Power-law cross-correlations estimation under heavy tails

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F16%3A00472030" target="_blank" >RIV/67985556:_____/16:00472030 - isvavai.cz</a>

Nalezeny alternativní kódy

RIV/00216208:11230/16:10324303

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.cnsns.2016.04.010" target="_blank" >http://dx.doi.org/10.1016/j.cnsns.2016.04.010</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cnsns.2016.04.010" target="_blank" >10.1016/j.cnsns.2016.04.010</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Power-law cross-correlations estimation under heavy tails

Popis výsledku v původním jazyce

We examine the performance of six estimators of the power-law cross-correlations -- the detrended cross-correlation analysis, the detrending moving-average cross-correlation analysis, the height cross-correlation analysis, the averaged periodogram estimator, the cross-periodogram estimator and the local cross-Whittle estimator -- under heavy-tailed distributions. The selection of estimators allows to separate these into the time and frequency domain estimators. By varying the characteristic exponent of the $alpha$-stable distributions which controls the tails behavior, we report several interesting findings. First, the frequency domain estimators are practically unaffected by heavy tails bias-wise. Second, the time domain estimators are upward biased for heavy tails but they have lower estimator variance than the other group for short series. Third, specific estimators are more appropriate depending on distributional properties and length of the analyzed series. In addition, we provide a discussion of implications of these results for empirical applications as well as theoretical explanations.

Název v anglickém jazyce

Power-law cross-correlations estimation under heavy tails

Popis výsledku anglicky

We examine the performance of six estimators of the power-law cross-correlations -- the detrended cross-correlation analysis, the detrending moving-average cross-correlation analysis, the height cross-correlation analysis, the averaged periodogram estimator, the cross-periodogram estimator and the local cross-Whittle estimator -- under heavy-tailed distributions. The selection of estimators allows to separate these into the time and frequency domain estimators. By varying the characteristic exponent of the $alpha$-stable distributions which controls the tails behavior, we report several interesting findings. First, the frequency domain estimators are practically unaffected by heavy tails bias-wise. Second, the time domain estimators are upward biased for heavy tails but they have lower estimator variance than the other group for short series. Third, specific estimators are more appropriate depending on distributional properties and length of the analyzed series. In addition, we provide a discussion of implications of these results for empirical applications as well as theoretical explanations.

Druh

J_x - Nezařazeno - Článek v odborném periodiku (Jimp, Jsc a Jost)

CEP obor

AH - Ekonomie

OECD FORD obor

—

Projekt

<a href="/cs/project/GP14-11402P" target="_blank" >GP14-11402P: Analýza dvoudimenzionální dlouhé paměti ve finančních časových řadách</a><br>

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Communications in Nonlinear Science and Numerical Simulation

ISSN

1007-5704

e-ISSN

—

Svazek periodika

40

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

10

Strana od-do

163-172

Kód UT WoS článku

000377294100015

EID výsledku v databázi Scopus

2-s2.0-84964868782