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首页> 外文期刊>The North American journal of economics and finance >Improving volatility forecasting based on Chinese volatility index information: Evidence from CSI 300 index and futures markets
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Improving volatility forecasting based on Chinese volatility index information: Evidence from CSI 300 index and futures markets

机译：基于中国波动性指数信息改善波动性预测：来自CSI 300指数和期货市场的证据

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摘要

This paper investigates whether iVX, the newly launched implied volatility index in China contains incremental information about volatility forecasting. We use high frequency data of Chinese CSI 300 stock index and futures to calculate realized volatility, then estimate various constant coefficients and time-varying coefficients HAR models (TVC-HAR), and finally adopt one-step and smooth multi-step rolling forecasting methods to evaluate the forecasting errors. Our analysis confirms that iVX does have significant influence to the realized volatility forecasting. Both the in-sample and out-of-sample forecasting errors indicate that iVX plays a crucial role to volatility forecasting, combining both continuous volatility, jump volatility and iVX information leads to best performance. TVC-HAR models outperform HAR models for multi-step ahead forecasting while with iVX as regressor perform best for one-step ahead forecasting. TVC-HAR models with iVX as driven factor is more suitable for index while models with time as the driven factor perform better for futures. MCS test further confirms the superiority of the selected models in volatility forecasting. Our study is important for financial market risk management and the healthy development of derivatives market in China.
机译：本文调查了IVX，中国新推出的暗示波动性指数是否包含有关波动预测的增量信息。我们使用中国CSI 300股指数和期货的高频数据来计算实现的波动率，然后估计各种恒定系数和时变系数Har模型（TVC-HAR），最后采用一步和平滑的多步轧制预测方法评估预测错误。我们的分析证实，IVX对实现的波动性预测产生了重大影响。样本内和样本外预测误差都表明IVX对波动性预测发挥着至关重要的作用，结合连续波动性，跳跃波动和IVX信息导致最佳性能。 TVC-HAR模型优于多步前预测的HAR模型，同​​时使用IVX作为退化器最适合一步前进预测。带有IVX的TVC-HAR模型作为驱动因子更适合索引，而模型随着驱动因子而言，对于期货而言更好。 MCS测试进一步证实了挥发性预测中所选模型的优越性。我们的研究对于中国金融市场风险管理和中国衍生品市场的健康发展至关重要。

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