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Random orthogonal matrix simulation with exact means, covariances, and multivariate skewness

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  • Hanke, Michael
  • Penev, Spiridon
  • Schief, Wolfgang
  • Weissensteiner, Alex

Abstract

We develop a simulation algorithm that generates multivariate samples with exact means, covariances, and multivariate skewness. If required for financial applications, absence of arbitrage can be ensured. Potential applications include the simulation of risk factors for the risk management of financial institutions. We use the Kollo measure of multivariate skewness, which is more informative for these applications than the Mardia skewness previously used in this context.

Suggested Citation

  • Hanke, Michael & Penev, Spiridon & Schief, Wolfgang & Weissensteiner, Alex, 2017. "Random orthogonal matrix simulation with exact means, covariances, and multivariate skewness," European Journal of Operational Research, Elsevier, vol. 263(2), pages 510-523.
    • Handle: RePEc:eee:ejores:v:263:y:2017:i:2:p:510-523
    DOI: 10.1016/j.ejor.2017.05.023
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    Cited by:

    1. Alexander, Carol & Meng, Xiaochun & Wei, Wei, 2022. "Targeting Kollo skewness with random orthogonal matrix simulation," European Journal of Operational Research, Elsevier, vol. 299(1), pages 362-376.
    2. Carol Alexander & Xiaochun Meng & Wei Wei, 2020. "Targetting Kollo Skewness with Random Orthogonal Matrix Simulation," Papers 2004.06586, arXiv.org, revised Sep 2021.

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