The effects of degrees of freedom estimation in the Asymmetric GARCH model with Student-t Innovations

TL;DR

This paper explores how using Jeffreys priors for degrees of freedom affects Bayesian inference in asymmetric Student-t GARCH models, with simulations and real stock data demonstrating its impact.

Contribution

It introduces a Jeffreys prior approach for degrees of freedom estimation in asymmetric Student-t GARCH models, addressing likelihood issues and improving inference.

Findings

Jeffreys prior improves degrees of freedom estimation accuracy.

Small sample sizes and misspecification affect parameter estimation.

Application to stock data shows model's practical usefulness.

Abstract

This work investigates the effects of using the independent Jeffreys prior for the degrees of freedom parameter of a Student-t model in the asymmetric generalised autoregressive conditional heteroskedasticity (GARCH) model. To capture asymmetry in the reaction to past shocks, smooth transition models are assumed for the variance. We adopt the fully Bayesian approach for inference, prediction and model selection We discuss problems related to the estimation of degrees of freedom in the Student-t model and propose a solution based on independent Jeffreys priors which correct problems in the likelihood function. A simulated study is presented to investigate how the estimation of model parameters in the Student-t GARCH model are affected by small sample sizes, prior distributions and misspecification regarding the sampling distribution. An application to the Dow Jones stock market data illustrates the usefulness of the asymmetric GARCH model with Student-t errors.