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Testing for Long-Range Dependence in Financial Time Series

Author

Listed:
  • Manveer Kaur Mangat

    (University of Vienna)

  • Erhard Reschenhofer

    (University of Vienna)

Abstract

Various trading strategies have been proposed that use estimates of the Hurst coefficient, which is an indicator of long-range dependence, for the calculation of buy and sell signals. This paper introduces frequency-domain tests for longrange dependence which do, in contrast to conventional procedures, not assume that the number of used periodogram ordinates grow with the length of the time series. These tests are applied to series of gold price returns and stock index returns in a rolling analysis. The results suggest that there is no long-range dependence, indicating that trading strategies based on fractal dynamics have no sound statistical basis.

Suggested Citation

  • Manveer Kaur Mangat & Erhard Reschenhofer, 2019. "Testing for Long-Range Dependence in Financial Time Series," Central European Journal of Economic Modelling and Econometrics, Central European Journal of Economic Modelling and Econometrics, vol. 11(2), pages 93-106, June.
    • Handle: RePEc:psc:journl:v:11:y:2019:i:2:p:93-106
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    References listed on IDEAS

    as
    1. Sowell, Fallaw, 1992. "Maximum likelihood estimation of stationary univariate fractionally integrated time series models," Journal of Econometrics, Elsevier, vol. 53(1-3), pages 165-188.
    2. Jonathan A. Batten & Cetin Ciner & Brian M. Lucey & Peter G. Szilagyi, 2013. "The structure of gold and silver spread returns," Quantitative Finance, Taylor & Francis Journals, vol. 13(4), pages 561-570, March.
    3. Koop, Gary & Ley, Eduardo & Osiewalski, Jacek & Steel, Mark F. J., 1997. "Bayesian analysis of long memory and persistence using ARFIMA models," Journal of Econometrics, Elsevier, vol. 76(1-2), pages 149-169.
    4. 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.
    5. Auer, Benjamin R., 2016. "On time-varying predictability of emerging stock market returns," Emerging Markets Review, Elsevier, vol. 27(C), pages 1-13.
    6. Koop, Gary & Osiewalski, Jacek & Steel, Mark F J, 1994. "Posterior Properties of Long-Run Impulse Responses," Journal of Business & Economic Statistics, American Statistical Association, vol. 12(4), pages 489-492, October.
    7. Clifford M. Hurvich & Rohit Deo & Julia Brodsky, 1998. "The mean squared error of Geweke and Porter‐Hudak's estimator of the memory parameter of a long‐memory time series," Journal of Time Series Analysis, Wiley Blackwell, vol. 19(1), pages 19-46, January.
    8. 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.
    9. C. W. J. Granger & Roselyne Joyeux, 1980. "An Introduction To Long‐Memory Time Series Models And Fractional Differencing," Journal of Time Series Analysis, Wiley Blackwell, vol. 1(1), pages 15-29, January.
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    Citations

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    Cited by:

    1. Erhard Reschenhofer & Manveer K. Mangat, 2020. "Reducing the Bias of the Smoothed Log Periodogram Regression for Financial High-Frequency Data," Econometrics, MDPI, vol. 8(4), pages 1-15, October.
    2. Erhard Reschenhofer & Thomas Stark & Manveer K. Mangat, 2020. "Robust Estimation of the Memory Parameter," Journal of Statistical and Econometric Methods, SCIENPRESS Ltd, vol. 9(4), pages 1-5.
    3. Manveer Kaur Mangat & Erhard Reschenhofer, 2020. "Frequency-Domain Evidence for Climate Change," Econometrics, MDPI, vol. 8(3), pages 1-15, July.

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    More about this item

    Keywords

    long-range dependence; fractionally integrated process; frequency domain test; Kolmogorov-Smirnov goodness-of-fit-test;
    All these keywords.

    JEL classification:

    • C12 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Hypothesis Testing: General
    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • C15 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Statistical Simulation Methods: General
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics

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