Random Orthogonal Matrix Simulation with Exact Means, Covariances, and Multivariate Skewness

European Journal of Operational Research, 263(2), 510-523

Posted: 11 Aug 2016 Last revised: 9 Jun 2020

See all articles by Michael Hanke

Michael Hanke

University of Liechtenstein

Spiridon Penev

University of New South Wales (UNSW) - School of Mathematics

Wolfgang Schief

University of New South Wales (UNSW) - School of Mathematics and Statistics

Alex Weissensteiner

Free University of Bolzano Bozen

Date Written: July 30, 2016

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.

Keywords: ROM simulation, multivariate skewness, risk factors

JEL Classification: C63, C15

Suggested Citation

Hanke, Michael and Penev, Spiridon and Schief, Wolfgang and Weissensteiner, Alex, Random Orthogonal Matrix Simulation with Exact Means, Covariances, and Multivariate Skewness (July 30, 2016). European Journal of Operational Research, 263(2), 510-523, Available at SSRN: https://ssrn.com/abstract=2816279 or http://dx.doi.org/10.2139/ssrn.2816279

Michael Hanke (Contact Author)

University of Liechtenstein ( email )

Fuerst Franz Josef-Strasse
Vaduz, FL-9490
Liechtenstein

Spiridon Penev

University of New South Wales (UNSW) - School of Mathematics ( email )

Department of Statistics
Sydney, New South Wales
2053 Australia

Wolfgang Schief

University of New South Wales (UNSW) - School of Mathematics and Statistics ( email )

Sydney, 2052
Australia

Alex Weissensteiner

Free University of Bolzano Bozen ( email )

Universitätsplatz 1
Bolzano, 39100
+39 0471 013496 (Phone)

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