Copulas and long memory
File(s)
Published version
OA Location
Author(s)
Ibragimov, R
Lentzas, George
Type
Journal Article
Abstract
This paper focuses on the analysis of persistence propertiesof copula-based time series. We obtain theoretical results that demonstratethat Gaussian and Eyraud-Farlie-Gumbel-Morgenstern copulas always pro-duce short memory stationary Markov processes. We further show via sim-ulations that, in finite samples, stationary Markov processes, such as thosegenerated by Clayton copulas, may exhibit a spurious long memory-like be-havior on the level of copulas, as indicated by standard methods of inferenceand estimation for long memory time series. We also discuss applicationsof copula-based Markov processes to volatility modeling and the analysisof nonlinear dependence properties of returns in real financial markets thatprovide attractive generalizations of GARCH models. Among other conclu-sions, the results in the paper indicate non-robustness of the copula-levelanalogues of standard procedures for detecting long memory on the levelof copulas and emphasize the necessity of developing alternative inferencemethods.
Date Issued
2017-12-12
Date Acceptance
2017-12-01
Citation
Probability Surveys, 2017, 14, pp.289-327
ISSN
1549-5787
Publisher
Institute of Mathematical Statistics
Start Page
289
End Page
327
Journal / Book Title
Probability Surveys
Volume
14
Copyright Statement
Copyright for all articles in Probability Surveys is CC BY 4.0 (https://creativecommons.org/licenses/by/4.0/)
Subjects
0102 Applied Mathematics
0104 Statistics
Publication Status
Published