Portfolio construction with Bayesian GARCH forecasts

Mean-variance portfolio construction depends on forecasts of the variance of the assets in the portfolio. Recent studies have shown that the portfolio weights are very sensitive to changes in the one step ahead variance forecasts. Thus, we propose a Bayesian approach where prior information could be included in the model to reduce the variability of the optimal portfolio weights. We compare the performance of a global regional portfolio (North America, Europe and the Pacific) based on variance forecasts by a Bayesian VAR-GARCH in mean model with the benchmark (MSCI World index).