Pitfalls in the Use of Systemic Risk Measures

2018 ◽  
Vol 53 (1) ◽  
pp. 269-298 ◽  
Author(s):  
Gunter Löffler ◽  
Peter Raupach
Keyword(s):  
Side Effects ◽  
Systemic Risk ◽  
Risk Measures ◽  
Systematic Risk ◽  
Idiosyncratic Risk ◽  
Adverse Side Effects ◽  
Heavy Tailed Distributions ◽  
Linear And Nonlinear ◽  
Heavy Tailed

We examine pitfalls in the use of return-based measures of systemic risk contributions (SRCs). For both linear and nonlinear return frameworks, assuming normal and heavy-tailed distributions, we identify nonexotic cases in which a change in a bank’s systematic risk, idiosyncratic risk, size, or contagiousness increases the risk of the system but lowers the measured SRC of the bank. Assessments based on estimated SRCs could thus produce false interpretations and incentives. We also identify potentially adverse side effects: A change in a bank’s risk structure can make the measured SRC of its competitors increase more strongly than its own.

ESTIMATION OF RISK MEASURES FROM HEAVY TAILED DISTRIBUTIONS

2021 ◽  
Vol 62 (1) ◽  
pp. 35-80
Author(s):  
El Hadji Deme ◽  
Mouhamad M. Allaya ◽  
Siradhi Deme ◽  
Hamza Dhaker ◽  
Ali Souleyman Dabye
Keyword(s):  
Risk Measures ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Estimation Of Risk

Conditional excess risk measures and multivariate regular variation

2019 ◽  
Vol 36 (1-4) ◽  
pp. 1-23
Author(s):  
Bikramjit Das ◽  
Vicky Fasen-Hartmann
Keyword(s):  
Risk Factors ◽  
Asymptotic Behavior ◽  
Systemic Risk ◽  
Regular Variation ◽  
Risk Measures ◽  
Excess Risk ◽  
Multivariate Regular Variation ◽  
Marginal Expected Shortfall ◽  
Broad Framework ◽  
Heavy Tailed

Abstract Conditional excess risk measures like Marginal Expected Shortfall and Marginal Mean Excess are designed to aid in quantifying systemic risk or risk contagion in a multivariate setting. In the context of insurance, social networks, and telecommunication, risk factors often tend to be heavy-tailed and thus frequently studied under the paradigm of regular variation. We show that regular variation on different subspaces of the Euclidean space leads to these risk measures exhibiting distinct asymptotic behavior. Furthermore, we elicit connections between regular variation on these subspaces and the behavior of tail copula parameters extending previous work and providing a broad framework for studying such risk measures under multivariate regular variation. We use a variety of examples to exhibit where such computations are practically applicable.

scholarly journals Estimating -Functionals for Heavy-Tailed Distributions and Application

2010 ◽  
Vol 2010 ◽  
pp. 1-34 ◽  
Author(s):  
Abdelhakim Necir ◽  
Djamel Meraghni
Keyword(s):  
Asymptotic Normality ◽  
Extreme Value Theory ◽  
Financial Risk ◽  
Risk Measures ◽  
Value Theory ◽  
Statistical Parameters ◽  
Linear Combinations ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
Actuarial Risk

-functionals summarize numerous statistical parameters and actuarial risk measures. Their sample estimators are linear combinations of order statistics (-statistics). There exists a class of heavy-tailed distributions for which the asymptotic normality of these estimators cannot be obtained by classical results. In this paper we propose, by means of extreme value theory, alternative estimators for -functionals and establish their asymptotic normality. Our results may be applied to estimate the trimmed -moments and financial risk measures for heavy-tailed distributions.

scholarly journals Comparing downside risk measures for heavy tailed distributions

2006 ◽  
Vol 92 (2) ◽  
pp. 202-208 ◽  
Author(s):  
Jón Daníelsson ◽  
Bjørn N. Jorgensen ◽  
Mandira Sarma ◽  
Casper G. de Vries
Keyword(s):  
Risk Measures ◽  
Downside Risk ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed

Stand-alone vs systemic risk-taking of financial institutions

2016 ◽  
Vol 17 (4) ◽  
pp. 374-389 ◽  
Author(s):  
Sascha Strobl
Keyword(s):  
Financial Institutions ◽  
Risk Taking ◽  
Systemic Risk ◽  
Risk Measures ◽  
Risk Measure ◽  
Idiosyncratic Risk ◽  
Empirical Literature ◽  
Content Type ◽  
State Of The Economy ◽  
The Usa

Purpose This study investigates the risk-taking behavior of financial institutions in the USA. Specifically, differences between taking risks that affect primarily the shareholders of the institution and risks contributing to the overall systemic risk of the financial sector are examined. Additionally, differences between risk-taking before, during and after the financial crisis of 2007/2008 are examined. Design/methodology/approach To analyze the determinants of stand-alone and systemic risk, a generalized linear model including size, governance, charter value, business cycle, competition and control variables is estimated. Furthermore, Granger causality tests are conducted. Findings The results show that systemic risk has a positive effect on valuation and that corporate governance has no significant effect on risk-taking. The influence of competition is conditional on the state of the economy and the risk measure used. Systemic risk Granger-causes idiosyncratic risk but not vice versa. Research limitations/implications The major limitations of this study are related to the analyzed subset of large financial institutions and important risk-culture variables being omitted. Practical implications The broad policy implication of this paper is that systemic risk cannot be lowered by market discipline due to the moral hazard problem. Therefore, regulatory measures are necessary to ensure that individual financial institutions are not endangering the financial system. Originality/value This study contributes to the empirical literature on bank risk-taking in several ways. First, the characteristics of systemic risk and idiosyncratic risk are jointly analyzed. Second, the direction of causality of these two risk measures is examined. Moreover, this paper contributes to the discussion of the effect of competition on risk-taking.

scholarly journals Monetary risk measures for stochastic processes via Orlicz duality

2021 ◽  
Author(s):  
Christos E. Kountzakis ◽  
Damiano Rossello
Keyword(s):  
Financial Performance ◽  
Stochastic Processes ◽  
Continuous Time ◽  
Risk Measures ◽  
Cash Flows ◽  
The Other ◽  
Future Evolution ◽  
Heavy Tailed Distributions ◽  
Heavy Tailed ◽  
The One

AbstractIn this article, we extend the framework of monetary risk measures for stochastic processes to account for heavy tailed distributions of random cash flows evolving over a fixed trading horizon. To this end, we transfer the $$L^p$$ L p -duality underlying the representation of monetary risk measures to a more flexible Orlicz duality, in spaces of stochastic processes modelling random future evolution of financial values in continuous time over a finite horizon. This contributes, on the one hand, to the theory of real-valued monetary risk measures for processes and, on the other hand, supports a new representation of acceptability indices of financial performance.

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