International Journal of Financial Studies

Risk management has become increasingly important in the financial world. Considering its importance, it is necessary to measure these risks. The financial market uses two risk measures: Value at Risk (VaR) and Expected Shortfall (ES). After the subprime crisis, the market began to emphasize ES instead of VaR. The hypothesis of this paper to be tested is that longer periods provide better information than shorter, more recent periods for measuring ES volatility to hedge trades. The ES can be adopted using parametric, semi-parametric, and non-parametric methods, and the analyses of the log return indicators started on 3 January 2000 and ended on 5 May 2023. The analyses carried out to evaluate these log return indicators covered the period from 6 May 2023 to 1 August 2025, where it was found that the exchange rate volatility of the Brazilian Real exceeded the VaR limits and even reached the Expected Shortfall risk zone. Then, a different analysis was performed, starting on 11 March 2020 and ending on 5 May 2023. This second analysis, as the first analysis, was carried out to evaluate these log return indicators that covered the period from 6 May 2023 to 1 August 2025. In this latest period analysis, the exchange rate volatility of the Brazilian Real reached the Exchange Shortfall risk zone in a different way compared to the first way. All three types of methods—parametric, non-parametric, and semi-parametric—show distinct behaviors depending on the period evaluated. The hypothesis was rejected, but the hedging strategies should account for asset volatility. The software used to calculate the estimators was Microsoft Excel 365 and Stata 14.2.

The effect of corporate social responsibility (CSR) disclosure on zero-leverage policies is examined for listed firms at the Pakistan Stock Exchange (PSX) from 2010 to 2021. Binary logistic regression models show a statistically significant positive relationship between CSR disclosure and zero leverage. Increased CSR disclosure raises the propensity of firms to have zero leverage. Moreover, the negative effect of CSR disclosure on debt ratios further confirms these findings. Results show that highly disclosed CSR firms face less information asymmetry and prefer equity financing over bank debt. Regulators should develop incentive programs to increase their CSR disclosure and strengthen stakeholders’ relationships.

Artificial intelligence (AI) has become an integral driver of digital transformation in the banking sector, fundamentally influencing operational efficiency, resource allocation, and profitability. This study investigates how AI adoption affects the profitability of Chinese commercial banks and through which mechanisms these effects occur, within the context of the country’s broader financial digitalization process. Using panel data for 17 A-share listed banks in China from 2009 to 2022, we employ a multi-period difference-in-differences (DID) framework—whose validity rests on the parallel trend assumption, empirically verified through an event-study specification—and combine it with propensity score matching (PSM) and placebo simulations to ensure credible causal identification. The results indicate that AI adoption significantly improves bank profitability. Mechanism analyses suggest that AI enhances profitability through two overarching channels—operational efficiency and resource allocation—manifested in (i) higher cost elasticity of income, (ii) improved deposit–loan turnover adaptability via more efficient liquidity and funding-cycle management, and (iii) optimized cross-business capital allocation efficiency through better risk–return matching in diversified operations. The effects are stronger for banks with higher digital investment intensity and tighter customer stickiness–liability cost coupling, and vary systematically across ownership types, bank sizes, and policy cycles. Overall, the findings provide policy-relevant evidence on how AI-driven digital transformation can enhance bank performance and risk management in modern financial systems. This study contributes by constructing a disclosure-based AI adoption measure from bank annual reports and exploiting staggered adoption with a multi-period DID design to provide causal evidence from China’s listed banking sector.