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Improved Algorithms for Computing Worst Value-at-Risk: Numerical Challenges and the Adaptive Rearrangement Algorithm

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Listed:
  • Marius Hofert
  • Amir Memartoluie
  • David Saunders
  • Tony Wirjanto

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.

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  • Marius Hofert & Amir Memartoluie & David Saunders & Tony Wirjanto, 2015. "Improved Algorithms for Computing Worst Value-at-Risk: Numerical Challenges and the Adaptive Rearrangement Algorithm," Papers 1505.02281, arXiv.org, revised Dec 2015.
    • Handle: RePEc:arx:papers:1505.02281
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    File URL: http://arxiv.org/pdf/1505.02281
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    Cited by:

    1. Edgars Jakobsons & Steven Vanduffel, 2015. "Dependence Uncertainty Bounds for the Expectile of a Portfolio," Risks, MDPI, vol. 3(4), pages 1-25, December.

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