This study examines the relationship between risk, capital and inefficiency in ASEAN banking. We test whether bank risk taking is related to its capital position and inefficiency. In this study, measures of risk, capital and inefficiency were based on accounting ratios. Data for the study includes seven countries in ASEAN taken from Bankscope database for the period 2003 to 2008. A 3-stage Least Squares method is used to capture endogenity between risk, capital and inefficiency and to avoid simultaneous bias for estimated coefficients when they are estimated separately. On the risk equation, in general, the results indicate that capital and inefficiency are negatively related with risk. On the capital equation, there is negative relationship with risk but not with efficiency. In the inefficiency equation, the results indicate that capital and are negatively related to inefficiency. However, risk surprisingly is not significant.
Keywords: risk, capital, inefficiency, 3 stage least squares method, ASEAN banking
1. Introduction
Banking is the most regulated industry in the world. Apart from the product and its service, banking regulation also cover its institution. The aim of the bank regulation is to increase prudential practices that will reduce the level of risk bank are exposed to. Furthermore, bank is also very important to the economy as the failure of banking will inhibit economic crisis. This motivation is known as systemic risk reduction motivation. In general, banking regulation is for the interest of depositors. In general capital regulation is very important because it plays an important role in banks' health and risk taking behavior, and its impact on competitiveness banks. In practice, a key aspect of the regulatory capital is calculated minimum regulatory capital, which is usually based on credit.
The most important part of banking regulation is regulation on capital. According to Mehta and Fung (2004), capital regulation has rooted since 1930. USA is a pioneer in such regulation when they proposed a proposal in 1986 that require US bank to maintain capita that reflect the riskiness of bank asset. After the establishment of Basel Committee on Banking Supervision (BCBS), in July 1988, Central bank governors endorse BCBS's document "International Convergence of Capital Measurement and Capital Standards", or "Basel Capital Accord", to be implemented by the end of 1992. The aim is to prevent banks from excessive risk-taking, regulators soon tried to link the required capital to the risk of the loan portfolio. By 1988, the time of the first international initiative (Basel Accord), most countries had already introduced one or another form of risk-sensitive capital regulation. The Basel Accord was signed by the G10 countries and was intended to apply only to internationally active banks. The accord assigns assets to different risk buckets. The assets in a bucket have to be backed by a bucket-specific capital requirement and the total minimum requirement is set at 8% of capital to risk-weighted assets. (BIS Web)
As capital availability is relatively constant, it means it is becoming a constraint to engage on expansion other risky business. It makes banks are forced to manage the risk efficiency to prevent from putting more capital. The so called risk management process is aims to the purpose of allocating capital efficiently in order to obtain optimal benefits and reduce cost of capital. The method used was the Bank earning assets as well choose the activities that banks may be effectively measured in terms of risk and risk adjusted return of company culture, the ability of capital, organization and infrastructure. It is important for banks to understand business issues and investment in which the Bank to invest so that the Bank may benefit optimum amount of capital, risk and return.
The objective of this study is to assess the relationship between risk, capital and their impact to efficiency in ASEAN banking. This kind of study using ASEAN banks is, as far our knowledge, is not available. Ahmad et. al. (2007) discuss the determinant of capital ratio in Asian banking as a one way process. Jeitschko and Shin (2007) on the other hand, study on the relationship between portfolio risk and capitalization in Korean banking. In adition, Konishi and Yasuda (2004) analyze the factors determining the risk taking behavior among Japanese commercial bank. This study is an effort to fill the gap in empirical study on this area in ASEAN setting.
2. Literature Review
Every study on capital in the banking industry departed from the same point with theory of capital structure in the financial theory. The most referred paper is the theory of the frictionless world of Modigliani and Miller. The Capital Structure Theory is the first modern theory of Modigliani and Miller (MM theory). They argue that capital structure is irrelevant or does not affect the value of the company. However if we look at the bank capital structure, the MM is not valid because of two things; first, the presence of the regulatory safety net that protects safety and soundness of banking and likely to lower capital. Second, regulatory capital requirement that raise capital of some banks, may give negative impact to the value of the banking firm.
Many studies focused on the relationship between risk and capital especially after the introduction of minimum capital regulation. Capital regulation is one of important tools that is used to prevent bank from failure. However, theoretical literature offer contradictory results as to the optimum design of capital adequacy regulation and to the effect of capital regulation on bank risk taking incentive and performance. It means theoretical issue of how higher capital ratios reduce overall banking risk is not yet solved entirely. On the other hand capital regulation should be set as a part of other prudential regulation (Altunbas et. al, 2007).
