Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
5076909 | Insurance: Mathematics and Economics | 2012 | 10 Pages |
This paper analyzes whether the skew-normal and skew-student distributions recently discussed in the finance literature are reasonable models for describing claims in property-liability insurance. We consider two well-known datasets from actuarial science and fit a number of parametric distributions to these data. Also the non-parametric transformation kernel approach is considered as a benchmark model. We find that the skew-normal and skew-student are reasonably competitive compared to other models in the literature when describing insurance data. In addition to goodness-of-fit tests, tail risk measures such as value at risk and tail value at risk are estimated for the datasets under consideration.
⺠The skew-normal and skew-student distributions are recently discussed in finance literature. ⺠Are these reasonable models for describing claims in property-liability insurance? ⺠We empirically apply these two distributions to two well known datasets. ⺠We consider various goodness of fit tests and estimate tail risk measures. ⺠Result: the two models are reasonably good compared to other models used in literature.