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Detection of volatility regime-switching for crude oil price modeling and forecasting

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  • Liu, Yue
  • Sun, Huaping
  • Zhang, Jijian
  • Taghizadeh-Hesary, Farhad

Abstract

For crude oil price modeling and forecasting, time-discrete models like GARCH and HAR-RV have been further developed with Markovian regime-switching in recent years. Questioning on the ubiquity of regime-switching, we establish a time-continuous diffusion model governing the oil prices and detecting whether volatility regime-switching exists in different time horizons over the past decade. Model analysis and comparison with existed methods show that, during the second period we investigated, there exists no obvious volatility regime-switching, hence combining with regime-switching contributes little to its modeling. Besides, shown by this paper, existence of regime-switching and the transition rate of regime depict the long-term structure of volatility, which could be an intrinsic market property.

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  • Liu, Yue & Sun, Huaping & Zhang, Jijian & Taghizadeh-Hesary, Farhad, 2020. "Detection of volatility regime-switching for crude oil price modeling and forecasting," Resources Policy, Elsevier, vol. 69(C).
  • Handle: RePEc:eee:jrpoli:v:69:y:2020:i:c:s0301420719306439
    DOI: 10.1016/j.resourpol.2020.101669
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    1. Sébastien Laurent & Jeroen V. K. Rombouts & Francesco Violante, 2012. "On the forecasting accuracy of multivariate GARCH models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 27(6), pages 934-955, September.
    2. Stéphane Goutte & Amine Ismail & Huyên Pham, 2017. "Regime-switching stochastic volatility model: estimation and calibration to VIX options," Applied Mathematical Finance, Taylor & Francis Journals, vol. 24(1), pages 38-75, January.
    3. Gong, Xu & Lin, Boqiang, 2018. "The incremental information content of investor fear gauge for volatility forecasting in the crude oil futures market," Energy Economics, Elsevier, vol. 74(C), pages 370-386.
    4. Chkili, Walid & Nguyen, Duc Khuong, 2014. "Exchange rate movements and stock market returns in a regime-switching environment: Evidence for BRICS countries," Research in International Business and Finance, Elsevier, vol. 31(C), pages 46-56.
    5. Zhang, Yue-Jun & Zhang, Lu, 2015. "Interpreting the crude oil price movements: Evidence from the Markov regime switching model," Applied Energy, Elsevier, vol. 143(C), pages 96-109.
    6. Liu, Qun & Jiang, Daqing & Shi, Ningzhong, 2018. "Threshold behavior in a stochastic SIQR epidemic model with standard incidence and regime switching," Applied Mathematics and Computation, Elsevier, vol. 316(C), pages 310-325.
    7. Balcilar, Mehmet & Gupta, Rangan & Miller, Stephen M., 2015. "Regime switching model of US crude oil and stock market prices: 1859 to 2013," Energy Economics, Elsevier, vol. 49(C), pages 317-327.
    8. Wen, Fenghua & Gong, Xu & Cai, Shenghua, 2016. "Forecasting the volatility of crude oil futures using HAR-type models with structural breaks," Energy Economics, Elsevier, vol. 59(C), pages 400-413.
    9. Chen, Jinyu & Zhu, Xuehong & Zhong, Meirui, 2019. "Nonlinear effects of financial factors on fluctuations in nonferrous metals prices: A Markov-switching VAR analysis," Resources Policy, Elsevier, vol. 61(C), pages 489-500.
    10. Peter R. Hansen & Asger Lunde & James M. Nason, 2011. "The Model Confidence Set," Econometrica, Econometric Society, vol. 79(2), pages 453-497, March.
    11. Martens, Martin & van Dijk, Dick & de Pooter, Michiel, 2009. "Forecasting S&P 500 volatility: Long memory, level shifts, leverage effects, day-of-the-week seasonality, and macroeconomic announcements," International Journal of Forecasting, Elsevier, vol. 25(2), pages 282-303.
    12. Franc Klaassen, 2002. "Improving GARCH volatility forecasts with regime-switching GARCH," Empirical Economics, Springer, vol. 27(2), pages 363-394.
