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Time-varying jump intensity and volatility forecasting of crude oil returns

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  • Zhang, Lei
  • Chen, Yan
  • Bouri, Elie

Abstract

Modelling and forecasting of crude oil volatility have been widely examined using GARCH-type models, and evidence suggests the presence of time-varying jumps in the crude oil market. This paper proposes a novel approach to model and forecast crude oil volatility by incorporating two time-varying jump intensities (State-dependent and Hawkes process) into the GARCH-Jump model. Our in-sample and out-of-sample analysis demonstrates that considering jump intensity as an explanatory variable significantly enhances the forecasting accuracy of WTI and Brent crude oil volatility. For WTI crude oil volatility, the more complex the jump intensity model, the better its forecasting power. For Brent crude oil volatility, the picture is different, indicating that the non-linear characteristics of volatility provide more informative forecasts. Further analysis shows that, during the COVID-19 crisis period, the Hawkes Jump Intensity (HJI)-GARCH model consistently improves the volatility forecasting performance. These results highlight the importance of jump intensity in modelling and predicting crude oil price volatility.

Suggested Citation

  • Zhang, Lei & Chen, Yan & Bouri, Elie, 2024. "Time-varying jump intensity and volatility forecasting of crude oil returns," Energy Economics, Elsevier, vol. 129(C).
    • Handle: RePEc:eee:eneeco:v:129:y:2024:i:c:s014098832300734x
    DOI: 10.1016/j.eneco.2023.107236
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    as
    1. Chai, Jian & Lu, Quanying & Hu, Yi & Wang, Shouyang & Lai, Kin Keung & Liu, Hongtao, 2018. "Analysis and Bayes statistical probability inference of crude oil price change point," Technological Forecasting and Social Change, Elsevier, vol. 126(C), pages 271-283.
    2. Vlaar, Peter J G & Palm, Franz C, 1993. "The Message in Weekly Exchange Rates in the European Monetary System: Mean Reversion, Conditional Heteroscedasticity, and Jumps," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 351-360, July.
    3. Chiou, Jer-Shiou & Lee, Yen-Hsien, 2009. "Jump dynamics and volatility: Oil and the stock markets," Energy, Elsevier, vol. 34(6), pages 788-796.
    4. Bahel, Eric & Marrouch, Walid & Gaudet, Gérard, 2013. "The economics of oil, biofuel and food commodities," Resource and Energy Economics, Elsevier, vol. 35(4), pages 599-617.
    5. Nelson, Daniel B, 1991. "Conditional Heteroskedasticity in Asset Returns: A New Approach," Econometrica, Econometric Society, vol. 59(2), pages 347-370, March.
    6. Hu, Genhua & Jiang, Haifeng, 2023. "Time-varying jumps in China crude oil futures market impacted by COVID-19 pandemic," Resources Policy, Elsevier, vol. 82(C).
    7. Bates, David S, 1991. "The Crash of '87: Was It Expected? The Evidence from Options Markets," Journal of Finance, American Finance Association, vol. 46(3), pages 1009-1044, July.
    8. Alvarez-Ramirez, Jose & Rodriguez, Eduardo & Martina, Esteban & Ibarra-Valdez, Carlos, 2012. "Cyclical behavior of crude oil markets and economic recessions in the period 1986–2010," Technological Forecasting and Social Change, Elsevier, vol. 79(1), pages 47-58.
    9. Pan, Jun, 2002. "The jump-risk premia implicit in options: evidence from an integrated time-series study," Journal of Financial Economics, Elsevier, vol. 63(1), pages 3-50, January.
    10. Carr, Peter & Wu, Liuren, 2017. "Leverage Effect, Volatility Feedback, and Self-Exciting Market Disruptions," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 52(5), pages 2119-2156, October.
    11. Bakshi, Gurdip & Cao, Charles & Chen, Zhiwu, 1997. "Empirical Performance of Alternative Option Pricing Models," Journal of Finance, American Finance Association, vol. 52(5), pages 2003-2049, December.
