The Co-CoVaR and some other Fair Systemic Risk Measures with Model Risk Corrections" ✌

SSRN Electronic Journal, 2017

The general consensus on the need to enhance the resilience of the financial system has led to the imposition of higher capital requirements for certain institutions, supposedly based on their contribution to systemic risk. Global Systemically Important Banks (G-SIBs) are divided into buckets based on their required additional capital buffers ranging from 1% to 3.5%. We measure the marginal contribution to systemic risk of 26 G-SIBs using the Distressed Insurance Premium methodology proposed by Huang et al. (2009) and examine ranking consistency with that using the SRISK of Acharya et al. (2012). We then compare bucketing using the two academic approaches and supervisory buckets. Because it leads to capital surcharges, bucketing should be consistent, irrespective of methodology. Instead, discrepancies in the allocation between buckets emerge and this suggests the complementary use of other methodologies.

2017

Based on accounting data, we propose an alternative measure, i.e. aggregate z-score and minus one z-score, for assessing systemic risk contributions. The z-score based systemic risk measure is developed on the idea that systemic risk contribution of each bank can be captured by the risk taking of a banking system including all banks and all-banks-but one, which is defined as the Leave-One-Out approach. Using an international sample of 61 large banks from 17 countries, we test the effectiveness of the z-score based approach in measuring systemic risk contributions, with the comparisons with commonly-used marketbased methods. The z-score based method can clearly identify greater systemic significance of global systemically important banks, although different measures cannot agree on the ranking of individual banks’ systemic significance. We find positive rank correlations between the z-score based approach and MES or ∆CoVaR. The easy computation of the zscore based measure and its abi...

Physica A: Statistical Mechanics and its Applications, 2015

h i g h l i g h t s

2010

We present a simple model of systemic risk and show how each fi nancial institution's contribution to systemic risk can be measured and priced. An institution's contribution, denoted systemic expected shortfall (SES), is its propensity to be undercapitalized when the system as a whole is undercapitalized, which increases in its leverage, volatility, correlation, and tail-dependence. Institutions internalize their externality if they are "taxed" based on their SES. Through several examples, we demonstrate empirically the ability of components of SES to predict emerging systemic risk during the nancial crisis of 2007-2009.

Cogent Business & Management

Systemic risk is of concern for economic welfare as systemic financial crisis has the potential to inefficiently lower the supply of credit to the nonfinancial sector. Conventional systemic risk measures are parsimonious in nature and are used to assess the current systemic risk contributions of financial institutions and does not highlight future systemic risk. In order to overcome the limitations of conventional systemic risk, ΔCoVaR $ is used predict future systemic risk and the literature outlines the use of firm and state level variables in the computation. This study contributes to the existing body of knowledge by providing empirical evidence on improved computation of forward systemic risk by corroborating the distinctive impact of sector's nature: munificence, dynamism and level of concentration. The comparison of conventional and proposed forward CoVaR provides interesting insights into the role of sector level variables in the buildup of systemic risk. The results highlight that the forecasting ability of forward ∆CoVaR predicted with sector level variables is reasonably higher than that of predicted without sector level variables. The study provides guidelines to the policy makers by providing an improved measure of future systemic risk that can that can help to avoid procycality pitfall by introducing countercyclical policies before the happening of systemic ABOUT THE AUTHOR

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.

2017

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.

2012

The recent financial crisis has shown how interconnected the financial world has become. Shocks in one location or asset class can have a sizable impact on the stability of institutions and markets around the world. But systemic risk analysis is severely hampered by the lack of consistent data that capture the international dimensions of finance. While currently available data can be used more effectively, supervisors and other agencies need more and better data to construct even rudimentary measures of risks in the international financial system. Similarly, market participants need better information on aggregate positions and linkages to appropriately monitor and price risks. Ongoing initiatives that will help close data gaps include the G20 Data Gaps Initiative, which recommends the collection of consistent banklevel data for joint analyses and enhancements to existing sets of aggregate statistics, and enhancements to the BIS international banking statistics. JEL Classification Numbers: F21, F34, G15, G18, Y1

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...

Journal of Banking & Finance, 2009

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.

Journal of Financial Stability, 2018

We measure systemic risk in the network of financial market infrastructures (FMIs) as the probability that two or more FMIs have a large credit risk exposure to the same FMI participant. We construct indicators of credit risk exposures in three main Canadian FMIs during the period 2007-11 and use extreme value methods to estimate this probability. We find large differences in the contribution to systemic risk across participants. We also find that when participants are in financial distress, they tend to create large credit exposures in two or more FMIs. Our results suggest that an appropriate oversight of FMIs may benefit from an in-depth system-wide analysis, which may have useful implications for the macroprudential regulation of the financial system.

Journal of Banking & Finance, 2013

In this paper, we define a financial institution's contribution to financial systemic risk as the increase in financial systemic risk conditional on the crash of the financial institution. The higher the contribution is, the more systemically important is the institution for the system. Based on relevant but different measurements of systemic risk, we propose a set of market-based measures on the systemic importance of financial institutions, each designed to capture certain aspects of systemic risk. Multivariate extreme value theory approach is used to estimate these measures. Using six big Canadian banks as the proxy for Canadian banking sector, we apply these measures to identify systemically important banks in Canadian banking sector and major risk contributors from international financial institutions to Canadian banking sector. The empirical evidence reveals that (i) the top three banks, RBC Financial Group, TD Bank Financial Group, and Scotiabank are more systemically important than other banks, although with different order from different measures, while we also find that the size of a financial institution should not be considered as a proxy of systemic importance; (ii) compared to the European and Asian banks, the crashes of U.S. banks, on average, are the most damaging to the Canadian banking sector, while the risk contribution to the Canadian banking sector from Asian banks is quite lower than that from banks in U.S. and euro area; (iii) the risk contribution to the Canadian banking sector exhibits " home bias ", that is, crosscountry risk contribution tends to be smaller than domestic risk contribution.

Finance Research Letters, 2019

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