IDEAS home Printed from https://ideas.repec.org/p/pra/mprapa/26947.html
   My bibliography  Save this paper

A trend deduction model of fluctuating oil prices

Author

Listed:
  • Xu, Haiyan
  • Zhang, ZhongXiang

Abstract

Crude oil prices have been fluctuating over time and by a large range. It is the disorganization of oil price series that makes it difficult to deduce the changing trends of oil prices in the middle- and long-terms and predict their price levels in the short-term. Following a price-state classification and state transition analysis of changing oil prices from January 2004 to August 2009, this paper first verifies that the observed crude oil price series during the soaring period follow a Markov Chain. Next, the paper deduces the changing trends of oil prices by the limit probability of a Markov Chain. We then undertake a probability distribution analysis and find that the oil price series have a log-normality distribution. On this basis, we integrate the two models to deduce the changing trends of oil prices from the short-term to the middle- and long-terms, thus making our deduction academically sound. Our results match the actual changing trends of oil prices, and show the possibility of re-emerging soaring oil prices.

Suggested Citation

  • Xu, Haiyan & Zhang, ZhongXiang, 2010. "A trend deduction model of fluctuating oil prices," MPRA Paper 26947, University Library of Munich, Germany, revised 17 Nov 2010.
  • Handle: RePEc:pra:mprapa:26947
    as

    Download full text from publisher

    File URL: https://mpra.ub.uni-muenchen.de/26947/2/MPRA_paper_26947.pdf
    File Function: original version
    Download Restriction: no
    ---><---

    Other versions of this item:

