Bayesian Nonparametric Panel Markov-Switching GARCH Models

Bibliographic Data

ID19418629
AuthorsRoberto Casarin (0000-0003-1746-9190, Ca’ Foscari University of Venice, Venice, Italy, corresponding author), Mauro Costantini (0000-0002-6991-5159, University of L’Aquila, L’Aquila, Italy), Anthony Osuntuyi (Ca’ Foscari University of Venice, Venice, Italy)
Year2024
Volume42
Issue1
Pages135-146
Publication date2024-01-02
Open AccessYes
TypeARTICLE
VenueJournal of Business and Economic Statistics (JOURNAL)
Journal identifiersISSN: 0735-0015 • E-ISSN: 1537-2707
PublisherInforma UK Limited (PUBLISHER • GB)
DOI10.1080/07350015.2023.2166049
OpenAlexW3117148402
LanguageEN
Citations received1
References cited62

This article proposes Bayesian nonparametric inference for panel Markov-switching GARCH models. The model incorporates series-specific hidden Markov chain processes that drive the GARCH parameters. To cope with the high-dimensionality of the parameter space, the article assumes soft parameter pooling through a hierarchical prior distribution and introduces cross sectional clustering through a Bayesian nonparametric prior distribution. An MCMC posterior approximation algorithm is developed and its efficiency is studied in simulations under alternative settings. An empirical application to financial returns data in the United States is offered with a portfolio performance exercise based on forecasts. A comparison shows that the Bayesian nonparametric panel Markov-switching GARCH model provides good forecasting performances and economic gains in optimal asset allocation

Autoregressive conditional heteroskedasticity · Bayesian inference · Bayesian probability · Econometrics · Markov chain · Markov chain Monte Carlo · Nonparametric statistics · Pooling · Statistics · Variable-order Bayesian network · Computer Science · Financial Risk and Volatility Modeling · Mathematics · Monetary Policy and Economic Impact · Spatial and Panel Data Analysis · Artificial Intelligence

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