    13. David D. Yao & Qing Zhang & Xun Yu Zhou, 2006. "A Regime-Switching Model for European Options," International Series in Operations Research & Management Science, in: Houmin Yan & George Yin & Qing Zhang (ed.), Stochastic Processes, Optimization, and Control Theory: Applications in Financial Engineering, Queueing Networks, and Manufacturing Systems, chapter 0, pages 281-300, Springer.
    14. Beine, Michel & Laurent, Sebastien & Lecourt, Christelle, 2003. "Official central bank interventions and exchange rate volatility: Evidence from a regime-switching analysis," European Economic Review, Elsevier, vol. 47(5), pages 891-911, October.
    15. Tang, Bao-Jun & Zhou, Hui-Ling & Chen, Hao & Wang, Kai & Cao, Hong, 2017. "Investment opportunity in China's overseas oil project: An empirical analysis based on real option approach," Energy Policy, Elsevier, vol. 105(C), pages 17-26.
    16. Fulvio Corsi, 2009. "A Simple Approximate Long-Memory Model of Realized Volatility," Journal of Financial Econometrics, Oxford University Press, vol. 7(2), pages 174-196, Spring.
    17. 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.
    18. Hamilton, James D, 1989. "A New Approach to the Economic Analysis of Nonstationary Time Series and the Business Cycle," Econometrica, Econometric Society, vol. 57(2), pages 357-384, March.
    19. Cui, Zhenyu & Kirkby, J. Lars & Nguyen, Duy, 2017. "Equity-linked annuity pricing with cliquet-style guarantees in regime-switching and stochastic volatility models with jumps," Insurance: Mathematics and Economics, Elsevier, vol. 74(C), pages 46-62.
    20. Choi, Kyongwook & Hammoudeh, Shawkat, 2010. "Volatility behavior of oil, industrial commodity and stock markets in a regime-switching environment," Energy Policy, Elsevier, vol. 38(8), pages 4388-4399, August.
    21. Hsu, Pao-Peng, 2017. "Examination of Taiwan's travel and tourism market cycle through a two-period Markov regime-switching model," Tourism Management, Elsevier, vol. 63(C), pages 201-208.
    22. Sun, Huaping & Edziah, Bless Kofi & Sun, Chuanwang & Kporsu, Anthony Kwaku, 2019. "Institutional quality, green innovation and energy efficiency," Energy Policy, Elsevier, vol. 135(C).
    23. Marcucci Juri, 2005. "Forecasting Stock Market Volatility with Regime-Switching GARCH Models," Studies in Nonlinear Dynamics & Econometrics, De Gruyter, vol. 9(4), pages 1-55, December.
    24. Laurent E. Calvet, 2004. "How to Forecast Long-Run Volatility: Regime Switching and the Estimation of Multifractal Processes," Journal of Financial Econometrics, Oxford University Press, vol. 2(1), pages 49-83.
    25. Yue‐Jun Zhang & Jin‐Liang Zhang, 2018. "Volatility forecasting of crude oil market: A new hybrid method," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 37(8), pages 781-789, December.
    26. Chang, Yoosoon & Choi, Yongok & Park, Joon Y., 2017. "A new approach to model regime switching," Journal of Econometrics, Elsevier, vol. 196(1), pages 127-143.
    27. Liu, Lily Y. & Patton, Andrew J. & Sheppard, Kevin, 2015. "Does anything beat 5-minute RV? A comparison of realized measures across multiple asset classes," Journal of Econometrics, Elsevier, vol. 187(1), pages 293-311.
    28. Roubaud, David & Arouri, Mohamed, 2018. "Oil prices, exchange rates and stock markets under uncertainty and regime-switching," Finance Research Letters, Elsevier, vol. 27(C), pages 28-33.
    29. Huiming Zhu & Xianfang Su & Wanhai You & Yinghua Ren, 2017. "Asymmetric effects of oil price shocks on stock returns: evidence from a two-stage Markov regime-switching approach," Applied Economics, Taylor & Francis Journals, vol. 49(25), pages 2491-2507, May.
