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- W3121825701 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." @default.
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- W3121825701 date "2004-03-01" @default.
- W3121825701 modified "2023-09-25" @default.
- W3121825701 title "Evaluating Interest Rate Covariance Models within a Value-at-Risk Framework" @default.
- W3121825701 doi "https://doi.org/10.24148/wp2004-03" @default.
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