A Multivariate GARCH-Jump Mixture Model

Li, Chenxing and Maheu, John M (2020): A Multivariate GARCH-Jump Mixture Model.

Abstract

This paper proposes a new parsimonious multivariate GARCH-jump (MGARCH-jump) mixture model with multivariate jumps that allows both jump sizes and jump arrivals to be correlated among assets. Dependent jumps impact the conditional moments of returns as well as beta dynamics of a stock. Applied to daily stock returns, the model identifies co-jumps well and shows that both jump arrivals and jump sizes are highly correlated. The jump model has better predictions compared to a benchmark multivariate GARCH model.

• Item Type: MPRA Paper
• Language: English
• Keywords: Multivariate GARCH; Jumps; Multinomial; Co-jump; beta dynamics; Value-at-Risk
• Subjects: 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 > C5 - Econometric Modeling > C53 - Forecasting and Prediction Methods ; Simulation Methods; C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics; G - Financial Economics > G1 - General Financial Markets; G - Financial Economics > G1 - General Financial Markets > G10 - General

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