0343525 20240111140740.220100830235959.9 000280385600015 77955303263 10.1016/j.physa.2010.05.025 20 s. www cav_un_epca*02574230378-437138918 (2010)3844-3855Elsevier high frequency data analysis heavy tails Hurst exponent cav_un_auth*0242028 Baruník Jozef Ekonometrie Department of Econometrics E E UTIA-B Department of Econometrics Ústav teorie informace a automatizace AV ČR, v. v. i. cav_un_auth*0256902 Krištoufek Ladislav Ekonometrie Department of Econometrics E E UTIA-B Department of Econometrics Ústav teorie informace a automatizace AV ČR, v. v. i. pdf http://library.utia.cas.cz/separaty/2010/E/barunik-0343525.pdf 118310 GA UK CZ cav_un_auth*0274537 GA402/09/0965 GA ČR cav_un_auth*0253176 46108 GA UK CZ CEZ:AV0Z10750506 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. 2011 AH 4 A 4a 20231122134044.8 http://hdl.handle.net/11104/0185985 PHYSICSMULTIDISCIPLINARY 1.467 0.382 6.7 0.0383 0.52 13244 617 87 0.881 31.56 1.74 Q2 45.662 66.875 Q3 Q2 2010 Soubory v repozitáři: barunik-0343525.pdf 77955303263 SCOPUS 000280385600015 WOS pdf cav_un_epca*0257423 Physica. A : Statistical Mechanics and its Applications 0378-4371 1873-2119 Roč. 389 č. 18 2010 3844 3855 Elsevier