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An information theory perspective on the informational efficiency of gold price

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  • Bariviera, Aurelio F.
  • Font-Ferrer, Alejandro
  • Sorrosal-Forradellas, M. Teresa
  • Rosso, Osvaldo A.

Abstract

This paper studies the informational efficiency of the gold market, and its variability due to economic distress situations. The period under study goes from 1968 until 2017. We use quantifiers derived from Information Theory in order to analyze the stochastic dynamics of gold price. In particular, we use permutation entropy, permutation statistical complexity and Fisher Information Measure, to assess the time varying dynamics of price time series. We find that the stochastic regime in the time series exhibits three distinct dynamics, roughly divided in years 1968–1981, 1981–2003, 2003–2017. Additionally, informational efficiency is affected by major economic and political events. Finally, we detect a strong persistence in volatility.

Suggested Citation

  • Bariviera, Aurelio F. & Font-Ferrer, Alejandro & Sorrosal-Forradellas, M. Teresa & Rosso, Osvaldo A., 2019. "An information theory perspective on the informational efficiency of gold price," The North American Journal of Economics and Finance, Elsevier, vol. 50(C).
    • Handle: RePEc:eee:ecofin:v:50:y:2019:i:c:s1062940818304534
    DOI: 10.1016/j.najef.2019.101018
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    as
    1. Dirk G. Baur & Brian M. Lucey, 2010. "Is Gold a Hedge or a Safe Haven? An Analysis of Stocks, Bonds and Gold," The Financial Review, Eastern Finance Association, vol. 45(2), pages 217-229, May.
    2. Hoang, Thi Hong Van & Lahiani, Amine & Heller, David, 2016. "Is gold a hedge against inflation? New evidence from a nonlinear ARDL approach," Economic Modelling, Elsevier, vol. 54(C), pages 54-66.
    3. Białkowski, Jędrzej & Bohl, Martin T. & Stephan, Patrick M. & Wisniewski, Tomasz P., 2015. "The gold price in times of crisis," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 329-339.
    4. Bariviera, Aurelio F. & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2016. "Libor at crossroads: Stochastic switching detection using information theory quantifiers," Chaos, Solitons & Fractals, Elsevier, vol. 88(C), pages 172-182.
    5. Andrew W. Lo, A. Craig MacKinlay, 1988. "Stock Market Prices do not Follow Random Walks: Evidence from a Simple Specification Test," The Review of Financial Studies, Society for Financial Studies, vol. 1(1), pages 41-66.
    6. Virginie Coudert & Hélène Raymond-Feingold, 2011. "Gold and financial assets: Are there any safe havens in bear markets?," Economics Bulletin, AccessEcon, vol. 31(2), pages 1613-1622.
    7. Aurelio F. Bariviera & M. Belen Guercio & Lisana B. Martinez & Osvaldo A. Rosso, 2015. "A permutation Information Theory tour through different interest rate maturities: the Libor case," Papers 1509.00217, arXiv.org.
    8. Dieter Hess & He Huang & Alexandra Niessen, 2008. "How do commodity futures respond to macroeconomic news?," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 22(2), pages 127-146, June.
    9. J. Doyne Farmer & Andrew W. Lo, 1999. "Frontiers of Finance: Evolution and Efficient Markets," Working Papers 99-06-039, Santa Fe Institute.
    10. Hiroyuki Kawakatsu & Matthew R. Morey, 1999. "An Empirical Examination Of Financial Liberalization And The Efficiency Of Emerging Market Stock Prices," Journal of Financial Research, Southern Finance Association;Southwestern Finance Association, vol. 22(4), pages 385-411, December.
    11. Baur, Dirk G. & McDermott, Thomas K., 2010. "Is gold a safe haven? International evidence," Journal of Banking & Finance, Elsevier, vol. 34(8), pages 1886-1898, August.
    12. Beckmann, Joscha & Czudaj, Robert, 2013. "Gold as an inflation hedge in a time-varying coefficient framework," The North American Journal of Economics and Finance, Elsevier, vol. 24(C), pages 208-222.
    13. Thi Hong Van Hoang & Amine Lahiani & David Heller, 2016. "Is gold a hedge against inflation? New evidence from a nonlinear ARDL approach," Post-Print hal-02012307, HAL.
    14. Ito, Mikio & Sugiyama, Shunsuke, 2009. "Measuring the degree of time varying market inefficiency," Economics Letters, Elsevier, vol. 103(1), pages 62-64, April.
    15. Cajueiro, Daniel O. & Gogas, Periklis & Tabak, Benjamin M., 2009. "Does financial market liberalization increase the degree of market efficiency? The case of the Athens stock exchange," International Review of Financial Analysis, Elsevier, vol. 18(1-2), pages 50-57, March.
