Modeling the Conditional Distribution of Daily Stock Index Returns: An Alternative Bayesian Semiparametric Model

摘要

This article introduces a new family of Bayesian semiparametric models for the conditional distribution of daily stock index returns. The proposed models capture key stylized 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 modeled parametrically. The new model is applied to the daily returns of the S&P 500, FTSE 100, and EUROSTOXX 50 indices and is compared with GARCH, stochastic volatility, and other Bayesian semiparametric models.