IDEAS home Printed from https://ideas.repec.org/a/eee/intfor/v41y2025i2p763-780.html
   My bibliography  Save this article

Skew–Brownian processes for estimating the volatility of crude oil Brent

Author

Listed:
  • Bufalo, Michele
  • Liseo, Brunero
  • Orlando, Giuseppe

Abstract

To predict the volatility of crude oil Brent price, we propose a novel econometric model 11The numerical results presented in this manuscript were reproduced by the Editor-in-Chief on 30 June 2024. where the explanatory variables are a combination of macroeconomic variables (i.e. price pressure), trade data (freight shipment index), and market sentiment (gold volatility). The model is proposed in two alternative variants: first, we assume Gaussian distributed quantities; alternatively, we consider the potential presence of skewness and adopt a Skew–Brownian process. We show that the suggested approach outperforms the selected baseline model as well as other models proposed in the literature, especially when turbulent periods occur.

Suggested Citation

  • Bufalo, Michele & Liseo, Brunero & Orlando, Giuseppe, 2025. "Skew–Brownian processes for estimating the volatility of crude oil Brent," International Journal of Forecasting, Elsevier, vol. 41(2), pages 763-780.
  • Handle: RePEc:eee:intfor:v:41:y:2025:i:2:p:763-780
    DOI: 10.1016/j.ijforecast.2024.06.009
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0169207024000633
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijforecast.2024.06.009?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Salisu, Afees A. & Fasanya, Ismail O., 2013. "Modelling oil price volatility with structural breaks," Energy Policy, Elsevier, vol. 52(C), pages 554-562.
    2. Jawadi, Fredj & Louhichi, Waël & Ameur, Hachmi Ben & Cheffou, Abdoulkarim Idi, 2016. "On oil-US exchange rate volatility relationships: An intraday analysis," Economic Modelling, Elsevier, vol. 59(C), pages 329-334.
    3. John C. Cox & Jonathan E. Ingersoll Jr. & Stephen A. Ross, 2005. "A Theory Of The Term Structure Of Interest Rates," World Scientific Book Chapters, in: Sudipto Bhattacharya & George M Constantinides (ed.), Theory Of Valuation, chapter 5, pages 129-164, World Scientific Publishing Co. Pte. Ltd..
    4. Charles, Amélie & Darné, Olivier, 2014. "Volatility persistence in crude oil markets," Energy Policy, Elsevier, vol. 65(C), pages 729-742.
    5. Giuseppe Orlando & Michele Bufalo, 2022. "A generalized two‐factor square‐root framework for modeling occurrences of natural catastrophes," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(8), pages 1608-1622, December.
    6. Wei, Yu & Wang, Yudong & Huang, Dengshi, 2010. "Forecasting crude oil market volatility: Further evidence using GARCH-class models," Energy Economics, Elsevier, vol. 32(6), pages 1477-1484, November.
    7. Reinhard Ellwanger & Stephen Snudden, 2023. "Futures Prices are Useful Predictors of the Spot Price of Crude Oil," The Energy Journal, , vol. 44(4), pages 65-82, July.
    8. Anthonie W. Van Der Stoep & Lech A. Grzelak & Cornelis W. Oosterlee, 2014. "The Heston Stochastic-Local Volatility Model: Efficient Monte Carlo Simulation," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 17(07), pages 1-30.
    9. Liu, Li & Wang, Yudong & Yang, Li, 2018. "Predictability of crude oil prices: An investor perspective," Energy Economics, Elsevier, vol. 75(C), pages 193-205.
    10. Reinhard Ellwanger and Stephen Snudden, 2023. "Futures Prices are Useful Predictors of the Spot Price of Crude Oil," The Energy Journal, International Association for Energy Economics, vol. 0(Number 4).
    11. Gao, Lin & Hitzemann, Steffen & Shaliastovich, Ivan & Xu, Lai, 2022. "Oil volatility risk," Journal of Financial Economics, Elsevier, vol. 144(2), pages 456-491.
    12. Soriano, Pilar & Torró, Hipòlit, 2022. "The response of Brent crude oil to the European central bank monetary policy," Finance Research Letters, Elsevier, vol. 46(PA).
    13. Ani Shabri & Ruhaidah Samsudin, 2014. "Daily Crude Oil Price Forecasting Using Hybridizing Wavelet and Artificial Neural Network Model," Mathematical Problems in Engineering, Hindawi, vol. 2014, pages 1-10, July.
    14. Adelchi Azzalini & Antonella Capitanio, 2003. "Distributions generated by perturbation of symmetry with emphasis on a multivariate skew t‐distribution," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 367-389, May.
    15. T. R. A. Corns & S. E. Satchell, 2007. "Skew Brownian Motion and Pricing European Options," The European Journal of Finance, Taylor & Francis Journals, vol. 13(6), pages 523-544.
    16. Viviane Naimy & Omar Haddad & Gema Fernández-Avilés & Rim El Khoury, 2021. "The predictive capacity of GARCH-type models in measuring the volatility of crypto and world currencies," PLOS ONE, Public Library of Science, vol. 16(1), pages 1-17, January.
    17. Fujiwara, Okitsugu & Perera, U. L. J. S. R., 1993. "EOQ models for continuously deteriorating products using linear and exponential penalty costs," European Journal of Operational Research, Elsevier, vol. 70(1), pages 104-114, October.
