A0167
Title: Hysteretic multivariate Bayesian structural GARCH model with soft information
Authors: Shih-Feng Huang - National Central University (Taiwan) [presenting]
Abstract: A hysteretic multivariate Bayesian structural GARCH model with soft information, denoted by SH-MBS-GARCH, is proposed to describe multidimensional financial time-series dynamics. First, the GARCH effects inherent is filtered in each financial time series by the De-GARCH technique. Next, a hysteretic multivariate Bayesian structural model is established for the multidimensional De-GARCH time series to simultaneously capture the trend, seasonal, cyclic, and endogenous (or exogenous) covariates effects. In particular, soft information is extracted from the daily financial news and added to the model's hysteretic part to reflect economic effects on the time-series behaviour. An MCMC algorithm is proposed for parameter estimation. The empirical study employs the Dow Jones Industrial, Nasdaq, and Philadelphia Semiconductor indices from January 2016 to December 2020 to investigate the performances of the proposed model. Numerical results reveal that the SH-MBS-GARCH model has better fitting and prediction performances than competitors.