    12. Torben G. Andersen & Luca Benzoni & Jesper Lund, 2002. "An Empirical Investigation of Continuous‐Time Equity Return Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1239-1284, June.
    13. David S. Bates, 2019. "How Crashes Develop: Intradaily Volatility and Crash Evolution," Journal of Finance, American Finance Association, vol. 74(1), pages 193-238, February.
    14. Jun Pan & Darrell Duffie, 2001. "Analytical value-at-risk with jumps and credit risk," Finance and Stochastics, Springer, vol. 5(2), pages 155-180.
    15. Gronwald, Marc, 2012. "A characterization of oil price behavior — Evidence from jump models," Energy Economics, Elsevier, vol. 34(5), pages 1310-1317.
    16. Mohammadi, Hassan & Su, Lixian, 2010. "International evidence on crude oil price dynamics: Applications of ARIMA-GARCH models," Energy Economics, Elsevier, vol. 32(5), pages 1001-1008, September.
    17. Nieuwland, Frederick G M C & Verschoor, Willem F C & Wolff, Christian C P, 1994. "Stochastic trends and jumps in EMS exchange rates," Journal of International Money and Finance, Elsevier, vol. 13(6), pages 699-727, December.
    18. Das, Sanjiv Ranjan & Sundaram, Rangarajan K., 1999. "Of Smiles and Smirks: A Term Structure Perspective," Journal of Financial and Quantitative Analysis, Cambridge University Press, vol. 34(2), pages 211-239, June.
    19. Chan, Wing H & Maheu, John M, 2002. "Conditional Jump Dynamics in Stock Market Returns," Journal of Business & Economic Statistics, American Statistical Association, vol. 20(3), pages 377-389, July.
    20. Wang, Yudong & Wu, Chongfeng, 2012. "Forecasting energy market volatility using GARCH models: Can multivariate models beat univariate models?," Energy Economics, Elsevier, vol. 34(6), pages 2167-2181.
    21. Zhang, Lei & Bouri, Elie & Chen, Yan, 2023. "Co-jump dynamicity in the cryptocurrency market: A network modelling perspective," Finance Research Letters, Elsevier, vol. 58(PB).
    22. Bouri, Elie & Lien, Donald & Roubaud, David & Shahzad, Syed Jawad Hussain, 2018. "Directional predictability of implied volatility: From crude oil to developed and emerging stock markets," Finance Research Letters, Elsevier, vol. 27(C), pages 65-79.
    23. Ramzi Nekhili & Jahangir Sultan, 2020. "Jump Driven Risk Model Performance in Cryptocurrency Market," IJFS, MDPI, vol. 8(2), pages 1-18, April.
    24. Elie Bouri, 2019. "The Effect of Jumps in the Crude Oil Market on the Sovereign Risks of Major Oil Exporters," Risks, MDPI, vol. 7(4), pages 1-15, December.
    25. Zhang, Jin-Liang & Zhang, Yue-Jun & Zhang, Lu, 2015. "A novel hybrid method for crude oil price forecasting," Energy Economics, Elsevier, vol. 49(C), pages 649-659.
    26. Zhi-Fu Mi & Yi-Ming Wei & Bao-Jun Tang & Rong-Gang Cong & Hao Yu & Hong Cao & Dabo Guan, 2017. "Risk assessment of oil price from static and dynamic modelling approaches," Applied Economics, Taylor & Francis Journals, vol. 49(9), pages 929-939, February.
    27. Anupam Dutta & Elie Bouri & David Roubaud, 2021. "Modelling the volatility of crude oil returns: Jumps and volatility forecasts," International Journal of Finance & Economics, John Wiley & Sons, Ltd., vol. 26(1), pages 889-897, January.
    28. Hsieh, David A, 1989. "Testing for Nonlinear Dependence in Daily Foreign Exchange Rates," The Journal of Business, University of Chicago Press, vol. 62(3), pages 339-368, July.
    29. Philippe Jorion, 1988. "On Jump Processes in the Foreign Exchange and Stock Markets," The Review of Financial Studies, Society for Financial Studies, vol. 1(4), pages 427-445.
    30. 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.
    31. Akgiray, Vedat & Booth, G Geoffrey, 1988. "Mixed Diffusion-Jump Process Modeling of Exchange Rate Movements," The Review of Economics and Statistics, MIT Press, vol. 70(4), pages 631-637, November.
    32. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    33. Ma, Feng & Liao, Yin & Zhang, Yaojie & Cao, Yang, 2019. "Harnessing jump component for crude oil volatility forecasting in the presence of extreme shocks," Journal of Empirical Finance, Elsevier, vol. 52(C), pages 40-55.
