A multivariate GARCH model with an infinite hidden Markov mixture

Li, Chenxing (2022): A multivariate GARCH model with an infinite hidden Markov mixture.

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.

• Item Type: MPRA Paper
• Language: English
• Keywords: Multivariate GARCH; IHMM; Bayesian nonparametric; Portfolio allocation; Transaction costs
• Subjects: C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C11 - Bayesian Analysis: General; C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C32 - Time-Series Models ; Dynamic Quantile Regressions ; Dynamic Treatment Effect Models ; Diffusion Processes ; State Space Models; C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C34 - Truncated and Censored Models ; Switching Regression Models; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics

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