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

35 Pages Posted: 11 Nov 2012 Last revised: 18 May 2013

See all articles by Reza Solgi

Reza Solgi

Swiss Finance Institute

Antonietta Mira

Università della Svizzera italiana - InterDisciplinary Institute of Data Science

Date Written: May 18, 2013

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

Suggested Citation

Solgi, Reza and Mira, Antonietta, A Bayesian Semiparametric Multiplicative Error Model with an Application to Realized Volatility (May 18, 2013). Available at SSRN: https://ssrn.com/abstract=2173197 or http://dx.doi.org/10.2139/ssrn.2173197

Reza Solgi (Contact Author)

Swiss Finance Institute ( email )

c/o University of Geneva
40, Bd du Pont-d'Arve
CH-1211 Geneva 4
Switzerland

Antonietta Mira

Università della Svizzera italiana - InterDisciplinary Institute of Data Science ( email )

Via Giuseppe Buffi 13
CH-6900 Lugano, CH-6904
Switzerland

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
143
Abstract Views
860
rank
261,689
PlumX Metrics