What Kind Of Systemic Risks Do We Face In The European Banking Sector? The Approach Of CoVaR Measure (original) (raw)
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The evolution of systemic risk in the European banking sector
This paper examines how systemic risk has evolved and developed over the past 18 years within the European Banking sector, using a combination of the ∆CoVaR methodology, and network analysis. A sample of banks is taken from the Stoxx Europe 600 Banks Index, and a systemic risk rank is formed for each. The paper investigates the impact on systemic risk of economic state revealing variables, such as the European volatility index VSTOXX, and Euribor. As well as this, the effect of key balance sheet variables such as total assets are also analysed and subsequently found to be significant. The paper notes the usefulness of ∆CoVaR as a measure of systemic risk, and finds that ∆CoVaR provides additional information over VaR.
Measuring Systemic Risks in the Turkish Banking Sector
Business and Economics Research Journal, 2020
This paper focused on measuring the systemic risks in Turkey's banking sector by using two major measures that have been proposed in the literature as conditional value at risk (CoVaR) and marginal expected shortfall (MES). In order to compute the contribution of banking sector to systemic risks, the MES and ΔCoVaR measures are estimated for the six Turkish banks, which are listed, on the Borsa Istanbul (BIST) during 2000-2016 period by using Engle's dynamic conditional correlation model. The preliminary results of this study show that although the measures provide different rankings for the systemic risk contributions, they turn out to be qualitatively very similar in explaining the cross-sectional differences in systemic risk contributions. Secondly, both systemic risk measures (MES and ΔCoVaR) are analyzed to determine the relationships between some variables associated with bank characteristics (e.g., VaR, size and leverage ratio) and banks' systemic risk contributions, via simple panel data regression methods.
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Journal of Financial Services Research
We analyze the systemic risk of Italian banks with the CoVaR from a bivariate normal GARCH model. The results show that it is a good measure of systemic risk and is applicable to the ranking of Italian other systemically important institutions. Using an elastic-net approach, we identify the balance sheet and market variables that explain the CoVaR of Italian banks. The analysis confirms that these variables are key determinants of systemic importance and highlights how higher capitalization is beneficial to tackling systemic risk. And, we detect a connection between CoVaR and some variables for trading and investment banking.
Measurement of Systemic Risk – a Critical Review of Selected Models
2018
Financial instability can lead to financial crises due to its spillover effects to other parts of the economy. Having an accurate measure of systemic risk gives academicians and policy makers the ability to make proper policies in order to predict systemic risks in advance and prevent a financial crisis as soon as warning signals indicate a possible economic disaster. For the purpose of this study the works of past researchers have been analyzed to study the effectiveness of various measures of systemic risk based on market data. Several experts have undergone systematic studies related to this area and have quantified the aggregate level of systemic risk in the economy. The results show that each measure predicts the systemic risk significantly. However each measure suffers its own set of limitations. SRISK, MES and CCA are more accurate in comparison to CoVaR, Granger Causality for identifying systemically important financial institutions.
SSRN Electronic Journal, 2020
This study has two objectives. It first assesses the output and inflation effects of systemic risk-taking in the euro area banking sector using a factor-augmented vector autoregressive model that exploits a 519 time-series rich dataset, including coherent measures of systemic risk in all its forms and its time dimension. Then, it evaluates whether the systemic risk measures can be used as early warning signals of severe negative nominal growth outcomes using the Receiver Operating Characteristic approach. The main findings are that, overall, real GDP growth and inflation react negatively to a one-standard deviation shock to systemic risk measures of the euro area banking industry. Inflation depicts a more pronounced response than GDP. There is heterogeneity in the strength of the responses across diverse forms of systemic risk and their time dimension. Specifically, short-term systemic risk measures tend to portray stronger effects on output and inflation than their conditional forward counterparts. In particular, systemic risk in the form of banking sector vulnerability associated with interconnectedness and contagion plays a considerable role in depressing economic activity in the euro area. Finally, all but one systemic risk measure predict with high accuracy the extreme macro-financial instability in the euro area that followed Lehman Brothers’ collapse. The systemic risk measures are potentially good candidates to be included in a macroprudential-policy toolkit for calibrating instruments at thresholds that reflect policymakers’ risk preferences.
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BIS Working Papers are written by members of the Monetary and Economic Department of the Bank for International Settlements, and from time to time by other economists, and are published by the Bank. The papers are on subjects of topical interest and are technical in character. The views expressed in them are those of their authors and not necessarily the views of the BIS.
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Theory and Practice, 2015
The financial crisis of 2007/08 has demonstrated that factors for financial distress of large parts of the economy depend to a large extent on the interrelations between the financial institutions. Risks threatening the financial sector can be decomposed into risks based in the individual factors for single institutions and risks which can be attributed to the financial system as a whole. This part of the risks is called systemic risk. We review several approaches for quantifying systemic risk, most of them based on structural credit modeling. In particular we present an approach which is inspired by the fact that the joint probability distributions can be represented by their individual marginals and the copula function, which represents the interrelations.
Chapter XX Measuring Systemic Risk – Structural Approaches
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The financial crisis of 2007/08 has demonstrated that factors for financial distress of large parts of the economy depend to a large extent on the interrelations between the financial institutions. Risks threatening the financial sector can be decomposed into risks based in the individual factors for single institutions and risks which can be attributed to the financial system as a whole. This part of the risks is called systemic risk. We review several approaches for quantifying systemic risk, most of them based on structural credit modeling. In particular we present an approach which is inspired by the fact that the joint probability distributions can be represented by their individual marginals and the copula function, which represents the interrelations.
Risks
The aim of this work is to assess systemic risk of Tunisian listed banks. The goal is to identify the institutions that contribute the most to systemic risk and that are most exposed to it. We use the CoVaR that considered the systemic risk as the value at risk (VaR) of a financial institution conditioned on the VaR of another institution. Thus, if the CoVaR increases with respect to the VaR, the spillover risk also increases among the institutions. The difference between these measurements is termed △CoVaR, and it allows for estimating the exposure and contribution of each bank to systemic risk. Results allow classifying Tunisian banks in terms of systemic risk involvement. They show that public banks occupy the top places, followed by the two largest private banks in Tunisia. These five banks are the main systemic players in the Tunisian banking sector. It seems that they are the least sensitive to the financial difficulties of existing banks and the most important contributors to...