运用高频金融数据建模和预测中国有色金属期货市场波动率,并探索已实现波动率的波动时变性和杠杆效应.拓展了LHAR-CJ模型,并对上海期货交易所铜和铝期货进行实证研究.研究表明,已实现波动率存在动态依赖性和时变性,它们均可通过长记忆性的HAR-GARCH结构体现.此外,中国有色金属期货市场波动率存在显著的周杠杆效应.最后,样本内预测和样本外预测的结果表明,考虑了已实现波动率的波动时变性和杠杆效应的HAR-CJ-G模型能有效地提高解释能力和样本外预测能力.
This paper seeks to model and forecast the Chinese nonferrous metals futures market volatility and allows new insights into the time-varying volatility of realized volatility and leverage effects using high-frequency data. The LHAR-CJ model is extended and the empirical research on copper and aluminum futures in Shanghai Futures Exchange suggests the dynamic dependencies and time-varying volatility of realized volatility, which are captured by long memory HAR-GARCH model. Besides, the findings also show the significant weekly leverage effects in Chinese nonferrous metals futures market volatility. Finally, in-sample and out-of-sample forecasts are investigated, and the results show that the LHAR-CJ-G model, considering time-varying volatility of realized volatility and leverage effects, effectively improves the explanatory power as well as out-of sample predictive performance.
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