key: cord-0818046-167revkb authors: Rizwan, Muhammad Suhail; Ahmad, Ghufran; Ashraf, Dawood title: Systemic Risk: The Impact of COVID-19tn1]() date: 2020-07-04 journal: Financ Res Lett DOI: 10.1016/j.frl.2020.101682 sha: c216d475642edcf9a21a922bee98e9396f3baa50 doc_id: 818046 cord_uid: 167revkb Banking sectors across the globe are under immense stress due to the evolving COVID-19 situation and policy responses thereto. This study investigates how COVID-19 impacted the systemic risk in the banking sectors of eight of the most COVID-19 affected countries. We find a significant increase in systemic risk among the sample countries initially, while stagnancy (at an elevated level) is observed during April 2020 except for China, which is showing some recovery. By using spillover measures, we also identify systemically important institutions. The findings of this study testify to the benefits of policy responses in containing systemic risk. The impact of the COVID-19 pandemic is felt beyond the health sector and exhibits severe economic consequences. The global economy is projected to decline by 3% in real GDP for 2020 (6.1% decline for developed economies). 1 Most governments responded immediately to manage economic and financial shocks * We are thankful to the editor, Dr. Samuel Vigne, and two anonymous referees for their helpful comments. The views expressed in this paper are those of the authors and do not reflect the views of the Islamic Research and Training Institute or the Islamic Development Bank Group. All errors are the responsibility of the authors. by providing fiscal, monetary, and macro-financial stimuli. Globally, regulators responded by easing regulatory requirements, loan payment deferments, and interim non-classification of non-performing loans (NPLs). However, extended lockdowns, loan payments deferments, and an uncertain political outlook have increased the systemic vulnerability of the banking sector, and experts believe that "Vulnerabilities in credit markets, emerging countries and banks could even cause a new financial crisis". 2 This study is motivated by this issue and provides an initial exploration of the systemic risk evolution in a sample of eight of the most COVID-19 affected countries. The banking system's elevated systemic risk vulnerabilities are attributed to three reasons. First, liquidity risk due to economic slowdowns, financial forbearance and reduced access to capital markets due to potential credit rating downgrades. 3 Second, loss of intermediation income caused by regulatory and policy responses including loan payment reprieves and availability of government-guaranteed loans at ultra-low interest rates. 4 Although these measures help in curbing immediate default risk, a significant increase in NPLs is unavoidable [12] . Finally, a severe decline in intermediation business can adversely affect the ability to finance operations and funding costs of financial institutions. These risks may spread like a contagion through interconnected financial institutions. In this study, we explore the probable contagion effect of the COVID-19 in the financial systems of eight of the most affected countries; Canada, China, France, Germany, Italy, Spain, the UK, and the USA. We include the Global Financial Crisis (GFC) of 2007-09 in the estimation for comparison purposes. The GFC was an endogenous shock, especially for the USA, and the regulatory frameworks, at the time, were micro-prudential. The current situation is exogenous with macro-prudential regulatory frameworks in place Buffer of the systemically important banks has been lowered from 2.25% to 1% of the risk-weighted assets. The Canadian government has introduced a fiscal package of CAD193 billion and has planned to purchase insured mortgages of CAD150 billion to provide liquidity. The aforementioned policy responses are intended to manage the economic and financial shocks. In the coming sections, we provide estimation methodology of systemic risk and analyze its evolution to see if these policy responses have provided the intended benefits. An overview of systemic risk measures can be found in [5] and [8] . Due to severe data limitations during the pandemic, we rely on CATFIN proposed by [1] . [1] defined CATFIN as the arithmetic mean of value-atrisk (VaR) estimated using three different methodologies; the generalized Pareto Distribution (GPD) of [9] , the skewed generalized error distribution (SGED), and a non-parametric estimation. Using the closed-form solution of VaR for the GPD, provided by [2, 3] , the VaR threshold (VaR GPD ) for the loss probability level α can be calculated as: where µ, σ and ε are the location, scale and shape parameters of the GPD, respectively, while n and N are the number of extremes, and the total number of observations, respectively. Let f (R; µ, σ, κ, λ) be the probability density function for the SGED where µ and σ represent the mean and standard deviation of the excess stock return R, and the parameters κ and λ control the shape and skewness of the distribution, respectively. Then, the VaR threshold for the SGED (VaR SGED ), at the loss probability level α, is numerically calculated as: The non-parametric estimation of VaR (VaR NP ) is based on the cutoff point for the lower α percentile of the excess return. Therefore, [1] defined CATFIN as follows: [8] shows that, among the individual systemic risk measures, CATFIN performs quite well in forecasting macroeconomic shocks. However, they define CATFIN as follows: Therefore, following [8] , we estimate CATFIN as defined in the equation (4). 7 In addition to systemic risk, we also estimated the systemic importance of the financial institutions using the connectedness measures -"Spillover-To-Others" (STO) and "Spillover-From-Others" (SFO) -proposed by [6] . The required components of CATFIN were estimated using the Systemic Risk repository by [4] . d M ij is the generalized variance decomposition, introduced by [11] , given as: where σ ii is the i-th diagonal element of Σ, e i is a selection vector with i-th element as unity and other elements as zeros, A m is the coefficient matrix for the m-th lagged shock vector of the moving average representation of the vector autoregressive model, and Σ is the covariance matrix of the error term in the vector autoregressive model. Then, d M ij denotes the proportion of the forecast error variance in variable i due to the shocks in variable j. The STO and SFO measures, for an institution i, are defined as: Therefore, STO i measures institution i's total share of forecast error variance to other institutions while SFO i measures institution i's forecast error variance from other institutions. To enable regulators to devise appropriate policies for managing the systemic risk, we use the methodology explained in Section 3.2, and identified Systemically Important Financial Institutions (SIFI) based on their ability to affect others (STO) and their vulnerability of being affected from others (SFO) during this pandemic. Figure 3 provides heat diagrams of the SIFI based on STO. 9 Figure 3 shows that for China, Spain, the UK, and the USA, the majority of the financial institutions can have spillover on other institutions. However, for Canada, France, Germany, and Italy, the SIFI are smaller in number which provides an opportunity for managing systemic risk by regulating these firms. 9 Due to space limitations, only codes are provided here. Institution names are available on request. As an alternative to CATFIN, we estimated Absorption Ratio (AR), proposed by [10] , as: where σ 2 ν k is the variance of the k-th eigenvector of the covariance matrix of asset returns, and σ 2 Ai is the variance of the i-th asset. So, higher values of AR indicate that the risk sources are more unified compared to lower values. Does systemic risk in the financial sector predict future economic downturns? An extreme value approach to estimating volatility and value at risk A generalized extreme value approach to financial risk measurement Systemic risk A survey of systemic risk analytics On the network topology of variance decompositions: Measuring the connectedness of financial firms Double-dip recession: Previous experience and current prospect. Library of Congress Systemic risk and the macroeconomy: An empirical evaluation Statistical inference using extreme order statistics Principal components as a measure of systemic risk Generalized impulse response analysis in linear multivariate models Covid-19 and non-performing loans: lessons from past crises Interconnectedness and systemic risk of china's financial institutions Paper Reference No. FRL_2020_656 Muhammad Suhail Rizwan: Conceptualization, Data curation, Formal analysis, Investigation, Writingoriginal draft Methodology, Visualization, Software, Investigation, Writing -original draft Project administration, Supervision, Validation, Writing -Review and Editing