What Factors Drive the Banks Systemic Risk among South Asian Countries

Purpose: This study designs to examine the determinants (size, liquidity ratio, leverage ratio, deposit ratio, asset growth, net interest income ratio and return on asset ratio) of bank’s systemic risk. We use the data of listed commercial banks of the South Asian countries (Pakistan, Bangladesh, and India). Design/Methodology/Approach: The sample consists 30 banks from Bangladesh, 87 banks from India and 22 banks from Pakistan. This study covers the period from 2006 to 2018. The data is collected from the published annual reports of banks and stock exchanges of respective country. The panel data analysis is performed for the estimation of research models. Findings: The findings demonstrate that larger banks contribute lower in the systemic risk of banks. Additionally, highly liquid banks enhance the systemic risk of the banking system. Moreover, the banks with greater reliance on the deposits, net interest income and with high return on asset reduce the systemic risk contribution of the banks. Implications/Originality/Value: This study provides the justification to devise the banking policies like enhance the proportion of liquidity among assets, reliance on net interest income and promote the financing needs through deposits to limit the systemic risk contribution of the banking system.

Systemic Risk Contribution and Contagion of Industrial Sectors in China: From the Global Financial Crisis to the COVID-19 Pandemic

This paper investigates the risk contribution of 29 industrial sectors to the China stock market by using one-factor with Durante generator copulas (FDG) and component expected shortfall (CES) analyses. Risk contagion between the systemically most important sector and other sectors is examined using a copula-based ∆CoVaR approach. The data cover the 2008 global financial crisis and the beginning of the COVID-19 pandemic. The empirical results show that the banking sector contributed most to systemic risk before and during the global financial crisis. Nonbank finance became equally important in 2020, and the COVID-19 pandemic promoted the position of the computer and pharmaceuticals sectors. The spillover effect diminishes over time, but there remains risk contagion between sectors. The risk spillover trend is consistent with that of systemic risk.

On topological properties of COVID-19: predicting and assessing pandemic risk with network statistics

The spread of coronavirus disease 2019 (COVID-19) has caused more than 80 million confirmed infected cases and more than 1.8 million people died as of 31 December 2020. While it is essential to quantify risk and characterize transmission dynamics in closed populations using Susceptible-Infection-Recovered modeling, the investigation of the effect from worldwide pandemic cannot be neglected. This study proposes a network analysis to assess global pandemic risk by linking 164 countries in pandemic networks, where links between countries were specified by the level of ‘co-movement’ of newly confirmed COVID-19 cases. More countries showing increase in the COVID-19 cases simultaneously will signal the pandemic prevalent over the world. The network density, clustering coefficients, and assortativity in the pandemic networks provide early warning signals of the pandemic in late February 2020. We propose a preparedness pandemic risk score for prediction and a severity risk score for pandemic control. The preparedness risk score contributed by countries in Asia is between 25% and 50% most of the time after February and America contributes around 40% in July 2020. The high preparedness risk contribution implies the importance of travel restrictions between those countries. The severity risk score of America and Europe contribute around 90% in December 2020, signifying that the control of COVID-19 is still worrying in America and Europe. We can keep track of the pandemic situation in each country using an online dashboard to update the pandemic risk scores and contributions.

Bank mergers: the cyclical behaviour of regulation, risk and returns

Purpose The purpose of this paper is to examine the effects of bank mergers on systemic and systematic risks on the relative merits of product and market diversification strategies. It also observes determinants of M&A deals criteria, product and market diversification positioning, crisis threshold and other regulatory and market factors. Design/methodology/approach This research examines the impact and association between merger announcements and regulatory reforms at bank and system levels by investigating the impact of various bank consolidation strategies on firms’ risks. We estimate beta(s) as an index of financial institutions’ systematic risk. We then develop an index of the estimated equity value loss as the long-rum marginal expected shortfall (LRMES). LRMES contributes to compute systemic risk (SRISK) contribution of these firms, which is the capital that a firm is expected to need if we have another financial crisis. Findings Large acquiring banks decrease systemic risk contribution in cross-border M&As with a non-bank financial institution, and witness profitability (ROA) gains, supporting geographic diversification stability. Capital requirements, activity restrictions and bank concentration increase systemic risk contribution in national mergers. Bank mergers with investment FIs targets enhance productivity but impair technical efficiency, contrary to bank-real estate deals where technical efficiency change accompanied lower systemic risk contribution. Practical implications Financial institutions are recommended to avoid trapped capital and liquidity by efficiently using local balance sheet and strengthening them via implementing models that clearly set diversification and netting benefits to determine capital reserves and to drive capital efficiency through the clarity on product–activity–geography diversification and focus. This contributes to successful ringfencing, decreases compliance costs and maximises returns and minimises several risks including systemic risk. Social implications Policy implications: the adversative properties of bank mergers in respect of systemic risk require strict and innovative monitoring of bank mergers from the bidding level by both acquirers and targets and regulators and competition supervisory bodies. Moreover, emphasis on regulators/governments intervention and role, as it provides a stabilising factor of the markets and consecutively lower systemic risk even if the systematic idiosyncratic risk contribution was significant. However, such roles have to be well planned and scaled to avoid providing motives for banks to seek too-big-too-fail or too-big-to-discipline status. Originality/value This research contributes to the renewing regulatory debate on banks sustainable structures by examining the risk effect of bank diversification versus focus. The authors aim to address the multidimensional impacts and risks inherent to M&A deals, by examining the extent of the interconnectedness of M&A and its implications within and beyond the banking sector.

CoCDaR and mCoCDaR: New Approach for Measurement of Systemic Risk Contributions

Systemic risk is the risk that the distress of one or more institutions trigger a collapse of the entire financial system. We extend CoVaR (value-at-risk conditioned on an institution) and CoCVaR (conditional value-at-risk conditioned on an institution) systemic risk contribution measures and propose a new CoCDaR (conditional drawdown-at-risk conditioned on an institution) measure based on drawdowns. This new measure accounts for consecutive negative returns of a security, while CoVaR and CoCVaR combine together negative returns from different time periods. For instance, ten 2% consecutive losses resulting in 20% drawdown will be noticed by CoCDaR, while CoVaR and CoCVaR are not sensitive to relatively small one period losses. The proposed measure provides insights for systemic risks under extreme stresses related to drawdowns. CoCDaR and its multivariate version, mCoCDaR, estimate an impact on big cumulative losses of the entire financial system caused by an individual firm’s distress. It can be used for ranking individual systemic risk contributions of financial institutions (banks). CoCDaR and mCoCDaR are computed with CVaR regression of drawdowns. Moreover, mCoCDaR can be used to estimate drawdowns of a security as a function of some other factors. For instance, we show how to perform fund drawdown style classification depending on drawdowns of indices. Case study results, data, and codes are posted on the web.