Search results

This paper aims to examine the effect of bank-specific, financial structure and macroeconomic factors on the risk-taking behavior, stability and profitability of banks in Gulf Cooperation Council (GCC) economies during 1998–2017.

The authors use a two-step system generalized method of moments dynamic model to analyze the data.

The results show that non-traditional activities increase the risk and decrease the stability and profitability of banks that are highly capitalized, highly liquid and large. Banks in this group are less engaged in securities investments and their higher degree of loan exposure leads to a decrease in risk and an increase in their stability and profitability. Higher concentration increases the risk and decreases the stability and profitability of banks that are less capitalized, less liquid and small. Banks with a higher share of non-traditional activities are riskier and less stable and less profitable before the financial crisis. The study finds that banks with relatively higher capitalization and high lending growth rates are riskier, profitable and less stable during the crisis. Larger commercial banks are less risky and more stable and profitable than smaller banks before the global financial crisis. Islamic banks performed better in terms of fee income, capitalization, liquidity, asset quality and have higher market concentration than conventional banks.

The study provides the first comprehensive empirical evidence on the drivers of risk-taking behavior, stability and profitability of the GCC banks. It also investigates the differences across these variables based on the characteristics of financial strength such as capitalization, liquidity and size; before, during and after the financial crisis; and differences between Islamic and conventional banks.

This study aims to investigate the factors that drive housing loan default in India based on unique micro-level data drawn from a public sector bank's credit files with a national presence in India. The authors address endogeneity in the loan to value ratio (LTV) while deciphering the drivers of default.

The study uses a probit regression approach to analyze the relationship between the probability of default and the explanatory variables. The authors introduce two instrumental variables to address the issue of endogeneity. The authors also add state-level demographic and several other control variables, including an indicator variable that captures the recent regulatory change. The authors’ analysis is based on 102,327 housing loans originated by the bank between January 2001 and December 2017.

The authors find that addressing the endogeneity issue is essential to specify default drivers, especially LTV, correctly. The nature of employment, gender, socio-religious category and age have a distinct bearing on housing loan defaults. Apart from the LTV ratio, the other key determinants of default are the interest rate, frequency of repayment, prepayment options and the loan period. The findings suggest that the population classification of branch location plays a significant role in loan default. The authors find that an increase in per capita income and an increase in the number of employed people in the state, which reflects borrowers' ability to pay by borrowers, reduce the probability of default. The change in the regulatory classification of loan assets by the Reserve Bank of India did not bear the main results.

The non-availability of the house price index in analyzing the default dynamics in the Indian housing finance market for the period covered under the study has constrained our analysis. The applicability of the equity theory of default, strategic default, borrowers' characteristics and personality traits are potential research areas in the Indian housing finance market.

The study's findings are expected to provide valuable inputs to the banks and the housing finance companies to explore and formulate appropriate strategic options in lending to this sector. It has highlighted various vistas of tailor-making housing loan product offerings by the commercial banks to ensure and steady and healthy growth of their loan portfolio. It has also highlighted the regulatory and policy underpinnings to ensure the healthy growth of the Indian housing finance market.

The study provides a fresh perspective on the default drivers in the Indian housing finance market based on micro-level data. In our analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Indian housing market not addressed by earlier papers on the Indian housing market. The authors also control for the regulatory changes in the Indian housing finance market and include state-level control variables like per capita GDP and the number of workers per thousand to capture the borrowers' ability to pay characteristics.

This study investigates factors contributing to residential mortgage loans default by utilizing a unique dataset of borrowers' default data from one of the pioneer lending institutions in Malaysia that provides home financing to the public. Studies on mortgage loan default have been extensively examined, but limited studies utilize the individual borrower's data, as financial institutions generally hesitant to reveal their customers' data due to confidentiality issue.

This study uses logistic regression model to analyze 47,158 housing loan borrowers' data for the year 2016.

The findings suggest that male borrowers, Malay and other type of ethnicity, guarantor availability, loan original balance, loan tenure, loan interest rate and loan-to-value (LTV) ratio are the significant factors that influence mortgage loans default in Malaysia.

