On Hurst exponent estimation under heavy-tailed distributions

0343525 - ÚTIA 2011 RIV NL eng J - Journal Article
Baruník, Jozef - Krištoufek, Ladislav
On Hurst exponent estimation under heavy-tailed distributions.
Physica. A : Statistical Mechanics and its Applications. Roč. 389, č. 18 (2010), s. 3844-3855. ISSN 0378-4371. E-ISSN 1873-2119
R&D Projects: GA ČR GA402/09/0965
Grant - others:GA UK(CZ) 118310; GA UK(CZ) 46108
Institutional research plan: CEZ:AV0Z10750506
Keywords : high frequency data analysis * heavy tails * Hurst exponent
Subject RIV: AH - Economics
Impact factor: 1.521, year: 2010
http://library.utia.cas.cz/separaty/2010/E/barunik-0343525.pdf

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.
Permanent Link: http://hdl.handle.net/11104/0185985