The ‘competition–stability/fragility’ nexus: A comparative analysis of Islamic and conventional banks

TLDR: In this paper, the authors investigated the relationship between competition and stability/fragility in different countries and regions, using data from both types of banks drawn from 16 developing economies over the period 2000-12.

About: This article is published in International Review of Financial Analysis.The article was published on 2017-03-01 and is currently open access. It has received 117 citations till now. The article focuses on the topics: Lerner index & Quantile regression.

Figures

Figure 7 These graphs present the impulse response functions to shocks, PVAR baseline model with four variables: GDP, Size (log of total assets), Lerner and DD for all sample banks. The solid lines plot the impulse responses. The dashed lines are 5% standard error confidences around the estimate. Errors generated by Monte-Carlo with 500 repetitions.

Table 1 This table presents the definition of the variables used in the analysis and their sources

Figure 3 These graphs present the impulse response functions to shocks, PVAR baseline model with four variables: GDP, Size ( log of total assets), Lerner and Z-score for Islamic banks. The solid lines plot the impulse responses. The dashed lines are 5% standard error confidences around the estimate. Errors generated by Monte-Carlo with 500 repetitions.

Table 4 This table presents the variance decomposition for all, conventional and Islamic banks. Each row represents the variance decomposition of the column variable. Each cell shows how much the column variable affects the variance of each row variable.

Figure 8 These graphs present the impulse response functions to shocks, PVAR baseline model with four variables: GDP, Size (log of total assets), Lerner and DD for conventional banks. The solid lines plot the impulse responses. The dashed lines are 5% standard error confidences around the estimate. Errors generated by Monte-Carlo with 500 repetitions.

Figures 10–12 plot the coefficients obtained from the different quantiles for all, conventional, and Islamic banks, respectively. These figures illustrate a one-unit change of in the LI on stability, all else held constant. The vertical axis is the covariate effect and the horizontal line represents the different quantile scale. As reported earlier, while in both all and conventional banks the estimated coefficients increase with the quantile, they decrease for Islamic banks. One notable similarity across all of the bank types is that from the 80th quantile onwards, the coefficient has a decreasing trend, indicating safer banks benefit less in reducing credit risk by gaining market power than riskier banks. Altogether, we have established a positive relationship between market power and stability, thereby supporting the competition– fragility hypothesis.

Frequently asked questions

Q1. What are the contributions in "The 'competition-stability/fragility' nexus: a comparative analysis of islamic and conventional banks" ?

In this paper, the authors investigated the competition-stability/fragility hypothesis in the context of Islamic and conventional banks and found that the competition can enhance bank stability through bringing about efficiency, promoting new product innovation and enhancing loan portfolio diversification. 

Q2. What did the authors do to investigate the relationship between competition and stability?

The authors also derived impulse response functions and undertook variance decompositions to investigate the relationship between competition and stability. 

Q3. What is the reason for having popularity of the Z-score as a proxy of financial?

One of the important reasons for having popularity of the Z-score as a proxy of financial stability is that it can be computed for both listed and unlisted banks, whereas market-based credit risk measurement such as DD can be calculated only for listed banks. 

Q4. What is the effect of the Lerner index on the NPL ratio?

That is, as banks get larger, they have more resources for better screening and monitoring procedures, and more flexibility in rejecting marginal loan applicants, and thus lower their NPL ratios. 

Q5. How did Keeley (1990) show that an increase in competition accounted for a?

Using a state preference model, Keeley (1990) showed that an increase in competition accounted for a decline in the charter value of the bank, thereby increasing the probability of bankruptcy. 

Q6. What is the effect of the LTD on the NPL ratio in conventional banks?

the loan to deposit ratio (LCD) has no significant impact on the NPL ratio in either conventional or Islamic banks, while the Economic Freedom Index (EFI) is significant only for conventional banks. 

Q7. What is the symmetry condition and linear homogeneity restrictions?

To impose the symmetry condition and linear homogeneity restrictions, the authors divide total cost and the prices of all inputs by the price of labor. 

Q8. What is the effect of LI on the NPL ratio for all banks?

As shown, LI has a significant negative impact on the NPL ratio across all quantiles for all banks, implying that an increase in market power significantly lowers the NPL ratio, thus supporting the competition–fragility hypothesis. 

Q9. What countries were selected to investigate the competition–stability hypothesis?

To investigate the competition–stability hypothesis, the authors selected 16 countries with both Islamic and conventional banks over the period 2000–12. 

Q10. What methods are used to estimate the relationship between competition and stability?

the authors use two different methodologies, namely, PVAR and two-stage quantile regression, to estimate the relationship between competition and stability. 

Q11. What is the main reason why Islamic banks have grown so rapidly?

This has attracted the attention of policymakers, regulators, and investors, particularly after the recent global financial crisis, when the possibility emerged that Islamic banking could serve as a viable alternative to the conventional banking system. 

Q12. What are the impulse response functions and variance decompositions?

To understand further the relationship between competition and stability, the authors direct their attention to the impulse response functions and variance decompositions. 

Q13. What data sets were used to examine the competition–stability hypothesis?

For instance, Nicoló et al. (2006) examined the competition–stability hypothesis using two different data sets, one cross-sectional data on US banks and the other bank-year data from 134 nonindustrial countries.