ROM Simulation: Applications to Stress Testing and VaR
Carol Alexander () and
ICMA Centre Discussion Papers in Finance from Henley Business School, University of Reading
Most banks employ historical simulation for Value-at-Risk (VaR) calculations, where VaR is computed from a lower quantile of a forecast distribution for the portfolio's profit and loss (P&L) that is constructed from a single, multivariate historical sample on the portfolio's risk factors. The implicit assumption is that history will repeat itself for certain over the forecast horizon. Until now, the only alternative is to assume the historical sample is generated by a multivariate, parametric risk factor distribution and (except in special cases where an ana- lytic solution is available) to simulate P&L via Monte Carlo (MC). This paper introduces a methodology that encompasses historical and MC VaR as special cases, which is much faster than MC simulation and which avoids the single-sample bias of historical simulation. Ran- dom orthogonal matrix (ROM) simulation is a fast matrix-based simulation method that applies directly to an historical sample, or to a parametric distribution. Each simulation matches the first four multivariate sample moments to those of the observed sample, or of the target distribution. Stressed VaR is typically computed from an historical sample using the Duffie-Pan methodology, whereby the sample is transformed to have a stressed covari- ance matrix. ROM simulation extends this methodology to generate very large samples, which furthermore have stressed values for the first four multivariate moments values.
Keywords: Random orthogonal matrix; Value-at-Risk; Stressed VaR; Basel II; Market risk capital (search for similar items in EconPapers)
JEL-codes: C14 C15 C53 C63 G17 G21 G28 (search for similar items in EconPapers)
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