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Impact of speculator's expectations of returns and time scales of investment on crude oil price behaviors

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  • He, Ling-Yun
  • Fan, Ying
  • Wei, Yi-Ming

Abstract

Based on time series of crude oil prices (daily spot), this paper analyses price fluctuation with two significant parameters [tau] (speculators' time scales of investment) and [epsilon] (speculators' expectations of return) by using Zipf analysis technique, specifically, by mapping [tau]-returns of prices into 3-alphabeted sequences (absolute frequencies) and 2-alphabeted sequences (relative frequencies), containing the fundamental information of price fluctuations. This paper empirically explores parameters and identifies various types of speculators' cognition patterns of price behavior. In order to quantify the degree of distortion, a feasible reference is proposed: an ideal speculator. Finally, this paper discusses the similarities and differences between those cognition patterns of speculators' and those of an ideal speculator. The resultant analyses identify the possible distortion of price behaviors by their patterns.

Suggested Citation

  • He, Ling-Yun & Fan, Ying & Wei, Yi-Ming, 2009. "Impact of speculator's expectations of returns and time scales of investment on crude oil price behaviors," Energy Economics, Elsevier, vol. 31(1), pages 77-84, January.
    • Handle: RePEc:eee:eneeco:v:31:y:2009:i:1:p:77-84
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    1. Turiel, Antonio & Pérez-Vicente, Conrad J., 2003. "Multifractal geometry in stock market time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 629-649.
    2. Ling-Yun He & Ying Fan & Yi-Ming Wei, 2007. "The empirical analysis for fractal features and long-run memory mechanism in petroleum pricing systems," International Journal of Global Energy Issues, Inderscience Enterprises Ltd, vol. 27(4), pages 492-502.
    3. Mandelbrot, Benoit B, 1971. "When Can Price Be Arbitraged Efficiently? A Limit to the Validity of the Random Walk and Martingale Models," The Review of Economics and Statistics, MIT Press, vol. 53(3), pages 225-236, August.
    4. Alvarez-Ramirez, Jose & Cisneros, Myriam & Ibarra-Valdez, Carlos & Soriano, Angel, 2002. "Multifractal Hurst analysis of crude oil prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 313(3), pages 651-670.
    5. Kim, Kyungsik & Yoon, Seong-Min, 2004. "Multifractal features of financial markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 272-278.
    6. Kaushik Matia & Yosef Ashkenazy & H. Eugene Stanley, 2003. "Multifractal Properties of Price Fluctuations of Stocks and Commodities," Papers cond-mat/0308012, arXiv.org.
    7. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    8. Matteo, T. Di & Aste, T. & Dacorogna, Michel M., 2005. "Long-term memories of developed and emerging markets: Using the scaling analysis to characterize their stage of development," Journal of Banking & Finance, Elsevier, vol. 29(4), pages 827-851, April.
    9. Apostolos Serletis & Ioannis Andreadis, 2007. "Random Fractal Structures in North American Energy Markets," World Scientific Book Chapters, in: Quantitative And Empirical Analysis Of Energy Markets, chapter 18, pages 245-255, World Scientific Publishing Co. Pte. Ltd..
    10. Adrangi, Bahram & Chatrath, Arjun & Dhanda, Kanwalroop Kathy & Raffiee, Kambiz, 2001. "Chaos in oil prices? Evidence from futures markets," Energy Economics, Elsevier, vol. 23(4), pages 405-425, July.
    11. Ausloos, M., 2000. "Statistical physics in foreign exchange currency and stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 285(1), pages 48-65.
    12. Maull, Hanns, 1977. "The price of crude oil in the international energy market : A political analysis," Energy Policy, Elsevier, vol. 5(2), pages 142-157, June.
    13. Ferreira, Fernando F. & de Oliveira, Viviane M. & Crepaldi, Antônio F. & Campos, Paulo R.A., 2005. "Agent-based model with heterogeneous fundamental prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 357(3), pages 534-542.
    14. Radalj, Kim F. & McAleer, Michael, 2005. "Speculation and destabilisation," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 69(1), pages 151-161.
    15. Benoit Mandelbrot, 2015. "The Variation of Certain Speculative Prices," World Scientific Book Chapters, in: Anastasios G Malliaris & William T Ziemba (ed.), THE WORLD SCIENTIFIC HANDBOOK OF FUTURES MARKETS, chapter 3, pages 39-78, World Scientific Publishing Co. Pte. Ltd..
    16. Panas, Epaminondas & Ninni, Vassilia, 2000. "Are oil markets chaotic? A non-linear dynamic analysis," Energy Economics, Elsevier, vol. 22(5), pages 549-568, October.
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