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Asymptotics of multivariate conditional risk measures for Gaussian risks

Chengxiu Ling

Insurance: Mathematics and Economics, 2019, vol. 86, issue C, 205-215

Abstract: This paper investigates accurate approximations of marginal moment excess, marginal conditional tail moment and marginal moment shortfall for multivariate Gaussian system risks. Based on the dimension reduction property via the quadratic programming problem, the super-exponential and polynomial convergence speeds are specified. Two interesting questions involved in risk management are well addressed, namely the minimal additional risk capital injection to avoid infinite risk contagion and a sufficient and necessary condition to alternate the convergence speeds. Numerical study and typical examples are given to illustrate the efficiency of our findings. Due to the flexible moment order, additional applications may involve in risk management, including tail mean–variance portfolio and multivariate conditional risk measures of tail covariance, tail skewness with dependence and extremal risk contagion under consideration.

Keywords: Gaussian system risk; Multivariate risk measures; Risk contagion; Quadratic programming problem; Marginal moment excess (search for similar items in EconPapers)
JEL-codes: C13 G10 G32 (search for similar items in EconPapers)
Date: 2019
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Insurance: Mathematics and Economics is currently edited by R. Kaas, Hansjoerg Albrecher, M. J. Goovaerts and E. S. W. Shiu

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