from the conventional banks suffer as a result of a high based lending rate (BLR).


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Table 4(a): Result of Fixed Effect Model for the Islamic Banks (Dependent variable is the Zrisk Index)

Independent variable Model 1(a) Model 1(b) Model 1(c) Model 2 Model 3 Model 4

C -0.17824 (-0.09652)

2.190864 (1.333)

3.135337 (1.519234)

4.603214** (2.071691)

1.933311 (1.313727)

1.486281 (0.122759)

BPS 3.171841*** (3.421744)

RE 1.904869*** (2.963037)

Risky 1.597425** (2.390668)

LCC 0.04302 (0.102607)

SPEC 2.399466*** (3.826118)

VART -37.9217 (-1.2111)

TL 0.503194 (0.877518)

0.046674 (0.089225)

0.137402 (0.217369)

-0.48189 (-0.84851)

-0.20877 (-0.31482)

0.073433 (0.037169)

PLL 27.03244 (1.255814)

32.70282* (1.950677)

30.82505 (1.62262)

38.43044*** (2.679275)

41.55427*** (2.902458)

-28.5132 (-0.47099)

TE 111.5781*** (22.60479)

106.9709*** (27.59419)

108.139*** (24.39377)

105.0098*** (20.74422)

107.4865*** (29.78733)

130.2652*** (16.85821)

GAP 1.034152* (1.789496)

0.857046 (1.495185)

0.610922 (0.992487)

0.250907 (0.351312)

0.22754 (0.378621)

1.82174 (1.654041)

INTEXP 6.941337 (0.605764)

9.306749 (0.981914)

8.457365 (0.81505)

5.083599 (0.462531)

5.672099 (0.56441)

27.35011* (1.885182)

INV -2.33247*** (-3.25297)

-2.23312*** (-3.01486)

-2.6797*** (-3.22371)

-2.65914*** (-2.90317)

-2.8897*** (-3.23188)

-2.82661 (-1.44645)

LTA 0.015155 (0.061863)

-0.20003 (-0.83188)

-0.3545 (-1.18466)

-0.45337 (-1.48636)

-0.21922 (-1.02646)

0.268996 (0.141047)

NONII 52.12042*** (5.810824)

43.38507*** (5.133602)

37.6017*** (3.90378)

46.75419*** (4.176816)

46.77667*** (4.190864)

52.63393** (2.091674)

MGT -0.1422 (-0.12986)

0.282703 (0.33959)

0.322441 (0.323746)

0.936792 (0.976251)

0.778442 (0.896707)

-3.23249*** (-2.83096)

AR(1) 0.292681*** (3.065974)

0.339116*** (3.541641)

0.33033*** (3.645797)

0.371848*** (4.246387)

0.300425*** (3.06329)

0.395188*** (2.770079)

R2 0.960494 0.966827 0.961544 0.964675 0.968708 0.999972

Adj R2 0.951106 0.958945 0.952406 0.956281 0.961272 0.999959

S.E. reg 2.450388 2.581173 2.560345 2.584523 2.565988 2.246942

F-stats 101.4533 117.9968 107.0357 107.1675 119.9008 945.0669

Prob (F) 0 0 0 0 0 0 D.W stat 1.644601 1.722141 1.680373 1.686569 1.685719 2.093364

Note: 1.Figures in parentheses are t-statistics

2. ***, **, * denotes significant at 1 %, 5% and 10% confidence level, respectively. 3. As Zrisk index is a ‘safety index’, a high index means a low bank insolvency risk

exposure; thus the relationship between independent variables and bank insolvency risk exposure is reversed from the sign in this table.


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Table 4(b): Result of Fixed Effect Model for the Conventional Banks (Dependent variable is the Zrisk Index)

Independent variable Model 1(a) Model 1(b) Model 1(c) Model 2 Model 3 Model 4

C 46.37998*** (7.461419)

43.87366*** (7.055968)

46.9645*** (8.239815)

63.28651*** (4.225073)

47.1079*** (8.151519)

59.85465*** (2.909578)

BPS -3.44868*

(-1.84059)

RE -3.23756** (-2.07606)

Risky -1.10489

(-0.82575)

LCC -2.72715*** (-2.70199)

SPEC -0.2564

(-0.07375)

VART 8.206099

(0.392531)

