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The expanded sovereign bond portfolios from the sizeable public interventions in the financial sector during the current crisis need close monitoring and analysis of emerging vulnerabilities. This chapter presents some conventional and new measures of market, credit, and liquidity risks for government bond portfolios, considered from the perspective of a sovereign debt manager. In particular, it examines duration, convexity, and VaR statistics as measures of market exposure; the contingent-claims approach as the most promising measure of credit risk exposure; and a VaR statistic as a measure of liquidity risk.

Probability of Default (PD) is a crucial credit risk parameter. International accords have motivated banks and credit institutions to adopt objective systems of evaluating and monitoring the PD. This study examines retail unsecured loans of a major Greek bank during the period of the financial crisis. It focusses on the stochastic behaviour of the financial states of the loans. It is tested whether a first-order Markov chain (MC) model describes sufficiently the transitions from one state to another. Moreover, Poisson regression models are estimated in order to calculate the limiting transition matrix, the limiting state probabilities and the PD. It is proved that the MC of the financial states of loans is non-homogeneous suggesting that the transition probabilities from one financial state to another are not constant across time. From the Poisson regression models, the transition probability matrix is estimated from one state to another in alternative time periods. From the limiting transition matrix, it is shown that if a loan is delayed then it is very likely to move towards the next worst case. The findings of this research could be useful for bank management.

Introduction: Commercial banks play a vital role in the global economy, facilitating economic growth and providing essential financial services. As key intermediaries between savers and borrowers, these institutions operate in a dynamic and complex environment characterised by various risk factors that can significantly impact their profitability and overall stability. Understanding the interconnected relationships between credit risk, interest rate risk, liquidity risk, and profitability is crucial for effective risk management strategies and the development of appropriate regulatory frameworks.

Purpose: Commercial banks play a critical role in the global economy by facilitating economic growth and providing financial services. This study examines the interconnected relationships between credit risk, interest rate risk, liquidity risk, and profitability in commercial banking.

Methodology: The sample consists of licenced scheduled commercial banks on the Bombay Stock Exchange (BSE) from 2015 to 2022. Using the Smart PLS-SEM 3.0 path analysis technique, the study evaluates the combined influence of these risk factors on profitability and provides evidence-based recommendations for risk management strategies.

Findings: The findings can assist banks in enhancing their risk management practices, and regulators in developing appropriate regulatory frameworks. By understanding the key risk factors and their impact on profitability, banks and regulators can mitigate risks, enhance transparency, and promote stability within the banking sector.

Significance/value: The value of this study lies in its focus on the interconnectedness of risk factors, profitability, and the potential implications for decision-making, risk management strategies, regulatory frameworks, and the overall stability of the commercial banking sector.