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Evaluating Value at Risk Methodologies: Accuracy versus Computational Time

Evaluating Value at Risk Methodologies: Accuracy versus Computational Time Recent research has shown that different methods of computing Value at Risk (VAR) generate widely varying results, suggesting the choice of VAR method is very important. This article examines six VAR methods, and compares their computational time requirements and their accuracy when the sole source of inaccuracy is errors in approximating nonlinearity. Simulations using portfolios of foreign exchange options showed fairly wide variation in accuracy and unsurprisingly wide variation in computational time. When the computational time and accuracy of the methods were examined together, four methods were superior to the others. The article also presents a new method for using order statistics to create confidence intervals for the errors and errors as a per cent of true value at risk for each VAR method. This makes it possible to easily interpret the implications of VAR errors for the size of shortfalls or surpluses in a firm's risk-based capital. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Journal of Financial Services Research Springer Journals

Evaluating Value at Risk Methodologies: Accuracy versus Computational Time

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References (21)

Publisher
Springer Journals
Copyright
Copyright © 1997 by Kluwer Academic Publishers
Subject
Finance; Financial Services; Macroeconomics/Monetary Economics//Financial Economics
ISSN
0920-8550
eISSN
1573-0735
DOI
10.1023/A:1007978820465
Publisher site
See Article on Publisher Site

Abstract

Recent research has shown that different methods of computing Value at Risk (VAR) generate widely varying results, suggesting the choice of VAR method is very important. This article examines six VAR methods, and compares their computational time requirements and their accuracy when the sole source of inaccuracy is errors in approximating nonlinearity. Simulations using portfolios of foreign exchange options showed fairly wide variation in accuracy and unsurprisingly wide variation in computational time. When the computational time and accuracy of the methods were examined together, four methods were superior to the others. The article also presents a new method for using order statistics to create confidence intervals for the errors and errors as a per cent of true value at risk for each VAR method. This makes it possible to easily interpret the implications of VAR errors for the size of shortfalls or surpluses in a firm's risk-based capital.

Journal

Journal of Financial Services Research – Springer Journals

Published: Oct 16, 2004

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