Evaluating interest rate covariance models within a value-at-risk framework
AbstractWe find that covariance matrix forecasts for an international interest rate portfolio generated by a model that incorporates interest-rate level volatility effects perform best with respect to statistical loss functions. However, within a value-at-risk (VaR) framework, the relative performance of the covariance matrix forecasts depends greatly on the VaR distributional assumption. Simple forecasts based just on weighted averages of past observations perform best using a VaR framework. In fact, we find that portfolio variance forecasts that ignore the individual assets in the portfolio generate the lowest regulatory capital charge, a key economic decision variable for commercial banks. Our results provide empirical support for the commonly-used VaR models based on simple covariance matrix forecasts and distributional assumptions.
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Bibliographic InfoPaper provided by Federal Reserve Bank of San Francisco in its series Working Paper Series with number 2004-03.
Date of creation: 2004
Date of revision:
Publication status: Published in Journal of Financial Econometrics, v. 3, no. 1 (Winter 2005) pp. 126-168
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