Počet záznamů: 1

On Hurst exponent estimation under heavy-tailed distributions

  1. 1.
    0343525 - UTIA-B 2011 RIV NL eng J - Článek v odborném periodiku
    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
    Grant CEP: GA ČR GA402/09/0965
    Grant ostatní:GA UK(CZ) 118310; GA UK(CZ) 46108
    Výzkumný záměr: CEZ:AV0Z10750506
    Klíčová slova: high frequency data analysis * heavy tails * Hurst exponent
    Kód oboru RIV: AH - Ekonomie
    Impakt faktor: 1.521, rok: 2010
    http://library.utia.cas.cz/separaty/2010/E/barunik-0343525.pdf 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.
    Trvalý link: http://hdl.handle.net/11104/0185985