TL -1.72546

(-1.35559) -1.51755

(-1.21423) -1.53566

(-1.20374) -2.16472*

(-1.68127) -1.18416

(-0.71108) -6.40551*** (-3.21073)

PLL -41.3399*** (-2.80603)

-42.7359*** (-2.83768)

-40.8938** (-2.50066)

-47.0027*** (-3.10986)

-43.0299** (-2.51323)

-68.344*** (-3.72481)

TE 56.05368*** (19.18249)

56.24606*** (18.8034)

54.59593***

(19.92156) 46.18734***

(12.0378) 53.68179*** (19.37484)

44.05853*** (9.342978)

GAP 0.297238

(0.287837) 0.227108

(0.218445) 0.455927

(0.424439) 0.239653

(0.255734) 0.32293

(0.328168) -0.8249

(-0.46207)

INV -3.18617

(-0.89131) -3.49554

(-1.01183) -3.56242

(-1.00851) -3.90195

(-1.22081) -3.86192

(-1.09132) -5.08138

(-1.60844)

LTA -5.09876*** (-7.31739)

-4.78745* **(-6.79582)

-5.2432*** (-8.01748)

-7.03039*** (-3.70236)

-5.27689*** (-7.22298)

-6.56386** (-2.38296)

NONII -133.477*** (-8.07198)

-133.131*** (-7.99074)

-129.23*** (-7.2639)

-102.096*** (-5.35652)

-126.508*** (-6.98093)

-81.6664*** (-4.22641)

MGT 5.892993*** (3.565727)

5.850076*** (3.520329)

5.60628*** (3.079072)

4.521552** (2.105361)

5.15225** (2.165039)

3.963549* (1.919373)

AR(1) 0.491161*** (7.793447)

0.493285*** (7.715212)

0.49363*** (8.024112)

0.404535*** (6.98632)

0.490943*** (7.710761)

0.378556*** (5.298047)

R2 0.954823 0.954447 0.952762 0.967259 0.952855 0.979654

Adj R2 0.946656 0.946211 0.944221 0.960366 0.944331 0.972055

S.E. reg 3.010207 3.010895 3.015683 2.691161 3.007784 2.471303

F-stats 147.3214 146.2497 140.4208 143.5580 140.5168 128.9192

Prob (F) 0 0 0 0 0 0

D.W stat 1.884375 1.882816 1.884414 1.731577 1.891825 2.000991 Note: 1.Figures in parentheses are t-statistics

2. ***, **, * denotes significant at 1 %, 5% and 10% confidence level, respectively. 3. As Zrisk index is a ‘safety index’, a high index means a low bank insolvency risk

exposure; thus the relationship between independent variables and bank insolvency risk exposure is reversed from the sign in this table.


204

For the case of conventional banks, the results of BPS, RE, and LCC show an inverse relationship with the Zrisk index (positive association with the insolvency risk exposure). Given this, we can say that the insignificant relationship of RISKY shows that lending to ‘purchased security’ and ‘consumption credit’ does not increase the conventional banks’ insolvency risk exposure. This contradicts the views by Obiyatullah (1999) who suggest that BPS, purchased security and consumption credit as the ‘risky sector lending’ in Malaysia. Instead, our finding offers evidence that only BPS or RE is the ‘risky sector lending’. The fact that real estate sectors are speculative in nature might be a possible justification for this observation. Some people buy real estate now with a hope that the price will grow up in a near future. Then they sell it to obtain profit from the difference in the buying and selling price. Therefore, if they thought that the price of that premises won’t go up, they would just default the loan, which increases the insolvency risk exposure. Also, the result of LCC infers that increasing stability of lending structure in the short run increases the conventional banks’ insolvency risk exposure. This concern is pronounced since BPS is a risky investment for the conventional banks and on average, more than 30% of lending is given to that sector. While loan expansion (TL) is not significant, the finding for provision of loan default (PLL) merits some discussion. As the impact of PLL is significant to the insolvency risk exposure, interestingly the coefficient sign for the Islamic and conventional banks contradicts. For the Islamic banks, it is negatively related to insolvency risk exposure (positively related to the Zrisk index). For the conventional banks, it is positively related to insolvency risk exposure (inversely related to the Zrisk index). Recall back that the inclusion of PLL as a proxy for loan default source from two reasons: 1) following Hassan (1993) and Madura et al (1994) and Ahmad and Ariff (2003, 2004) to proxy loan default; and 2) to broaden the sample size.22 However, because of the surprising finding, we repeat the test using NPL and lag of NPL despite a limited number of observations.23 For the case of Islamic banks, the finding for NPL is consistent with PLL. Interestingly, our findings for lag NPL show positive association, but not statistically significant.24 Against this background, we can infer that the positive association between lag NPL and Islamic bank insolvency risk seems to suggest that the expected positive relationship materialized in a lagged time period. However for the case of conventional banks, both NPL and lag NPL show insignificant results.25 The inconsistent results raise an issue of the accuracy of PLL in forecasting loan default; hence, an extensive study on PLL and NPL deserves further exploration. For the capital related variable (TE), both Islamic and conventional banks show a negative relationship between capital buffer and bank insolvency risk (positive relationship – if looking at the index). This finding conforms


