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Title:

Bayesian inference for multivariate copulas using pair-copula constructions

Document type:
Zeitschriftenaufsatz
Author(s):
Min, A.; Czado, C.
Non-TUM Co-author(s):
nein
Cooperation:
-
Abstract:
We provide a Bayesian analysis of pair-copula constructions (PCC's) (Aas et al. (2009)), which outperform many other multivariate copula constructions in modeling dependencies in financial data. We use bivariate t-copulas as building blocks in a PCC to allow extreme events in bivariate margins individually. While parameters may be estimated by maximum likelihood, confidence intervals are difficult to obtain. Consequently, we develop a Markov chain Monte Carlo (MCMC) algorithm and compute credibl...     »
Intellectual Contribution:
Discipline-based Research
Journal title:
Journal of Financial Econometrics
Year:
2010
Journal volume:
8
Journal issue:
4
Pages contribution:
511-546
Reviewed:
ja
Language:
en
Format:
Text
Judgement review:
0
Key publication:
Ja
Peer reviewed:
Ja
International:
Ja
Book review:
Nein
Commissioned:
not commissioned
Professional Journal:
Nein
Technology:
Nein
Mission statement:
;
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