A review of copula models for economic time series

被引:353
|
作者
Patton, Andrew J. [1 ]
机构
[1] Duke Univ, Dept Econ, Durham, NC 27708 USA
关键词
Correlation; Inference; Multivariate models; Semiparametric estimation; Time series; DENSITY FORECAST EVALUATION; OF-FIT TESTS; EFFICIENT ESTIMATION; SEMIPARAMETRIC ESTIMATION; STATISTICAL-INFERENCE; ASYMMETRIC DEPENDENCE; ARCHIMEDEAN COPULAS; MAXIMUM-LIKELIHOOD; BAYESIAN-INFERENCE; RISK-MANAGEMENT;
D O I
10.1016/j.jmva.2012.02.021
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
This survey reviews the large and growing literature on copula-based models for economic and financial time series. Copula-based multivariate models allow the researcher to specify the models for the marginal distributions separately from the dependence structure that links these distributions to form a joint distribution. This allows for a much greater degree of flexibility in specifying and estimating the model, freeing the researcher from considering only existing multivariate distributions. The author surveys estimation and inference methods and goodness-of-fit tests for such models, as well as empirical applications of these copulas for economic and financial time series. (C) 2012 Elsevier Inc. All rights reserved.
引用
收藏
页码:4 / 18
页数:15
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