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Tail Conditional Expectations for Elliptical Distributions

Tail Conditional Expectations for Elliptical Distributions Abstract Significant changes in the insurance and financial markets are giving increasing attention to the need for developing a standard framework for risk measurement. Recently, there has been growing interest among insurance and investment experts to focus on the use of a tail conditional expectation because it shares properties that are considered desirable and applicable in a variety of situations. In particular, it satisfies requirements of a “coherent” risk measure in the spirit developed by Artzner et al. (1999). This paper derives explicit formulas for computing tail conditional expectations for elliptical distributions, a family of symmetric distributions that includes the more familiar normal and student-t distributions. The authors extend this investigation to multivariate elliptical distributions allowing them to model combinations of correlated risks. They are able to exploit properties of these distributions, naturally permitting them to decompose the conditional expectation, and allocate the contribution of individual risks to the aggregated risks. This is meaningful in practice, particularly in the case of computing capital requirements for an institution that may have several lines of correlated business and is concerned about fairly allocating the total capital to these constituents. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png North American Actuarial Journal Taylor & Francis

Tail Conditional Expectations for Elliptical Distributions

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References (32)

Publisher
Taylor & Francis
Copyright
Copyright Taylor & Francis Group, LLC
ISSN
2325-0453
eISSN
1092-0277
DOI
10.1080/10920277.2003.10596118
Publisher site
See Article on Publisher Site

Abstract

Abstract Significant changes in the insurance and financial markets are giving increasing attention to the need for developing a standard framework for risk measurement. Recently, there has been growing interest among insurance and investment experts to focus on the use of a tail conditional expectation because it shares properties that are considered desirable and applicable in a variety of situations. In particular, it satisfies requirements of a “coherent” risk measure in the spirit developed by Artzner et al. (1999). This paper derives explicit formulas for computing tail conditional expectations for elliptical distributions, a family of symmetric distributions that includes the more familiar normal and student-t distributions. The authors extend this investigation to multivariate elliptical distributions allowing them to model combinations of correlated risks. They are able to exploit properties of these distributions, naturally permitting them to decompose the conditional expectation, and allocate the contribution of individual risks to the aggregated risks. This is meaningful in practice, particularly in the case of computing capital requirements for an institution that may have several lines of correlated business and is concerned about fairly allocating the total capital to these constituents.

Journal

North American Actuarial Journal – Taylor & Francis

Published: Oct 1, 2003

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