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Document type:
Zeitschriftenaufsatz 
Author(s):
Almeida, C. and Czado, C. 
Title:
Efficient Bayesian inference for stochastic time-varying copula models 
Abstract:
There is strong empirical evidence that dependence in multivariate financial time series varies over time. To incorporate this effect we suggest a time varying copula class, which allows for stochastic autoregressive (SCAR) copula time dependence. For this we introduce latent variables which are analytically related to Kendall’s τ , specifically we introduce latent variables that are the Fisher transformation of Kendall’s τ allowing for easy comparison of different copula families su...    »
 
Keywords:
time varying dependence, non Gaussian copulas, Kendall’s ¿ , Bayesian inference, Markov Chain Monte Carlo 
Journal title:
Computational Statistics and Data Analysis 
Year:
2012 
Journal volume:
56 
Year / month:
2012-06 
Journal issue:
Pages contribution:
1511–1527 
Reviewed:
ja 
Language:
en 
Status:
Verlagsversion / published 
Semester:
SS 10 
TUM Institution:
Lehrstuhl für Mathematische Statistik 
Format:
Text