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Short- and long-run causality across the implied volatility of crude oil and agricultural commodities

Author

Listed:
  • Elie Bouri

    (Holy Spirit University of Kaslik, Lebanon)

  • Imad Kachacha

    (Holy Spirit University of Kaslik, Lebanon)

  • Donald Lien

    (University of Texas at San Antonio, USA)

  • David Roubaud

    (Montpellier Business School, France)

Abstract

Unlike prior studies which often use realized variance or return series within a GRACH framework to examine time-domain linkages, this study employs CBOE implied volatility data from July 27, 2012 to September 30, 2016 within the frequency-domain causality framework in order to uncover short-, medium-, and long-run causal relations across crude oil, wheat and corn markets. Overall, the results show that the volatility causal relation differs between high and low frequencies. Specifically, we provide evidence in further support of the argument that the crude oil market dominates the corn market. The results also indicate that structural breaks characterize the causal relation between the implied volatilities of corn and wheat, which is found to differ between the short- and long-run. Implications for the analysis of hedging and risk management are discussed.

Suggested Citation

  • Elie Bouri & Imad Kachacha & Donald Lien & David Roubaud, 2017. "Short- and long-run causality across the implied volatility of crude oil and agricultural commodities," Economics Bulletin, AccessEcon, vol. 37(2).
    • Handle: RePEc:ebl:ecbull:eb-17-00098
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    References listed on IDEAS

    as
    1. Rafiq, Shuddhasattwa & Bloch, Harry, 2016. "Explaining commodity prices through asymmetric oil shocks: Evidence from nonlinear models," Resources Policy, Elsevier, vol. 50(C), pages 34-48.
    2. Pataracchia, Beatrice, 2011. "The spectral representation of Markov switching ARMA models," Economics Letters, Elsevier, vol. 112(1), pages 11-15, July.
    3. Fernandez-Perez, Adrian & Frijns, Bart & Tourani-Rad, Alireza, 2016. "Contemporaneous interactions among fuel, biofuel and agricultural commodities," Energy Economics, Elsevier, vol. 58(C), pages 1-10.
    4. Nicola, Francesca de & De Pace, Pierangelo & Hernandez, Manuel A., 2016. "Co-movement of major energy, agricultural, and food commodity price returns: A time-series assessment," Energy Economics, Elsevier, vol. 57(C), pages 28-41.
    5. Managi, Shunsuke & Okimoto, Tatsuyoshi, 2013. "Does the price of oil interact with clean energy prices in the stock market?," Japan and the World Economy, Elsevier, vol. 27(C), pages 1-9.
    6. Lucotte, Yannick, 2016. "Co-movements between crude oil and food prices: A post-commodity boom perspective," Economics Letters, Elsevier, vol. 147(C), pages 142-147.
    7. Aurélien Leroy & Yannick Lucotte, 2016. "Co-movements between crude oil and food prices: A post-commodity boom perspective," Post-Print hal-03528419, HAL.
    8. George J. Jiang & Yisong S. Tian, 2005. "The Model-Free Implied Volatility and Its Information Content," The Review of Financial Studies, Society for Financial Studies, vol. 18(4), pages 1305-1342.
    9. Wen-Den Chen, 2006. "Estimating the long memory granger causality effect with a spectrum estimator," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 25(3), pages 193-200.
    10. repec:cii:cepiie:2014-q4-140-60 is not listed on IDEAS
    11. Walid Mensi & Makram Beljid & Shunsuke Managi, 2014. "Structural breaks and the time-varying levels of weak-form efficiency in crude oil markets: Evidence from the Hurst exponent and Shannon entropy methods," International Economics, CEPII research center, issue 140, pages 89-106.
    12. Rangan Gupta & Luis A. Gil-Alana & Olaoluwa S. Yaya, 2015. "Do sunspot numbers cause global temperatures? Evidence from a frequency domain causality test," Applied Economics, Taylor & Francis Journals, vol. 47(8), pages 798-808, February.
    13. Du, Xiaodong & Yu, Cindy L. & Hayes, Dermot J., 2011. "Speculation and volatility spillover in the crude oil and agricultural commodity markets: A Bayesian analysis," Energy Economics, Elsevier, vol. 33(3), pages 497-503, May.
    14. repec:cii:cepiei:2014-q4-140-6 is not listed on IDEAS
    15. Awartani, Basel & Aktham, Maghyereh & Cherif, Guermat, 2016. "The connectedness between crude oil and financial markets: Evidence from implied volatility indices," Journal of Commodity Markets, Elsevier, vol. 4(1), pages 56-69.
    16. Baffes, John, 2007. "Oil spills on other commodities," Resources Policy, Elsevier, vol. 32(3), pages 126-134, September.
    17. Diks, Cees & Panchenko, Valentyn, 2006. "A new statistic and practical guidelines for nonparametric Granger causality testing," Journal of Economic Dynamics and Control, Elsevier, vol. 30(9-10), pages 1647-1669.
    18. Mensi, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong & Yoon, Seong-Min, 2014. "Dynamic spillovers among major energy and cereal commodity prices," Energy Economics, Elsevier, vol. 43(C), pages 225-243.
    19. Huchet, Nicolas & Fam, Papa Gueye, 2016. "The role of speculation in international futures markets on commodity prices," Research in International Business and Finance, Elsevier, vol. 37(C), pages 49-65.
    20. Kumar, Surender & Managi, Shunsuke & Matsuda, Akimi, 2012. "Stock prices of clean energy firms, oil and carbon markets: A vector autoregressive analysis," Energy Economics, Elsevier, vol. 34(1), pages 215-226.
    21. Hiemstra, Craig & Jones, Jonathan D, 1994. "Testing for Linear and Nonlinear Granger Causality in the Stock Price-Volume Relation," Journal of Finance, American Finance Association, vol. 49(5), pages 1639-1664, December.
    22. Bouri, Elie & Jain, Anshul & Biswal, P.C. & Roubaud, David, 2017. "Cointegration and nonlinear causality amongst gold, oil, and the Indian stock market: Evidence from implied volatility indices," Resources Policy, Elsevier, vol. 52(C), pages 201-206.
    23. Maghyereh, Aktham I. & Awartani, Basel & Bouri, Elie, 2016. "The directional volatility connectedness between crude oil and equity markets: New evidence from implied volatility indexes," Energy Economics, Elsevier, vol. 57(C), pages 78-93.
    24. Cavicchioli, Maddalena, 2013. "Spectral density of Markov-switching VARMA models," Economics Letters, Elsevier, vol. 121(2), pages 218-220.
    25. Henriques, Irene & Sadorsky, Perry, 2008. "Oil prices and the stock prices of alternative energy companies," Energy Economics, Elsevier, vol. 30(3), pages 998-1010, May.
    26. Nicolas Huchet & Papa Gueye Fam, 2016. "The role of speculation in international futures markets on commodity prices," Post-Print hal-03591560, HAL.
    27. Lemmens, Aurélie & Croux, Christophe & Dekimpe, Marnik G., 2008. "Measuring and testing Granger causality over the spectrum: An application to European production expectation surveys," International Journal of Forecasting, Elsevier, vol. 24(3), pages 414-431.
    28. Breitung, Jorg & Candelon, Bertrand, 2006. "Testing for short- and long-run causality: A frequency-domain approach," Journal of Econometrics, Elsevier, vol. 132(2), pages 363-378, June.
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    Citations

