**4.1 Result of full sample**

We report the parameter estimates of risk, capital, and efficiency in a system of equations that account for fixed effects and country-time effects (see **Table 1**). The relationship of risk and cost efficiency is not evidenced as the coefficient is not significant. The small but significant and positive coefficient of L.CAP indicates that higher capital ratio can improve cost efficiency as larger capital reduces the conflict between shareholders and debtholders, thereby lower agency cost [22]. In the five ASEAN countries, commercial banks that are better capitalized appear to be more efficient. This finding is consistent with results from the study of Tahir and Mongid [13] in ASEAN region and Prakash et al. [14] in Indian banks. Evidence of reverse causation from efficiency to capital is not supported. Lagged capital significantly decreases credit risk as proxied by loan loss; and vice versa, risk decreases capital significantly (at the 5% level). This bidirectional relationship supports moral hazard behavior also found by other researchers [1, 8, 45].

To confirm the effects of the three variables, we look at how one variable responds to the shock in another variable. The graphs of impulse-response functions and variance decomposition (**Figure 1**) can help explain those relationships.


*Note: CE, LLOSS, CAP stand for cost efficiency, credit risk, and capital ratio respectively. L.LLOSS, L.CAP, L.CE are lagged value of the three variables. p-value reported in parentheses. \*\*\*, \*\*, and \* indicate 1, 5, and 10% significance levels respectively.*

### **Table 1.**

*Results of full sample.*

Row 2, column 1 of **Figure 1** displays the response of cost efficiency to a shock in capital, confirming the result of **Table 1** in visual form. The positive response of cost efficiency to the impulse in capital supports the agency cost shareholders-debtholders hypothesis. The response reaches the peak in year 2 and then reverts to zero after 10 years.

Row 2, column 3 of **Figure 1** shows the response of credit risk to the shock in capital. The response is negative and significant, bottoming out in year 3 and converging to equilibrium after 10 years. Large confidence interval after year 3 suggests cautious conclusion on long-term causality. The result supports moral hazard theory where capital can have impact on risk-taking. Row 3, column 2, depicts the response of capital to a shock in risk. Capital appears to decrease following an increase in credit risk. This negative influence bottoms out after 2 years and then reverts to zero. We observe a bidirectional causal relationship between capital and risk from the IRF graph, confirming evidence of moral hazard behavior.

The variance decomposition (VDC) analysis reported in **Table 2** shows the percentage of variation in one variable that is explained by the shock in another variable.

Twelve percent of variation in cost efficiency is explained by the shock in capital and only 2.69% of cost efficiency justified by risk. The variance decomposition of cost efficiency confirms again the hypothesis of agency cost shareholders-debtholders. The explanatory power of capital on variation in risk is 9%, whereas the explanation of efficiency on risk is negligible. This result suggests that banks with low capitalization take on higher credit risk due to nonperforming loans, implying the moral hazard behavior. Credit risk and cost efficiency explain only 2.78 and 4.06% of the variation in capital. The significant influence of bank capital on credit risk and cost efficiency

*Causal Relationship Among Bank Capitalization, Efficiency, and Risk-Taking in ASEAN… DOI: http://dx.doi.org/10.5772/intechopen.109120*

### **Figure 1.**

*Impulse-response functions for risk (LLOSS), capital (CAP), and cost efficiency (CE).*


*Note: Each cell indicates the percentage of variation in the row variable over a 10-year period explained by a shock to the column variable.*

### **Table 2.**

*Variance decompositions for the full sample.*

advocates the enhancing capital base of banks from regulatory capital requirement as enforced by international Basel standards.

### **4.2 Result of subsamples**

In what follows, we divide our data two subsamples, one of banks with lagged cost efficiency higher than the median, and one of banks at or below median efficiency.

