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This study tends to investigate how the outbreak of the coronavirus disease 2019 (COVID-19) pandemic has affected banks' contribution to systemic risk. In addition, the authors examine whether the impact of the pandemic may vary across advanced/emerging economies, and with banks with differed characteristics.

The authors construct the bank-specific conditional value at risk (CoVaR) and marginal expected shortfall (MES) to measure their contribution to systemic risk and define the outbreak of the COVID-19 pandemic by the timing when countries report more than 100 confirmed cases. The authors use the approach of difference-in-differences to assess the impact of the COVID-19 pandemic on banks' contribution to systemic risk. This sample comprises monthly panel data of around 900 listed commercial banks in 39 advanced and emerging economies.

The authors find that, firstly, the COVID-19 pandemic increased banks' contribution to systemic risk significantly around the world. Secondly, the impact of the COVID-19 virus was more pronounced in developed countries than in emerging economies. Finally, banks with a larger size and higher loan-to-deposit ratio are more greatly affected by the COVID-19 pandemic, while a higher capitalization for banks is insufficient to shelter them from the adverse impact of such pandemic.

The authors assess the impact of the COVID-19 pandemic on banks' contribution to systemic risk. Using the conditional value at risk (marginal expected shortfall) of banks as the measure, this study’s results suggest that banks' contribution to systemic risk increases by around 25% (48%) amid the COVID-19 pandemic. This study’s findings may shed some light on the potential policies that financial regulators may employ to ameliorate the adverse outcomes of the ongoing pandemic.

We employ three systemic risk measures of banks, including the systemic risk index (SRISK) and marginal expected shortfall (MES) of Brownlees and Engle (2017) and the conditional Value-at-Risk (ΔCoVaR) of Adrian and Brunnermeier (2016), to analyze bank's exposure and contribution to systemic risk in the banking system when a financial crisis occurs. We find evidence that time-varying systemic risk exists, and systemic risk exposures escalate with the interconnectedness of banks. We also find revenue diversification is another significant factor that reduces a bank's exposure to systemic risk but not for banks in Taiwan and Singapore.

Viewing the domestic downside risk as a “pushing” factor for outward foreign direct investment (OFDI), this study aims to examine the surge in Chinese cross-border acquisitions (CBAs) between 2008 and 2017, a unique window when private firms in China were allowed to conduct CBAs.

This study examines the effect of down-side risk on cross-border acquisition performance by using the sample of Chinese A-share listed companies from 2008 to 2017. Specifically, this study considers three kinds of systemic risk, systematic risk and idiosyncratic risk, and respectively examines their impact on CBAs activities; this study also investigates their subsequent results after CBAs activities. The contingency effect of state ownership on the above relationship is also discussed.

The findings reveal that pre-CBA systemic risk explains the volume of CBA activities; CBAs are followed by a reduction in systemic risk; the interactions between systemic risk and CBAs decrease with the level of state ownership; and the above results do not hold for traditional risk measures (i.e. systematic risk and idiosyncratic risk).

This study contributes to the literature by revealing the role of systemic risk as a “pushing” factor in the context of OFDI and suggesting an alternative explanation for CBAs from China: Chinese firms (especially private firms) took advantage of the rare opportunity between 2008 and 2017 given by the government to transfer assets overseas through CBA.

This paper sheds light on the impact of market risk measures on systemic risk. Market risk, which is captured by the volatility of stock market returns, is also decomposed into systematic and idiosyncratic risks.

The author uses the five-factor asset pricing model and systemic risk methodologies to derive market and systemic risk measures, respectively. Using a sample of 2,667 US banks for over 30 years and employing panel data estimation techniques, the author tests the said relationship.

It is shown that idiosyncratic risk can surge systemic risk, while systematic risk plays a less important role. Results survive a battery of tests, including different systemic risk measures, controlling causality and interacting with bank size, market fear and crisis periods.

These findings call for regulatory intervention, especially for large banks with high idiosyncratic risk.

This is the first paper that provides a more granular picture of the relationship between market and systemic risk from the US banking industry for more than 30 years.

The study measures the total systemic risks and connectedness across commodities, stocks, exchange rates and bond markets in Africa during the Covid-19 pandemic.

