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Persistence and Cycles in Historical Oil Prices Data

Author

Listed:
  • Luis A. Gil-Alana

    (University of Navarra, Faculty of Economics, Edificio Biblioteca, Entrada Este, E-31080 Pamplona, Spain)

  • Rangan Gupta

    (Department of Economics, University of Pretoria)

Abstract

This paper deals with the analysis of two observed features in historical oil prices data. In particular, persistence and cyclicity. Using monthly data from September 1859 to October 2013, we observe that the series presents two peaks in the spectrum, one occurring at the long run or zero frequency and the other at a cyclical frequency. These features can be well described in terms of a long memory model that incorporates both peaks in the spectrum. It is found that the order of integration at the zero frequency is about 0.6, and the one at the cyclical frequency is substantially smaller (of about 0.3) with the length of the cycles being approximately of about 74 periods (months), which is consistent with the length suggested by the business cycles theory.

Suggested Citation

  • Luis A. Gil-Alana & Rangan Gupta, 2013. "Persistence and Cycles in Historical Oil Prices Data," Working Papers 201375, University of Pretoria, Department of Economics.
    • Handle: RePEc:pre:wpaper:201375
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    Cited by:

    1. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2020. "Modeling US historical time-series prices and inflation using alternative long-memory approaches," Empirical Economics, Springer, vol. 58(4), pages 1491-1511, April.
    2. Gil-Alana, Luis A. & Chang, Shinhye & Balcilar, Mehmet & Aye, Goodness C. & Gupta, Rangan, 2015. "Persistence of precious metal prices: A fractional integration approach with structural breaks," Resources Policy, Elsevier, vol. 44(C), pages 57-64.
    3. Nima Nonejad, 2019. "Modeling Persistence and Parameter Instability in Historical Crude Oil Price Data Using a Gibbs Sampling Approach," Computational Economics, Springer;Society for Computational Economics, vol. 53(4), pages 1687-1710, April.
    4. Vaibhavi Aher, 2023. "Einführung und Überblick," Springer Books, in: Vaibhavi Aher (ed.), Statistische und mathematische Methoden in der Wirtschaft, chapter 1, pages 1-71, Springer.
    5. Nima Nonejad, 2021. "Should crude oil price volatility receive more attention than the price of crude oil? An empirical investigation via a large‐scale out‐of‐sample forecast evaluation of US macroeconomic data," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(5), pages 769-791, August.
    6. Moses Tule & Afees Salisu & Charles Chiemeke, 2020. "Improving Nigeria’s Inflation Forecast with Oil Price: The Role of Estimators," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 18(1), pages 191-229, March.
    7. Lynda Khalaf & Beatriz Peraza López, 2020. "Simultaneous Indirect Inference, Impulse Responses and ARMA Models," Econometrics, MDPI, vol. 8(2), pages 1-26, April.
    8. Luis A. Gil-Alana & Christophe André & Rangan Gupta & Tsangyao Chang & Omid Ranjbar, 2016. "The Feldstein--Horioka puzzle in South Africa: A fractional cointegration approach," The Journal of International Trade & Economic Development, Taylor & Francis Journals, vol. 25(7), pages 978-991, October.
    9. Nima Nonejad, 2024. "Point forecasts of the price of crude oil: an attempt to “beat” the end-of-month random-walk benchmark," Empirical Economics, Springer, vol. 67(4), pages 1497-1539, October.
    10. Nonejad, Nima, 2021. "The price of crude oil and (conditional) out-of-sample predictability of world industrial production," Journal of Commodity Markets, Elsevier, vol. 23(C).
    11. 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.
    12. Giorgio Canarella & Luis A. Gil-Alana & Rangan Gupta & Stephen M. Miller, 2016. "Modeling U.S. Historical Time-Series Prices and Inflation Using Various Linear and Nonlinear Long-Memory Approaches," Working Papers 201683, University of Pretoria, Department of Economics.
    13. Nonejad, Nima, 2022. "Equity premium prediction using the price of crude oil: Uncovering the nonlinear predictive impact," Energy Economics, Elsevier, vol. 115(C).
    14. Maneejuk, Paravee & Kaewtathip, Nuttaphong & Yamaka, Woraphon, 2024. "The influence of the Ukraine-Russia conflict on renewable and fossil energy price cycles," Energy Economics, Elsevier, vol. 129(C).
    15. Oloko, Tirimisiyu F. & Ogbonna, Ahamuefula E. & Adedeji, Abdulfatai A. & Lakhani, Noman, 2021. "Oil price shocks and inflation rate persistence: A Fractional Cointegration VAR approach," Economic Analysis and Policy, Elsevier, vol. 70(C), pages 259-275.
    16. Nima Nonejad, 2020. "Does the price of crude oil help predict the conditional distribution of aggregate equity return?," Empirical Economics, Springer, vol. 58(1), pages 313-349, January.
    17. Bravo Caro, José Manuel & Golpe, Antonio A. & Iglesias, Jesús & Vides, José Carlos, 2020. "A new way of measuring the WTI – Brent spread. Globalization, shock persistence and common trends," Energy Economics, Elsevier, vol. 85(C).
    18. Ateeque Anwer, 2024. "How Oil Price Shocks Influence on Inflation Rate? Evidence from Malaysian Economy," Bulletin of Business and Economics (BBE), Research Foundation for Humanity (RFH), vol. 13(2), pages 350-356.
    19. Jair N. Ojeda-Joya & Oscar Jaulin-Mendez & Juan C. Bustos-Peláez, 2019. "The Interdependence Between Commodity-Price and GDP Cycles: A Frequency-Domain Approach," Atlantic Economic Journal, Springer;International Atlantic Economic Society, vol. 47(3), pages 275-292, September.
    20. Nonejad, Nima, 2022. "Understanding the conditional out-of-sample predictive impact of the price of crude oil on aggregate equity return volatility," The North American Journal of Economics and Finance, Elsevier, vol. 62(C).

    More about this item

    Keywords

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    JEL classification:

    • 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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