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

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  • Fredj Jawadi, Waël Louhichi, Hachmi Ben Ameur, and 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-movements (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, and Zied Ftiti, 2019. "Do Jumps and Co-jumps Improve Volatility Forecasting of Oil and Currency Markets?," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    • Handle: RePEc:aen:journl:ej40-si2-louhichi
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    Cited by:

    1. Fredj Jawadi & Mohamed Sellami, 2022. "On the effect of oil price in the context of Covid‐19," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 3924-3933, October.
    2. Semei Coronado & Jose N. Martinez & Victor Gualajara & Rafael Romero-Meza & Omar Rojas, 2023. "Time-Varying Granger Causality of COVID-19 News on Emerging Financial Markets: The Latin American Case," Mathematics, MDPI, vol. 11(2), pages 1-18, January.
    3. Vincenzo Candila & Denis Maximov & Alexey Mikhaylov & Nikita Moiseev & Tomonobu Senjyu & Nicole Tryndina, 2021. "On the Relationship between Oil and Exchange Rates of Oil-Exporting and Oil-Importing Countries: From the Great Recession Period to the COVID-19 Era," Energies, MDPI, vol. 14(23), pages 1-18, December.
    4. 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.
    5. Louhichi, Waël & Ftiti, Zied & Ameur, Hachmi Ben, 2021. "Measuring the global economic impact of the coronavirus outbreak: Evidence from the main cluster countries," Technological Forecasting and Social Change, Elsevier, vol. 167(C).
    6. Oguzhan Cepni, Duc Khuong Nguyen, and Ahmet Sensoy, 2022. "News Media and Attention Spillover across Energy Markets: A Powerful Predictor of Crude Oil Futures Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Special I).
    7. Zhang, Yue-Jun & Zhang, Han, 2023. "Volatility forecasting of crude oil futures market: Which structural change-based HAR models have better performance?," International Review of Financial Analysis, Elsevier, vol. 85(C).
    8. Yue-Jun Zhang & Han Zhang, 2023. "Volatility Forecasting of Crude Oil Market: Which Structural Change Based GARCH Models have Better Performance?," The Energy Journal, , vol. 44(1), pages 175-194, January.
    9. Cui, Xin & Sensoy, Ahmet & Nguyen, Duc Khuong & Yao, Shouyu & Wu, Yiyao, 2022. "Positive information shocks, investor behavior and stock price crash risk," Journal of Economic Behavior & Organization, Elsevier, vol. 197(C), pages 493-518.
    10. Oguzhan Cepni & Duc Khuong Nguyen & Ahmet Sensoy, 2022. "News Media and Attention Spillover across Energy Markets: A Powerful Predictor of Crude Oil Futures Prices," The Energy Journal, , vol. 43(1_suppl), pages 1-30, June.
    11. Haithem Awijen & Hachmi Ben Ameur & Zied Ftiti & Waël Louhichi, 2025. "Forecasting oil price in times of crisis: a new evidence from machine learning versus deep learning models," Annals of Operations Research, Springer, vol. 345(2), pages 979-1002, February.
    12. Ftiti, Zied & Ben Ameur, Hachmi & Louhichi, Waël, 2021. "Does non-fundamental news related to COVID-19 matter for stock returns? Evidence from Shanghai stock market," Economic Modelling, Elsevier, vol. 99(C).
    13. 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.
    14. Mohamed Arbi Madani & Zied Ftiti, 2024. "Understanding Intraday Oil Price Dynamics during the COVID-19 Pandemic: New Evidence from Oil and Stock Investor Sentiments," The Energy Journal, , vol. 45(3), pages 57-86, May.

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    JEL classification:

    • F0 - International Economics - - General

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