Dynamic Portfolio Optimization Using Generalized Dynamic Conditional Heteroskedastic Factor Models

Abstract

We model large panels of financial time series by means of generalized dynamic factor models with multivariate GARCH idiosyncratic components. Such models combine the features of dynamic factors with those of a generalized smooth transition conditional correlation (GSTCC) model, which belongs to the class of time-varying conditional correlation models. The model is applied to dynamic portfolio allocation with Value at Risk constraints on 6.5 years of daily TOPIX Sector Indexes. Results show that the proposed model yields better portfolio performance than other multivariate models proposed in the literature, including the traditional mean-variance approach.