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On the relationship between the Hurst exponent, the ratio of the mean square successive difference to the variance, and the number of turning points

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  • Tarnopolski, Mariusz

Abstract

The long range dependence of the fractional Brownian motion (fBm), fractional Gaussian noise (fGn), and differentiated fGn (DfGn) is described by the Hurst exponent H. Considering the realizations of these three processes as time series, they might be described by their statistical features, such as half of the ratio of the mean square successive difference to the variance, A, and the number of turning points, T. This paper investigates the relationships between A and H, and between T and H. It is found numerically that the formulae A(H)=aebH in case of fBm, and A(H)=a+bHc for fGn and DfGn, describe well the A(H) relationship. When T(H) is considered, no simple formula is found, and it is empirically found that among polynomials, the fourth and second order description applies best. The most relevant finding is that when plotted in the space of (A,T), the three process types form separate branches. Hence, it is examined whether A and T may serve as Hurst exponent indicators. Some real world data (stock market indices, sunspot numbers, chaotic time series) are analyzed for this purpose, and it is found that the H’s estimated using the H(A) relations (expressed as inverted A(H) functions) are consistent with the H’s extracted with the well known wavelet approach. This allows to efficiently estimate the Hurst exponent based on fast and easy to compute A and T, given that the process type: fBm, fGn or DfGn, is correctly classified beforehand. Finally, it is suggested that the A(H) relation for fGn and DfGn might be an exact (shifted) 3/2 power-law.

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  • Tarnopolski, Mariusz, 2016. "On the relationship between the Hurst exponent, the ratio of the mean square successive difference to the variance, and the number of turning points," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 461(C), pages 662-673.
    • Handle: RePEc:eee:phsmap:v:461:y:2016:i:c:p:662-673
    DOI: 10.1016/j.physa.2016.06.004
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    References listed on IDEAS

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    1. 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.
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    1. Tarnopolski, Mariusz, 2018. "Correlation between the Hurst exponent and the maximal Lyapunov exponent: Examining some low-dimensional conservative maps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 490(C), pages 834-844.
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    3. Mateos, Diego M. & Zozor, Steeve & Olivares, Felipe, 2020. "Contrasting stochasticity with chaos in a permutation Lempel–Ziv complexity — Shannon entropy plane," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 554(C).
    4. Mihailović, Dragutin T. & Nikolić-Đorić, Emilija & Arsenić, Ilija & Malinović-Milićević, Slavica & Singh, Vijay P. & Stošić, Tatijana & Stošić, Borko, 2019. "Analysis of daily streamflow complexity by Kolmogorov measures and Lyapunov exponent," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 525(C), pages 290-303.
    5. Gaël Kermarrec, 2020. "On Estimating the Hurst Parameter from Least-Squares Residuals. Case Study: Correlated Terrestrial Laser Scanner Range Noise," Mathematics, MDPI, vol. 8(5), pages 1-23, April.
    6. David, S.A. & Inácio, C.M.C. & Quintino, D.D. & Machado, J.A.T., 2020. "Measuring the Brazilian ethanol and gasoline market efficiency using DFA-Hurst and fractal dimension," Energy Economics, Elsevier, vol. 85(C).
    7. Mariusz Tarnopolski, 2017. "Modeling the price of Bitcoin with geometric fractional Brownian motion: a Monte Carlo approach," Papers 1707.03746, arXiv.org, revised Aug 2017.

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