MODEL ACA(p,q) AND VALUE-AT-RISK PREDICTION
Claim severity is a sum of money that must be paid by insurance companies to policyholder for the occurrence of disasters that cause loss to policyholder. That claim severity is potentially cause losses to the insurance company. Therefore, the claim severity must be modeled into a good forecasting m...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/26221 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | Claim severity is a sum of money that must be paid by insurance companies to policyholder for the occurrence of disasters that cause loss to policyholder. That claim severity is potentially cause losses to the insurance company. Therefore, the claim severity must be modeled into a good forecasting model. One model that is used is textit{Autoregressive Conditional Amount}(p,q) stochastic model. In addition to choosing the right model, the selection of parameter estimation method also needs to be considered. One parameter estimation methods for ACA model is Maximum Likelihood method. The method has a principle that the estimation parameter is a parameter which can maximise likelihood function. Furthermore, ACA model is used to measure the risk which is claim severity. <br />
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The risk measure that have been exploited is Value-at-Risk (VaR). VaR is minimum claim severity reserve that must be provided by insurance companies to minimize the risk. |
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