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Revisiting Fama–French factors' predictability with Bayesian modelling and copula‐based portfolio optimization
Author(s) -
Zhao Yang,
Stasinakis Charalampos,
Sermpinis Georgios,
Fernandes Filipa Da Silva
Publication year - 2019
Publication title -
international journal of finance and economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.505
H-Index - 39
eISSN - 1099-1158
pISSN - 1076-9307
DOI - 10.1002/ijfe.1742
Subject(s) - predictability , econometrics , copula (linguistics) , autoregressive model , portfolio , diversification (marketing strategy) , economics , bayesian probability , portfolio optimization , covariance , factor analysis , computer science , mathematics , financial economics , statistics , business , marketing
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 nonlinear models. Next, the individual forecasts are combined through dynamic model averaging, and their performance is benchmarked by the best performing individual predictor and other forecast combination techniques. Finally, we use the generalized autoregressive score model and the skewed t copula method to estimate the correlation of assets. The generalized autoregressive score performance is also compared with other traditional approaches such as dynamic conditional correlation model and asymmetric dynamic conditional correlation. The performance of the constructed portfolios is assessed through traditional metrics and ratios accounting for the conditional value‐at‐risk and the conditional diversification benefits approach. Our results show that combining Bayesian forecast combinations with copulas is leading to significant improvements in the portfolio optimization process, and forecasting covariance accounting for asymmetric dependence between the factors adds diversification benefits to the obtained portfolios.