    34. Afees A. Salisu & Rangan Gupta & Elie Bouri & Qiang Ji, 2022. "Mixed‐frequency forecasting of crude oil volatility based on the information content of global economic conditions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(1), pages 134-157, January.
    35. Angelos Kanas, 2013. "The risk-return relation and VIX: evidence from the S&P 500," Empirical Economics, Springer, vol. 44(3), pages 1291-1314, June.
    36. Bjørn Eraker & Michael Johannes & Nicholas Polson, 2003. "The Impact of Jumps in Volatility and Returns," Journal of Finance, American Finance Association, vol. 58(3), pages 1269-1300, June.
    37. Karaki, Mohamad B., 2017. "Nonlinearities in the response of real GDP to oil price shocks," Economics Letters, Elsevier, vol. 161(C), pages 146-148.
    38. 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.
    39. Zhang, Yue-Jun & Yao, Ting & He, Ling-Yun & Ripple, Ronald, 2019. "Volatility forecasting of crude oil market: Can the regime switching GARCH model beat the single-regime GARCH models?," International Review of Economics & Finance, Elsevier, vol. 59(C), pages 302-317.
    40. Zhang, Chuanguo & Chen, Xiaoqing, 2011. "The impact of global oil price shocks on China’s stock returns: Evidence from the ARJI(-ht)-EGARCH model," Energy, Elsevier, vol. 36(11), pages 6627-6633.
    41. Manickavasagam, Jeevananthan & Visalakshmi, S. & Apergis, Nicholas, 2020. "A novel hybrid approach to forecast crude oil futures using intraday data," Technological Forecasting and Social Change, Elsevier, vol. 158(C).
    42. Jun Yu, 2004. "Empirical Characteristic Function Estimation and Its Applications," Econometric Reviews, Taylor & Francis Journals, vol. 23(2), pages 93-123.
    43. repec:wyi:journl:002190 is not listed on IDEAS
    44. Aït-Sahalia, Yacine & Cacho-Diaz, Julio & Laeven, Roger J.A., 2015. "Modeling financial contagion using mutually exciting jump processes," Journal of Financial Economics, Elsevier, vol. 117(3), pages 585-606.
    45. Zhou, Chunyang & Wu, Chongfeng & Wang, Yudong, 2019. "Dynamic portfolio allocation with time-varying jump risk," Journal of Empirical Finance, Elsevier, vol. 50(C), pages 113-124.
    46. David E. Rapach & Jack K. Strauss & Guofu Zhou, 2010. "Out-of-Sample Equity Premium Prediction: Combination Forecasts and Links to the Real Economy," The Review of Financial Studies, Society for Financial Studies, vol. 23(2), pages 821-862, February.
    47. Jarque, Carlos M. & Bera, Anil K., 1980. "Efficient tests for normality, homoscedasticity and serial independence of regression residuals," Economics Letters, Elsevier, vol. 6(3), pages 255-259.
    48. Peter Fortune, 1999. "Are stock returns different over weekends? a jump diffusion analysis of the \"weekend effect\"," New England Economic Review, Federal Reserve Bank of Boston, issue Sep, pages 3-19.
    49. Muyi Li & Wai Keung Li & Guodong Li, 2013. "On Mixture Memory Garch Models," Journal of Time Series Analysis, Wiley Blackwell, vol. 34(6), pages 606-624, November.
    50. 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.
    51. Morana, Claudio, 2001. "A semiparametric approach to short-term oil price forecasting," Energy Economics, Elsevier, vol. 23(3), pages 325-338, May.
    52. Dutta, Anupam & Soytas, Ugur & Das, Debojyoti & Bhattacharyya, Asit, 2022. "In search of time-varying jumps during the turmoil periods: Evidence from crude oil futures markets," Energy Economics, Elsevier, vol. 114(C).
    53. Korotayev, Andrey & Bilyuga, Stanislav & Belalov, Ilya & Goldstone, Jack, 2018. "Oil prices, socio-political destabilization risks, and future energy technologies," Technological Forecasting and Social Change, Elsevier, vol. 128(C), pages 304-310.
    54. Hou, Aijun & Suardi, Sandy, 2012. "A nonparametric GARCH model of crude oil price return volatility," Energy Economics, Elsevier, vol. 34(2), pages 618-626.
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