| Abstract: | This article provides a solution to the curse of dimensionalityassociated to multivariate generalized autoregressive conditionallyheteroskedastic (GARCH) estimation. We work with univariateportfolio GARCH models and show how the multivariate dimensionof the portfolio allocation problem may be recovered from theunivariate approach. The main tool we use is "variance sensitivityanalysis," the change in the portfolio variance induced by aninfinitesimal change in the portfolio allocation. We suggesta computationally feasible method to find minimum variance portfoliosand estimate full variance-covariance matrices. An applicationto real data portfolios implements our methodology and comparesits performance against that of selected popular alternatives. |
