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Unit root behavior in energy futures prices

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  • Serletis, Apostolos

Abstract

This paper re-examines the empirical evidence for random walk type behavior in energy futures prices. In doing so, tests for unit roots in the univariate time-series representation of the daily crude oil, heating oil, and unleaded gasoline series are performed using recent state-of-the-art methodology. The results show that the unit root hypothesis can be rejected if allowance is made for the possibility of a one-time break in the intercept and the slope of the trend function at an unknown point in time.

Suggested Citation

  • Serletis, Apostolos, 1992. "Unit root behavior in energy futures prices," MPRA Paper 1744, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:1744
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    References listed on IDEAS

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    1. 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.
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    Citations

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    Cited by:

    1. Belbute, José, 2013. "Does final demand for energy in Portugal exhibit long memory?," MPRA Paper 45717, University Library of Munich, Germany.
    2. Lean, Hooi Hooi & Smyth, Russell, 2009. "Long memory in US disaggregated petroleum consumption: Evidence from univariate and multivariate LM tests for fractional integration," Energy Policy, Elsevier, vol. 37(8), pages 3205-3211, August.
    3. Wang, Yudong & Wu, Chongfeng, 2012. "Long memory in energy futures markets: Further evidence," Resources Policy, Elsevier, vol. 37(3), pages 261-272.
    4. Shrestha, Keshab, 2014. "Price discovery in energy markets," Energy Economics, Elsevier, vol. 45(C), pages 229-233.
    5. Ratti, Ronald A. & Vespignani, Joaquin L., 2015. "OPEC and non-OPEC oil production and the global economy," Energy Economics, Elsevier, vol. 50(C), pages 364-378.
    6. Vinod Mishra & Russell Smyth, 2014. "Unit root properties of natural gas spot and futures prices: The relevance of heteroskedasticity in high frequency data," Monash Economics Working Papers 20-14, Monash University, Department of Economics.
    7. Población, Javier & Serna, Gregorio, 2016. "Is the refining margin stationary?," International Review of Economics & Finance, Elsevier, vol. 44(C), pages 169-186.
    8. John Elder & Apostolos Serletis, 2008. "Long memory in energy futures prices," Review of Financial Economics, John Wiley & Sons, vol. 17(2), pages 146-155.
    9. Liu, Wei-han, 2016. "A re-examination of maturity effect of energy futures price from the perspective of stochastic volatility," Energy Economics, Elsevier, vol. 56(C), pages 351-362.
    10. Herbert, John H, 1995. "Trading volume, maturity and natural gas futures price volatility," Energy Economics, Elsevier, vol. 17(4), pages 293-299, October.
    11. Sensoy, Ahmet & Hacihasanoglu, Erk, 2014. "Time-varying long range dependence in energy futures markets," Energy Economics, Elsevier, vol. 46(C), pages 318-327.
    12. Yuliya Lovcha & Alejandro Perez-Laborda, 2017. "Structural shocks and dynamic elasticities in a long memory model of the US gasoline retail market," Empirical Economics, Springer, vol. 53(2), pages 405-422, September.
    13. Hooi Hooi Lean & Russell Smyth, 2015. "Testing for weak-form efficiency of crude palm oil spot and future markets: new evidence from a GARCH unit root test with multiple structural breaks," Applied Economics, Taylor & Francis Journals, vol. 47(16), pages 1710-1721, April.
    14. Ali Akarca & Dimitri Andrianacos, 1998. "The relationship between crude oil and gasoline prices," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 4(3), pages 282-288, August.
    15. Maslyuk, Svetlana & Smyth, Russell, 2008. "Unit root properties of crude oil spot and futures prices," Energy Policy, Elsevier, vol. 36(7), pages 2591-2600, July.
    16. Carlos Pestana Barros & Luis A. Gil-Alana, 2011. "Oil Prices: Persistence and Breaks," Faculty Working Papers 09/11, School of Economics and Business Administration, University of Navarra.
    17. Yudong Wang & Chongfeng Wu, 2013. "Efficiency of Crude Oil Futures Markets: New Evidence from Multifractal Detrending Moving Average Analysis," Computational Economics, Springer;Society for Computational Economics, vol. 42(4), pages 393-414, December.
    18. Mishra, Vinod & Smyth, Russell, 2016. "Are natural gas spot and futures prices predictable?," Economic Modelling, Elsevier, vol. 54(C), pages 178-186.
    19. Yaya, OlaOluwa Simon & Gil-Alana, Luis Alberiko & Carcel, Hector, 2015. "Testing fractional persistence and non-linearities in the natural gas market: An application of non-linear deterministic terms based on Chebyshev polynomials in time," Energy Economics, Elsevier, vol. 52(PA), pages 240-245.
    20. Perera, Devmali & Białkowski, Jędrzej & Bohl, Martin T., 2020. "Does the tea market require a futures contract? Evidence from the Sri Lankan tea market," Research in International Business and Finance, Elsevier, vol. 54(C).
    21. Ghoshray, Atanu & Johnson, Ben, 2010. "Trends in world energy prices," Energy Economics, Elsevier, vol. 32(5), pages 1147-1156, September.
    22. Ali Akarca & Dimitri Andrianacos, 1997. "Detecting break in oil price series using the Box-Tiao method," International Advances in Economic Research, Springer;International Atlantic Economic Society, vol. 3(2), pages 217-224, May.
    23. Noguera, José, 2013. "Oil prices: Breaks and trends," Energy Economics, Elsevier, vol. 37(C), pages 60-67.
    24. Ozdemir, Zeynel Abidin & Gokmenoglu, Korhan & Ekinci, Cagdas, 2013. "Persistence in crude oil spot and futures prices," Energy, Elsevier, vol. 59(C), pages 29-37.

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    More about this item

    Keywords

    Futures; Energy; Unit Roots;
    All these keywords.

    JEL classification:

    • P28 - Political Economy and Comparative Economic Systems - - Socialist and Transition Economies - - - Natural Resources; Environment
    • G13 - Financial Economics - - General Financial Markets - - - Contingent Pricing; Futures Pricing
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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