    18. Minxian Yang, 2008. "Normal log-normal mixture, leptokurtosis and skewness," Applied Economics Letters, Taylor & Francis Journals, vol. 15(9), pages 737-742.
    19. Giuseppe Orlando & Michele Bufalo, 2021. "Empirical Evidences on the Interconnectedness between Sampling and Asset Returns’ Distributions," Risks, MDPI, vol. 9(5), pages 1-35, May.
    20. Mensi, Walid & Nekhili, Ramzi & Vo, Xuan Vinh & Kang, Sang Hoon, 2021. "Oil and precious metals: Volatility transmission, hedging, and safe haven analysis from the Asian crisis to the COVID-19 crisis," Economic Analysis and Policy, Elsevier, vol. 71(C), pages 73-96.
    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. Rao, Amar & Sharma, Gagan Deep & Tiwari, Aviral Kumar & Hossain, Mohammad Razib & Dev, Dhairya, 2025. "Crude oil Price forecasting: Leveraging machine learning for global economic stability," Technological Forecasting and Social Change, Elsevier, vol. 216(C).
    2. Charles, Amélie & Darné, Olivier, 2017. "Forecasting crude-oil market volatility: Further evidence with jumps," Energy Economics, Elsevier, vol. 67(C), pages 508-519.
    3. Liu, Jing & Ma, Feng & Tang, Yingkai & Zhang, Yaojie, 2019. "Geopolitical risk and oil volatility: A new insight," Energy Economics, Elsevier, vol. 84(C).
    4. Manabu Asai & Michael McAleer & Jun Yu, 2006. "Multivariate Stochastic Volatility," Microeconomics Working Papers 22058, East Asian Bureau of Economic Research.
    5. Tian, Yingxu & Zhang, Haoyan, 2018. "Skew CIR process, conditional characteristic function, moments and bond pricing," Applied Mathematics and Computation, Elsevier, vol. 329(C), pages 230-238.
    6. Scarcioffolo, Alexandre R. & Etienne, Xiaoli L., 2021. "Regime-switching energy price volatility: The role of economic policy uncertainty," International Review of Economics & Finance, Elsevier, vol. 76(C), pages 336-356.
    7. Andrei Cozma & Christoph Reisinger, 2015. "Exponential integrability properties of Euler discretization schemes for the Cox-Ingersoll-Ross process," Papers 1601.00919, arXiv.org.
    8. Tarek Bouazizi & Mongi Lassoued & Zouhaier Hadhek, 2021. "Oil Price Volatility Models during Coronavirus Crisis: Testing with Appropriate Models Using Further Univariate GARCH and Monte Carlo Simulation Models," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 281-292.
    9. Mehmet Balcilar & Zeynel Abidin Ozdemir, 2017. "The nexus between the oil price and its volatility in a stochastic volatility in mean model with time-varying parameters," Working Papers 15-33, Eastern Mediterranean University, Department of Economics.
    10. Balcilar, Mehmet & Ozdemir, Zeynel Abidin, 2019. "The nexus between the oil price and its volatility risk in a stochastic volatility in the mean model with time-varying parameters," Resources Policy, Elsevier, vol. 61(C), pages 572-584.
    11. Stéphane Goutte & David Guerreiro & Bilel Sanhaji & Sophie Saglio & Julien Chevallier, 2019. "International Financial Markets," Post-Print halshs-02183053, HAL.
    12. Zavadska, Miroslava & Morales, Lucía & Coughlan, Joseph, 2020. "Brent crude oil prices volatility during major crises," Finance Research Letters, Elsevier, vol. 32(C).
    13. Kristoufek, Ladislav, 2014. "Leverage effect in energy futures," Energy Economics, Elsevier, vol. 45(C), pages 1-9.
    14. Kais Tissaoui & Taha Zaghdoudi & Abdelaziz Hakimi & Mariem Nsaibi, 2023. "Do Gas Price and Uncertainty Indices Forecast Crude Oil Prices? Fresh Evidence Through XGBoost Modeling," Computational Economics, Springer;Society for Computational Economics, vol. 62(2), pages 663-687, August.
    15. Nonejad, Nima, 2017. "Parameter instability, stochastic volatility and estimation based on simulated likelihood: Evidence from the crude oil market," Economic Modelling, Elsevier, vol. 61(C), pages 388-408.
    16. Wang, Lu & Ma, Feng & Hao, Jianyang & Gao, Xinxin, 2021. "Forecasting crude oil volatility with geopolitical risk: Do time-varying switching probabilities play a role?," International Review of Financial Analysis, Elsevier, vol. 76(C).
    17. Ewing, Bradley T. & Malik, Farooq, 2017. "Modelling asymmetric volatility in oil prices under structural breaks," Energy Economics, Elsevier, vol. 63(C), pages 227-233.
    18. Lin, Yu & Xiao, Yang & Li, Fuxing, 2020. "Forecasting crude oil price volatility via a HM-EGARCH model," Energy Economics, Elsevier, vol. 87(C).
    19. Yingying Xu & Donald Lien, 2022. "Forecasting volatilities of oil and gas assets: A comparison of GAS, GARCH, and EGARCH models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(2), pages 259-278, March.
    20. 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.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    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:eee:intfor:v:41:y:2025:i:2:p:763-780. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijforecast .

    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.