Improved algorithms for computing worst Value-at-Risk

Hofert Marius (), Memartoluie Amir (), Saunders David () and Wirjanto Tony ()
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Hofert Marius: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada
Memartoluie Amir: Cheriton School of Computer Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada
Saunders David: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada
Wirjanto Tony: Department of Statistics and Actuarial Science, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

Statistics & Risk Modeling, 2017, vol. 34, issue 1-2, 13-31

Abstract: Numerical challenges inherent in algorithms for computing worst Value-at-Risk in homogeneous portfolios are identified and solutions as well as words of warning concerning their implementation are provided. Furthermore, both conceptual and computational improvements to the Rearrangement Algorithm for approximating worst Value-at-Risk for portfolios with arbitrary marginal loss distributions are given. In particular, a novel Adaptive Rearrangement Algorithm is introduced and investigated. These algorithms are implemented using the R package qrmtools and may be of interest in any context in which it is required to find columnwise permutations of a matrix such that the minimal (maximal) row sum is maximized (minimized).

Keywords: Risk aggregation; model uncertainty; Value-at-Risk; Adaptive Rearrangement Algorithm (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1515/strm-2015-0028

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