MEASURING SPATIAL RISK USING CONDITIONAL AUTOREGRESSIVE (CAR) MODEL
Premium charges happen to be at the centre of every insurance business. The amount of premium charged will determine if the company will be in or out of business. Claim frequency and claim severity are the two major components in determining the premium rate. Generalized Linear Models (GLM) have...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/54885 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Premium charges happen to be at the centre of every insurance business. The
amount of premium charged will determine if the company will be in or out of
business. Claim frequency and claim severity are the two major components in
determining the premium rate. Generalized Linear Models (GLM) have been used
in traditional insurance to estimate the claim frequency. GLM have shortcomings
that make the insurance estimates inaccurate. By applying the Generalized Linear
Mixed Model (GLMM) to model claim frequency, we have shown that it possible
to model frequency using GLMM. The GLMM model has a spatial random effect
which is modelled using the Conditional Autoregressive (CAR) model which is
multivariate normal therefore gives room for having both negative and positive
values for the spatial effects. The spatial effects for claim frequency were
determined by using CAR model. The unknown parameters were estimated using
R packages. Based on the estimates, the claim frequency and severity are simulated
and applied to calculate pure premium for COVID-19. The simulation for claim
frequency with spatial random effects had better pure premium estimate compared
to the claim frequency without spatial effects. |
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