The study uses the Diebold-Yilmaz spillover and connectedness measures in a generalized VAR framework. The author calculates the net transmitters or receivers of shocks between two assets and visualizes their strength using a network analysis tool.

The study found low systemic risks across all assets and countries. However, we found higher systemic risks in the forex market than in the stock and bond markets, and in South Africa than in other countries. The dynamic analysis found time-varying connectedness return shocks, which increased during the peak periods of the first and second waves of the pandemic. We found both gold and oil as net receivers of shocks. Overall, over half of all assets were net receivers, and others were net transmitters of return shocks. The network connectedness plot shows high net pairwise connectedness from Morocco to South Africa stock market.

The study has implications for policymakers to develop the capacities of local investors and markets to limit portfolio outflows during a crisis.

Previous studies have analyzed spillovers across asset classes in a single country or a single asset across countries. This paper contributes to the literature on network connectedness across assets and countries.

This study aims to analyse the systemic risk emitted by all publicly listed commercial banks in a key emerging economy, India.

The study makes use of the Tobias and Brunnermeier (2016) estimator to quantify the systemic risk (ΔCoVaR) that banks contribute to the system. The methodology addresses a classification problem based on the probability that a particular bank will emit high systemic risk or moderate systemic risk. The study applies machine learning models such as logistic regression, random forest (RF), neural networks and gradient boosting machine (GBM) and addresses the issue of imbalanced data sets to investigate bank’s balance sheet features and bank’s stock features which may potentially determine the factors of systemic risk emission.

The study reports that across various performance matrices, the authors find that two specifications are preferred: RF and GBM. The study identifies lag of the estimator of systemic risk, stock beta, stock volatility and return on equity as important features to explain emission of systemic risk.

The findings will help banks and regulators with the key features that can be used to formulate the policy decisions.

This study contributes to the existing literature by suggesting classification algorithms that can be used to model the probability of systemic risk emission in a classification problem setting. Further, the study identifies the features responsible for the likelihood of systemic risk.

Sudden and unannounced policy changes by the government that provide banks with windfall deposits creates a challenge in terms of resource deployment. In the process, there is an impact on their risk and returns. Using data on domestic Indian commercial banks, this study aims to examine the impact of such an announcement – the 2016 demonetisation episode – on bank behaviour.

Using data on domestic Indian commercial banks during 2010–2020, the paper investigates the effect of a sudden and unannounced policy change on their risk and returns. Using the demonetisation undertaken in November 2016 as a natural experiment, the paper applies the difference-in-differences methodology to tease out the causal impact.

The findings reveal a decline in risk and an increase in returns of state-owned banks, consistent with a flight-to-safety. The response differed in terms of market and accounting measures and across state-owned banks with differing levels of capital and asset quality.

Although several aspects of the demonetisation episode have been well analysed, its impact on banks – the main conduits of the exercise – and in particular on their risk and returns, is an unaddressed area of research. Viewed from this standpoint, this is one of the early studies to undertake a comprehensive empirical analysis on this aspect.

The purpose of this paper is to assess the effects of prompt corrective action on bank risk and returns in an empirical framework.

The paper uses a difference-in-difference specification to analyse whether and how PCA affects bank risk and returns. As part of robustness, the analysis also uses a fixed effects specification with Driscoll–Kraay standard errors to account for serial correlation and cross-sectional dependence.

The findings reveal that banks under PCA framework contribute less to systemic risk and exhibit higher market valuation. These findings differ across recapitalised versus non-recapitalised banks and for banks with differing asset quality, capital and profitability. The overall price impact is a decline in lending rates and deposit costs.

To the best of the author’s understanding, this is one of the early studies in the Indian context to carefully examine the linkage between PCA and bank behaviour.

The purpose of this paper is to construct an index for systemic risk in China.

This paper develops a systemic risk index for China (SRIC) using textual information from 26 leading newspapers in China. Our index measures the systematic risk from 21 topics relating to China’s economy and provides narratives of the sources of systemic risk.

SRIC effectively predicts changes in GDP, aggregate financing to the real economy and the purchasing managers’ index. Moreover, SRIC explains several other commonly used macroeconomic indicators. Our risk measure provides a helpful monitoring tool for policymakers to manage systemic risk.

The paper construct an index of systemic risk based on the information extracted from newspaper articles. This approach is new to the literature.