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Commodity predictability analysis with a permutation information theory approach

Author

Listed:
  • Zunino, Luciano
  • Tabak, Benjamin M.
  • Serinaldi, Francesco
  • Zanin, Massimiliano
  • Pérez, Darío G.
  • Rosso, Osvaldo A.

Abstract

It is widely known that commodity markets are not totally efficient. Long-range dependence is present, and thus the celebrated Brownian motion of prices can be considered only as a first approximation. In this work we analyzed the predictability in commodity markets by using a novel approach derived from Information Theory. The complexity–entropy causality plane has been recently shown to be a useful statistical tool to distinguish the stage of stock market development because differences between emergent and developed stock markets can be easily discriminated and visualized with this representation space [L. Zunino, M. Zanin, B.M. Tabak, D.G. Pérez, O.A. Rosso, Complexity–entropy causality plane: a useful approach to quantify the stock market inefficiency, Physica A 389 (2010) 1891–1901]. By estimating the permutation entropy and permutation statistical complexity of twenty basic commodity future markets over a period of around 20 years (1991.01.02–2009.09.01), we can define an associated ranking of efficiency. This ranking is quantifying the presence of patterns and hidden structures in these prime markets. Moreover, the temporal evolution of the commodities in the complexity–entropy causality plane allows us to identify periods of time where the underlying dynamics is more or less predictable.

