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Time series analysis of persistence in crude oil price volatility across bull and bear regimes

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  • Gil-Alana, Luis A.
  • Gupta, Rangan
  • Olubusoye, Olusanya E.
  • Yaya, OlaOluwa S.

Abstract

This paper deals with the analysis of crude oil prices in the context of fractional integration and using bull and bear phases over monthly periods between September, 1859 to July, 2015. We examine both the log prices series as well as volatility, approximated by means of the absolute and the squared returns. The results for the whole sample indicate that the log-prices are nonstationary, with an order of integration close to 1 or even higher than 1, while the squared and absolute returns show evidence of long memory behavior. Upon separating the sample according to bull and bear periods, we observe an increase in the order of integration in both the log-prices and the two measures of volatility. Our results have important policy implications.

Suggested Citation

  • Gil-Alana, Luis A. & Gupta, Rangan & Olubusoye, Olusanya E. & Yaya, OlaOluwa S., 2016. "Time series analysis of persistence in crude oil price volatility across bull and bear regimes," Energy, Elsevier, vol. 109(C), pages 29-37.
  • Handle: RePEc:eee:energy:v:109:y:2016:i:c:p:29-37
    DOI: 10.1016/j.energy.2016.04.082
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    1. Bradley T. Ewing & Farooq Malik, 2010. "Estimating Volatility Persistence in Oil Prices Under Structural Breaks," The Financial Review, Eastern Finance Association, vol. 45(4), pages 1011-1023, November.
    2. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models—Rejoinder," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 211-219.
    3. Charles, Amélie & Darné, Olivier, 2009. "The efficiency of the crude oil markets: Evidence from variance ratio tests," Energy Policy, Elsevier, vol. 37(11), pages 4267-4272, November.
    4. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.
    5. Donald W. Jones, Paul N. Leiby and Inja K. Paik, 2004. "Oil Price Shocks and the Macroeconomy: What Has Been Learned Since 1996," The Energy Journal, International Association for Energy Economics, vol. 0(Number 2), pages 1-32.
    6. Lutkepohl, Helmut & Claessen, Holger, 1997. "Analysis of cointegrated VARMA processes," Journal of Econometrics, Elsevier, vol. 80(2), pages 223-239, October.
    7. Hilde C. Bjørnland & Vegard H. Larsen & Junior Maih, 2018. "Oil and Macroeconomic (In)stability," American Economic Journal: Macroeconomics, American Economic Association, vol. 10(4), pages 128-151, October.
    8. Christiane Baumeister & Gert Peersman, 2013. "The Role Of Time‐Varying Price Elasticities In Accounting For Volatility Changes In The Crude Oil Market," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(7), pages 1087-1109, November.
    9. Robert B. Barsky & Lutz Kilian, 2004. "Oil and the Macroeconomy Since the 1970s," Journal of Economic Perspectives, American Economic Association, vol. 18(4), pages 115-134, Fall.
    10. Nathan S. Balke & Stephen P. A. Brown & Mine K. Yücel, 2010. "Oil price shocks and U.S. economic activity: an international perspective," Working Papers 1003, Federal Reserve Bank of Dallas.
    11. Christian Kascha, 2012. "A Comparison of Estimation Methods for Vector Autoregressive Moving-Average Models," Econometric Reviews, Taylor & Francis Journals, vol. 31(3), pages 297-324.
    12. Charles, Amélie & Darné, Olivier, 2014. "Volatility persistence in crude oil markets," Energy Policy, Elsevier, vol. 65(C), pages 729-742.
    13. Kang, Wensheng & Ratti, Ronald A., 2013. "Structural oil price shocks and policy uncertainty," Economic Modelling, Elsevier, vol. 35(C), pages 314-319.
    14. Koop, Gary & Korobilis, Dimitris, 2010. "Bayesian Multivariate Time Series Methods for Empirical Macroeconomics," Foundations and Trends(R) in Econometrics, now publishers, vol. 3(4), pages 267-358, July.
    15. Lutz Kilian, 2008. "The Economic Effects of Energy Price Shocks," Journal of Economic Literature, American Economic Association, vol. 46(4), pages 871-909, December.
