# Bayesian nonparametric panel Markov-switching GARCH models

arXiv: https://arxiv.org/abs/2012.10124 · HTML: https://mlsanity.com/paper/2012.10124/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 18 Dec 2020.

## Built on by (most cited first, 2 of 2)

- [Bayesian Nonlinear Regression using Sums of Simple Functions](https://mlsanity.com/paper/2312.01881/) (2023)
- [Bayesian Autoregressive Online Change-Point Detection with Time-Varying Parameters](https://mlsanity.com/paper/2407.16376/) (2024)

## Abstract (arXiv)

This paper introduces a new model for panel data with Markov-switching GARCH effects. The model incorporates a series-specific hidden Markov chain process that drives the GARCH parameters. To cope with the high-dimensionality of the parameter space, the paper exploits the cross-sectional clustering of the series by first assuming a soft parameter pooling through a hierarchical prior distribution with two-step procedure, and then introducing clustering effects in the parameter space through a nonparametric prior distribution. The model and the proposed inference are evaluated through a simulation experiment. The results suggest that the inference is able to recover the true value of the parameters and the number of groups in each regime. An empirical application to 78 assets of the SP&100 index from 6th January 2000 to 3rd October 2020 is also carried out by using a two-regime Markov switching GARCH model. The findings shows the presence of 2 and 3 clusters among the constituents in the first and second regime, respectively.