Tisa Maria Antony

Credit risk is a significant factor affecting the financial stability of banks. Keeping the credit risk under control is essential to maintain a bank’s cash flow. This paper examines the various profitability, microeconomic and macroeconomic indicators that affect a bank’s credit risk. The study uses the dataset of 31 banks from 2012 to 2021 and employs a panel data modelling approach to account for any variations in risk-taking behavior. The results revealed a statistically significant negative relationship between return on equity and credit risk when nonperforming loans proxy credit risk. This finding was consistent across fixed effect, random effect, and pooled OLS methods, at 1 percent significance (P value < 0.00), indicating that the extent of credit risk decreases as profitability increases. It was further found that bank age and ownership type positively affect a bank’s credit risk, while factors such as bank size and operational efficiency negatively affect credit risk when nonperforming loans proxy credit risk. Further, macroeconomic variables showed that gross domestic product is positively associated with credit risk, while inflation negatively affects credit risk. Overall, the findings of this paper demonstrated that credit risk is affected by both micro and macroeconomic factors. The paper also addresses significant policy implications as it helps various stakeholders to examine the determinants of credit risk, make credit decisions, and ultimately lower their credit risk.

Liquidity risk is a significant financial threat that must be handled carefully. Underestimation or mismanagement of liquidity risk may lead to severe financial losses or even bank failures. Therefore, timely and adequately estimating liquidity risk and examining factors that affect liquidity risk are essential. On that account, this paper aims to examine the determinants of liquidity risk for Indian commercial banks from 2013 to 2022. For this purpose, the study has employed a panel data regression model with pooled OLS, fixed effect, and random effect methods and has considered bank-specific and macroeconomic variables. The findings show that liquidity risk is affected by both bank-specific variables and macroeconomic variables. Bank-specific variables, such as bank age, have a negative impact on liquidity risk at the 1 percent significance using pooled OLS, FE, and RE models. In contrast, bank size and bank capitalization positively impacted liquidity risk. However, the operational efficiency of banks was found to have no significant impact on liquidity risk using both the liquid asset to total assets ratio and the loan to deposit ratio. In addition, the results show that macroeconomic variables such as GDP and inflation have a positive impact on liquidity risk. The study’s findings are expected to assist various stakeholders in making appropriate policies, decisions and managing their liquidity risk.