IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v11y2023i3p528-d1040353.html
   My bibliography  Save this article

Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests

Author

Listed:
  • Bechir Raggad

    (Department of Business Administration, College of Business Administration in Majmaah, Majmaah University, Majmaah 11952, Saudi Arabia
    Faculty of Economics and Management in Nabeul, University of Carthage, Carthage 2085, Tunisia
    BESTMOD Laboratory, Higher Institute of Management of Tunis, University of Tunis, Tunis 1002, Tunisia)

  • Elie Bouri

    (School of Business, Lebanese American University, Beirut P.O. Box 13-5053, Lebanon)

Abstract

We examine the daily dependence and directional predictability between the returns of crude oil and the Crude Oil Volatility Index (OVX). Unlike previous studies, we apply a battery of quantile-based techniques, namely the quantile unit root test, the causality-in-quantiles test, and the cross-quantilogram approach. Our main results show evidence of significant bi-directional predictability that is quantile-dependent and asymmetric. A significant positive Granger causality runs from oil (OVX) returns to OVX (oil) returns when both series are in similar lower (upper) quantiles, as well as in opposite quantiles. The Granger causality from OVX returns to oil returns is only significant during periods of high volatility, although it is not always positive. The findings imply that the forward-looking estimate of oil volatility, reflecting the sentiment of oil market participants, should be considered when studying price variations in the oil market, and that crude oil returns can be used to predict oil implied volatility during bearish market conditions. Therefore, the findings have implications regarding predictability under various conditions for oil market participants.

