Dynamic partial (co)variance forecasting model
提出动态部分（协）方差预测模型，通过引入动态模型平均处理参数和阈值组合的时变性，实证表明该模型在统计和经济意义上均优于单一模型，且预测效果稳健。
In this study, we propose a dynamic partial (co)variance forecasting model (DPCFM) by introducing a dynamic model averaging (DMA) approach into a partial (co)variance forecasting model. The dynamic partial (co)variance forecasting model considers the time-varying property of the model's parameters and optimal threshold combinations used to construct partial (co)variance. Our empirical results suggest that in both variance and covariance cases, the dynamic partial variance forecasting model can generate more accurate forecasts than an individual partial (co)variance forecasting model in both the statistical and economic sense. The superiority of the dynamic partial (co)variance forecasting model is robust to various forecast horizons.