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Analysis and Bayes statistical probability inference of crude oil price change point

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  • Chai, Jian
  • Lu, Quanying
  • Hu, Yi
  • Wang, Shouyang
  • Lai, Kin Keung
  • Liu, Hongtao

Abstract

This paper introduces the Poisson distribution, power-law distribution, and logarithmic-normal distribution as the prior distributions to construct Bayes statistical probability inference model for the simulation of the monthly crude oil price change point trends. Based on basic statistical cognition and product partition model (PPM), the historical change points are defined, identified, and analyzed. The PPM-KM integration model is established by combining PPM model and K-means method to measure, cluster, and identify the posterior probability of change points. The appearance probabilities of change points under different scenarios are calculated and compared for single recursive probabilistic predictions. The results showed there were 37 significant change points during 1986–2015. In different time points, unbalance of market supply-demand structure, sudden geopolitical event, the US dollar index, and global economic development situation have become the main reasons for oil price catastrophes. The monthly crude oil price change point complied with the power-law distribution hypothesis. It provides a new analytical perspective and is beneficial to governments, enterprises and investors to understand the market trends, avoid investment risks and make the right investment decisions.

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  • 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.
    • Handle: RePEc:eee:tefoso:v:126:y:2018:i:c:p:271-283
    DOI: 10.1016/j.techfore.2017.09.007
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    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. Inclan, Carla, 1993. "Detection of Multiple Changes of Variance Using Posterior Odds," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(3), pages 289-300, July.
    3. 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.
    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. Arouri, Mohamed El Hédi & Lahiani, Amine & Lévy, Aldo & Nguyen, Duc Khuong, 2012. "Forecasting the conditional volatility of oil spot and futures prices with structural breaks and long memory models," Energy Economics, Elsevier, vol. 34(1), pages 283-293.
    6. Zhang, Xun & Yu, Lean & Wang, Shouyang & Lai, Kin Keung, 2009. "Estimating the impact of extreme events on crude oil price: An EMD-based event analysis method," Energy Economics, Elsevier, vol. 31(5), pages 768-778, September.
    7. Regnier, Eva, 2007. "Oil and energy price volatility," Energy Economics, Elsevier, vol. 29(3), pages 405-427, May.
    8. Donald W. K. Andrews, 2003. "Tests for Parameter Instability and Structural Change with Unknown Change Point: A Corrigendum," Econometrica, Econometric Society, vol. 71(1), pages 395-397, January.
    9. Masih, Rumi & Peters, Sanjay & De Mello, Lurion, 2011. "Oil price volatility and stock price fluctuations in an emerging market: Evidence from South Korea," Energy Economics, Elsevier, vol. 33(5), pages 975-986, September.
    10. Chang, Kuang-Liang & Yu, Shih-Ti, 2013. "Does crude oil price play an important role in explaining stock return behavior?," Energy Economics, Elsevier, vol. 39(C), pages 159-168.
    11. Lutz Kilian, 2009. "Not All Oil Price Shocks Are Alike: Disentangling Demand and Supply Shocks in the Crude Oil Market," American Economic Review, American Economic Association, vol. 99(3), pages 1053-1069, June.
    12. Lutz Kilian & Cheolbeom Park, 2009. "The Impact Of Oil Price Shocks On The U.S. Stock Market," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 50(4), pages 1267-1287, November.
    13. Chiroma, Haruna & Abdulkareem, Sameem & Herawan, Tutut, 2015. "Evolutionary Neural Network model for West Texas Intermediate crude oil price prediction," Applied Energy, Elsevier, vol. 142(C), pages 266-273.
    14. Chen, Pei-Fen & Lee, Chien-Chiang & Zeng, Jhih-Hong, 2014. "The relationship between spot and futures oil prices: Do structural breaks matter?," Energy Economics, Elsevier, vol. 43(C), pages 206-217.
    15. Baruník, Jozef & Malinská, Barbora, 2016. "Forecasting the term structure of crude oil futures prices with neural networks," Applied Energy, Elsevier, vol. 164(C), pages 366-379.
