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Multiple risk measures for multivariate dynamic heavy–tailed models

Multiple risk measures for multivariate dynamic heavy–tailed models
Multiple risk measures for multivariate dynamic heavy–tailed models

The dynamic evolution of tail–risk interdependence among institutions is of primary importance when extreme events such as financial crisis occur. In this paper we introduce two new risk measures that generalise the Conditional Value–at–Risk and the Conditional Expected Shortfall in a multiple setting. The proposed risk measures aim to capture extreme tail co–movements among several multivariate connected market participants experiencing contemporaneous distress instances. Analytical expressions for the risk measures are obtained under a parametric model that postulates a joint dynamic evolution of the underlying institutions' losses and gains. We consider a multivariate Student–t version of Markov Switching models as a robust alternative to the usual multivariate Gaussian specification, accounting for heavy–tails and time varying non–linear correlations. An empirical application to US banks is considered to show that our model–based risk measurement framework provides a better characterisation of the dynamic evolution of the overall risk of a financial system and a more complete picture of how the risk spreads among institutions.

Markov–Switching models, Multiple conditional expected shortfall, Multiple conditional Value–at–Risk, Risk measures, Systemic risk, Tail risk interdependence
0927-5398
1-32
Bernardi, Mauro
0295dc28-c830-4ee8-a6db-7de82557407d
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Petrella, Lea
bf351458-2a5a-452e-be73-496a19c4060a
Bernardi, Mauro
0295dc28-c830-4ee8-a6db-7de82557407d
Maruotti, Antonello
7096256c-fa1b-4cc1-9ca4-1a60cc3ee12e
Petrella, Lea
bf351458-2a5a-452e-be73-496a19c4060a

Bernardi, Mauro, Maruotti, Antonello and Petrella, Lea (2017) Multiple risk measures for multivariate dynamic heavy–tailed models. Journal of Empirical Finance, 43, 1-32. (doi:10.1016/j.jempfin.2017.04.005).

Record type: Article

Abstract

The dynamic evolution of tail–risk interdependence among institutions is of primary importance when extreme events such as financial crisis occur. In this paper we introduce two new risk measures that generalise the Conditional Value–at–Risk and the Conditional Expected Shortfall in a multiple setting. The proposed risk measures aim to capture extreme tail co–movements among several multivariate connected market participants experiencing contemporaneous distress instances. Analytical expressions for the risk measures are obtained under a parametric model that postulates a joint dynamic evolution of the underlying institutions' losses and gains. We consider a multivariate Student–t version of Markov Switching models as a robust alternative to the usual multivariate Gaussian specification, accounting for heavy–tails and time varying non–linear correlations. An empirical application to US banks is considered to show that our model–based risk measurement framework provides a better characterisation of the dynamic evolution of the overall risk of a financial system and a more complete picture of how the risk spreads among institutions.

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More information

Accepted/In Press date: 26 April 2017
e-pub ahead of print date: 5 May 2017
Published date: 1 September 2017
Keywords: Markov–Switching models, Multiple conditional expected shortfall, Multiple conditional Value–at–Risk, Risk measures, Systemic risk, Tail risk interdependence

Identifiers

Local EPrints ID: 413176
URI: https://eprints.soton.ac.uk/id/eprint/413176
ISSN: 0927-5398
PURE UUID: f1ff42d5-ceb4-4fc0-a595-7d635e9c0393

Catalogue record

Date deposited: 17 Aug 2017 16:30
Last modified: 13 Mar 2019 19:32

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