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Improved algorithms for computing worst Value-at-Risk

Author

Listed:
  • 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)

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).

Suggested Citation

  • Hofert Marius & Memartoluie Amir & Saunders David & Wirjanto Tony, 2017. "Improved algorithms for computing worst Value-at-Risk," Statistics & Risk Modeling, De Gruyter, vol. 34(1-2), pages 13-31, June.
    • Handle: RePEc:bpj:strimo:v:34:y:2017:i:1-2:p:13-31:n:3
    DOI: 10.1515/strm-2015-0028
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    References listed on IDEAS

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    Cited by:

    1. Marius Hofert, 2020. "Implementing the Rearrangement Algorithm: An Example from Computational Risk Management," Risks, MDPI, vol. 8(2), pages 1-28, May.

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