COPULA DOUBLE EXPONENSIAL UNTUK PREDIKSI MODEL KERUGIAN AGGREGATE

• Main Authors: , PUJI LESTARI, , Dr. Adhitya Ronnie Effendi
• 格式: Theses and Dissertations NonPeerReviewed
• 出版: [Yogyakarta] : Universitas Gadjah Mada 2014
• 主題: ETD
• 在線閱讀: https://repository.ugm.ac.id/131831/; http://etd.ugm.ac.id/index.php?mod=penelitian_detail&sub=PenelitianDetail&act=view&typ=html&buku_id=72337

Aggregate loss model is total amount paid on all claims occurring in a fixed time period on a defined set of insurance contracts. For a model of aggregate losses, the interest is in predicting both the claims number process as well as the claims amount process. This minithesis develops predictor of aggregate losses using a longitudinal data. In longitudinal data, one encounters data from cross-section of risk classes with a history of insurance claims available for each risk class. To help explain and predict both the claims number and claims amount process we need explanatory variables. For the marginal claims distributions this minithesis uses generalized linear models (GLM). The claims number process is represented using a Poisson regression models that is conditioned on a sequence of latent variables. These latent variables drive the serial dependencies among claims number, their joint distribution is represented using an elliptical copula. This minithesis presents an illustrative example of Massachusetts automobile claims. Estimates of the latent claims process parameters are derived and simulated predictions are provided.