Evaluating interest rate covariance models within a value-at-risk framework
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.
Status: Published in Journal of Financial Econometrics, v. 3, no. 1 (Winter 2005) pp. 126-168
File(s): File format is application/pdf http://www.frbsf.org/economic-research/files/wp04-03bk.pdf
Provider: Federal Reserve Bank of San Francisco
Part of Series: Working Paper Series
Publication Date: 2004