    16. Dvir, Eyal & Rogoff, Kenneth, 2014. "Demand effects and speculation in oil markets: Theory and evidence," Journal of International Money and Finance, Elsevier, vol. 42(C), pages 113-128.
    17. Hamilton, James D., 2011. "Nonlinearities And The Macroeconomic Effects Of Oil Prices," Macroeconomic Dynamics, Cambridge University Press, vol. 15(S3), pages 364-378, November.
    18. Canova, Fabio, 1993. "Modelling and forecasting exchange rates with a Bayesian time-varying coefficient model," Journal of Economic Dynamics and Control, Elsevier, vol. 17(1-2), pages 233-261.
    19. Erten, Bilge & Ocampo, José Antonio, 2013. "Super Cycles of Commodity Prices Since the Mid-Nineteenth Century," World Development, Elsevier, vol. 44(C), pages 14-30.
    20. Paap, Richard & van Dijk, Herman K, 2003. "Bayes Estimates of Markov Trends in Possibly Cointegrated Series: An Application to U.S. Consumption and Income," Journal of Business & Economic Statistics, American Statistical Association, vol. 21(4), pages 547-563, October.
    21. Gil-Alana, Luis A. & Gupta, Rangan, 2014. "Persistence and cycles in historical oil price data," Energy Economics, Elsevier, vol. 45(C), pages 511-516.
    22. Timothy Cogley & Thomas J. Sargent, 2005. "Drift and Volatilities: Monetary Policies and Outcomes in the Post WWII U.S," Review of Economic Dynamics, Elsevier for the Society for Economic Dynamics, vol. 8(2), pages 262-302, April.
    23. Sangjoon Kim & Neil Shephard & Siddhartha Chib, 1998. "Stochastic Volatility: Likelihood Inference and Comparison with ARCH Models," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 65(3), pages 361-393.
    24. Peiris, M. Shelton, 1988. "On the study of some functions of multivariate ARMA processes," Journal of Multivariate Analysis, Elsevier, vol. 25(1), pages 146-151, April.
    25. Gary M. Koop, 2013. "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 177-203, March.
    26. Dimitris Korobilis, 2013. "Assessing the Transmission of Monetary Policy Using Time-varying Parameter Dynamic Factor Models-super-," Oxford Bulletin of Economics and Statistics, Department of Economics, University of Oxford, vol. 75(2), pages 157-179, April.
    27. Sungbae An & Frank Schorfheide, 2007. "Bayesian Analysis of DSGE Models," Econometric Reviews, Taylor & Francis Journals, vol. 26(2-4), pages 113-172.
    28. James D. Hamilton, 2009. "Causes and Consequences of the Oil Shock of 2007-08," Brookings Papers on Economic Activity, Economic Studies Program, The Brookings Institution, vol. 40(1 (Spring), pages 215-283.
    29. Christian Kascha & Carsten Trenkler, 2015. "Simple Identification and Specification of Cointegrated Varma Models," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 30(4), pages 675-702, June.
    30. Ben S. Bernanke & Jean Boivin & Piotr Eliasz, 2005. "Measuring the Effects of Monetary Policy: A Factor-Augmented Vector Autoregressive (FAVAR) Approach," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 120(1), pages 387-422.
    31. John Elder & Apostolos Serletis, 2010. "Oil Price Uncertainty," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 42(6), pages 1137-1159, September.
    32. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," NBER Working Papers 14028, National Bureau of Economic Research, Inc.
    33. repec:ulb:ulbeco:2013/13388 is not listed on IDEAS
    34. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    35. 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.
    36. Christiane Baumeister & Gert Peersman, 2013. "Time-Varying Effects of Oil Supply Shocks on the US Economy," American Economic Journal: Macroeconomics, American Economic Association, vol. 5(4), pages 1-28, October.
    37. Hannan, E J, 1976. "The Identification and Parameterization of ARMAX and State Space Forms," Econometrica, Econometric Society, vol. 44(4), pages 713-723, July.
    38. Anton Nakov & Andrea Pescatori, 2010. "Oil and the Great Moderation," Economic Journal, Royal Economic Society, vol. 120(543), pages 131-156, March.