Capital regulation is one of key instrument of modern banking regulation. The regulation aims to increase a cushion during economic crisis and a mechanism to restrain bank to take excessive risk taking. During economic downturn, the quality bank asset decrease impacted to reduce capital. As we know, theoretical foundation on the relationship between capital and risk mainly base on theory of moral hazard that existed because agency problem. Capital. They tested whether increase capital regulation forces bank to increase their risk or vice versa (Jokipii and Alistair & Milne, 2009). Shrieves and Dahl (1992) argue that positive relationship between key variables in line several hypotheses which include the unintended effect of minimum capital regulation, regulatory cost; bankruptcy cost avoidance as well as managerial risk aversion. Jacques and Nigro (1997) on other hand find a negative relationship between change in capital regulation and risk level.
Empirical evidence on the relationship between capital requirement and risk taking is mixed. In USA study by Sheldon (1996) find that asset volatility rise and decrease for both bank that increased capital and that did not. Calem and Rob (1999) quantify the effect of capital based regulation and find the U-shaped relationship between capital and risk taking. The U-shaped means that undercapitalized bank take maximum risk and a bank's capital rise they take less risk. When capital increases again, they will take higher risk again. They find that undercapitalized bank take higher risk because the cost of bankruptcy is shifted to deposit insurance. For well capitalized banks, they take higher risk because it is more profitable and low probability of bankruptcy. In Japan, higher capital requirement is responded by lowering asset volatility. Konishi and Yasuda (2004) analyzing the factors determining the risk taking behavior among Japanese commercial bank and found that risk taking activities are reduced when capital regulation is introduced (see Ford and Weston, 2008)
Iannota et. al. (2006) compare the performance and risk of 181 large banks from 15 European countries over the 1999 - 2004 related to the ownership type. After controlling for size, output mix, asset quality, country and years effects they find that ownership type and ownership concentration play important role on risk and performance. Private bank is more profitable than mutual and public bank. However, private banks are more profitable due to their earning asset structure rather than from superior cost efficiency. Public sector banks have poorer loan quality and higher insolvency risk. This means public bank is relatively less profitable and riskier than other type of ownership. Public banks rely their funding on wholesale interbank and capital market but they have higher liquidity and lower loan level. It is different from private bank that rely their funding from customer deposit and provide more loan. For the mutual bank, the behavior is similar to private bank in term of favorable customer relationship, higher loan ratio and quality. In term of cost, private and mutual banks have lower operating cost. In term of ownership concentration, there is no much different impact on profitability. However when the ownership concentration linked to other variable such as loan quality, asset risk and insolvency risk, higher concentration attributable to better loan quality, better asset risk and lower insolvency risk. Dispersed ownership banks are incurring higher cost per dollar income than concentrated one. It is in accordance to agency theory framework.
Brewer et. al. (2009) study the determinant of capital ratios across 12 countries in Europe, USA, and Japan. They model bank capital ratio as function of public policy, regulatory, bank specific, macroeconomic and country level financial condition. The model estimated with annual data from 1992 to 2005 for unbalanced panel the 78 largest private banks. The study found that banks maintain their higher capital ratio when the banking sector is relatively smaller and when regulator practice prompt corrective action more actively. Higher capital ratio is also related to the existence of stringent capital regulation and better good corporate governance mechanism. In general, capital ratio difference among counties under investigation is in part explained by the public policy and regulatory regime applied in the countries.
Kazion (2009) studies the role of capital in the bank's danger of a default and its implication for regulatory purposes. Using dynamic model, bank can adjust its deposit to a desired level in continuous -time model, Bank adjust the volume of its deposit voluntary, because of two purposes: reduce leverage or increase deposit volume. As the bank must comply with the leverage regulation, any increase in deposit will end to capital binding. If restructuring asset cost apply, when bank increase deposit, bank must incur the cost and reduce the deposit to prevent from a violation of the regulation in the future. The findings are: in line with empirical studies, banks do not hold the minimum capital but have voluntary capital buffer. When banks do not have attractive investment possibility, bank prefers to reduce the deposits and increase it later in the future. This deposit reduction strategy also reduces the default cost (asset quality) and can be considered as a countervailing effect. Surprisingly, when the higher volatility of asset value and a lower deposit growth exist, end to lower cost of default cost.
