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Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets

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  • Chen, Yixiang
  • Ma, Feng
  • Zhang, Yaojie

Abstract

In this article, we investigate the impacts of jumps, cojumps and their signed components on forecasting oil futures price volatility in the framework of the heterogeneous autoregressive realized volatility model. Our empirical results reveal several noteworthy findings. First, the effects of signed jumps and cojumps based on the daily and intraday jump tests on future volatility are asymmetric, and the negative components are much more powerful in forecasting volatility. Moreover, our proposed models, including the signed jump and cojump components, are able to generate higher forecasting accuracy, and we find that disentangling the effects of positive and negative jumps and cojumps can significantly improve forecasts of future volatility. Lastly, our findings are reliable for various robustness checks and our study provides some new insights into forecasting oil price realized volatility, which are useful for researchers, market participants, and policymakers.

Suggested Citation

  • Chen, Yixiang & Ma, Feng & Zhang, Yaojie, 2019. "Good, bad cojumps and volatility forecasting: New evidence from crude oil and the U.S. stock markets," Energy Economics, Elsevier, vol. 81(C), pages 52-62.
  • Handle: RePEc:eee:eneeco:v:81:y:2019:i:c:p:52-62
    DOI: 10.1016/j.eneco.2019.03.020
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