APPLYING PROPORTIONAL HAZARD TRANSFORM ON REINSURANCE DATA
The distribution of reinsurance data is right skewed and often heavy tailed and.Stop loss cover reinsurance is known as one form of risk transfer that deals with dataset which have values more than a given retention. The data consists of some extreme values and are spread at the tail of the distribu...
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| Format: | Theses |
| Language: | Indonesia |
| Online Access: | https://digilib.itb.ac.id/gdl/view/16889 |
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| Institution: | Institut Teknologi Bandung |
| Language: | Indonesia |
| Summary: | The distribution of reinsurance data is right skewed and often heavy tailed and.Stop loss cover reinsurance is known as one form of risk transfer that deals with dataset which have values more than a given retention. The data consists of some extreme values and are spread at the tail of the distribution. Modeling the entire data may not capture the characteristics of those extreme values at the tail area. <br />
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This thesis attempts to discuss the implementation of peak over threshold (POT) method in modeling extreme values at the tail of a distribution. The application of proportional hazard (ph) transform in calculating risk premium of a stop loss cover reinsurance point of view is also discussed. A case study on a (secondary) dataset of fire reinsurance is conducted. It can be inferred at a 5% significance level that the tail area of the distribution may be modeled by Generalized Pareto. The risk premium amount is increased when risk aversion approaches 0. |
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