Lindquist (2004) studies the excess capital both for commercial and saving bank in Norway using panel data approach. In general, saving bank is holding more capital than commercial banks. In relation to the risk, saving bank excess capital has negative relationship. Effect of credit risk to excess capital is not significant but previous profit is. In general, high risk bank is not poorly capitalized but in reality low risk bank is too much capital. In connection to price of subordinated debt, there is negative relationship which supports the assumption that excess capital is insurance against the cost related to market discipline and supervisory action due to lower capital condition. Small bank hold higher capital buffer than big banks. GDP growth is not significant to influence the capital buffer.
3. Data and Variables
3.1 Data
In this study we will use a panel set of individual commercial bank from economically important countries in pacific region from 2003 to 2008. Data is collected from bank's balance sheet, income statements and off-balance sheet obtained from the Bankscope database to construct standard accounting measure of banking activities. The sample will cover banks from 8 countries in ASEAN; Indonesia, Malaysia, Thailand, the Philippines, Singapore, Cambodia, Brunei and Vietnam. Samples are selected merely base on the availability of the data in the Bankscope database. All variable in this study are measured in thousand US dollar. Table 1 presents the distribution of samples for the study.
Table 1: Sample Distribution, 2003-2008
Number
Countries
Samples
Contribution
1
Indonesia
226
34
2
Malaysia
110
16
3
Thailand
95
14
4
The Philippines
71
11
5
Singapore
24
4
6
Cambodia
110
16
7
Brunei
26
4
8
Vietnam
6
1
Total
668
100
From the sample, Indonesian banks had the largest sample (34 percent), followed by Malaysia and Vietnam 16 percent each, Thailand 14 percent, Singapore and Cambodia 4 percent and Brunei 1 percent.
3.2 Variables
Variables to be used in this study are variable that theoretically and empirically plausible. The variables and definition are presented below:
Table 2: Variables Used
Variables
Definitions
Risk
Loan Loss Reserve to total loan
CAP
Total Equity to total assets
INEFF
Cost inefficiency is measured by total banking cost divided by total income
NLTA
Net loan to total asset
SIZE
Logarithm of total asset as indicator of bank size operation.
ROA
profit before tax to total asset as indicator of profitability.
OBSTA
ratio of off balance sheet activities to total asset.
IRC
Total interest revenue divided by total asset
We expect that that risk has negative relationship with capital, positive to inefficiency, size and net loan to total asset. On the capital side, we expect the negative relationship with risk meaning that less capitalized bank take excessive risk. On inefficiency side, we expect positive sign with capital meaning well capitalized bank operate less efficient. Risk will produce negative sign as riskier bank increase inefficiency. In term of size, we expect negative sign as it means the economies of scale hold.
4. Methodology
From the literatures above, they underline that relationship between capital and risk are made simultaneously and are interrelated. This situation is called as endogenity. Since the relationship between capital and risk is a over-identified simultaneous system, if we use the OLS to run the estimation, we may have simultaneous bias and inconsistent problem in the estimated results. Capital equation is over-identified, it means the reduced-form method cannot be used to get the exact estimation indirectly, because there will be more than one solution to obtain the original postulated parameters (α) from the estimated coefficients of reduced-form equations.
Implication is the modeling requires the use of a simultaneous equation specification and estimation methodology. To simplify, we follow the approach adopted by Altunbas et. al. (2007), using level data. This approach solves the availability of the data. To make possible for simultaneous estimation between bank risk, bank capital and bank operating efficiency, a system equation is used and estimated using three stage least square (3SLS) approach using panel data technique as follows:
Risk = α0 + α1 CAP + α2 INEFF + α3 Size + α4 NLTA (1)
CAP = β0 + β 1 RISK + β 2 INEFF + β 3 SIZE + β4 ROA + β5 IRC (2)
INEFF = γ0 + γ1 CAP + γ2 RISK + γ3 SIZE + γ4 OBSTA (3)
To estimate the equation (1), equation (2) is used as instrumental variables. As the Three -Stage Least Squares (3SLS) has been programmed in STATA, we will use this software to estimate regression equation. The use of 3SLS is necessary as it will avoid simultaneous bias for estimated coefficients.
Several studies have focused on understanding the relationship between risk and capital. They tested whether an increase in capital regulation forces bank to increase their risk or vise versa (Jokipii and Alistair & Milne, 2009). Shrieves and Dahl (1992) argue that positive relationship between capital and risk is in line to several hypotheses which include the unintended effect of minimum capital regulation, regulatory cost, bankruptcy cost avoidance as well as managerial risk aversion. Jacques and Nigro (1997) on other hand find a negative relationship between change in capital regulation and risk level.
