A Bayesian Semiparametric Multiplicative Error Model with an Application to Realized Volatility

Abstract

A semiparametric multiplicative error model (MEM) is proposed. In traditional MEM, the innovations are typically assumed to be Gamma distributed (with one free parameter that ensures unit mean of the innovations and thus identifiability of the model), however empirical investigations unveils the inappropriateness of this choice. In the proposed approach, the conditional mean of the time series is modeled parametrically, while we model its conditional distribution nonparametrically by Dirichlet process mixture of Gamma distributions. Bayesian inference is performed using Markov chain Monte Carlo simulation. This model is applied to the time series of daily realized volatility of some indices, and is compared to similar parametric models available in the literature. Our simulations and empirical studies show better predictive performance, flexibility and robustness to misspecification of our Bayesian semiparametric approach.

Keywords: Dirichlet process mixture model, multiplicative error model, slice sampler, realized volatility, parameter expansion

JEL Classification: C11, C22, C51