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Do Jumps and Co-jumps Improve Volatility Forecasting of Oil and Currency Markets?

Author

Listed:
  • Fredj Jawadi
  • Waël Louhichi
  • Hachmi Ben Ameur
  • Zied Ftiti

Abstract

This paper aims at modeling and forecasting volatility in both oil and USD exchange rate markets using high frequency data. We test whether extreme co-move-ments (co-jumps) between these markets, as well as intraday unexpected news, help to improve volatility forecasting or not. Accordingly, we propose different extensions of Corsi (2009)’s model by including co-jumps and news. Our analysis provides two interesting findings. First, we find that both markets exhibit significant co-jumps driven by unexpected macroeconomic news. Second, we show that our model outperforms Corsi (2009)’s model and provides more accurate forecasts. In particular, while co-jumps constitute a key variable in forecasting oil price volatility, the unexpected news is relevant to forecasts of USD exchange rate volatility.

Suggested Citation

  • Fredj Jawadi & Waël Louhichi & Hachmi Ben Ameur & Zied Ftiti, 2019. "Do Jumps and Co-jumps Improve Volatility Forecasting of Oil and Currency Markets?," The Energy Journal, , vol. 40(2_suppl), pages 131-156, December.
    • Handle: RePEc:sae:enejou:v:40:y:2019:i:2_suppl:p:131-156
    DOI: 10.5547/01956574.40.SI2.fjaw
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    References listed on IDEAS

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    Cited by:

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    2. Dimitris Anastasiou & Apostolos Katsafados & Christos Tzomakas, 2026. "Banks’ stock price crash risk prediction with textual analysis: a machine learning approach," Annals of Operations Research, Springer, vol. 357(1), pages 89-111, February.
    3. Jialu Gao & Jianzhou Wang & Danxiang Wei & Bo Zeng, 2026. "An innovative decision-making system integrating multifractal analysis and volatility forecasting," Annals of Operations Research, Springer, vol. 357(1), pages 45-87, February.
    4. Zied Ftiti & Wael Louhichi & Hachmi Ben Ameur, 2023. "Cryptocurrency volatility forecasting: What can we learn from the first wave of the COVID-19 outbreak?," Annals of Operations Research, Springer, vol. 330(1), pages 665-690, November.
    5. Shijia Song & Handong Li, 2025. "Improving Price Generation: A Novel Agent-Based Model for Capturing Persistent Jumps in Asset Prices," Computational Economics, Springer;Society for Computational Economics, vol. 66(1), pages 421-452, July.

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