**Table 3** presents results for subsamples of high- and low-efficiency banks. In the efficiency equation, capital causes an increase in cost efficiency, but the result is significant only for the group of low efficiency banks. The result of capital equation shows contrasting relation between efficiency and capital for the two groups. A shock in cost efficiency results in increase in capital of low-efficiency banks implying evidence of franchise value hypothesis. Low-efficiency banks tend to preserve their franchise value generated from lower returns to protect the banks from financial distress; hence, they will not assume the risk of lowering their capital base. Nevertheless, in high-efficiency banks, increase in efficiency causes a decrease in capital, supporting efficiency risk hypothesis. High efficiency banks can take on higher leverage and maintain less capital because of lower expected costs of financial distress. These findings confirm the breakpoint in the association of capital and efficiency


### **Table 3.**

*Result for subsamples of high- and low-efficiency banks.*

between high-efficiency and low-efficiency banks in the study of Bagntarasian and Mamatzakis [25] in European banks. The risk equation shows a significant negative causation of capital on risk for both subsamples. The lagged cost efficiency has negative coefficient but significant only at 10% suggesting weak evidence of bad management behavior for both high and low efficiency banks.

An impulse response function graph may help explain the impact of cost efficiency on bank capital (**Figure 2**).

The response of capital to a shock in cost efficiency is displayed in Row 1, column 2 of **Figure 2**. Contrasting responses are shown in the graphs of the two subsamples. Visual evidence in high-efficiency banks confirms the efficiency risk hypothesis where the shock in efficiency causes negative response in capital and the effect reaches the trough after 3 years and reverts to zero. High-efficiency banks expect high earnings from better efficiency to substitute for equity capital in the event of financial distress [10]. For low-efficiency banks, following the shock in cost efficiency, banks respond by an increase in capital with peak reached in 2 years and subsequently

### **Figure 2.**

*Impulse response functions for subsamples. Subsample of high-efficiency banks. Subsample of low-efficiency banks.*

*Causal Relationship Among Bank Capitalization, Efficiency, and Risk-Taking in ASEAN… DOI: http://dx.doi.org/10.5772/intechopen.109120*


### **Table 4.**

*VDC for subsamples of high- and low-efficiency banks.*

converge to equilibrium after 10 years. This result provides visual evidence for franchise value hypothesis. Low-efficiency banks tend to protect their economic rent by increasing capital.

The result of variance decomposition of the two subsamples in **Table 4** is consistent with findings from IRFs. In the group of high-efficiency banks, capital shock can explain 19.72% of the variation in cost efficiency. The franchise value hypothesis has strongly evidenced in VDC analysis of low-efficiency banks with 20.49% explanatory power of cost efficiency on capital. On the contrary, in high-efficiency bank subsample, evidence of efficiency risk hypothesis is weaker with only 8.27% explanation. Bad management behavior is apparently evidenced in low efficiency banks with 12.59% risk variation explained by cost efficiency as compared to only 3.61% in the group of high-efficiency banks. Capital explains 11% of forecast error variance in risk for high-efficiency banks but only 8% for low-efficiency banks. The result supports the moral hazard hypothesis.

### **4.3 Sensitivity analysis**

The empirical study is extended to how the causal relationship of capital, risk, and efficiency is sensitive to the differences in ownership type, size of banks, as well as the 2008 global crisis.

### *4.3.1 Results for foreign banks and domestic banks*

Literature suggests that the behavior of banks can alter with different types of ownership. Therefore we divide the sample into two groups of domestic and foreign banks. **Table 5** reports parameter estimates of the model for the subsamples of foreign banks and domestic banks. There exists weak evidence of bad luck in foreign banks. The results indicate positive causation running from capital to efficiency, which supports agency cost shareholders-managers hypothesis for both foreign and domestic banks. However, the reverse causation is different among the two groups. In foreign banks, higher efficiency causes an increase in capital while efficiency in domestic banks causes a decline in capital. Bank capital negatively causes risk regardless of type of ownership, which is consistent with regulatory hypothesis.

The IRF graphs (**Figure 3**) confirm the regression results. The effect of one standard deviation shock in risk on cost efficiency is negative, bottoming out within a year and reverts to zero as shown in row 3, column 1. Row 2, column 1 indicates the shock of capital on efficiency with longer impact of more than 2 years for domestic banks as compared to 1-year influence in foreign banks. Different responses of the two groups are visualized in row 1, column of **Figure 3** as foreign banks have higher capital in a year and domestic banks reduce capital in 2 years following an efficiency shock.

