COPULA DOUBLE EXPONENSIAL UNTUK PREDIKSI MODEL KERUGIAN AGGREGATE
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...
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| Main Authors: | , |
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| 格式: | Theses and Dissertations NonPeerReviewed |
| 出版: |
[Yogyakarta] : Universitas Gadjah Mada
2014
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| 主題: | |
| 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 |
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| 機構: | Universitas Gadjah Mada |
| 總結: | 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. |
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