    39. John Geweke & Gianni Amisano, 2011. "Hierarchical Markov normal mixture models with applications to financial asset returns," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 26(1), pages 1-29, January/F.
    40. Afees A. Salisu, 2014. "Modelling oil price volatility before, during and after the global financial crisis," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 38(4), pages 469-495, December.
    41. James H. Stock & Mark W. Watson, 2007. "Why Has U.S. Inflation Become Harder to Forecast?," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(s1), pages 3-33, February.
    42. Tokic, Damir, 2015. "The 2014 oil bust: Causes and consequences," Energy Policy, Elsevier, vol. 85(C), pages 162-169.
    43. Nathan S. Balke & Stephen P.A. Brown & Mine K. Yucel, 2002. "Oil Price Shocks and the U.S. Economy: Where Does the Asymmetry Originate?," The Energy Journal, International Association for Energy Economics, vol. 0(Number 3), pages 27-52.
    44. Ortiz-Cruz, Alejandro & Rodriguez, Eduardo & Ibarra-Valdez, Carlos & Alvarez-Ramirez, Jose, 2012. "Efficiency of crude oil markets: Evidences from informational entropy analysis," Energy Policy, Elsevier, vol. 41(C), pages 365-373.
    45. Giorgio E. Primiceri, 2005. "Time Varying Structural Vector Autoregressions and Monetary Policy," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 72(3), pages 821-852.
    46. Hamilton, James D, 1983. "Oil and the Macroeconomy since World War II," Journal of Political Economy, University of Chicago Press, vol. 91(2), pages 228-248, April.
    47. Koop, Gary & Korobilis, Dimitris, 2013. "Large time-varying parameter VARs," Journal of Econometrics, Elsevier, vol. 177(2), pages 185-198.
    48. Tokic, Damir, 2010. "The 2008 oil bubble: Causes and consequences," Energy Policy, Elsevier, vol. 38(10), pages 6009-6015, October.
    49. Lutkepohl, Helmut & Poskitt, D S, 1996. "Specification of Echelon-Form VARMA Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 14(1), pages 69-79, January.
    50. Konstantinos Metaxoglou & Aaron Smith, 2007. "Maximum Likelihood Estimation of VARMA Models Using a State‐Space EM Algorithm," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 666-685, September.
    51. Nikolaos Antonakakis & Ioannis Chatziantoniou & George Filis, 2014. "Dynamic Spillovers of Oil Price Shocks and Policy Uncertainty," Department of Economics Working Papers wuwp166, Vienna University of Economics and Business, Department of Economics.
    52. Athanasopoulos, George & Vahid, Farshid, 2008. "VARMA versus VAR for Macroeconomic Forecasting," Journal of Business & Economic Statistics, American Statistical Association, vol. 26, pages 237-252, April.
    53. Melike Bildirici & Özgür Ömer Ersin, 2014. "Nonlinearity, Volatility and Fractional Integration in Daily Oil Prices: Smooth Transition Autoregressive ST-FI(AP)GARCH Models," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(3), pages 108-135, October.
    54. J. Stevens & P. de Lamirande, 2014. "Testing the efficiency of the futures market for crude oil in the presence of a structural break," Applied Economics, Taylor & Francis Journals, vol. 46(33), pages 4053-4059, November.
    55. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Dynamic spillovers of oil price shocks and economic policy uncertainty," Energy Economics, Elsevier, vol. 44(C), pages 433-447.
    56. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    57. Poskitt, D.S., 2016. "Vector autoregressive moving average identification for macroeconomic modeling: A new methodology," Journal of Econometrics, Elsevier, vol. 192(2), pages 468-484.
    58. Xiaoyi Mu and Haichun Ye, 2015. "Small Trends and Big Cycles in Crude Oil Prices," The Energy Journal, International Association for Energy Economics, vol. 0(Number 1).
    59. George Athanasopoulos & D. Poskitt & Farshid Vahid, 2012. "Two Canonical VARMA Forms: Scalar Component Models Vis-à-Vis the Echelon Form," Econometric Reviews, Taylor & Francis Journals, vol. 31(1), pages 60-83.
    60. Marta Banbura & Domenico Giannone & Lucrezia Reichlin, 2010. "Large Bayesian vector auto regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 25(1), pages 71-92.
