Luo Jiawen, Chen Zhenbiao, Wang Shengquan(通讯作者). Realized Volatility Forecast of Financial Futures Using Time-varying HAR Latent Factor Models. Journal of Management Science and Engineering, 2023, 8(2): 214-243.
论文简介
We forecast realized volatilities by developing a time-varying heterogeneous autoregressive (HAR) latent factor model with dynamic model average (DMA) and dynamic model selection (DMS) approaches. The number of latent factors is determined using Chan and Grant's (2016) deviation information criteria. The predictors in our model include lagged daily, weekly, and monthly volatility variables, the corresponding volatility factors, and a speculation variable. In addition, the time-varying properties of the best-performing DMA(DMS)-HAR-2FX models, including size, inclusion probabilities, and coefficients, are examined. We find that the proposed DMA(DMS)-HAR-2FX model outperforms the competing models for both in-sample and out-of-sample forecasts. Furthermore, the speculation variable displays strong predictability for forecasting the realized volatility of financial futures in China.
我们通过开发一个具有动态模型平均（DMA）和动态模型选择（DMS）方法的时变异构自回归（HAR）潜因子模型来预测实际波动率。潜因子的数量使用Chan and Grant (2016)的偏差信息准则来确定。我们模型中的预测因子包括滞后的日、周和月波动率变量、相应的波动率因子和一个投机变量。此外，研究了表现最好的DMA（DMS）-HAR-2FX模型的时变特性，包括大小、包含概率和系数。我们发现所提出的DMA（DMS）-HAR-2FX模型在样本内和样本外预测方面都优于竞争模型。此外，投机变量在预测中国金融期货实际波动率方面表现出很强的可预测性。