    16. Lutz Kilian, 2014. "Oil Price Shocks: Causes and Consequences," Annual Review of Resource Economics, Annual Reviews, vol. 6(1), pages 133-154, October.
    17. Joel Popkin, 1974. "Commodity Prices and the U.S. Price Level," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 5(1), pages 249-260.
    18. Perron, P, 1993. "Erratum [The Great Crash, the Oil Price Shock and the Unit Root Hypothesis]," Econometrica, Econometric Society, vol. 61(1), pages 248-249, January.
    19. Labys, W.C. & Badillo, D. & Lesourd, J.B., 1995. "The Cyclical Behavior of Individual Commodity Price Series," G.R.E.Q.A.M. 95b03, Universite Aix-Marseille III.
    20. Wu, Gang & Zhang, Yue-Jun, 2014. "Does China factor matter? An econometric analysis of international crude oil prices," Energy Policy, Elsevier, vol. 72(C), pages 78-86.
    21. Gong, Xu & Wen, Fenghua & Xia, X.H. & Huang, Jianbai & Pan, Bin, 2017. "Investigating the risk-return trade-off for crude oil futures using high-frequency data," Applied Energy, Elsevier, vol. 196(C), pages 152-161.
    22. Hamilton, James D. & Wu, Jing Cynthia, 2014. "Risk premia in crude oil futures prices," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 9-37.
    23. Loschi, R. H. & Cruz, F. R. B., 2002. "An analysis of the influence of some prior specifications in the identification of change points via product partition model," Computational Statistics & Data Analysis, Elsevier, vol. 39(4), pages 477-501, June.
    24. Wang, Yudong & Liu, Li & Diao, Xundi & Wu, Chongfeng, 2015. "Forecasting the real prices of crude oil under economic and statistical constraints," Energy Economics, Elsevier, vol. 51(C), pages 599-608.
    25. Loschi, R.H. & Cruz, F.R.B., 2005. "Extension to the product partition model: computing the probability of a change," Computational Statistics & Data Analysis, Elsevier, vol. 48(2), pages 255-268, February.
    26. Vuong, Quang H, 1989. "Likelihood Ratio Tests for Model Selection and Non-nested Hypotheses," Econometrica, Econometric Society, vol. 57(2), pages 307-333, March.
    27. James D. Hamilton, 2012. "Oil Prices, Exhaustible Resources, and Economic Growth," NBER Working Papers 17759, National Bureau of Economic Research, Inc.
    28. Cunado, Juncal & Perez de Gracia, Fernando, 2014. "Oil price shocks and stock market returns: Evidence for some European countries," Energy Economics, Elsevier, vol. 42(C), pages 365-377.
    29. Canova, Fabio, 1994. "Detrending and turning points," European Economic Review, Elsevier, vol. 38(3-4), pages 614-623, April.
    30. Leybourne, Stephen J. & C. Mills, Terence & Newbold, Paul, 1998. "Spurious rejections by Dickey-Fuller tests in the presence of a break under the null," Journal of Econometrics, Elsevier, vol. 87(1), pages 191-203, August.
    31. Labys, Walter C. & Maizels, Alfred, 1993. "Commodity price fluctuations and macroeconomic adjustments in the developed economies," Journal of Policy Modeling, Elsevier, vol. 15(3), pages 335-352, June.
    32. Plourde, André & Watkins, G. C., 1998. "Crude oil prices between 1985 and 1994: how volatile in relation to other commodities?," Resource and Energy Economics, Elsevier, vol. 20(3), pages 245-262, September.
    33. 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.
    34. Fernando A. Quintana & Pilar L. Iglesias, 2003. "Bayesian clustering and product partition models," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(2), pages 557-574, May.
    35. Ewing, Bradley T. & Malik, Farooq, 2017. "Modelling asymmetric volatility in oil prices under structural breaks," Energy Economics, Elsevier, vol. 63(C), pages 227-233.
    36. Fan, Liwei & Pan, Sijia & Li, Zimin & Li, Huiping, 2016. "An ICA-based support vector regression scheme for forecasting crude oil prices," Technological Forecasting and Social Change, Elsevier, vol. 112(C), pages 245-253.
    37. Askari, Hossein & Krichene, Noureddine, 2008. "Oil price dynamics (2002-2006)," Energy Economics, Elsevier, vol. 30(5), pages 2134-2153, September.
    38. Chai, Jian & Guo, Ju-E. & Meng, Lei & Wang, Shou-Yang, 2011. "Exploring the core factors and its dynamic effects on oil price: An application on path analysis and BVAR-TVP model," Energy Policy, Elsevier, vol. 39(12), pages 8022-8036.
    39. 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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