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A multivariate GARCH model with an infinite hidden Markov mixture

Chenxing Li

MPRA Paper from University Library of Munich, Germany

Abstract: This paper proposes a new Bayesian semiparametric model that combines a multivariate GARCH (MGARCH) component and an infinite hidden Markov model. The new model nonparametrically approximates both the shape of unknown returns distributions and their short-term evolution. It also captures the smooth trend of the second moment with the MGARCH component and the potential skewness, kurtosis, and volatility roughness with the Bayesian nonparametric component. The results show that this more-sophisticated econometric model not only has better out-of-sample density forecasts than benchmark models, but also provides positive economic gains for a CRRA investor at different risk-aversion levels when transaction costs are assumed. After considering the transaction costs, the proposed model dominates all benchmark models/portfolios when No Short-Selling or No Margin-Trading restriction is imposed.

Keywords: Multivariate GARCH; IHMM; Bayesian nonparametric; Portfolio allocation; Transaction costs (search for similar items in EconPapers)
JEL-codes: C11 C14 C32 C34 C53 C58 (search for similar items in EconPapers)
Date: 2022-03-16
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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https://mpra.ub.uni-muenchen.de/112792/1/MPRA_paper_112792.pdf original version (application/pdf)
https://mpra.ub.uni-muenchen.de/118470/1/draft17.pdf revised version (application/pdf)

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Persistent link: https://EconPapers.repec.org/RePEc:pra:mprapa:112792

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