Bayesian inference for latent factor GARCH models

TL;DR

This paper introduces an efficient particle Gibbs algorithm for Bayesian estimation of latent factor GARCH models, overcoming previous challenges with slow mixing and limited scalability.

Contribution

It presents a scalable, generalizable particle Gibbs method that improves Bayesian inference for complex, multi-factor GARCH models.

Findings

Efficient estimation of factor GARCH models using particle Gibbs.

Method scales well with increasing model dimension.

Applicable to various GARCH family models.

Abstract

Latent factor GARCH models are difficult to estimate using Bayesian methods because standard Markov chain Monte Carlo samplers produce slowly mixing and inefficient draws from the posterior distributions of the model parameters. This paper describes how to apply the particle Gibbs algorithm to estimate factor GARCH models efficiently. The method has two advantages over previous approaches. First, it generalises in a straightfoward way to models with multiple factors and to various members of the GARCH family. Second, it scales up well as the dimension of the o, bservation vector increases.