Soft splicing model: bridging the gap between composite model and finite mixture model
Tsz Chai Fung,
Himchan Jeong and
George Tzougas
Scandinavian Actuarial Journal, 2024, vol. 2024, issue 2, 168-197
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.
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:taf:sactxx:v:2024:y:2024:i:2:p:168-197
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DOI: 10.1080/03461238.2023.2234914
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