Future studies may expand the sample by employing data from other types of financial institutions that would give greater insights as findings might vary due to differences in objectives, functions and regulations. In addition, the findings are subjected to the censoring bias where future studies could perform the survival analysis to control for censoring bias and re-validating the findings of the present study.

The findings provide valuable insights for lending institutions and the government to formulate housing loan policy in Malaysia.

To the best of the authors' knowledge, this is the first study in the context of emerging economies that uses financial institution's internal data to investigate factors of mortgage loan default.

The purpose of this paper is to empirically assess the efficiency (transaction efficiency, intermediation efficiency and profit efficiency) of the retail branches of a large bank and identify the driver parameters of the same.

The authors use the non-parametric data envelopment analysis approach to analyze the financial performance of 247 branches in 2014, spread over 14 states of a country. After checking for possible misspecification bias, the authors use the fractional regression approach in the second stage of the analysis to assess possible drivers of the efficiency of bank branches in terms of the size of business, funding mix, per employee contribution to business and profit and business per transaction. In addition, the authors included spatial parameters like economic condition and competitive position of branches in their analysis.

The authors find that on an overall basis, there might be a deliberate initiative of the top management of the bank to over-branch in order to improve the output at the aggregate level which is above the level of cost minimization. The study clearly indicates to the top management that low-cost deposit is a significant driver of branch efficiency apart from business per transaction, income per employee. Moreover, it is found that branches located in areas of high branch concentration are more efficient, and local economic condition does drive efficiency of branches.

The authors address the dilemma faced by the top management of banks in arriving at an appropriate scientific benchmark to assess the performance of branches based on the drivers of efficiency and initiate suitable strategic interventions to improve their efficiency.

The integrated assessment of the efficiency of bank branches and arriving at the drivers of efficiency using the fractional regression model framework are likely to prove beneficial in the benchmarking exercise of the performance of bank branches.

The purpose of this paper is to examine the drivers of default in the Malaysian housing market in the light of various policy interventions by the country’s central bank, and the government’s expressed concern to ensure balanced growth in the market. This paper assesses the importance of considering the endogeneity of loan-to-value (LTV) in predicting housing loan default and its implications.

In this paper, the author addresses the endogeneity problem in the LTV variable using two instrumental variables (IV) in this probit regression: national residential property gains tax and the statutory reserve ratio of Bank Negara Malaysia. This study uses the instrumental variable probit model to consider endogeneity bias. This study assumes a latent (unobservable) variable (Y*), representing a borrower’s tendency to default, which is associated linearly with the borrower’s and loan characteristics and other variables (Xi). This study uses individual borrower-level information of 43,156 housing loan borrowers from the files of a well-established housing bank in Malaysia.

This study’s results confirm that endogeneity causes a substantial difference in the magnitude of the estimated effects of LTV on the default tendency. At the lower values of LTV, the probability of default is over-estimated, and at the higher values, the default probability is substantially underestimated. Endogeneity bias also affects the estimated coefficients of loan and borrower characteristics. The authors find that the interest rate is less relevant in predicting loan default. Other loan characteristics, such as loan age, tenure, payment amount and the built-up area, are relevant. This study’s result confirms that the borrower’s location matters, and an increase in state gross domestic product per capita and an increase in the supply of residential units reduce default probability.

The present study did not explore the applicability of the “equity theory of default” in the Malaysian housing market. This study did not assess “strategic default” issues and the effect of borrowers’ characteristics, personality traits and self-control of Malaysian housing loan borrowers in the mortgage decision-making process. The evolving dynamics of the Malaysian housing market microstructure in property valuation remained unexplored in the present study.

The findings have crucial relevance in the decision-making process of commercial banks, the central bank and the government to frame policies to foster balanced growth and development in the housing market. The authors argue that striking a subtle balance between the concerns of financial stability and productive risk-taking by commercial banks in Malaysia remains a continuing challenge for the country’s central bank. The authors also argue that designing suitable taxation policies by the government can deliver its cherished goal of balanced development in the housing market.

