Heterogeneous Volatility Information Content for the Realized GARCH Modeling and Forecasting Volatility

Wen Xu, Pakorn Aschakulporn, Jin E. Zhang
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Abstract

As the demand for accuracy in volatility modeling and forecasting increases, the literature tends to incorporate different volatility measures with heterogeneous information content to construct the hybrid volatility model. This study focuses on one of the popular hybrid volatility models: the Realized Generalized Autoregressive Heteroskedasticity (Realized GARCH) and embeds various volatility measures, including the CBOE VIX, VIX1D, Realized Volatility, and Daily Range to examine their heterogeneous impact on the conditional volatility estimation and forecasting. To evaluate the impact of the volatility measures, we first construct a volatility response function. This involves calculating the difference in one-step-ahead conditional volatility forecasts that incorporate information from both return and volatility measures against the forecasts based on return innovations only. Subsequently, the variance share is calculated to evaluate its role in explaining future variations in the Realized GARCH. Our results show that among these four volatility measures, VIX is the most informative volatility. Although VIX1D is overemphasized by the literature, its significance in volatility forecasting remains substantial, confirming that risk-neutral volatility measures are generally more informative than physical measures. Finally, we also find that incorporating multiple risk-neutral volatility measures does not improve forecasting performance compared to using a single measure due to overlapping information.
实现 GARCH 建模和预测波动的异质波动信息含量
随着对波动率建模和预测准确性的要求越来越高,相关文献倾向于结合具有异质性信息含量的不同波动率指标来构建混合波动率模型。本研究聚焦于流行的混合波动率模型之一:实变广义自回归异方差模型(实变 GARCH),并嵌入了各种波动率指标,包括 CBOE VIX、VIX1D、实变波动率和每日范围,以检验它们对条件波动率估计和预测的异质性影响。为了评估波动率指标的影响,我们首先构建了波动率响应函数。这包括计算包含收益率和波动率指标信息的一步前条件波动率预测与仅基于收益率创新的预测之间的差异。随后,计算方差份额,以评估其在解释实现 GARCH 未来变化中的作用。我们的结果表明,在这四种波动率指标中,VIX 是信息量最大的波动率指标。虽然 VIX1D 在文献中被过分强调,但它在波动率预测中的重要性仍然很大,这证实了风险中性波动率指标通常比物理指标更有参考价值。最后,我们还发现,由于信息重叠,与使用单一指标相比,采用多种风险中性波动指标并不能提高预测性能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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