According to Deelchand and Padgett (2009), their study confirmed that risk, capital and efficiency are determined simultaneously. Using Japanese cooperative banks, empirical model shows a negative relationship between risk and level of capital. Inefficient cooperative banks operate higher risk but also hold more capital. The situation may reflect the existence of moral hazards problem. In this study, basically we adopt approach taken by Deelchand and Padget (2009) and Heid et. al. (2003), who treat risk, capital and efficiency, simultaneously. However, their approach is not fully adopted as their efficiency measure is specified using stochastic frontier approach (SFA). We use accounting ratio to measure efficiency i.e., cost to income ratio.
5. Result
Before we conducted estimation, we did unit root test to see if the data is stationery or not. As the data is a mixture of time series and cross sectional, and the sample period is only six year series, the risk that the data tend to be non stationary is viable. In addition, some observations have been deleted due to unavailable problem. This make the time frame become less evident. To solve the problem, we conduct simple Augmented Dickey Fuller (ADF) and found that all variable is stationer at 1 percent level. We also performed Hausman testing to investigate the endogenity. We compared the residuals and predicted value to see the correlation and found that there is no significant correlation. It means the endogenity exist and the OLS and 3SLS estimator should differ only by sampling error.
Table 3 presents the description variables used in this estimation. From 668 observations, we can see that variable RISK has a mean value of 6.33 meaning that on average the ratio of loan loss reserve to total loan is around 6.33 percent with minimum ratio is zero and maximum value is four times of its loan. In term of SIZE, the mean is 14.31 in log value. The loan is still very dominant asset in ASEAN Banking. It can be seen from the NLTA where the mean is 52.4 percent and the maximum value is 90 percent. Off Balance sheet to total asset (OBSA) is also quite dominant in ASEAN Banking. The mean value is 30 percent of asset and standard deviation 112 percent.
Table 3. Descriptive Statistics of Variables
Variable
N
Mean
Std. Dev.
Min
Max
RISK
668
6.333467
19.78811
0
403.7348
SIZE
668
14.31244
1.837777
9.890858
18.9994
NLTA
668
52.39291
18.20338
1
90
OBSTA
668
29.44967
111.929
0
1524.05
CAP
668
13.3388
9.825273
-35.0247
74.12313
IRC
668
3.264568
1.625736
.0272294
14.11911
INEF
668
52.86924
25.60653
3
269.
ROA
668
1.741238
1.554551
-6.958635
8.638091
IRC, as a ratio of interest income to asset, has mean value 3.3 percent with standard deviation of 1.63 percent. The minimum value is 0.027 percent and maximum value is 14.11. INEF as a measure of cost to income ratio indicate the inefficiency level; higher the value, higher the inefficiency. The most inefficient is 3 percent and the most inefficient is 269 percent. The mean value is 52 percent with standard deviation of 25.6 percent. For ROA, the mean value is 1.74 percent and standard deviation is 1.6 percent. The lowest is -6.96 percent, meaning that the bank is experiencing a loss. The highest value of ROA is 8.64 percent.
Table 4 present the capabilities of the model to link risk, capital and efficiency. In general the model is capable to explain the relationship between capital, risk and efficiency. All chi-square is significant at 1% meaning that at least one instrumental variable (IV) have non zero relationship with endogenous variables: RISK, CAP and INEF. Exogenous variables used in this study are SIZE, NLTA, ROA, IRC and OBSTA. We did not consider R-Squared as this measure is not usable in 3SLS as the model in 3SLS focuses more on structural relationship.
Table 4: Capability of the model
Equation
N
Parms
RMSE
R-sq
Chi-square
P
RISK
668
3
41.67017
-4.4476
60.36
0.0000
CAP
668
6
8.591538
0.0838
789.82
0.0000
INEF
668
4
43.54805
-1.8841
654.74
0.0000
Table 5 presents the regression result for the estimation of the risk equation derived from the simultaneous regression. In this model, risk is defined using accounting data. An accounting measure of banking risk is derived from the ratio of loan to total asset. We used this term to link with portfolio theory on the relationship between risk and return. Bigger loan portion, the bigger profitability of the bank in the future. The used non performing loan breach this relationship.
The CAP as a measure of equity to asset has negative relationship and significant with risk. It means stronger capital is associated with less risk taking behavior. This relationship provides further evidence that banking sector in ASEAN behaves similarly from other study that provides negative coefficient. It means that the possibility of moral hazard by increasing risk to get higher return on the cost of depositors is valid. When the deposit insurance is exist, the evident provide similar evidence from US setting that lower capita tend to increase risk. The moral hazard problem may exist due to various reason for example binding capital regulation in the area is less effective.