    30. Alain Bensoussan & SingRu Hoe & ZhongFeng Yan & George Yin, 2017. "Real Options With Competition And Regime Switching," Mathematical Finance, Wiley Blackwell, vol. 27(1), pages 224-250, January.
    31. Biswas, Arunangshu & Goswami, Anindya & Overbeck, Ludger, 2018. "Option pricing in a regime switching stochastic volatility model," Statistics & Probability Letters, Elsevier, vol. 138(C), pages 116-126.
    32. Efimova, Olga & Serletis, Apostolos, 2014. "Energy markets volatility modelling using GARCH," Energy Economics, Elsevier, vol. 43(C), pages 264-273.
    33. Duan, Yinying & Chen, Wang & Zeng, Qing & Liu, Zhicao, 2018. "Leverage effect, economic policy uncertainty and realized volatility with regime switching," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 493(C), pages 148-154.
    34. He, Xin-Jiang & Zhu, Song-Ping, 2016. "An analytical approximation formula for European option pricing under a new stochastic volatility model with regime-switching," Journal of Economic Dynamics and Control, Elsevier, vol. 71(C), pages 77-85.
    35. Stéphane Goutte & Amine Ismail & Huyên Pham, 2017. "Regime-switching Stochastic Volatility Model : Estimation and Calibration to VIX options," Working Papers hal-01212018, HAL.
    36. Alizadeh, Amir H. & Huang, Chih-Yueh & van Dellen, Stefan, 2015. "A regime switching approach for hedging tanker shipping freight rates," Energy Economics, Elsevier, vol. 49(C), pages 44-59.
    37. Gong, Xu & Lin, Boqiang, 2017. "Forecasting the good and bad uncertainties of crude oil prices using a HAR framework," Energy Economics, Elsevier, vol. 67(C), pages 315-327.
    38. Jean-David Fermanian, 2017. "Recent Developments in Copula Models," Econometrics, MDPI, vol. 5(3), pages 1-3, July.
    39. Arunangshu Biswas & Anindya Goswami & Ludger Overbeck, 2017. "Option Pricing in a Regime Switching Stochastic Volatility Model," Papers 1707.01237, arXiv.org, revised Jan 2018.
    40. Roubaud, David & Arouri, Mohamed, 2018. "Oil prices, exchange rates and stock markets under uncertainty and regime-switching," Finance Research Letters, Elsevier, vol. 27(C), pages 28-33.
    41. Ardia, David & Bluteau, Keven & Rüede, Maxime, 2019. "Regime changes in Bitcoin GARCH volatility dynamics," Finance Research Letters, Elsevier, vol. 29(C), pages 266-271.
    42. Pan, Zhiyuan & Wang, Yudong & Wu, Chongfeng & Yin, Libo, 2017. "Oil price volatility and macroeconomic fundamentals: A regime switching GARCH-MIDAS model," Journal of Empirical Finance, Elsevier, vol. 43(C), pages 130-142.
    43. 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.
    44. 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.
    45. Wang, Yudong & Wu, Chongfeng & Yang, Li, 2016. "Forecasting crude oil market volatility: A Markov switching multifractal volatility approach," International Journal of Forecasting, Elsevier, vol. 32(1), pages 1-9.
    46. Cai, Jun, 1994. "A Markov Model of Switching-Regime ARCH," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(3), pages 309-316, July.
    47. Klingelhöfer, Jan & Sun, Rongrong, 2018. "China's regime-switching monetary policy," Economic Modelling, Elsevier, vol. 68(C), pages 32-40.
    48. Yue Liu & Nicolas Privault, 2017. "Selling At The Ultimate Maximum In A Regime-Switching Model," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 20(03), pages 1-27, May.
    49. Robert J. Elliott & Katsumasa Nishide & Carlton‐James U. Osakwe, 2016. "Heston‐Type Stochastic Volatility with a Markov Switching Regime," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(9), pages 902-919, September.
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