Empirical research on the Malaysian housing market based on micro-level data is scarce due to a paucity of relevant data. This study is based on the individual borrower-level information of 43,156 housing loan borrowers from the files of a well-established housing bank in Malaysia. In this analysis, the authors find clear evidence of endogeneity in LTV and argue that any attempts to decipher the default drivers of housing loans without addressing the issue of endogeneity may lead to faulty interpretation. Therefore, this study is unique in recognizing endogeneity and has gone deeper in identifying the default drivers in the Malaysian housing market not addressed by earlier papers.

The authors analyse the determinants of loan defaults in micro, small and medium enterprises (MSME) loans in India from the survival duration perspective to draw inferences that have implications for lenders and policymakers.

The authors use the Kaplan–Meier survivor function and the Cox Proportional Hazard model to analyse 4.29 lakhs MSME loan account data originated by a large bank having a national presence from 1st January 2016 to 31st December 2020.

The estimated Kaplan–Meier survival function by various categories of loan and socio-demographic characteristics reflects heterogeneity and identifies the trigger points for actions. The authors identify the key identified default drivers. The authors find that the subsidy amount is more effective at the lower level and its effectiveness diminishes significantly beyond an optimum level. The simulated values show that the effects of rising interest rates on survival rates vary across industries and types of loans.

The identified points of inflection in the default dynamics would help banks to initiate actions to prevent loan defaults. The default drivers identified would foster more nuanced lending decisions. The study estimation of the survival rate based on the simulated values of interest rate and subsidy provides insight for policymakers.

This study is the first to investigate default drivers in MSME loans in India using micro-data. The study findings will act as signposts for the planners to guide the direction of the interest rate to be charged by banks in MSME loans, interest subvention and tailoring subsidy levels to foster sustainable growth.

The purpose of this paper is to examine and compare the impact of oil and gas prices shocks on the non-performing loans (NPLs) of banks at the aggregate as well as at the level of commercial and Islamic banks in Qatar over the period 2000-2016.

Using the West Texas Intermediate Database, BankScope Database, World Bank’s World Development Indicators Database, and International Monetary Fund Database, the authors use a one-step system generalized method of moments dynamic model to examine and compare the association between oil and gas prices shocks with NPLs in Qatari banks. The authors also test the hypotheses of direct and indirect impacts of oil price shocks and gas price shocks on bank NPLs.

The results indicate that oil price shocks and gas price shocks do not have directly affect NPLs of Qatari banks at the aggregate level, while they have indirect effects that are channeled through the country-specific macroeconomic and institutional factors. The authors find that oil and gas prices shocks affect NPLs of Qatari Islamic banks directly through extended oil and gas-related cash flows, while their impact on the NPLs of Qatari commercial banks is indirect. In other words, Islamic banks in Qatar greatly benefits from increased cash flow caused by the rise in the oil and gas prices, which make their NPLs, much lower than that in commercial banks. Better capital cushion, better managerial efficiency, better risk management, and liquidity management systems should be used by the Islamic banks in Qatar to expand their customer base. The authors also find that positive fiscal stance of the government reduces the NPLs in both commercial and Islamic banks.

The results of this study necessitate policy measures that can counter the effects of changes in oil and gas prices on the growth of bank NPLs.

It is widely recognized that oil and gas prices and the level of production are of great importance to the economic development of oil and gas-exporting countries. So far, however, no econometric study has been reported in the literature which analyses and compares the impact of oil and gas prices shocks on the NPLs of commercial and Islamic banks and also at the aggregate level in any of the oil economies. Thus, this study provides the first empirical evidence on distinct direct and indirect channels through which oil and gas prices shocks may affect bank NPLs.

The purpose of this paper is to investigate and compare the impact of oil and gas prices changes on bank deposits at the aggregate as well as at the level of commercial and Islamic banks in Qatar over the period 2000–2016.

