Hysteretic Multivariate Bayesian Structural GARCH Model with Soft Information

TL;DR

This paper presents the SH-MBS-GARCH model, a Bayesian multivariate GARCH framework that incorporates hysteretic effects and soft information for improved modeling of joint financial time series dynamics.

Contribution

It introduces a novel flexible method to embed soft information into the regime component and a Bayesian estimation approach combining adaptive MCMC, spike-and-slab regression, and a simulation smoother.

Findings

Model outperforms competitors in fit and prediction accuracy.

Effectively captures regime-switching dynamics.

Validated on major stock indices from 2016 to 2020.

Abstract

This study introduces the SH-MBS-GARCH model, a hysteretic multivariate Bayesian structural GARCH framework that integrates hard and soft information to capture the joint dynamics of multiple financial time series, incorporating hysteretic effects and addressing conditional heteroscedasticity through GARCH components. Various model specifications could utilize soft information to define the regime indicator in distinct ways. We propose a flexible, straightforward method for embedding soft information into the regime component, applicable across all SH-MBS-GARCH model variants. We further propose a generally applicable Bayesian estimation approach that combines adaptive MCMC, spike-and-slab regression, and a simulation smoother, ensuring accurate parameter estimation, validated through extensive simulations. Empirical analysis of the Dow Jones Industrial Average, NASDAQ Composite, and PHLX Semiconductor indices from January 2016 to December 2020 demonstrates that the SH-MBS-GARCH model outperforms competing models in fitting and prediction accuracy, effectively capturing regime-switching dynamics.