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Title:

Efficient Bayesian inference for stochastic time-varying copula models

Document type:
Zeitschriftenaufsatz
Author(s):
Almeida, C. and Czado, C.
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 such as...     »
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:
6
Pages contribution:
1511–1527
Reviewed:
ja
Language:
en
Fulltext / DOI:
doi:http://dx.doi.org/10.1016/j.csda.2011.08.015
WWW:
http://www.sciencedirect.com/science/article/pii/S0167947311003148
Status:
Verlagsversion / published
Semester:
SS 10
TUM Institution:
Lehrstuhl für Mathematische Statistik
Format:
Text
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