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This paper investigates the influence of the ongoing crisis of Russia's incursion on Ukraine on the risk dynamics of energy futures contracts with high-frequency data on four different futures contracts using risk metrics of value at risk (VaR) and conditional value at risk (CVaR) for the USA market.

The author used different generalised autoregressive conditional heteroscedasticity - Extreme Value Theory (GARCH)-EVT models and compared the performance of each of the competing models. Backtesting evidence shows that VaR and CVaR combined with GARCH-EVT better estimate risk.

The study results show that combined risk metrics are efficient and adaptive to estimating the risk dynamics and backtesting of the models, revealing that the autoregressive moving average (ARMA) (1,1)-asymmetric power autoregressive conditional heteroscedasticity (APARCH) model performs relatively better than other models.

The paper has practical implications for different market participants. From the risk manager's and day traders' angles, the market participants can estimate the risk exposure in the energy futures contract and take positions accordingly. The results are important for oil-importing countries due to the developing supply crisis and price escalation, which can brew inflation in the economy.

To the best of the author's knowledge, the paper is the first to throw light on the risk angle of energy futures contracts during the ongoing crisis of the Russia–Ukraine war.

This paper investigates the influence of three different sentiment indicators on the time-varying stock–bond correlation of 15 countries during the global crisis period of the coronavirus disease 2019 (COVID-19) pandemic.

The author uses the time-varying correlation estimated using the autoregressive moving average -dynamic conditional correlation - generalised autoregressive conditional heteroskedasticity (ARMA-DCC-GARCH) model to achieve this aim. The impact of investor sentiment on the stock–bond correlation was analysed using the Markov regime-switching regression.

The study results show that the sentiment indicators of fear, uncertainty and distress have a pronounced negative impact on the stock–bond correlation. They further provide evidence of a strong regime effect on the stock–bond correlation with sentiment indicators.

The paper has a relevant impact on policymakers and fund managers. First, the policymakers now have more insightful evidence of how the stock and bond markets react during crises. Second, the fund managers need to focus on behavioural variables as they may be driving factors in crisis periods that may impair portfolio management.

To the best of my knowledge, the paper is the first to throw light on the behaviour of the stock–bond correlation for 15 countries during the COVID-19 period.

Our study investigates the influence of peer performance on the earnings management decisions within publicly traded Indian companies. There is mixed evidence in the literature, with the impact of peer performance on earnings management in emerging markets being notably underexplored. Additionally, the study explores whether robust corporate governance mechanisms can mitigate earnings management practices. Our study offers policy insights into these areas.

Our study used a longitudinal panel dataset from 2011 to 2020, utilising idiosyncratic returns of peer firms as an external measure of peer performance. This approach is further enhanced by the usage of alternative discretionary accrual metrics, which could be a robust measure for both market leaders and followers.

Our study employs two distinct methods, accrual and real earnings management, to assess earnings management. The findings indicate that peer performance triggers earnings management within peer groups, showcasing managerial opportunism in financial reporting to align with peer achievements. Furthermore, the evidence suggests that robust corporate governance effectively curtails earnings management, especially in industries where peer influence is significant.

Our study offers valuable insights for regulators, highlighting that enhancing the institutional framework with stringent corporate governance mechanisms can effectively reduce earnings management in companies within emerging markets.

The paper is a novel attempt in emerging markets to show that managers engage in opportunistic reporting to align with the performance of their peers and that governance strategies effectively mitigate these practices in such markets.

This study examines a link between firms' product market power, industry concentration, the degree of earnings management and the role of governance in curbing earnings management.

The author uses different panel techniques of feasible generalized least squares (FGLS) and system generalized method of moments (GMM) to show robust study findings.

The study results reveal that firms lacking product market pricing power engage in earnings management, adding a new dimension to the existing literature. These findings mirror even at the industry level, where the authors document immense competitiveness led to earnings manipulation and stringent corporate governance mechanism has the potency to curb earnings management.

The paper has valuable insights and practical implications for policymakers and market participants. The results indicate robust institutional oversight mechanisms can deter earnings management in a concentrated market.

To the best of the authors’ knowledge, this is among the first paper from India, a growing emerging economy, to look at the various aspects of market characteristics, earnings management and the role of corporate governance.