Show simple item record

dc.contributor.authoroflaz, zarina
dc.contributor.authoryozgatligil, ceylan
dc.contributor.authorsevtap, kestel
dc.date.accessioned2019-07-10T12:17:12Z
dc.date.available2019-07-10T12:17:12Z
dc.date.issued2018-11-29
dc.identifier.urihttps://hdl.handle.net/20.500.12498/1125
dc.description.abstractIn this paper, we propose an approach for modeling claim dependence, with the assumption that the claim numbers and the aggregate claim amounts are mutually and serially dependent through an underlying hidden state and can be characterized by a hidden finite state Markov chain using bivariate Hidden Markov Model (BHMM). We construct three different BHMMs, namely Poisson–Normal HMM, Poisson–Gamma HMM, and Negative Binomial–Gamma HMM, stemming from the most commonly used distributions in insurance studies. Expectation Maximization algorithm is implemented and for the maximization of the state-dependent part of log-likelihood of BHMMs, the estimates are derived analytically. To illustrate the proposed model, motor third-party liability claims in Istanbul, Turkey, are employed in the frame of Poisson–Normal HMM under a different number of states. In addition, we derive the forecast distribution, calculate state predictions, and determine the most likely sequence of states. The results indicate that the dependence under indirect factors can be captured in terms of different states, namely low, medium, and high states.en_US
dc.language.isoenen_US
dc.publisherASTIN Bulletinen_US
dc.titleAGGREGATE CLAIM ESTIMATION USING BIVARIATE HIDDEN MARKOV MODELen_US
dc.typeArticleen_US


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record