Bayesian Inference for Multivariate Copulas Using Pair-Copula Constructions
AbstractWe provide a Bayesian analysis of pair-copula constructions (PCCs) (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 credible intervals. Standard errors obtained from MCMC output are compared to those obtained from a numerical Hessian matrix and bootstrapping. As applications, we consider Norwegian financial returns and Euro swap rates. Finally, we apply the Bayesian model selection approach of Congdon�(2006) to identify conditional independence, thus constructing more parsimonious PCCs. Copyright The Author 2010. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: email@example.com, Oxford University Press.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Bibliographic InfoArticle provided by Society for Financial Econometrics in its journal Journal of Financial Econometrics.
Volume (Year): 8 (2010)
Issue (Month): 4 (Fall)
Contact details of provider:
Postal: Oxford University Press, Great Clarendon Street, Oxford OX2 6DP, UK
Fax: 01865 267 985
Web page: http://jfec.oxfordjournals.org/
More information through EDIRC
You can help add them by filling out this form.
CitEc Project, subscribe to its RSS feed for this item.
- Zhiwei Shen & Martin Odening & Ostap Okhrin, 2013.
"Can expert knowledge compensate for data scarcity in crop insurance pricing?,"
SFB 649 Discussion Papers
SFB649DP2013-030, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
- Shen, Zhiwei & Odening, Martin & Okhrin, Ostap, 2013. "Can expert knowledge compensate for data scarcity in crop insurance pricing?," 2013 Annual Meeting, August 4-6, 2013, Washington, D.C. 149431, Agricultural and Applied Economics Association.
- Okhrin, Ostap & Okhrin, Yarema & Schmid, Wolfgang, 2013. "On the structure and estimation of hierarchical Archimedean copulas," Journal of Econometrics, Elsevier, vol. 173(2), pages 189-204.
- Stöber, Jakob & Joe, Harry & Czado, Claudia, 2013. "Simplified pair copula constructions—Limitations and extensions," Journal of Multivariate Analysis, Elsevier, vol. 119(C), pages 101-118.
- Gareth W. Peters & Alice X. D. Dong & Robert Kohn, 2012. "A Copula Based Bayesian Approach for Paid-Incurred Claims Models for Non-Life Insurance Reserving," Papers 1210.3849, arXiv.org, revised Dec 2012.
- Grundke, Peter & Polle, Simone, 2012. "Crisis and risk dependencies," European Journal of Operational Research, Elsevier, vol. 223(2), pages 518-528.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Oxford University Press) or (Christopher F. Baum).
If references are entirely missing, you can add them using this form.