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BY 4.0 license Open Access Published by De Gruyter Open Access December 21, 2020

On quantile based co-risk measures and their estimation

  • Sebastian Fuchs EMAIL logo and Wolfgang Trutschnig
From the journal Dependence Modeling


Conditional Value-at-Risk (CoVaR) is defined as the Value-at-Risk of a certain risk given that the related risk equals a given threshold (CoVaR=) or is smaller/larger than a given threshold (CoVaR</CoVaR). We extend the notion of Conditional Value-at-Risk to quantile based co-risk measures that are weighted mixtures of CoVaR at different levels and hence involve the stochastic dependence that occurs among the risks and that is captured by copulas. We show that every quantile based co-risk measure is a quantile based risk measure and hence fulfills all related properties. We further discuss continuity results of quantile based co-risk measures from which consistent estimators for CoVaR< and CoVaR based risk measures immediately follow when plugging in empirical copulas. Although estimating co-risk measures based on CoVaR= is a nontrivial endeavour since conditioning on events with zero probability is necessary we show that working with so-called empirical checkerboard copulas allows to construct strongly consistent estimators for CoVaR= and related co-risk measures under very mild regularity conditions. A small simulation study illustrates the performance of the obtained estimators for special classes of copulas.

MSC 2010: 62H05; 60E05; 91G70


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Received: 2020-11-02
Accepted: 2020-11-30
Published Online: 2020-12-21

© 2020 Sebastian Fuchs et al., published by De Gruyter

This work is licensed under the Creative Commons Attribution 4.0 International License.

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