    16. Dipak Ghosh & Eric Levin & Robert E Wright & The Centre for Economic Policy Research, "undated". "Gold as an Inflation Hedge?," Working Papers Series 96/10, University of Stirling, Division of Economics.
    17. Wright, Jonathan H, 2000. "Alternative Variance-Ratio Tests Using Ranks and Signs," Journal of Business & Economic Statistics, American Statistical Association, vol. 18(1), pages 1-9, January.
    18. R. Cont, 2001. "Empirical properties of asset returns: stylized facts and statistical issues," Quantitative Finance, Taylor & Francis Journals, vol. 1(2), pages 223-236.
    19. 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.
    20. Gallais-Hamonno, Georges & Hoang, Thi-Hong-Van & Oosterlinck, Kim, 2015. "Informational efficiency of the clandestine and official gold markets in Paris," Economics Letters, Elsevier, vol. 126(C), pages 28-30.
    21. Solt, Michael E & Swanson, Paul J, 1981. "On the Efficiency of the Markets for Gold and Silver," The Journal of Business, University of Chicago Press, vol. 54(3), pages 453-478, July.
    22. Kim, Jae H. & Shamsuddin, Abul & Lim, Kian-Ping, 2011. "Stock return predictability and the adaptive markets hypothesis: Evidence from century-long U.S. data," Journal of Empirical Finance, Elsevier, vol. 18(5), pages 868-879.
    23. Aurelio Fernández Bariviera & Luciano Zunino & María Belén Guercio & Lisana Martinez & Osvaldo Rosso, 2013. "Revisiting the European sovereign bonds with a permutation-information-theory approach," The European Physical Journal B: Condensed Matter and Complex Systems, Springer;EDP Sciences, vol. 86(12), pages 1-10, December.
    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. Cheung, Yin-Wong & Chinn, Menzie David, 2001. "Currency traders and exchange rate dynamics: a survey of the US market," Journal of International Money and Finance, Elsevier, vol. 20(4), pages 439-471, August.
    26. Stosic, Darko & Stosic, Dusan & Ludermir, Teresa B. & Stosic, Tatijana, 2019. "Exploring disorder and complexity in the cryptocurrency space," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 548-556.
    27. Aye, Goodness C. & Chang, Tsangyao & Gupta, Rangan, 2016. "Is gold an inflation-hedge? Evidence from an interrupted Markov-switching cointegration model," Resources Policy, Elsevier, vol. 48(C), pages 77-84.
    28. 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.
    29. Nicolau, Mihaela & Palomba, Giulio, 2015. "Dynamic relationships between spot and futures prices. The case of energy and gold commodities," Resources Policy, Elsevier, vol. 45(C), pages 130-143.
    30. Zunino, Luciano & Fernández Bariviera, Aurelio & Guercio, M. Belén & Martinez, Lisana B. & Rosso, Osvaldo A., 2012. "On the efficiency of sovereign bond markets," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(18), pages 4342-4349.
    31. Baur, Dirk G. & Beckmann, Joscha & Czudaj, Robert, 2014. "Gold Price Forecasts in a Dynamic Model Averaging Framework – Have the Determinants Changed Over Time?," Ruhr Economic Papers 506, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    32. Jun Cai & Yan‐Leung Cheung & Michael C. S. Wong, 2001. "What moves the gold market?," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 21(3), pages 257-278, March.
    33. Charles, Amélie & Darné, Olivier & Kim, Jae H., 2015. "Will precious metals shine? A market efficiency perspective," International Review of Financial Analysis, Elsevier, vol. 41(C), pages 284-291.
    34. Baur, Dirk G. & Glover, Kristoffer J., 2015. "Speculative trading in the gold market," International Review of Financial Analysis, Elsevier, vol. 39(C), pages 63-71.
    35. Majumder, Debasish, 2012. "When the market becomes inefficient: Comparing BRIC markets with markets in the USA," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 84-92.
    36. Olivares, Felipe & Plastino, Angelo & Rosso, Osvaldo A., 2012. "Ambiguities in Bandt–Pompe’s methodology for local entropic quantifiers," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(8), pages 2518-2526.
    37. 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.
    38. Christie-David, Rohan & Chaudhry, Mukesh & Koch, Timothy W., 2000. "Do macroeconomics news releases affect gold and silver prices?," Journal of Economics and Business, Elsevier, vol. 52(5), pages 405-421.
    39. Buguk, Cumhur & Wade Brorsen, B., 2003. "Testing weak-form market efficiency: Evidence from the Istanbul Stock Exchange," International Review of Financial Analysis, Elsevier, vol. 12(5), pages 579-590.
