Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization

This study is investigating the predictability of the five Fama-French factors and explores their optimal portfolio allocation for factor investing during 2000-2017. Firstly, we forecast each factor with a pool of linear and non-linear models. Next, the individual forecasts are combined through Dynamic Model Averaging (DMA), while their performance is benchmarked by the best performing individual predictor and other forecast combination techniques. Finally, we use the Generalized Autoregressive Score (GAS) model and the skewed t copula method to estimate the correlation of assets. The GAS performance is also compared with other traditional approaches such as Dynamic Conditional Correlation (DCC) model and Asymmetric Dynamic Conditional Correlation (ADCC). The performance of the constructed portfolios is assessed through traditional metrics and ratios accounting for the Conditional Value-at-Risk (CVaR) and the Conditional Diversification Benefits (CDB) approach. Our results show that combining Bayesian forecast combinations with copulas is leading to significant improvements in the portfolio optimization process, while forecasting covariance accounting for asymmetric dependence between the factors adds diversification benefits to the obtained portfolios.