English, Article, Journal or magazine article edition: Regulatory Evaluation of Value-at-Risk Models Jose A. Lopez

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Bookmark: http://trove.nla.gov.au/version/190120
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Regulatory evaluation of value-at-risk models
Author
  • Jose A. Lopez
Physical Description
  • preprint
Notes
  • Value-at-risk (VaR) models have been accepted by banking regulators as tools for setting capital requirements for market risk exposure. Three statistical methodologies for evaluating the accuracy of such models are examined; specifically, evaluation based on the binomial distribution, interval forecast evaluation as proposed by Christoffersen (1995), and distribution forecast evaluation as proposed by Crnkovic and Drachman (1995). These methodologies test whether the VaR forecasts in question exhibit properties characteristic of accurate VaR forecasts. However, the statistical tests used often have low power against alternative models. A new evaluation methodology, based on the probability forecasting framework discussed by Lopez (1995), is proposed. This methodology gauges the accuracy of VaR models using forecast evaluation techniques. It is argued that this methodology provides users, such as regulatory agencies, with greater flexibility to tailor the evaluations to their particular interests by defining the appropriate loss function. Simulation results indicate that this methodology is clearly capable of differentiating among accurate and alternative VaR models. This paper was presented at the Financial Institutions Center's October 1996 conference on "
  • RePEc:wop:pennin:96-51
  • Beginning 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.
  • Bank capital ; Risk ; Econometric models
  • RePEc:fip:fednrp:9710
  • Beginning in 1998, U.S. commercial banks may determine their regulatory capital requirements for financial market risk exposure using value-at-risk (VaR) models i.e., models of the time-varying distributions of portfolio returns. Currently, regulators have available three hypothesis-testing methods for evaluating the accuracy of VaR models: the binomial method, the interval forecast method and the distribution forecast method. These methods use hypothesis tests to examine whether the VaR forecasts in question exhibit properties characteristic of accurate VaR forecasts. However, given the low power often exhibited by these tests, these methods may often misclassify forecasts from inaccurate models as accurate. A new evaluation method that uses loss functions based on probability forecasts, is proposed. Simulation results indicate that this method is capable of differentiating between forecasts from accurate and inaccurate, alternative VaR models.
  • Risk ; Bank capital ; Econometric models ; Forecasting
  • RePEc:fip:fednsr:33
Language
  • English
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