Modelling the conditional distribution of daily stock index returns: an alternative Bayesian semiparametric model

This paper introduces a new family of Bayesian semi-parametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylised facts of such returns, namely heavy tails, asymmetry, volatility clustering, and the ‘leverage effect’. A Bayesian nonparametric prior is used to generate random density functions that are unimodal and asymmetric. Volatility is modelled parametrically. The new model is applied to the daily returns of the S&P 500, FTSE 100, and EUROSTOXX 50 indices and is compared to GARCH, Stochastic Volatility, and other Bayesian semi-parametric models