Bayesian sequential stock return prediction through copulas

Abstract

In this paper we perform density prediction for the equity returns in a non-linear manner by employing a copula-based approach. The use of asymmetric copulas allows to model asymmetric predictive densities and non-linear dependencies between equity returns and some predictor variable. In our proposed approach, the copula parameter and the marginals are estimated simultaneously by using Sequential Monte Carlo techniques. We apply proposed models to daily log returns of 20 assets traded at the NYSE. Among other findings, we show that in terms of predictive log Bayes Factors the asymmetric copula is preferred by more assets than the symmetric copula, advocating the use of non-linear models. Also, dividend yield is a better predictor variable than the lagged returns overall, but this result is reversed if we consider a volatile period only. These results have major implications for the investors when making portfolio decisions or measuring tail risk.