Table 5: Determinant of Risk Taking
Coef.
Std. Err.
z
P>z
CAP
-.0338
.0107
-3.13
0.002
INEF
-.0075
.0022
-3.30
0.001
SIZE
-.0934
.0287
-3.25
0.001
NLTA
.9994
.0006
1431
0.000
_cons
1.760
.5420
3.25
0.001
In term of cost to income ratio (INEF), the result is quite plausible. The coefficient of INEF is negative meaning the inefficient bank tend to be prudential by reducing risk. This situation is supported by unsophisticated market where basically small bank is very difficult to find investment opportunity other than loan. Inefficient banks are more sensitive to risk because they understand when they make loss, their bank will be easily operating under less capital that may cause action from bank regulator. This action may end to the closure or being taken over by other investors. This finding also support the view that inefficient bank is very sensitive to risk taking than efficient bank because the implication of risk taking behavior can be substantial to their capital. In other side, bank size (SIZE) is also risk sensitive. The coefficient for SIZE is negative mean that bigger bank is relatively more capable to reduce risk by more diversified portfolio and risk management especially credit risk management. Furthermore, if we look the SIZE, the result provide evident that bigger bank take lower credit risk as the coefficient is negative and significant.
The coefficient for NLTA is positive and significant meaning the existence of linear relationship between net loan and risk taking. Higher net loan to total asset can expose to higher credit risk. When the portion of loan to asset is bigger, it means bank asset is dominated by loan. In ASEAN countries where most important role of banking industry to perform intermediation, higher portion of NLTA has positive contribution to the credit risk.
Table 4 present the report of for CAP equation. We expect that risk has negative relationship with capital meaning that bank with higher risk taking has higher capital. In this equation we found that higher risk has negative and not significant coefficient. The negative sign means higher risk taking has negative association with lower capital. This finding confirms that the capital regulation is not binding strictly in ASEAN countries and enough room for bank to escape from this situation. Bank with substantial amount of problem loan is forced put more provision and this regulatory action usually implicated to lower capital.
INEF as a measure of operating inefficiency has negative sign it is not significant. The result confirm that less efficient bank will have less stronger capital position because less capacity to accumulate more capital from profit. Bank with higher inefficiency tend to less capable to generate profit. As capital is largely depending on the capability to generate internally, inefficient bank will be not capable to generate capital internally.
F.3 Capital Equation
Table 6: Capital Equation
Coef.
Std. Err.
z
P>z
RISK
-.0117
.0447
-0.26
0.794
INEF
-.1916
.3013
-0.64
0.525
SIZE
-2.5269
.6420
-3.94
0.000
ROA
318.5829
3658.523
0.09
0.931
IRC
-.0151
1.5399
-0.01
0.992
_cons
48.2308
25.5799
1.89
0.059
We found that capital is negatively related to risk mean when credit risk increase, bank capital tend to decrease when risk is higher. This relationship is plausible and indicating that higher risk means less capital. It is because we use loan losses to measure risk. It is basically refer to credit loss (ex post) rather than credit risk (ex ante). SIZE has negative coefficient and significant. The explanation can be provided by nature of market and regulatory conditions. Bigger banks enjoy bigger guarantee in term of failure. The ideology of "too big too fail" may reduce the intention to put more capital. In addition, additional capital is also costly for shareholder. In market perspective, bigger bank size also enjoy reputational benefit because of various reason such as network operation. Even the bigger bank own less capital ratio than small bank, public confidence is much higher than small bank and this reduce motivation for bigger capital.
ROA has positive relationship with capital as predicted before. However this relationship is not significant. The reason may come from the 1998 banking crisis in the region that end up to inflow of foreign investor and put more capital to their acquired bank. The domination of Indonesian bank in the sample is clear and at this time, foreign controlled banks occupy more than 65% of Indonesian banking asset. These reasons are also valid for other ASEAN countries. It means strong capital does not necessarily come from internal capital formation. The IRC, ratio of interest revenue to total asset, as indicator how bank can generate revenue from its asset has negative coefficient meaning that bank with higher IRC tend to reduce capital ratio. It may indicate the higher interest revenue may not mean higher profit and then to capital. Referring to ROA which has positive coefficient, we may conclude that higher interest may come from higher risk.