205

to the empirical findings that conclude when capital increases, the cushion against loss increases, thus decreases bank risk to become insolvent. For the liquidity variable, INV is only a factor for the case of Islamic banks, but not for the conventional banks. The positive relationship infers that when Islamic banks increase their holding in short term investment, their insolvency risk exposure increases. The historical data show that the obvious difference between Islamic and conventional banks is in terms of the securities held for trading. Islamic banks have several types of securities held for trading like Islamic accepted bills, Shahadah ad-Dayn, Bankers acceptance, and Islamic Unit trust. These instruments have different underlying contracts that could increase the Islamic banks’ insolvency risk. Besides, the less developed Islamic money market instruments could strengthen the risk impact due to the lack of risk management tools available for the Islamic banks. In contrast, securities held for trading for the conventional banks comprise of stable money market instruments such as Malaysian government treasury bills, Cagamas notes, and Bank Negara bills, which are well-known as low risk and high liquidity instruments. For the business operation related variables, size (LTA) is a factor to the conventional bank, but not to the Islamic bank insolvency risk exposure. The positive relationship implies that an increase in total asset would increase the conventional bank insolvency risk exposure. This finding suggests that bigger banks tend to embank into risky activities, either through lending in risky sectors or entering into non-traditional banking activities (fee-based transaction and off-balance sheet activities). Indeed, our finding for NONII confirms that a bigger conventional bank tends to embank in non-traditional banking activities which may increase their insolvency risk exposure. In contrast, the result of NONII for the Islamic banks is reverse. An increase in NONII reduces the Islamic bank’s insolvency risk. This can be due to the fact that most of its fee-based activities are based on short term self-liquidating trade related contingencies, which are considered as less risky. On the other hand, most of the conventional banks’ fee-based activities are associated to contracts on foreign exchange and interest rate, which are considered as more risky. With respect to MGT, our result shows that it is inversely related to conventional banks, but not to the Islamic banks. This suggests that an increase in earning asset of conventional banks is associated with a reduction in the bank insolvency risk. The intuition behind this finding is that the conventional bank is efficient. As the bank efficiency measures are growing, the impact of bank efficiency via other measures based on parametric and non-parametric approach is beneficial for a deeper understanding of this subject.


206

5. Conclusion In general, the impact of real estate lending on the Islamic and conventional banks’ insolvency risk exposures differs. As real estate lending reduces the Islamic bank insolvency risk, it increases the conventional bank insolvency risk exposure. Against this background, several suggestions for the regulatory bodies and practitioners are highlighted. First, as lending to real estate sectors reduces the Islamic banks’ insolvency risk, the central bank should introduce special guidelines for capital adequacy standard that taking into consideration the lower impact of real estate lending on bank insolvency risk for the case of the Islamic banks. This regulatory change should be motivated by the desire to lessen the capital constraint for the Islamic banks. In order to achieve the target set by BNM for the Islamic banks to command a 20 percent market share by year 2010, urgent action is pronounced as the Islamic banks have to compete hand in hand with the conventional banks. By incorporating a lower risk factor for real estate lending, the risk-weighted-asset (RWA) for capital adequacy standard for the Islamic banks can be reduced. Then, more loans can be distributed based on a limited capital, hence promoting the growth of the Islamic banking market share. Secondly, as the increasing price for real estate could affect the amount of loan default, this study suggests that this issue is tackled by restructuring the cost for fund. A separate rate should be given to: 1) the first and second onwards property buyers and 2) the local and foreign purchasers. This different rate can curtail an excessive demand for real estate prices besides segregating the real estate speculators and non-speculators. To the extent that the first timer property buyers want to hold the properties for good, the probability for them to default the loan decreases. Thirdly, as real estate lending appears not to be a risky investment for the Islamic banks and vice versa for the conventional banks, banks should restructure its lending strategy. By knowing that real estate lending does not increase the insolvency risk, the Islamic banks should be less likely to fall victim to the generalized panic of its conventional counterparts. Instead, specialization on real estate sector should be encouraged as it will not jeopardize the Islamic banks’ safety and soundness. Accordingly, cost of funding real estate sectors should be revised. The Islamic banks should be able to come out with their own benchmark, which is totally independent from the conventional banks’ rate.