    61. Lutz Kilian, 2008. "Exogenous Oil Supply Shocks: How Big Are They and How Much Do They Matter for the U.S. Economy?," The Review of Economics and Statistics, MIT Press, vol. 90(2), pages 216-240, May.
    62. Gary Koop & Simon M. Potter, 2007. "Estimation and Forecasting in Models with Multiple Breaks," The Review of Economic Studies, Review of Economic Studies Ltd, vol. 74(3), pages 763-789.
    63. Eric M. Leeper & Todd B. Walker & Shu-Chun Susan Yang, 2008. "Fiscal Foresight: Analytics and Econometrics," NBER Working Papers 14028, National Bureau of Economic Research, Inc.
    64. Sims, Christopher A, 1980. "Macroeconomics and Reality," Econometrica, Econometric Society, vol. 48(1), pages 1-48, January.
    65. James H. Stock & Mark W. Watson, 2007. "Erratum to "Why Has U.S. Inflation Become Harder to Forecast?"," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 39(7), pages 1849-1849, October.
    66. Kang, Wensheng & Ratti, Ronald A., 2013. "Oil shocks, policy uncertainty and stock market return," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 26(C), pages 305-318.
    67. Paresh Kumar Narayan & Stephan Popp, 2010. "A new unit root test with two structural breaks in level and slope at unknown time," Journal of Applied Statistics, Taylor & Francis Journals, vol. 37(9), pages 1425-1438.
    68. Cooley, Thomas F. & Dwyer, Mark, 1998. "Business cycle analysis without much theory A look at structural VARs," Journal of Econometrics, Elsevier, vol. 83(1-2), pages 57-88.
    69. Brown, Stephen P. A. & Yucel, Mine K., 2002. "Energy prices and aggregate economic activity: an interpretative survey," The Quarterly Review of Economics and Finance, Elsevier, vol. 42(2), pages 193-208.
    70. Afees A. Salisu & Ismail O. Fasanya, 2012. "Comparative Performance of Volatility Models for Oil Price," International Journal of Energy Economics and Policy, Econjournals, vol. 2(3), pages 167-183.
    71. Adrian R. Pagan & Kirill A. Sossounov, 2003. "A simple framework for analysing bull and bear markets," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(1), pages 23-46.
    72. Narayan, Paresh Kumar & Narayan, Seema, 2007. "Modelling oil price volatility," Energy Policy, Elsevier, vol. 35(12), pages 6549-6553, December.
    73. Jean-Marie Dufour & Dalibor Stevanović, 2013. "Factor-Augmented VARMA Models With Macroeconomic Applications," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 31(4), pages 491-506, October.
    74. Abadir, Karim M. & Distaso, Walter & Giraitis, Liudas, 2007. "Nonstationarity-extended local Whittle estimation," Journal of Econometrics, Elsevier, vol. 141(2), pages 1353-1384, December.
    75. Antonakakis, Nikolaos & Chatziantoniou, Ioannis & Filis, George, 2014. "Spillovers between oil and stock markets at times of geopolitical unrest and economic turbulence," MPRA Paper 59760, University Library of Munich, Germany.
    76. Wang, Yudong & Liu, Li, 2010. "Is WTI crude oil market becoming weakly efficient over time?: New evidence from multiscale analysis based on detrended fluctuation analysis," Energy Economics, Elsevier, vol. 32(5), pages 987-992, September.
    77. Fardous Alom & Bert D. Ward & Baiding Hu, 2012. "Modelling petroleum future price volatility: analysing asymmetry and persistency of shocks," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 36(1), pages 1-24, March.
    78. Susan Yang, Shu-Chun, 2005. "Quantifying tax effects under policy foresight," Journal of Monetary Economics, Elsevier, vol. 52(8), pages 1557-1568, November.
    79. Salisu, Afees A. & Mobolaji, Hakeem, 2013. "Modeling returns and volatility transmission between oil price and US–Nigeria exchange rate," Energy Economics, Elsevier, vol. 39(C), pages 169-176.
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    More about this item

    Keywords

    Bull and bear regimes; Oil price; Persistence; Volatility; West Texas Intermediate market;
    All these keywords.

    JEL classification:

    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices

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