Suggested Citation

  • Bechir Raggad & Elie Bouri, 2023. "Quantile Dependence between Crude Oil Returns and Implied Volatility: Evidence from Parametric and Nonparametric Tests," Mathematics, MDPI, vol. 11(3), pages 1-23, January.
    • Handle: RePEc:gam:jmathe:v:11:y:2023:i:3:p:528-:d:1040353
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/11/3/528/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/11/3/528/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Benedetto, Francesco & Mastroeni, Loretta & Quaresima, Greta & Vellucci, Pierluigi, 2020. "Does OVX affect WTI and Brent oil spot variance? Evidence from an entropy analysis," Energy Economics, Elsevier, vol. 89(C).
    2. Elie Bouri & Riza Demirer & Rangan Gupta & Christian Pierdzioch, 2020. "Infectious Diseases, Market Uncertainty and Oil Market Volatility," Energies, MDPI, vol. 13(16), pages 1-8, August.
    3. Galvao Jr., Antonio F., 2009. "Unit root quantile autoregression testing using covariates," Journal of Econometrics, Elsevier, vol. 152(2), pages 165-178, October.
    4. Zivot, Eric & Andrews, Donald W K, 2002. "Further Evidence on the Great Crash, the Oil-Price Shock, and the Unit-Root Hypothesis," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(1), pages 25-44, January.
    5. Aboura, Sofiane & Chevallier, Julien, 2013. "Leverage vs. feedback: Which Effect drives the oil market?," Finance Research Letters, Elsevier, vol. 10(3), pages 131-141.
    6. Roberto F. Aguilera & Roderick G. Eggert & Gustavo Lagos C.C. & John E. Tilton, 2009. "Depletion and the Future Availability of Petroleum Resources," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1), pages 141-174.
    7. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis," Energy Economics, Elsevier, vol. 70(C), pages 440-452.
    8. Agnolucci, Paolo, 2009. "Volatility in crude oil futures: A comparison of the predictive ability of GARCH and implied volatility models," Energy Economics, Elsevier, vol. 31(2), pages 316-321, March.
    9. Lv, Wendai, 2018. "Does the OVX matter for volatility forecasting? Evidence from the crude oil market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 916-922.
    10. Krzysztof Echaust & Małgorzata Just, 2021. "Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic," Energies, MDPI, vol. 14(14), pages 1-21, July.
    11. Liu, Bing-Yue & Ji, Qiang & Fan, Ying, 2017. "Dynamic return-volatility dependence and risk measure of CoVaR in the oil market: A time-varying mixed copula model," Energy Economics, Elsevier, vol. 68(C), pages 53-65.
    12. Glosten, Lawrence R & Jagannathan, Ravi & Runkle, David E, 1993. "On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks," Journal of Finance, American Finance Association, vol. 48(5), pages 1779-1801, December.
    13. Sakov, Anat & Bickel, Peter J., 2000. "An Edgeworth expansion for the m out of n bootstrapped median," Statistics & Probability Letters, Elsevier, vol. 49(3), pages 217-223, September.
    14. Dutta, Anupam & Bouri, Elie & Saeed, Tareq, 2021. "News-based equity market uncertainty and crude oil volatility," Energy, Elsevier, vol. 222(C).
    15. repec:dau:papers:123456789/9860 is not listed on IDEAS
    16. Jeng-Bau Lin & Wei Tsai, 2019. "The Relations of Oil Price Change with Fear Gauges in Global Political and Economic Environment," Energies, MDPI, vol. 12(15), pages 1-17, August.
    17. Xiao, Zhijie, 2009. "Quantile cointegrating regression," Journal of Econometrics, Elsevier, vol. 150(2), pages 248-260, June.
    18. Szakmary, Andrew & Ors, Evren & Kyoung Kim, Jin & Davidson, Wallace III, 2003. "The predictive power of implied volatility: Evidence from 35 futures markets," Journal of Banking & Finance, Elsevier, vol. 27(11), pages 2151-2175, November.
    19. Mencía, Javier & Sentana, Enrique, 2013. "Valuation of VIX derivatives," Journal of Financial Economics, Elsevier, vol. 108(2), pages 367-391.
    20. Jeng-Bau Lin & Chin-Chia Liang & Wei Tsai, 2019. "Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    21. Li, Shuping & Li, Jianfeng & Lu, Xinsheng & Sun, Yihong, 2022. "Exploring the dynamic nonlinear relationship between crude oil price and implied volatility indices: A new perspective from MMV-MFDFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    22. Terence D. Agbeyegbe, 2015. "An inverted U‐shaped crude oil price return‐implied volatility relationship," Review of Financial Economics, John Wiley & Sons, vol. 27(1), pages 28-45, November.
    23. Lux, Thomas & Segnon, Mawuli & Gupta, Rangan, 2016. "Forecasting crude oil price volatility and value-at-risk: Evidence from historical and recent data," Energy Economics, Elsevier, vol. 56(C), pages 117-133.