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    Cited by:

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    2. Jebabli, Ikram & Roubaud, David, 2018. "Time-varying efficiency in food and energy markets: Evidence and implications," Economic Modelling, Elsevier, vol. 70(C), pages 97-114.
    3. Shahbaz, Muhammad & Khan, Asad ul Islam & Mubarak, Muhammad Shujaat, 2023. "Roling-window bounds testing approach to analyze the relationship between oil prices and metal prices," The Quarterly Review of Economics and Finance, Elsevier, vol. 87(C), pages 388-395.
    4. Clark Lundberg & Tristan Skolrud & Bahram Adrangi & Arjun Chatrath, 2021. "Oil Price Pass through to Agricultural Commodities†," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(2), pages 721-742, March.
    5. Hu, Min & Zhang, Dayong & Ji, Qiang & Wei, Lijian, 2020. "Macro factors and the realized volatility of commodities: A dynamic network analysis," Resources Policy, Elsevier, vol. 68(C).
    6. Geng, Jiang-Bo & Chen, Fu-Rui & Ji, Qiang & Liu, Bing-Yue, 2021. "Network connectedness between natural gas markets, uncertainty and stock markets," Energy Economics, Elsevier, vol. 95(C).
    7. Shahzad, Syed Jawad Hussain & Bouri, Elie & Hernandez, Jose Areola & Roubaud, David, 2021. "Causal nexus between crude oil and US corporate bonds," The Quarterly Review of Economics and Finance, Elsevier, vol. 80(C), pages 577-589.
    8. Joel Oyeleke*, Olusola, 2021. "Frequency Domain Approach To Causality Among Fiscal Deficit, Interest Rates And Inflation In Nigeria," Ilorin Journal of Economic Policy, Department of Economics, University of Ilorin, vol. 8(1), pages 46-59, June.

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    More about this item

    Keywords

    Implied volatility; crude oil; corn; wheat; frequency-domain causality;
    All these keywords.

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • Q0 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - General

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