    References listed on IDEAS

    as
    1. Kosobud, Richard F & Stokes, Houston H, 1978. "Oil Market Share Dynamics: A Markov Chain Analysis of Consumer and Producer Adjustments," Empirical Economics, Springer, vol. 3(4), pages 253-275.
    2. Vo, Minh T., 2009. "Regime-switching stochastic volatility: Evidence from the crude oil market," Energy Economics, Elsevier, vol. 31(5), pages 779-788, September.
    3. Mark Holmes & Ping Wang, 2003. "Oil Price Shocks and the Asymmetric Adjustment of UK Output: A Markov-switching approach," International Review of Applied Economics, Taylor & Francis Journals, vol. 17(2), pages 181-192.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Ibrahim Abada, 2012. "Study of the evolution of the northwestern European natural gas markets using S-GaMMES," Working Papers 1203, Chaire Economie du climat.
    2. Ibrahim Abada, 2012. "A stochastic generalized Nash-Cournot model for the northwestern European natural gas markets with a fuel substitution demand function: The S-GaMMES model," Working Papers 1202, Chaire Economie du climat.
    3. Ibrahim Abada & Pierre-André Jouvet, 2013. "A stochastic generalized Nash-Cournot model for the northwestern European natural gas markets: The S-GaMMES model," Working Papers 1308, Chaire Economie du climat.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Urom, Christian & Onwuka, Kevin O. & Uma, Kalu E. & Yuni, Denis N., 2020. "Regime dependent effects and cyclical volatility spillover between crude oil price movements and stock returns," International Economics, Elsevier, vol. 161(C), pages 10-29.
    2. Ali Ahmed, Huson Joher & Wadud, I.K.M. Mokhtarul, 2011. "Role of oil price shocks on macroeconomic activities: An SVAR approach to the Malaysian economy and monetary responses," Energy Policy, Elsevier, vol. 39(12), pages 8062-8069.
    3. Baum, Christopher F. & Zerilli, Paola & Chen, Liyuan, 2021. "Stochastic volatility, jumps and leverage in energy and stock markets: Evidence from high frequency data," Energy Economics, Elsevier, vol. 93(C).
    4. Mengzhe Zhang & Leunglung Chan, 2016. "Pricing volatility swaps in the Heston’s stochastic volatility model with regime switching: A saddlepoint approximation method," International Journal of Financial Engineering (IJFE), World Scientific Publishing Co. Pte. Ltd., vol. 3(04), pages 1-20, December.
    5. Chen, Rongda & Xu, Jianjun, 2019. "Forecasting volatility and correlation between oil and gold prices using a novel multivariate GAS model," Energy Economics, Elsevier, vol. 78(C), pages 379-391.
    6. Melanie Parravano & Luis Enrique Pedauga, 2008. "Oil market dynamics: A Markow chain analysis," Economía, Instituto de Investigaciones Económicas y Sociales (IIES). Facultad de Ciencias Económicas y Sociales. Universidad de Los Andes. Mérida, Venezuela, vol. 33(25), pages 87-115, january-j.
    7. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    8. Jeff Fleming & Chris Kirby, 2013. "Component-Driven Regime-Switching Volatility," Journal of Financial Econometrics, Society for Financial Econometrics, vol. 11(2), pages 263-301, March.
    9. Lin, Sha & He, Xin-Jiang, 2021. "A closed-form pricing formula for forward start options under a regime-switching stochastic volatility model," Chaos, Solitons & Fractals, Elsevier, vol. 144(C).
    10. Gatfaoui, Hayette, 2016. "Linking the gas and oil markets with the stock market: Investigating the U.S. relationship," Energy Economics, Elsevier, vol. 53(C), pages 5-16.
    11. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    12. Cifarelli, Giulio & Paesani, Paolo, 2017. "On the difficulty of interpreting market behaviour in an uncertain world: the case of oil futures pricing between 2003 and 2016," MPRA Paper 84009, University Library of Munich, Germany.
    13. Gronwald, Marc, 2012. "A characterization of oil price behavior — Evidence from jump models," Energy Economics, Elsevier, vol. 34(5), pages 1310-1317.
    14. Akdoğan, Kurmaş, 2020. "Fundamentals versus speculation in oil market: The role of asymmetries in price adjustment?," Resources Policy, Elsevier, vol. 67(C).
    15. Gao, Shen & Hou, Chenghan & Nguyen, Bao H., 2020. "Forecasting natural gas prices using highly flexible time-varying parameter models," Working Papers 2020-01, University of Tasmania, Tasmanian School of Business and Economics.
    16. Jean Pierre Fernández Prada Saucedo & Gabriel Rodríguez, 2020. "Modeling the Volatility of Returns on Commodities: An Application and Empirical Comparison of GARCH and SV Models," Documentos de Trabajo / Working Papers 2020-484, Departamento de Economía - Pontificia Universidad Católica del Perú.
    17. Benedetto, F. & Giunta, G. & Mastroeni, L., 2016. "On the predictability of energy commodity markets by an entropy-based computational method," Energy Economics, Elsevier, vol. 54(C), pages 302-312.
    18. Lu Yang & Shigeyuki Hamori, 2018. "Modeling The Dynamics Of International Agricultural Commodity Prices: A Comparison Of Garch And Stochastic Volatility Models," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 13(03), pages 1-20, September.
    19. Aloui, Chaker & Jammazi, Rania, 2009. "The effects of crude oil shocks on stock market shifts behaviour: A regime switching approach," Energy Economics, Elsevier, vol. 31(5), pages 789-799, September.
    20. Don Bredin & Stuart Hyde, 2008. "Regime Change and the Role of International Markets on the Stock Returns of Small Open Economies," European Financial Management, European Financial Management Association, vol. 14(2), pages 315-346, March.

    More about this item

    Keywords

    Oil price; Log-normality distribution; Limit probability of a Markov Chain; Trend deduction model; OPEC;
    All these keywords.

    JEL classification:

    • Q41 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Demand and Supply; Prices
    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • F01 - International Economics - - General - - - Global Outlook
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting
    • O13 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Agriculture; Natural Resources; Environment; Other Primary Products
    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other

    NEP fields

    This paper has been announced in the following NEP Reports:

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:pra:mprapa:26947. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: . General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Joachim Winter (email available below). General contact details of provider: https://edirc.repec.org/data/vfmunde.html .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.