    24. Basher, Syed Abul & Sadorsky, Perry, 2016. "Hedging emerging market stock prices with oil, gold, VIX, and bonds: A comparison between DCC, ADCC and GO-GARCH," Energy Economics, Elsevier, vol. 54(C), pages 235-247.
    25. Bollerslev, Tim & Osterrieder, Daniela & Sizova, Natalia & Tauchen, George, 2013. "Risk and return: Long-run relations, fractional cointegration, and return predictability," Journal of Financial Economics, Elsevier, vol. 108(2), pages 409-424.
    26. Han, Heejoon & Linton, Oliver & Oka, Tatsushi & Whang, Yoon-Jae, 2016. "The cross-quantilogram: Measuring quantile dependence and testing directional predictability between time series," Journal of Econometrics, Elsevier, vol. 193(1), pages 251-270.
    27. Abakah, Emmanuel Joel Aikins & Tiwari, Aviral Kumar & Alagidede, Imhotep Paul & Gil-Alana, Luis Alberiko, 2022. "Re-examination of risk-return dynamics in international equity markets and the role of policy uncertainty, geopolitical risk and VIX: Evidence using Markov-switching copulas," Finance Research Letters, Elsevier, vol. 47(PA).
    28. Jushan Bai & Pierre Perron, 2003. "Computation and analysis of multiple structural change models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 1-22.
    29. Herrera, Ana María & Karaki, Mohamad B., 2015. "The effects of oil price shocks on job reallocation," Journal of Economic Dynamics and Control, Elsevier, vol. 61(C), pages 95-113.
    30. Liu, Zhenhua & Tseng, Hui-Kuan & Wu, Jy S. & Ding, Zhihua, 2020. "Implied volatility relationships between crude oil and the U.S. stock markets: Dynamic correlation and spillover effects," Resources Policy, Elsevier, vol. 66(C).
    31. Diaz, Elena Maria & Molero, Juan Carlos & Perez de Gracia, Fernando, 2016. "Oil price volatility and stock returns in the G7 economies," Energy Economics, Elsevier, vol. 54(C), pages 417-430.
    32. Ma, Feng & Wahab, M.I.M. & Huang, Dengshi & Xu, Weiju, 2017. "Forecasting the realized volatility of the oil futures market: A regime switching approach," Energy Economics, Elsevier, vol. 67(C), pages 136-145.
    33. Ji, Qiang & Fan, Ying, 2016. "Modelling the joint dynamics of oil prices and investor fear gauge," Research in International Business and Finance, Elsevier, vol. 37(C), pages 242-251.
    34. Roger Koenker & Zhijie Xiao, 2004. "Unit Root Quantile Autoregression Inference," Journal of the American Statistical Association, American Statistical Association, vol. 99, pages 775-787, January.
    35. Becker, Ralf & Clements, Adam E. & White, Scott I., 2007. "Does implied volatility provide any information beyond that captured in model-based volatility forecasts?," Journal of Banking & Finance, Elsevier, vol. 31(8), pages 2535-2549, August.
    36. 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.
    37. Johansen, Soren, 1995. "Likelihood-Based Inference in Cointegrated Vector Autoregressive Models," OUP Catalogue, Oxford University Press, number 9780198774501.
    38. Luo, Xingguo & Qin, Shihua, 2017. "Oil price uncertainty and Chinese stock returns: New evidence from the oil volatility index," Finance Research Letters, Elsevier, vol. 20(C), pages 29-34.
    39. Karaki, Mohamad B., 2017. "Nonlinearities in the response of real GDP to oil price shocks," Economics Letters, Elsevier, vol. 161(C), pages 146-148.
    40. Joo, Young C. & Park, Sung Y., 2017. "Oil prices and stock markets: Does the effect of uncertainty change over time?," Energy Economics, Elsevier, vol. 61(C), pages 42-51.
    41. Herrera, Ana María & Karaki, Mohamad B. & Rangaraju, Sandeep Kumar, 2017. "Where do jobs go when oil prices drop?," Energy Economics, Elsevier, vol. 64(C), pages 469-482.
    42. Batten, Jonathan A. & Kinateder, Harald & Szilagyi, Peter G. & Wagner, Niklas F., 2019. "Liquidity, surprise volume and return premia in the oil market," Energy Economics, Elsevier, vol. 77(C), pages 93-104.
    43. Raggad, Bechir, 2023. "Can implied volatility predict returns on oil market? Evidence from Cross-Quantilogram Approach," Resources Policy, Elsevier, vol. 80(C).
    44. Lundblad, Christian, 2007. "The risk return tradeoff in the long run: 1836-2003," Journal of Financial Economics, Elsevier, vol. 85(1), pages 123-150, July.
    45. Victor Troster, 2018. "Testing for Granger-causality in quantiles," Econometric Reviews, Taylor & Francis Journals, vol. 37(8), pages 850-866, September.
    46. Chen, Hongtao & Liu, Li & Li, Xiaolei, 2018. "The predictive content of CBOE crude oil volatility index," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 492(C), pages 837-850.
    47. 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.
    48. Yang, Minxian, 2019. "The risk return relationship: Evidence from index returns and realised variances," Journal of Economic Dynamics and Control, Elsevier, vol. 107(C), pages 1-1.
    Full references (including those not matched with items on IDEAS)

