Evaluating Interest Rate Covariance Models within a Value-at-Risk Framework

A key component of managing international interest rate portfolios is forecasts of the covariances between national interest rates and accompanying exchange rates. How should portfolio managers choose among the large number of covariance forecasting models available? We find that covariance matrix forecasts generated by models incorporating 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, and forecasts based just on weighted averages of past observations perform best. In addition, portfolio variance forecasts that ignore the covariance matrix 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