IDEAS home Printed from https://ideas.repec.org/a/aen/journl/ej40-si2-louhichi.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.iaee.org/en/publications/ejarticle.aspx?id=3232
    Download Restriction: Access to full text is restricted to IAEE members and subscribers.
    ---><---

    As the access to this document is restricted, you may want to look for a different version below or search for a different version of it.

    Other versions of this item:

    References listed on IDEAS

    as
    1. Domenico Ferraro & Ken Rogoff & Barbara Rossi, 2011. "Can Oil Prices Forecast Exchange Rates?," Working Papers 11-05, Duke University, Department of Economics.
    2. Maheu, John M. & McCurdy, Thomas H., 2011. "Do high-frequency measures of volatility improve forecasts of return distributions?," Journal of Econometrics, Elsevier, vol. 160(1), pages 69-76, January.
    3. Barnett, William A., 2012. "Getting it Wrong: How Faulty Monetary Statistics Undermine the Fed, the Financial System, and the Economy," MIT Press Books, The MIT Press, edition 1, volume 1, number 0262516888, December.
    4. William A. Barnett, 2000. "Economic Monetary Aggregates: An Application of Index Number and Aggregation Theory," Contributions to Economic Analysis, in: The Theory of Monetary Aggregation, pages 11-48, Emerald Group Publishing Limited.
    5. Suzanne S. Lee & Per A. Mykland, 2008. "Jumps in Financial Markets: A New Nonparametric Test and Jump Dynamics," The Review of Financial Studies, Society for Financial Studies, vol. 21(6), pages 2535-2563, November.
    6. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    7. Reboredo, Juan Carlos & Rivera-Castro, Miguel A. & Zebende, Gilney F., 2014. "Oil and US dollar exchange rate dependence: A detrended cross-correlation approach," Energy Economics, Elsevier, vol. 42(C), pages 132-139.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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).
    4. 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).
    5. 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).
    6. 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.
    7. 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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Post-Print halshs-01442618, HAL.
    2. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2020. "The contribution of intraday jumps to forecasting the density of returns," Post-Print halshs-02505861, HAL.
    3. Álvaro Cartea & Dimitrios Karyampas, 2016. "The Relationship between the Volatility of Returns and the Number of Jumps in Financial Markets," Econometric Reviews, Taylor & Francis Journals, vol. 35(6), pages 929-950, June.
    4. Liu, Yi & Liu, Huifang & Zhang, Lei, 2019. "Modeling and forecasting return jumps using realized variation measures," Economic Modelling, Elsevier, vol. 76(C), pages 63-80.
    5. Chorro, Christophe & Ielpo, Florian & Sévi, Benoît, 2020. "The contribution of intraday jumps to forecasting the density of returns," Journal of Economic Dynamics and Control, Elsevier, vol. 113(C).
    6. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Documents de travail du Centre d'Economie de la Sorbonne 17006, Université Panthéon-Sorbonne (Paris 1), Centre d'Economie de la Sorbonne.
    7. Ryadh M. Alkhareif & William A. Barnett, 2012. "Divisia Monetary Aggregates for the GCC Countries," International Symposia in Economic Theory and Econometrics, in: Recent Developments in Alternative Finance: Empirical Assessments and Economic Implications, pages 1-37, Emerald Group Publishing Limited.
    8. Yilun Zhang & Yuping Song & Ying Peng & Hanchao Wang, 2024. "Volatility forecasting incorporating intraday positive and negative jumps based on deep learning model," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 43(7), pages 2749-2765, November.
    9. Yang, Lu & Cai, Xiao Jing & Hamori, Shigeyuki, 2018. "What determines the long-term correlation between oil prices and exchange rates?," The North American Journal of Economics and Finance, Elsevier, vol. 44(C), pages 140-152.
    10. Florian Ielpo & Benoît Sévi, 2014. "Forecasting the density of oil futures," Working Papers 2014-601, Department of Research, Ipag Business School.
    11. Doureige J. Jurdi, 2020. "Intraday Jumps, Liquidity, and U.S. Macroeconomic News: Evidence from Exchange Traded Funds," JRFM, MDPI, vol. 13(6), pages 1-19, June.
    12. Denisa Banulescu-Radu & Christophe Hurlin & Bertrand Candelon & Sébastien Laurent, 2016. "Do We Need High Frequency Data to Forecast Variances?," Annals of Economics and Statistics, GENES, issue 123-124, pages 135-174.
    13. Sévi, Benoît, 2014. "Forecasting the volatility of crude oil futures using intraday data," European Journal of Operational Research, Elsevier, vol. 235(3), pages 643-659.
    14. Almut Veraart & Luitgard Veraart, 2012. "Stochastic volatility and stochastic leverage," Annals of Finance, Springer, vol. 8(2), pages 205-233, May.
    15. Barnett, William A. & Su, Liting, 2017. "Data sources for the credit-card augmented Divisia monetary aggregates," Research in International Business and Finance, Elsevier, vol. 39(PB), pages 899-910.
    16. Li, Xiafei & Liao, Yin & Lu, Xinjie & Ma, Feng, 2022. "An oil futures volatility forecast perspective on the selection of high-frequency jump tests," Energy Economics, Elsevier, vol. 116(C).
    17. Wu, Hanlin & Li, Pan & Cao, Jiawei & Xu, Zijian, 2024. "Forecasting the Chinese crude oil futures volatility using jump intensity and Markov-regime switching model," Energy Economics, Elsevier, vol. 134(C).
    18. Nguyen, Duc Binh Benno & Prokopczuk, Marcel, 2019. "Jumps in commodity markets," Journal of Commodity Markets, Elsevier, vol. 13(C), pages 55-70.
    19. Laurent, Sébastien & Shi, Shuping, 2020. "Volatility estimation and jump detection for drift–diffusion processes," Journal of Econometrics, Elsevier, vol. 217(2), pages 259-290.
    20. Yusui Tang & Feng Ma & Yaojie Zhang & Yu Wei, 2022. "Forecasting the oil price realized volatility: A multivariate heterogeneous autoregressive model," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 27(4), pages 4770-4783, October.

    More about this item

    JEL classification:

    • F0 - International Economics - - General

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:aen:journl:ej40-si2-louhichi. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: David Williams (email available below). General contact details of provider: https://edirc.repec.org/data/iaeeeea.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.