Power-law cross-correlations estimation under heavy tails

Result code in 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>

Alternative codes found

RIV/00216208:11230/16:10324303

Result on the web

<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>

Result language

angličtina

Original language name

Power-law cross-correlations estimation under heavy tails

Original language description

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.

Czech name

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Czech description

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Type

J_x - Unclassified - Peer-reviewed scientific article (Jimp, Jsc and Jost)

CEP classification

AH - Economics

OECD FORD branch

—

Project

<a href="/en/project/GP14-11402P" target="_blank" >GP14-11402P: Bivariate long memory analysis of financial time series</a><br>

Continuities

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Publication year

2016

Confidentiality

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

Name of the periodical

Communications in Nonlinear Science and Numerical Simulation

ISSN

1007-5704

e-ISSN

—

Volume of the periodical

40

Issue of the periodical within the volume

1

Country of publishing house

NL - THE KINGDOM OF THE NETHERLANDS

Number of pages

10

Pages from-to

163-172

UT code for WoS article

000377294100015

EID of the result in the Scopus database

2-s2.0-84964868782