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Estimation of the Hurst exponent from noisy data: a Bayesian approach

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Abstract

We consider a model based on the fractional Brownian motion under the influence of noise. We implement the Bayesian approach to estimate the Hurst exponent of the model. The robustness of the method to the noise intensity is tested using artificial data from fractional Brownian motion. We show that estimation of the parameters achieved when noise is considered explicitly in the model. Moreover, we identify the corresponding noise-amplitude level that allow to receive the correct estimation of the Hurst exponents in various cases.

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Authors and Affiliations

  1. Interdisciplinary Center for Dynamics of Complex Systems, University of Potsdam, Karl-Liebknecht-Str. 24, 14476, Potsdam, Germany

    N. Makarava & M. Holschneider

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  1. N. Makarava
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  2. M. Holschneider
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Correspondence to N. Makarava.

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Makarava, N., Holschneider, M. Estimation of the Hurst exponent from noisy data: a Bayesian approach. Eur. Phys. J. B 85, 272 (2012). https://doi.org/10.1140/epjb/e2012-30221-1

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