207

End notes: 1 In Madura et al. (1994), the deposit-taking institutions comprise the commercial banks and saving institutions. The ex-ante risk measure is implied based on call option price. They analyze nine variables that reflect credit, capital, interest rate, and business operation. 2 With regards to the depository institutions, real estate lending and real estate owned are the determinants for the implied risk exposures. Meanwhile, the real estate owned and capital buffer are the determinants for the case of commercial banks. 3 In Ahmad and Ariff (2003), the deposit-taking institutions comprise the commercial and merchant banks. 4 The BSV are: NPL/TL; Lag NPL; MGT (earning asset/TA); LEV(Tier2/Total Capital); RISKY sector loan (BPS + purchase of securities + consumption credit); Regulatory Capital (Tier 1/TL); Cost of Fund; Loan loss provision; Risk Weighted Asset; KLIBOR; SPREAD; GAP; Loan/Deposit; total asset. 5 The determinants for market risk exposure are loan quality, cost of fund, loan expansion, and lending structure. The determinants for unsystematic risk are loan quality, cost of fund, and interest rate. For total risk exposure, the unsystematic risk determinants remain significant plus additional variable, the loan expansion. Finally, the regulatory capital is the only significant determinant for equity risk. 6Credit variables: 1) Loan sales, 2)Loan Loss reserve, 3) Diversification index; capital variable: leverage; interest rate variable: GAP; Business variable: 1) Size; 2) Div Payout Ratio. All except size are deflated by total asset. 7 Please refer to Hassan (1993) for the detail explanation of the implied asset subordinated debt models. 8 1) Capital variable: book value of equity / (total asset - cash - fed funds sold -securities); 2 liquidity variable : (cash + net fed fund + securities) / Total Asset 3) 2 lending structure: (commercial + industrial loan) / Asset; and (commercial Real estate loans)/ Asset. 9 The four risk measures are: 1) σROE,, 2) σROA, 3) σLLP./TL, 4) σnpl/TL. 10 Business variables: size and non-interest income/TA; capital variable: TE/TA, Liquidity variable: liquid asset/TA; credit variable: PLL/TA and total loan/TA; Interest variable: net interest income/TA 11 Credit variable: TL/TA; Capital variable; TA/TE; investment variable: Investment securities/TA; (Sales Fed fund-purchased Fed fund)/TA (-); Mutual fund asset: MFA/TA; Business variable: size. 12 Business variables: 1) size, 2) profitability differences (ROA and ROE), 3) business differences (deposit to total asset) and 4) personnel expenses to total assets. Credit variable is total loan to total asset, and capital variable is total equity to total asset. 13 Please refer to Beaver et al.(1989), Hsio (2002), Gujarati (2003), Shahimi (2006) and Zakaria (2007) for further details on panel data regression technique. 14 GAP = total rate sensitive assets – total rate sensitive liabilities. 15 Cost of fund for conventional banks is total interest expense; while for the Islamic banks, it is the total income distributed to depositors and shareholders’ fund. 16 Non-interest-income for the Islamic banks is income from fee-based transactions. 17 Please refer to Hannan & Hanwreck (1988) for the detail derivation of the Zrisk index. 18 The Lending structure measures for LCC, SPEC and VART are adopted from Ibrarim and Amin (2004), Amin Gutierrez and Ferrantino (1997; 1999). 19 They are agriculture, hunting, forestry and fishing; mining and quarrying; manufacturing; electricity, gas and water; broad property sectors (construction, real estate, purchase of residential landed property, and purchase of non-residential of landed property); wholesale, retail trade, restaurants and hotels; transport, storage and communication; finance, insurance and business services; purchase of securities; purchase of transport vehicles; consumption credit; and others.


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