【專題演講】A Modified VAR-deGARCH Model for Asynchronous Multivariate Financial Time Series via Variational Bayesian Inference-Dr. Wei-Ting Lai(Graduate Institute of Statistics, National Central University)
A Modified VAR-deGARCH Model for Asynchronous Multivariate Financial Time Series via Variational Bayesian Inference
Wei-Ting Lai
1Graduate Institute of Statistics, National Central University
2 Department of Statistics, National Cheng-Kung University
時間：114年04月21日(星期一)下午1:30 -3：00
地點：民生校區五育樓402教室
Abstract
This study proposes a modified VAR-deGARCH model, called M-VAR-deGARCH, for modeling asynchronous multivariate financial time series with GARCH effects and simultaneously accommodating the latest market information. A variational Bayesian (VB) procedure is developed to infer the M-VAR-deGARCH model for structure selection and parameter estimation. We conduct extensive simulations and empirical studies to evaluate the fitting and forecasting performances of the M-VAR-deGARCH model. The simulation results reveal that the proposed VB procedure produces satisfactory selection performances. In addition, our empirical studies find that the latest market information in Asia can provide helpful information to predict market trends in Europe and South Africa, especially when momentous events occur.
Keywords: Asynchronous time series, GARCH, variational Bayesian inference, vector autoregressive model, variable selection
Wei-Ting Lai
1Graduate Institute of Statistics, National Central University
2 Department of Statistics, National Cheng-Kung University
時間：114年04月21日(星期一)下午1:30 -3：00
地點：民生校區五育樓402教室
Abstract
This study proposes a modified VAR-deGARCH model, called M-VAR-deGARCH, for modeling asynchronous multivariate financial time series with GARCH effects and simultaneously accommodating the latest market information. A variational Bayesian (VB) procedure is developed to infer the M-VAR-deGARCH model for structure selection and parameter estimation. We conduct extensive simulations and empirical studies to evaluate the fitting and forecasting performances of the M-VAR-deGARCH model. The simulation results reveal that the proposed VB procedure produces satisfactory selection performances. In addition, our empirical studies find that the latest market information in Asia can provide helpful information to predict market trends in Europe and South Africa, especially when momentous events occur.
Keywords: Asynchronous time series, GARCH, variational Bayesian inference, vector autoregressive model, variable selection
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