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Evaluating interest rate covariance models within a value-at-risk framework

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Miguel A. Ferreira
Jose A. Lopez

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Abstract

We 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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Paper provided by Federal Reserve Bank of San Francisco in its series Working Papers in Applied Economic Theory with number 2004-03.

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Date of creation: 2004
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Publication status: Published in Journal of Financial Econometrics, v. 3, no. 1 (Winter 2005) pp. 126-168
Handle: RePEc:fip:fedfap:2004-03

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Keywords: Interest rates ; Forecasting ; Risk management;

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  1. Andrew J. Patton & Kevin Sheppard, 2008. "Evaluating Volatility and Correlation Forecasts," OFRC Working Papers Series 2008fe22, Oxford Financial Research Centre. [Downloadable!]
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