.png){kind=icon}
.png){kind=icon}
Return
Copulas and Temporal Dependence
.png){kind=icon}
OA
AI
DOI：10.3982/ECTA8152.png)
.png){kind=icon}
.png){kind=icon}
Share.png){kind=icon}
Save
Abstract
En 中文
An emerging literature in time series econometrics concerns the modeling of potentially nonlinear temporal dependence in stationary Markov chains using copula functions. We obtain sufficient conditions for a geometric rate of mixing in models of this kind. Geometric beta-mixing is established under a rather strong sufficient condition that rules out asymmetry and tail dependence in the copula function. Geometric -mixing is obtained under a weaker condition that permits both asymmetry and tail dependence. We verify one or both of these conditions for a range of parametric copula functions that are popular in applied work.
Keywords:
Copula
Markov chain
maximal correlation
mean square contingency
mixing
canonical correlation
tail dependence
AI Summary
Key information extracted from the uploaded paper, including a brief overview, abstract, background, key highlights, visual analysis, and future outlook.
Journal
IF:
7.1
Papers:
3.0K
Citations:
4.3W
Organization
No organization information available