From the cross‐market perspective, this paper investigates crude oil volatility index (OVX) forecasts by proposing a hybrid method, which combines the data‐driven SVR technique and parametric models. In terms of parametric models, we utilize GARCH‐type models with jumps, and the forecasting effects of five non‐parametric jumps (including interday and intraday jump tests) of stock market are also explored. Empirical results show that our approach can substantially increase forecasting accuracy. In addition, the model confidence set test and robust test reaffirm the superiority of the novel hybrid method. From the assessment of economic significance, the advantages of the hybrid method for volatility index forecasting are further confirmed. All these findings imply that jumps of stock market can be helpful in forecasting OVX, especially after the introduction of the hybrid method. Our work can certainly provide a new insight for volatility forecasting and cross‐market research.

中文翻译：

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原油波动指数的混合预测：股市上涨的跨市场效应

本文从跨市场的角度出发，提出了一种混合方法来研究原油波动指数（OVX）预测，该方法结合了数据驱动的SVR技术和参数模型。在参数模型方面，我们利用带有跳跃的GARCH型模型，并探讨了股票市场的五种非参数跳跃（包括日间和盘中跳跃测试）的预测效果。实证结果表明，我们的方法可以显着提高预测准确性。此外，模型置信集测试和鲁棒性测试再次证实了新型混合方法的优越性。从经济意义的评估来看，进一步证实了混合方法进行波动率指数预测的优势。所有这些发现都表明股市的上涨有助于预测 OVX，特别是在引入混合方法之后。我们的工作无疑可以为波动性预测和跨市场研究提供新的见解。