Using the BankScope Database as well as bank-level balance sheet and financial statements data, the authors use one-step system GMM dynamic model to examine and compare the association between oil and gas prices changes with bank deposits in Qatar. The authors also test hypotheses of direct and indirect impacts of oil and gas prices changes on bank deposits.

The results indicate that oil and gas prices changes have a direct impact on deposits of banks at the aggregate level in Qatar. However, the authors find that oil and gas price changes significantly affect deposits of Qatari commercial banks directly prompting enhanced lending by banks and the consequent business activities in the economy, while their impact on the deposits of Qatari Islamic banks is indirect, i.e. the impact is permeated through the macroeconomic and institutional characteristics of the country that are reinforced by the growing expectations and commercial sentiment of the country. The authors find that significant association between oil price changes and deposit growth during the global financial crisis 2008 has been distorted. However, the authors find that there was a sharp rise in the deposits of Islamic banks during the period of global financial crisis.

The results of this study necessitate policy measures that can counter the effects of changes in oil and gas prices on the effectiveness of bank deposits.

It is widely recognized that oil and gas prices and the level of production are of great importance to the economic development of oil and gas exporting countries. So far, however, no econometric study has been reported in the literature which analyses and compares the impact of oil and gas prices changes on bank deposits of commercial and Islamic banks and also at the aggregate level in any of the oil-exporting economies. Thus, this study provides the first empirical evidence on distinct direct and indirect channels through which oil and gas prices changes may affect bank deposits.

This paper is a first attempt to empirically calibrate the default and asset correlation for large companies in India and elaborate its implications for credit risk capital estimation for a bank.

The authors estimate default probabilities and default correlations of long‐term bonds of 542 Indian corporates using rating transitions and pair‐wise migrations over ten year cohorts of firms. Further, the implicit asset correlation from the estimated default correlations and default thresholds are derived using the asymptotic single risk factor approach.

The authors find evidence that default correlations are time variant and vary across rating grades and industries. The highest correlations are observed between companies within the same rating grades (systematic risk impact) and within the same industry (industry specific impact). More interestingly, significantly smooth monotonic relationship between the probability of default (PD) and asset correlation as prescribed by the Basel II IRB document (2006) are not found. Moreover, it is found that the asset correlation range for Indian corporates do not match with what is prescribed for corporate exposures by BCBS.

The authors address the dilemma implied by the negative relationship between PD and asset correlation as suggested by BCBS IRB formula and other research for developed economies with estimates of asset correlation for and emerging market like India and demonstrate its implications on the estimation of credit risk capital.

The primary objective of the paper is to demonstrate the importance of borrower‐specific characteristics as well as local situation factors in determining the demand prospect as well as the risk of credit loss on residential housing loan repayment behavior in India.

Using 13,487 housing loan accounts (sanctioned from 1993‐2007) data from Banks and Housing Finance Cos (HFCs) in India, this paper attempts to find out the crucial factors that drive demand for housing and its correlation with borrower characteristics using a panel regression method. Next, using logistic regression, housing loan defaults and the major causative factors of the same are examined.

In analyzing the housing demand pattern, some special characteristics of the Indian residential housing market (demographic and social features) and the housing loan facility structure (loan process, loan margin, loan rate, collateral structure etc.), that have contributed to the safety and soundness of the Indian housing market have been deciphered. The empirical results suggest that borrower defaults on housing loan payments is mainly driven by change in the market value of the property vis‐à‐vis the loan amount and EMI to income ratio. A 10 percent decrease in the market value of the property vis‐à‐vis the loan amount raises the odds of default by 1.55 percent. Similarly, a 10 percent increase in EMI to income ratio raises the delinquency chance by 4.50 percent. However, one cannot ignore borrower characteristics like marital status, employment situation, regional locations, city locations, age profile and house preference which otherwise may inhibit the lender to properly assess credit risk in home loan business, as the results show that these parameters also act as default triggers.

This study contributes on the micro side of the housing market in India, since it uses unique and robust loan information data from banks and HFCs. The empirical results obtained in this paper are useful to regulators, policy makers, market players as well as the researchers to understand housing market demand and risk characteristics in an emerging market economy such as India.