About: On Hurst exponent estimation under heavy-tailed distributions     Goto   Sponge   NotDistinct   Permalink

An Entity of Type : http://linked.opendata.cz/ontology/domain/vavai/Vysledek, within Data Space : linked.opendata.cz associated with source document(s)

AttributesValues
rdf:type
Description
  • 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.
  • 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. (en)
Title
  • On Hurst exponent estimation under heavy-tailed distributions
  • On Hurst exponent estimation under heavy-tailed distributions (en)
skos:prefLabel
  • On Hurst exponent estimation under heavy-tailed distributions
  • On Hurst exponent estimation under heavy-tailed distributions (en)
skos:notation
  • RIV/67985556:_____/10:00343525!RIV11-GA0-67985556
http://linked.open...avai/riv/aktivita
http://linked.open...avai/riv/aktivity
  • P(GA402/09/0965), S, Z(AV0Z10750506), Z(MSM0021620841)
http://linked.open...iv/cisloPeriodika
  • 18
http://linked.open...vai/riv/dodaniDat
http://linked.open...aciTvurceVysledku
http://linked.open.../riv/druhVysledku
http://linked.open...iv/duvernostUdaju
http://linked.open...titaPredkladatele
http://linked.open...dnocenehoVysledku
  • 276726
http://linked.open...ai/riv/idVysledku
  • RIV/67985556:_____/10:00343525
http://linked.open...riv/jazykVysledku
http://linked.open.../riv/klicovaSlova
  • high frequency data analysis; heavy tails; Hurst exponent (en)
http://linked.open.../riv/klicoveSlovo
http://linked.open...odStatuVydavatele
  • NL - Nizozemsko
http://linked.open...ontrolniKodProRIV
  • [1CBA93B7CD18]
http://linked.open...i/riv/nazevZdroje
  • Physica. A : Statistical Mechanics and its Applications
http://linked.open...in/vavai/riv/obor
http://linked.open...ichTvurcuVysledku
http://linked.open...cetTvurcuVysledku
http://linked.open...vavai/riv/projekt
http://linked.open...UplatneniVysledku
http://linked.open...v/svazekPeriodika
  • 389
http://linked.open...iv/tvurceVysledku
  • Krištoufek, Ladislav
  • Baruník, Jozef
http://linked.open...ain/vavai/riv/wos
  • 000280385600015
http://linked.open...n/vavai/riv/zamer
issn
  • 0378-4371
number of pages
is http://linked.open...avai/riv/vysledek of
Faceted Search & Find service v1.16.118 as of Jun 21 2024


Alternative Linked Data Documents: ODE     Content Formats:   [cxml] [csv]     RDF   [text] [turtle] [ld+json] [rdf+json] [rdf+xml]     ODATA   [atom+xml] [odata+json]     Microdata   [microdata+json] [html]    About   
This material is Open Knowledge   W3C Semantic Web Technology [RDF Data] Valid XHTML + RDFa
OpenLink Virtuoso version 07.20.3240 as of Jun 21 2024, on Linux (x86_64-pc-linux-gnu), Single-Server Edition (126 GB total memory, 58 GB memory in use)
Data on this page belongs to its respective rights holders.
Virtuoso Faceted Browser Copyright © 2009-2024 OpenLink Software