Measuring the self-similarity exponent in Lévy stable processes of financial time series

M. Fernández-Martínez, M.A. Sánchez-Granero and J.E. Trinidad Segovia
Authors registered in the RePEc Author Service: Juan Evangelista Trinidad-Segovia ()

Physica A: Statistical Mechanics and its Applications, 2013, vol. 392, issue 21, 5330-5345

Abstract: Geometric method-based procedures, which will be called GM algorithms herein, were introduced in [M.A. Sánchez Granero, J.E. Trinidad Segovia, J. García Pérez, Some comments on Hurst exponent and the long memory processes on capital markets, Phys. A 387 (2008) 5543-5551], to efficiently calculate the self-similarity exponent of a time series. In that paper, the authors showed empirically that these algorithms, based on a geometrical approach, are more accurate than the classical algorithms, especially with short length time series. The authors checked that GM algorithms are good when working with (fractional) Brownian motions. Moreover, in [J.E. Trinidad Segovia, M. Fernández-Martínez, M.A. Sánchez-Granero, A note on geometric method-based procedures to calculate the Hurst exponent, Phys. A 391 (2012) 2209-2214], a mathematical background for the validity of such procedures to estimate the self-similarity index of any random process with stationary and self-affine increments was provided. In particular, they proved theoretically that GM algorithms are also valid to explore long-memory in (fractional) Lévy stable motions.

Keywords: Hurst exponent; Financial markets; Long memory; GM algorithms; Lévy stable motion (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (3)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:392:y:2013:i:21:p:5330-5345

DOI: 10.1016/j.physa.2013.06.026

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