| Abstract: | A key component of managing international interest rate portfoliosis forecasts of the covariances between national interest ratesand accompanying exchange rates. How should portfolio managerschoose among the large number of covariance forecasting modelsavailable? We find that covariance matrix forecasts generatedby models incorporating interest-rate level volatility effectsperform best with respect to statistical loss functions. However,within a value-at-risk (VaR) framework, the relative performanceof the covariance matrix forecasts depends greatly on the VaRdistributional assumption, and forecasts based just on weightedaverages of past observations perform best. In addition, portfoliovariance forecasts that ignore the covariance matrix generatethe lowest regulatory capital charge, a key economic decisionvariable for commercial banks. Our results provide empiricalsupport for the commonly used VaR models based on simple covariancematrix forecasts and distributional assumptions. |
