Markov Switching Smooth Transition GARCH Model

TL;DR

This paper introduces a Markov switching asymmetric GARCH model that captures leverage effects and negative skewness, using Bayesian estimation methods, and demonstrates its superior in-sample fit and forecasting performance on real data.

Contribution

It presents a novel Markov switching asymmetric GARCH model with Bayesian estimation, improving modeling of leverage effects and skewness in financial time series.

Findings

Model achieves the best in-sample fit via DIC.

Provides better forecasts when negative skewness is significant.

Demonstrates effectiveness on real financial data sets.

Abstract

A Markov switching asymmetric GARCH model which imposes more leverage effect of the negative shocks is considered. The asymptotic behavior of the second moment is investigated and an upper bound for it is calculated. A bayesian strategy through Gibbs and griddy Gibbs sampling is used to estimate the parameters. Finally we study the performance of the model by two real data sets. We show that this model has the best in-sample fit via DIC and provides a better forecast when the negative skewness is large enough.