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Hurst exponents, power laws, and efficiency in the Brazilian foreign exchange market

Author

Listed:
  • Sergio Da Silva

    (Department of Economics, Federal University of Santa Catarina)

  • Annibal Figueiredo

    (Department of Physics, University of Brasilia)

  • Iram Gleria

    (Institute of Physics, Federal University of Alagoas)

  • Raul Matsushita

    (Department of Statistics, University of Brasilia)

Abstract

We find evidence of weak informational efficiency in the Brazilian daily foreign exchange market using Hurst exponents (Hurst 1951, 1955, Feder 1988), which offer an alternative (from statistical physics) to traditional econometric gauges. We show that a trend toward efficiency has been reverted since the crisis of 1999. We also find power laws (Mantegna and Stanley 2000) in means, volatilities, the Hurst exponents, autocorrelation times, and complexity indices of returns for varying time lags.

Suggested Citation

  • Sergio Da Silva & Annibal Figueiredo & Iram Gleria & Raul Matsushita, 2007. "Hurst exponents, power laws, and efficiency in the Brazilian foreign exchange market," Economics Bulletin, AccessEcon, vol. 7(1), pages 1-11.
    • Handle: RePEc:ebl:ecbull:eb-06g10032
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    References listed on IDEAS

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    1. Laurini, M. P. & Portugal, M. S., 2003. "Markov Switching Based Nonlinear Tests for Market Efficiency Using the R$/US$ Exchange Rate," Finance Lab Working Papers flwp_51, Finance Lab, Insper Instituto de Ensino e Pesquisa.
    2. Laurini, Márcio Poletti & Portugal, Marcelo Savino, 2004. "Long memory in the R$ / US$ exchange rate: A robust analysis," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 24(1), May.
    3. Gordon R. Richards, 2004. "A fractal forecasting model for financial time series," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(8), pages 586-601.
    4. repec:ebl:ecbull:v:7:y:2002:i:3:p:1-12 is not listed on IDEAS
    5. Sergio Da Silva & Raul Matsushita & Iram Gleria, 2002. "Scaling power laws in the Sao Paulo Stock Exchange," Economics Bulletin, AccessEcon, vol. 7(3), pages 1-12.
    6. Katsumi Shimotsu, 2006. "Simple (but Effective) Tests Of Long Memory Versus Structural Breaks," Working Paper 1101, Economics Department, Queen's University.
    7. Sergio Da Silva & Guilherme Moura, 2005. "Is There a Brazilian J-Curve?," Economics Bulletin, AccessEcon, vol. 6(10), pages 1-17.
    8. 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.
    9. Lo, Andrew W, 1991. "Long-Term Memory in Stock Market Prices," Econometrica, Econometric Society, vol. 59(5), pages 1279-1313, September.
    10. Francois Schmitt & Daniel Schertzer & Shaun Lovejoy, 2000. "Multifractal Fluctuations In Finance," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 3(03), pages 361-364.
    11. repec:ebl:ecbull:v:6:y:2005:i:10:p:1-17 is not listed on IDEAS
    12. Couillard, Michel & Davison, Matt, 2005. "A comment on measuring the Hurst exponent of financial time series," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 348(C), pages 404-418.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Onali, Enrico & Goddard, John, 2011. "Are European equity markets efficient? New evidence from fractal analysis," International Review of Financial Analysis, Elsevier, vol. 20(2), pages 59-67, April.
    2. Garcin, Matthieu, 2017. "Estimation of time-dependent Hurst exponents with variational smoothing and application to forecasting foreign exchange rates," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 483(C), pages 462-479.
    3. Chaker Aloui & Ben hamida Hela, 2011. "Hurst's exponent behaviour, weak-form stock market efficiency and financial liberalization: the Tunisian case," Economics Bulletin, AccessEcon, vol. 31(1), pages 830-843.
    4. Vinodh Madhavan & Rakesh Arrawatia, 2016. "Relative Efficiency of G8 Sovereign Credit Default Swaps and Bond Scrips: An Adaptive Market Hypothesis Perspective," Studies in Microeconomics, , vol. 4(2), pages 127-150, December.
    5. Horta, Paulo & Lagoa, Sérgio & Martins, Luís, 2014. "The impact of the 2008 and 2010 financial crises on the Hurst exponents of international stock markets: Implications for efficiency and contagion," International Review of Financial Analysis, Elsevier, vol. 35(C), pages 140-153.
    6. Mynhardt, H. R. & Plastun, Alex & Makarenko, Inna, 2014. "Behavior of Financial Markets Efficiency During the Financial Market Crisis: 2007-2009," MPRA Paper 58942, University Library of Munich, Germany.
    7. Jin, Xiaoye, 2017. "Time-varying return-volatility relation in international stock markets," International Review of Economics & Finance, Elsevier, vol. 51(C), pages 157-173.
    8. Lazăr, Dorina & Todea, Alexandru & Filip, Diana, 2012. "Martingale difference hypothesis and financial crisis: Empirical evidence from European emerging foreign exchange markets," Economic Systems, Elsevier, vol. 36(3), pages 338-350.
    9. Lin William Cong & Xi Li & Ke Tang & Yang Yang, 2021. "Crypto Wash Trading," Papers 2108.10984, arXiv.org.
    10. Guglielmo Maria Caporale & Alex Plastun, 2022. "Persistence in High Frequency Financial Data," CESifo Working Paper Series 10045, CESifo.
    11. Asif, Raheel & Frömmel, Michael, 2022. "Testing Long memory in exchange rates and its implications for the adaptive market hypothesis," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 593(C).
    12. Abounoori, Esmaiel & Shahrazi, Mahdi & Rasekhi, Saeed, 2012. "An investigation of Forex market efficiency based on detrended fluctuation analysis: A case study for Iran," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 391(11), pages 3170-3179.

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    More about this item

    Keywords

    econophysics;

    JEL classification:

    • G1 - Financial Economics - - General Financial Markets
    • F3 - International Economics - - International Finance

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