    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. Raggad, Bechir, 2023. "Can implied volatility predict returns on oil market? Evidence from Cross-Quantilogram Approach," Resources Policy, Elsevier, vol. 80(C).
    2. Krzysztof Echaust & Małgorzata Just, 2021. "Tail Dependence between Crude Oil Volatility Index and WTI Oil Price Movements during the COVID-19 Pandemic," Energies, MDPI, vol. 14(14), pages 1-21, July.
    3. Liu, Bing-Yue & Ji, Qiang & Fan, Ying, 2017. "Dynamic return-volatility dependence and risk measure of CoVaR in the oil market: A time-varying mixed copula model," Energy Economics, Elsevier, vol. 68(C), pages 53-65.
    4. Mighri, Zouheir & Ragoubi, Hanen & Sarwar, Suleman & Wang, Yihan, 2022. "Quantile Granger causality between US stock market indices and precious metal prices," Resources Policy, Elsevier, vol. 76(C).
    5. Jeng-Bau Lin & Wei Tsai, 2019. "The Relations of Oil Price Change with Fear Gauges in Global Political and Economic Environment," Energies, MDPI, vol. 12(15), pages 1-17, August.
    6. Lee, Chi-Chuan & Lee, Chien-Chiang & Li, Yong-Yi, 2021. "Oil price shocks, geopolitical risks, and green bond market dynamics," The North American Journal of Economics and Finance, Elsevier, vol. 55(C).
    7. Fassas, Athanasios P. & Siriopoulos, Costas, 2021. "Implied volatility indices – A review," The Quarterly Review of Economics and Finance, Elsevier, vol. 79(C), pages 303-329.
    8. Troster, Victor & Shahbaz, Muhammad & Uddin, Gazi Salah, 2018. "Renewable energy, oil prices, and economic activity: A Granger-causality in quantiles analysis," Energy Economics, Elsevier, vol. 70(C), pages 440-452.
    9. Yoon, Seong-Min, 2022. "On the interdependence between biofuel, fossil fuel and agricultural food prices: Evidence from quantile tests," Renewable Energy, Elsevier, vol. 199(C), pages 536-545.
    10. Liu, Zhenhua & Tseng, Hui-Kuan & Wu, Jy S. & Ding, Zhihua, 2020. "Implied volatility relationships between crude oil and the U.S. stock markets: Dynamic correlation and spillover effects," Resources Policy, Elsevier, vol. 66(C).
    11. Jeng-Bau Lin & Chin-Chia Liang & Wei Tsai, 2019. "Nonlinear Relationships between Oil Prices and Implied Volatilities: Providing More Valuable Information," Sustainability, MDPI, vol. 11(14), pages 1-15, July.
    12. Mishra, Shekhar & Sharif, Arshian & Khuntia, Sashikanta & Meo, Muhammad Saeed & Rehman Khan, Syed Abdul, 2019. "Does oil prices impede Islamic stock indices? Fresh insights from wavelet-based quantile-on-quantile approach," Resources Policy, Elsevier, vol. 62(C), pages 292-304.
    13. Li, Shuping & Li, Jianfeng & Lu, Xinsheng & Sun, Yihong, 2022. "Exploring the dynamic nonlinear relationship between crude oil price and implied volatility indices: A new perspective from MMV-MFDFA," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 603(C).
    14. Maghyereh, Aktham & Abdoh, Hussein & Awartani, Basel, 2022. "Have returns and volatilities for financial assets responded to implied volatility during the COVID-19 pandemic?," Journal of Commodity Markets, Elsevier, vol. 26(C).
    15. Lee, Chi-Chuan & Lee, Chien-Chiang, 2020. "Insurance activity, real output, and geopolitical risk: Fresh evidence from BRICS," Economic Modelling, Elsevier, vol. 92(C), pages 207-215.
    16. Ahmed, Walid M.A., 2018. "On the interdependence of natural gas and stock markets under structural breaks," The Quarterly Review of Economics and Finance, Elsevier, vol. 67(C), pages 149-161.
    17. Kuang, Wei, 2023. "The equity-oil hedge: A comparison between volatility and alternative risk frameworks," Energy, Elsevier, vol. 271(C).
    18. Chang, Bisharat Hussain & Sharif, Arshian & Aman, Ameenullah & Suki, Norazah Mohd & Salman, Asma & Khan, Syed Abdul Rehman, 2020. "The asymmetric effects of oil price on sectoral Islamic stocks: New evidence from quantile-on-quantile regression approach," Resources Policy, Elsevier, vol. 65(C).
    19. Huang, Jionghao & Li, Ziruo & Xia, Xiaohua, 2021. "Network diffusion of international oil volatility risk in China's stock market: Quantile interconnectedness modelling and shock decomposition analysis," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 1-39.
    20. Sinha, Avik & Shah, Muhammad Ibrahim & Sengupta, Tuhin & Jiao, Zhilun, 2020. "Analyzing Technology-Emissions Association in Top-10 Polluted MENA Countries: How to Ascertain Sustainable Development by Quantile Modeling Approach," MPRA Paper 100253, University Library of Munich, Germany, revised 2020.

    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:gam:jmathe:v:11:y:2023:i:3:p:528-:d:1040353. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.