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On Hurst exponent estimation under heavy-tailed distributions
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SYSNO ASEP 0343525 Document Type J - Journal Article R&D Document Type Journal Article Subsidiary J Článek ve WOS Title On Hurst exponent estimation under heavy-tailed distributions Author(s) Baruník, Jozef (UTIA-B) RID, ORCID
Krištoufek, Ladislav (UTIA-B) RID, ORCIDSource Title Physica. A : Statistical Mechanics and its Applications. - : Elsevier - ISSN 0378-4371
Roč. 389, č. 18 (2010), s. 3844-3855Number of pages 20 s. Publication form www - www Language eng - English Country NL - Netherlands Keywords high frequency data analysis ; heavy tails ; Hurst exponent Subject RIV AH - Economics R&D Projects GA402/09/0965 GA ČR - Czech Science Foundation (CSF) CEZ AV0Z10750506 - UTIA-B (2005-2011) UT WOS 000280385600015 EID SCOPUS 77955303263 DOI 10.1016/j.physa.2010.05.025 Annotation In this paper, we show how the sampling properties of Hurst exponent methods of estimation change with the presence of heavy tails. We run extensive Monte Carlo simulations to find out how rescaled range anal- ysis (R/S), multifractal detrended fluctuation analysis (MF − DFA), detrending moving average (DMA) and generalized Hurst exponent ap- proach (GHE) estimate Hurst exponent on independent series with dif- ferent heavy tails. For this purpose, we generate independent random series from stable distribution with stability exponent α changing from 1.1 (heaviest tails) to 2 (Gaussian normal distribution) and we estimate Hurst exponent using the different methods. R/S and GHE prove to be robust to heavy tails in the underlying process. GHE provides the low- est variance and bias in comparison to the other methods regardless the presence of heavy tails in data and sample size. Workplace Institute of Information Theory and Automation Contact Markéta Votavová, votavova@utia.cas.cz, Tel.: 266 052 201. Year of Publishing 2011
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