Asymptotic Equivalence of Risk Measures Under Dependence Uncertainty

21 Pages Posted: 17 Jan 2018

See all articles by Jun Cai

Jun Cai

University of Waterloo - Department of Statistics and Actuarial Science

Haiyan Liu

Michigan State University - Department of Mathematics

Ruodu Wang

University of Waterloo - Department of Statistics and Actuarial Science

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Date Written: January 2018

Abstract

In this paper, we study the aggregate risk of inhomogeneous risks with dependence uncertainty, evaluated by a generic risk measure. We say that a pair of risk measures is asymptotically equivalent if the ratio of the worst‐case values of the two risk measures is almost one for the sum of a large number of risks with unknown dependence structure. The study of asymptotic equivalence is particularly important for a pair of a noncoherent risk measure and a coherent risk measure, as the worst‐case value of a noncoherent risk measure under dependence uncertainty is typically difficult to obtain. The main contribution of this paper is to establish general asymptotic equivalence results for the classes of distortion risk measures and convex risk measures under different mild conditions. The results implicitly suggest that it is only reasonable to implement a coherent risk measure for the aggregation of a large number of risks with uncertainty in the dependence structure, a relevant situation for risk management practice.

Keywords: risk aggregation, distortion risk measures, convex risk measures, dependence uncertainty, diversification

Suggested Citation

Cai, Jun and Liu, Haiyan and Wang, Ruodu, Asymptotic Equivalence of Risk Measures Under Dependence Uncertainty (January 2018). Mathematical Finance, Vol. 28, Issue 1, pp. 29-49, 2018, Available at SSRN: https://ssrn.com/abstract=3103417 or http://dx.doi.org/10.1111/mafi.12140

Jun Cai (Contact Author)

University of Waterloo - Department of Statistics and Actuarial Science ( email )

Waterloo, Ontario N2L 3G1
Canada

Haiyan Liu

Michigan State University - Department of Mathematics ( email )

619 Red Cedar Road
East Lansing, MI 48824
United States

Ruodu Wang

University of Waterloo - Department of Statistics and Actuarial Science ( email )

Waterloo, Ontario N2L 3G1
Canada

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