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 paper 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 paper also presents a new method for using order statistics to create confidence intervals for the errors and errors as a percent 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. This paper was presented at the Financial Institutions Center's October 1996 conference on "
|Date of creation:||Nov 1996|
|Date of revision:|
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- Darryll Hendricks, 1996. "Evaluation of value-at-risk models using historical data," Economic Policy Review, Federal Reserve Bank of New York, issue Apr, pages 39-69.
- Christopher Marshall & Michael Siegel, 1996. "Value at Risk: Implementing a Risk Measurement Standard," Center for Financial Institutions Working Papers 96-47, Wharton School Center for Financial Institutions, University of Pennsylvania.
- Arturo Estrella, 1995. "Taylor, Black and Scholes: series approximations and risk management pitfalls," Research Paper 9501, Federal Reserve Bank of New York.
- William Fallon, 1996. "Calculating Value-at-Risk," Center for Financial Institutions Working Papers 96-49, Wharton School Center for Financial Institutions, University of Pennsylvania.
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