Regulatory evaluation of value-at-risk models
AbstractBeginning in 1998, commercial banks may determine their regulatory capital requirements for market risk exposure using value-at-risk (VaR) models; i.e., time-series models of the distributions of portfolio returns. Currently, regulators have available three statistical methods for evaluating the accuracy of VaR models: the binomial method, the interval forecast method, and the distribution forecast method. These methods test whether the VaR forecasts in question exhibit properties characteristics of accurate VaR forecasts. However, the statistical tests can have low power against alternative models. A new evaluation method, based on proper scoring rules for probability forecasts, is proposed. Simulation results indicate that this method is clearly capable of differentiating among accurate and alternative VaR models.
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Bibliographic InfoPaper provided by Federal Reserve Bank of New York in its series Research Paper with number 9710.
Date of creation: 1997
Date of revision:
Other versions of this item:
- Jose A. Lopez, 1997. "Regulatory evaluation of value-at-risk models," Staff Reports 33, Federal Reserve Bank of New York.
- Jose A. Lopez, 1996. "Regulatory Evaluation of Value-at-Risk Models," Center for Financial Institutions Working Papers 96-51, Wharton School Center for Financial Institutions, University of Pennsylvania.
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