Abstract
Considerations of both the heavy-tail phenomenon and multi-modality of a claim severity distribution have been challenging in the actuarial literature and practices. In this article, we develop a novel class of soft splicing models that bridges the gap between pre-existing methods for handling the issues above. The proposed method is flexible enough to incorporate tail-heaviness and multi-modality with computational efficiency and nests finite mixture models and splicing models as its special and/or limiting cases. The soft splicing model is also more robust in extrapolating the tail-heaviness of distribution subject to model contamination. According to simulation studies and real insurance claim data analyses, it is shown that the proposed soft splicing model provides superior goodness-of-fit and more accurate estimates of tail risk measures than both finite mixture and composite models.
Original language | English |
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Pages (from-to) | 168-197 |
Number of pages | 30 |
Journal | Scandinavian Actuarial Journal |
Volume | 2024 |
Issue number | 2 |
Early online date | 13 Jul 2023 |
DOIs | |
Publication status | Published - 7 Feb 2024 |
Keywords
- Claim severity modeling
- mixture models
- multi-modal distribution
- risk management
- tail-heaviness
ASJC Scopus subject areas
- Statistics and Probability
- Economics and Econometrics
- Statistics, Probability and Uncertainty