Dimension Reduction in the Computation of Value‐at‐Risk

Emerald - 2002
CLAUDIOALBANESE1, KENJACKSON2, PETTERWIBERG3
1Associate professor of mathematics at the University of Toronto in Ontario, Canada
2Professor of computer science at the University of Toronto in Ontario, Canada
3Ph.D. candidate in the department of computer science at the University of Toronto in Ontario, Canada

Tóm tắt

Regulators require banks to employ value‐at‐risk (VaR) to estimate the exposure of their trading portfolios to market risk, in order to establish capital requirements. However, portfolio‐level VaR analysis is a high‐dimensional problem and hence computationally intensive. This article presents two new portfolio‐based approaches to reducing the dimensionality of the VaR analysis.

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