本研究提出了一種融合軟訊息的滯後多元貝葉斯結構GARCH模型，稱為SHMBS- GARCH，用於描述不同經濟狀態下的多維金融時間序列動態。我們首先運用 De-GARCH 技術去除每條金融時間序列中的GARCH 效應。接著，構建一個針對 De-GARCH 時間序列的滯後多元貝葉斯結構模型，同時捕捉趨勢、季節性、循環 模式以及內生（或外生）協變量效應。特別的是，我們將從每日金融新聞中提取 的軟訊息納入模型的滯後部分，以反映經濟對時間序列行為的影響。為估計參數， 我們提出了一種MCMC 算法，而模擬研究表示，所提出的算法能夠獲得好的估 計結果。實證研究利用2016 年1 月至2020 年12 月期間的道瓊工業、納斯達克 和費城半導體指數數據，評估了所提出模型的性能。數值分析顯示，所提出的模 型在擬合和預測精度方面優於競爭模型。;This study proposes a hysteretic multivariate Bayesian structural GARCH model integrating soft information, denoted by SH-MBS-GARCH, to describe multidimensional financial time series dynamics under different economic states. We first employ the De-GARCH technique to remove GARCH effects from each financial time series. Next, we construct a hysteretic multivariate Bayesian structural model for the De-GARCH time series, simultaneously capturing trends, seasonality, cyclic patterns, and endogenous (or exogenous) covariate effects. In particular, we incorporate soft information extracted from daily financial news into the model′s hysteretic part, reflecting economic influences on time-series behavior. An MCMC algorithm is proposed for parameters estimation. Simulation studies reveal that the proposed algorithm can obtain satisfactory estimation results. The empirical study utilizes data from the Dow Jones Industrial, Nasdaq, and Philadelphia Semiconductor indices spanning from January 2016 to December 2020 to evaluate the performance of the proposed model. Numerical analysis demonstrates that the proposed model outperforms competing models in terms of fitting and predictive accuracy.