IRC as ratio of interest revenue divided by total asset produce negative relationship with CAP. It is quite strange as bigger IRC means the bank can produce higher revenue. However as capital is measured by equity to total asset, implied that even when the bank can generate higher interest income, bank may still incur higher operating cost and loan loss provision. It means even the bank can generate higher interest revenue, if the profit is less, the impact is not plausible. This variable is also not significant.
F.5 Inefficiency Equation
Table 5 present INEF equation. From the table 6, we can see that bank capital (CAP) has negative coefficient and significant meaning that bank with higher capital operate more efficient. This finding is in accordance to many previous research which conclude the more capitalized bank has better efficient operation than bank with less capital. According to Berger and Young (1997), well capitalized bank are better run.
RISK also provide very provocative result. Bank that higher risk profile, tend to operate more efficient than less risky bank. Higher risk means reduce inefficiency. It is rational because higher risk bank tend to get higher revenue and so reduce the operating inefficiency score. However risk is not significant. Bank with higher portion of loan in its portfolio tend to operate more efficient. In line to RISK, we expect OBSTA also provide similar result that more bank involve in off balance sheet activities, bank tend to operate more efficient. This situation can be explained by revenue side of OBS activities that generate more revenue. Bank that more actively involved in the OBS activities operate more efficient or less inefficiency. We found this expectation is not valid as OBSTA is not significant.
Table 7: Inefficiency Equation
Equation 3
Coef.
Std. Err
z
P>z
[95% Conf.Interval]
CAP
-4.755307
.4834654
-9.84
0.000
-5.702881
RISK
-.0714054
.0869631
-0.82
0.412
-.24185
SIZE
-12.22023
1.472972
-8.30
0.000
-15.1072
OBSTA
-.0005254
.0124289
-0.04
0.966
-.0248856
_cons
295.8286
26.92864
10.99
0.000
243.0494
Bank size, measured by logarithm of asset has negative coefficient with inefficiency. In other word, bigger bank is more efficient. The relationship is theoretically strong and can be explained by both economies of scale as well as economic of scope. Bank can enjoy higher efficiency when they can managed a larger amount of loan.
5. Conclusion
This study investigated the relationship between risk, capital and efficiency based on accounting data. Using three stage least square regression model (3SLS), we found that bank risk taking is ASEAN has negative relation with capital position meaning that higher capital bank tend to reduce the risk. Inefficient bank tends to reduce risk because of two reasons. First, they do not want to get regulatory action when their risk taking end to loss. Second, because they are not efficient, therefore they tend not to take more risks. Furthermore, bigger banks tend to have lower risk than smaller one because the bigger one can generate income from other businesses such as cash management and other fee generated activities. Banks that owns more portion on loan in their portfolio has positive relation to risk. In this risk equation, we can conclude that capital general capital and inefficiency are negatively related.
In capital equation, there is negative relationship between risk and capital. Bank that has higher risk tend to have lower capital but his relation is not significant. Inefficient bank has negative relationship with capital and not significant. Size has negative relationship with capital meaning bigger bank tend to own less capital. This finding is not surprising because for bigger banks, they can attract more capital at faster and lower cost than smaller banks. In addition, bank with bigger size has lower capital because enjoy implicit guarantee form "too big to fail" principle. In this equation, there is no relationship between risk and inefficiency.
In inefficiency equation, we found that capital is negative and significant. It indicates bigger capital ratio increases efficiency of banking firm in ASEAN. However, risk is not significant. In term of size, bigger bank enjoy better efficiency than smaller bank. This relationship can be explained by economies of scale and scope. Risk is not significant to efficiency. Off-balance sheet (OBS) has negative relationship though not significant. It means bigger OBS activities increase operating efficiency though the coefficient is not significant. In this equation, we can conclude that inefficiency is determined by capital and size meaning more capital and bigger size can improve the operating efficiency of banking firms in ASEAN. Policy implementation is the effort by banking regulators in ASEAN to require banks putting capital should be intensified.
However, this study is without limitations. The data in this study is annual data that means a dynamic relationship between risk, capital and efficiency cannot be tested. We can conclude in this study that in general relationship between risk, capital and efficiency exist. This study is different from study by Shrives and Dahl (200) that used time series data that can cover the impact of capital regulation on risk taking. This study also excludes macroeconomic and regulatory variables. However as this kind of study is not performed before, future research should cover regulatory as well as macroeconomic condition of each countries. More importantly, definition of risk and capital should be changed to observe stronger theoretical foundation and more realistic base on regional characteristic. For efficiency variable, future study should use economic efficiency to insulate the data from managerial as well as accounting bias.
