A Kernel Technique for Forecasting the Variance-Covariance Matrix
In this paper we propose a novel methodology for forecasting variance convariance matrices (VCM) using kernel estimates. While the popular Riskmetrics methodology can be seen as a special case of our methodology, the generalisation is significant as it allows the researcher to use a number of variables to determine the kernel weights of past VCM. The complexity of the methodology scales with the number of explanatory variables used and not with the size of the VCM. This, as well as the automatic positive definiteness of the VCM forecasts are major improvements on currently available forecasting methods. An empirical analysis establishes the usefulness of our proposed methodology.
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