 Multivariate Modelling Using CopulasGuangyuan Gao ()
Chapter Chapter 6 in Bayesian Claims Reserving Methods in Non-life Insurance with Stan, 2018, pp 153-183 from Springer Abstract: Abstract Copulas are a family of multivariate distributions whose marginal distributions are uniform. At the end of reserving problems, we need to aggregate the outstanding liability distribution of each line of business or each type of benefit to get the total outstanding liability distribution. The dependence between them must be considered. In the Bayesian copulas framework, all the uncertainties and correlations are considered during the inferential process which is an advantage compared with the likelihood-based frequentist inference. In Sect. 6.1, the elements of copulas are reviewed, including Sklar’s theorem, parametric copulas, inference methods, etc. In Sect. 6.2, we discuss the usefulness of copulas in risk modelling generally. The copula is used to model the empirical dependence between risks while the marginal regression model is used to model the structural dependence. In Sect. 6.3, a bivariate Gaussian copula is used to aggregate the liabilities of the doctor benefit and the hospital benefit in WorkSafe Victoria. These two benefits are correlated positively even after removing the structural effects of the development periods. Date: 2018 There are no downloads for this item, see the EconPapers FAQ for hints about obtaining it. Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-981-13-3609-6_6 Ordering information: This item can be ordered from DOI: 10.1007/978-981-13-3609-6_6 Access Statistics for this chapter More chapters in Springer Books from Springer |
Is your work missing from RePEc? Here is how to contribute.
Questions or problems? Check the EconPapers FAQ or send mail to .
EconPapers is hosted by the
School of Business at Örebro University.