    40. Sjaastad, Larry A., 2008. "The price of gold and the exchange rates: Once again," Resources Policy, Elsevier, vol. 33(2), pages 118-124, June.
    41. 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.
    42. Benoit B. Mandelbrot, 1972. "Statistical Methodology for Nonperiodic Cycles: From the Covariance To R/S Analysis," NBER Chapters, in: Annals of Economic and Social Measurement, Volume 1, number 3, pages 259-290, National Bureau of Economic Research, Inc.
    43. Scott W. Barnhart, 1989. "The Effects of Macroeconomic Announcements on Commodity Prices," American Journal of Agricultural Economics, Agricultural and Applied Economics Association, vol. 71(2), pages 389-403.
    44. Aurelio F. Bariviera & Luciano Zunino & Osvaldo A. Rosso, 2018. "An analysis of high-frequency cryptocurrencies prices dynamics using permutation-information-theory quantifiers," Papers 1808.01926, arXiv.org.
    45. Tschoegl, Adrian E., 1980. "Efficiency in the gold market -- a note," Journal of Banking & Finance, Elsevier, vol. 4(4), pages 371-379, December.
    46. De Micco, L. & González, C.M. & Larrondo, H.A. & Martin, M.T. & Plastino, A. & Rosso, O.A., 2008. "Randomizing nonlinear maps via symbolic dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 387(14), pages 3373-3383.
    47. 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.
    48. Brock, William & Lakonishok, Josef & LeBaron, Blake, 1992. "Simple Technical Trading Rules and the Stochastic Properties of Stock Returns," Journal of Finance, American Finance Association, vol. 47(5), pages 1731-1764, December.
    49. Margaret A. Monroe & Richard A. Cohn, 1986. "The relative efficiency of the gold and treasury bill futures markets," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 6(3), pages 477-493, September.
    50. Hess, Dieter E. & Huang, He & Niessen-Ruenzi, Alexandra, 2008. "How do commodity futures respond to macroeconomic news?," CFR Working Papers 08-03, University of Cologne, Centre for Financial Research (CFR).
    51. Aurelio F. Bariviera & Angelo Plastino & George Judge, 2018. "Spurious Seasonality Detection: A Non-Parametric Test Proposal," Econometrics, MDPI, vol. 6(1), pages 1-15, January.
    52. Carpi, Laura C. & Saco, Patricia M. & Rosso, O.A., 2010. "Missing ordinal patterns in correlated noises," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(10), pages 2020-2029.
    53. K. Ahn & D. Lee & S. Sohn & B. Yang, 2019. "Stock market uncertainty and economic fundamentals: an entropy-based approach," Quantitative Finance, Taylor & Francis Journals, vol. 19(7), pages 1151-1163, July.
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    3. Chirwa, Themba G. & Odhiambo, Nicholas M., 2020. "Determinants of gold price movements: An empirical investigation in the presence of multiple structural breaks," Resources Policy, Elsevier, vol. 69(C).
    4. Brouty, Xavier & Garcin, Matthieu, 2024. "Fractal properties, information theory, and market efficiency," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
    5. Mensi, Walid & Sensoy, Ahmet & Vo, Xuan Vinh & Kang, Sang Hoon, 2020. "Impact of COVID-19 outbreak on asymmetric multifractality of gold and oil prices," Resources Policy, Elsevier, vol. 69(C).
    6. Zhang, Guangyong & Jiang, Le & Tian, Lixin & Fu, Min, 2021. "Analysis of the gold fixing price fluctuation in different times based on the directed weighted networks," The North American Journal of Economics and Finance, Elsevier, vol. 57(C).
    7. de Araujo, Fernando Henrique Antunes & Bejan, Lucian & Stosic, Borko & Stosic, Tatijana, 2020. "An analysis of Brazilian agricultural commodities using permutation – information theory quantifiers: The influence of food crisis," Chaos, Solitons & Fractals, Elsevier, vol. 139(C).
    8. Bhatia, Madhur, 2023. "On the efficiency of the gold returns: An econometric exploration for India, USA and Brazil," Resources Policy, Elsevier, vol. 82(C).
    9. Fernandes, Leonardo H.S. & Bouri, Elie & Silva, José W.L. & Bejan, Lucian & de Araujo, Fernando H.A., 2022. "The resilience of cryptocurrency market efficiency to COVID-19 shock," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 607(C).

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    More about this item

    Keywords

    Gold; Permutation entropy; Statistical complexity; Fisher Information Measure; Economic crisis;
    All these keywords.

    JEL classification:

    • G01 - Financial Economics - - General - - - Financial Crises
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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