Fractal approach towards power-law coherency to measure cross-correlations between time series

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F67985556%3A_____%2F17%3A00473066" target="_blank" >RIV/67985556:_____/17:00473066 - isvavai.cz</a>

Výsledek na webu

<a href="http://dx.doi.org/10.1016/j.cnsns.2017.02.018" target="_blank" >http://dx.doi.org/10.1016/j.cnsns.2017.02.018</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1016/j.cnsns.2017.02.018" target="_blank" >10.1016/j.cnsns.2017.02.018</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Fractal approach towards power-law coherency to measure cross-correlations between time series

Popis výsledku v původním jazyce

We focus on power-law coherency as an alternative approach towards studying power law cross-correlations between simultaneously recorded time series. To be able to study empirical data, we introduce three estimators of the power-law coherency parameter Hp based on popular techniques usually utilized for studying power-law cross-correlations detrended cross-correlation analysis (DCCA), detrending moving-average cross-correlation analysis (DMCA) and height cross-correlation analysis (HXA). In the finite sample properties study, we focus on the bias, variance and mean squared error of the estimators. We find that the DMCA-based method is the safest choice among the three. The HXA method is reasonable for long time series with at least 104 observations, which can be easily attainable in some disciplines but problematic in others. The DCCA-based method does not provide favorable properties which even deteriorate with an increasing time series length. The paper opens a new venue towards studying cross-correlations between time series.

Název v anglickém jazyce

Fractal approach towards power-law coherency to measure cross-correlations between time series

Popis výsledku anglicky

We focus on power-law coherency as an alternative approach towards studying power law cross-correlations between simultaneously recorded time series. To be able to study empirical data, we introduce three estimators of the power-law coherency parameter Hp based on popular techniques usually utilized for studying power-law cross-correlations detrended cross-correlation analysis (DCCA), detrending moving-average cross-correlation analysis (DMCA) and height cross-correlation analysis (HXA). In the finite sample properties study, we focus on the bias, variance and mean squared error of the estimators. We find that the DMCA-based method is the safest choice among the three. The HXA method is reasonable for long time series with at least 104 observations, which can be easily attainable in some disciplines but problematic in others. The DCCA-based method does not provide favorable properties which even deteriorate with an increasing time series length. The paper opens a new venue towards studying cross-correlations between time series.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

50202 - Applied Economics, Econometrics

Projekt

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

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2017

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

50

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

NL - Nizozemsko

Počet stran výsledku

8

Strana od-do

193-200

Kód UT WoS článku

000399513200015

EID výsledku v databázi Scopus

2-s2.0-85014923760