Suggested Citation

  • Zunino, Luciano & Tabak, Benjamin M. & Serinaldi, Francesco & Zanin, Massimiliano & Pérez, Darío G. & Rosso, Osvaldo A., 2011. "Commodity predictability analysis with a permutation information theory approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 390(5), pages 876-890.
  • Handle: RePEc:eee:phsmap:v:390:y:2011:i:5:p:876-890
    DOI: 10.1016/j.physa.2010.11.020
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    1. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2009. "Forbidden patterns, permutation entropy and stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(14), pages 2854-2864.
    2. Pawe{l} Sieczka & Janusz A. Ho{l}yst, 2008. "Correlations in commodity markets," Papers 0803.3884, arXiv.org, revised Jan 2009.
    3. Chstoph Bandt & Faten Shiha, 2007. "Order Patterns in Time Series," Journal of Time Series Analysis, Wiley Blackwell, vol. 28(5), pages 646-665, September.
    4. Grau-Carles, Pilar, 2000. "Empirical evidence of long-range correlations in stock returns," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 287(3), pages 396-404.
    5. Turvey, Calum G., 2007. "A note on scaled variance ratio estimation of the Hurst exponent with application to agricultural commodity prices," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 377(1), pages 155-165.
    6. Grech, D & Mazur, Z, 2004. "Can one make any crash prediction in finance using the local Hurst exponent idea?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(1), pages 133-145.
    7. Rosso, Osvaldo A. & Craig, Hugh & Moscato, Pablo, 2009. "Shakespeare and other English Renaissance authors as characterized by Information Theory complexity quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(6), pages 916-926.
    8. Power, Gabriel J. & Turvey, Calum G., 2010. "Long-range dependence in the volatility of commodity futures prices: Wavelet-based evidence," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(1), pages 79-90.
    9. He, Ling-Yun & Chen, Shu-Peng, 2010. "Are developed and emerging agricultural futures markets multifractal? A comparative perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3828-3836.
    10. B. M. Tabak & T. R. Serra & D. O. Cajueiro, 2010. "Topological properties of commodities networks," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 74(2), pages 243-249, March.
    11. Eom, Cheoljun & Choi, Sunghoon & Oh, Gabjin & Jung, Woo-Sung, 2008. "Hurst exponent and prediction based on weak-form efficient market hypothesis of stock markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(18), pages 4630-4636.
    12. Bentes, Sónia R. & Menezes, Rui & Mendes, Diana A., 2008. "Long memory and volatility clustering: Is the empirical evidence consistent across stock markets?," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(15), pages 3826-3830.
    13. Zunino, Luciano & Zanin, Massimiliano & Tabak, Benjamin M. & Pérez, Darío G. & Rosso, Osvaldo A., 2010. "Complexity-entropy causality plane: A useful approach to quantify the stock market inefficiency," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(9), pages 1891-1901.
    14. Barunik, Jozef & Kristoufek, Ladislav, 2010. "On Hurst exponent estimation under heavy-tailed distributions," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(18), pages 3844-3855.
    15. Calum G. Turvey & Jeffrey R. Stokes, 2008. "Market Structure and the Value of Agricultural Contingent Claims," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 56(1), pages 79-94, March.
    16. Vandewalle, N. & Ausloos, M., 1997. "Coherent and random sequences in financial fluctuations," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 246(3), pages 454-459.
    17. Chen, Shu-Peng & He, Ling-Yun, 2010. "Multifractal spectrum analysis of nonlinear dynamical mechanisms in China’s agricultural futures markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(7), pages 1434-1444.
    18. Carbone, A. & Castelli, G. & Stanley, H.E., 2004. "Time-dependent Hurst exponent in financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 344(1), pages 267-271.
    19. Arianos, Sergio & Carbone, Anna, 2007. "Detrending moving average algorithm: A closed-form approximation of the scaling law," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 382(1), pages 9-15.
    20. Cajueiro, Daniel O & Tabak, Benjamin M, 2004. "The Hurst exponent over time: testing the assertion that emerging markets are becoming more efficient," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 336(3), pages 521-537.
    21. Grech, Dariusz & Pamuła, Grzegorz, 2008. "The local Hurst exponent of the financial time series in the vicinity of crashes on the Polish stock exchange market," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(16), pages 4299-4308.
    22. Zunino, L. & Pérez, D.G. & Kowalski, A. & Martín, M.T. & Garavaglia, M. & Plastino, A. & Rosso, O.A., 2008. "Fractional Brownian motion, fractional Gaussian noise, and Tsallis permutation entropy," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(24), pages 6057-6068.
    23. Bassler, Kevin E. & Gunaratne, Gemunu H. & McCauley, Joseph L., 2006. "Markov processes, Hurst exponents, and nonlinear diffusion equations: With application to finance," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 343-353.
    24. 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.
    25. Di Matteo, T. & Aste, T. & Dacorogna, M.M., 2003. "Scaling behaviors in differently developed markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 324(1), pages 183-188.
    26. Fama, Eugene F, 1970. "Efficient Capital Markets: A Review of Theory and Empirical Work," Journal of Finance, American Finance Association, vol. 25(2), pages 383-417, May.
    27. L. Zunino & B. M. Tabak & D. G. Pérez & M. Garavaglia & O. A. Rosso, 2007. "Inefficiency in Latin-American market indices," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 60(1), pages 111-121, November.
    28. Carbone, Anna & Stanley, H. Eugene, 2007. "Scaling properties and entropy of long-range correlated time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 384(1), pages 21-24.
    29. 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..
    30. Keller, K. & Sinn, M., 2005. "Ordinal analysis of time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 356(1), pages 114-120.
    31. Coeurjolly, Jean-Francois, 2000. "Simulation and identification of the fractional Brownian motion: a bibliographical and comparative study," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 5(i07).
    32. Kevin E. Bassler & Gemunu H. Gunaratne & Joseph L. McCauley, 2006. "Markov Processes, Hurst Exponents, and Nonlinear Diffusion Equations with application to finance," Papers cond-mat/0602316, arXiv.org.
    33. Serinaldi, Francesco, 2010. "Use and misuse of some Hurst parameter estimators applied to stationary and non-stationary financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2770-2781.
    34. Lamberti, P.W & Martin, M.T & Plastino, A & Rosso, O.A, 2004. "Intensive entropic non-triviality measure," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 334(1), pages 119-131.
    35. Siqueira, Erinaldo Leite & Stošić, Tatijana & Bejan, Lucian & Stošić, Borko, 2010. "Correlations and cross-correlations in the Brazilian agrarian commodities and stocks," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(14), pages 2739-2743.
    36. Eom, Cheoljun & Oh, Gabjin & Jung, Woo-Sung, 2008. "Relationship between efficiency and predictability in stock price change," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(22), pages 5511-5517.
    37. Sieczka, Paweł & Hołyst, Janusz A., 2009. "Correlations in commodity markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1621-1630.
    38. Martin, M.T. & Plastino, A. & Rosso, O.A., 2006. "Generalized statistical complexity measures: Geometrical and analytical properties," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 369(2), pages 439-462.
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