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Multifactor cat bond pricing using distortion operator models with recurrent neural networks

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Tag12Valor
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001  MAP20260013343
003  MAP
005  20260603180824.0
008  260428e20260420bel|||p |0|||b|eng d
040  ‎$a‎MAP‎$b‎spa‎$d‎MAP
084  ‎$a‎6
100  ‎$0‎MAPA20260008004‎$a‎Chen, Xiaowei
24510‎$a‎Multifactor cat bond pricing using distortion operator models with recurrent neural networks‎$c‎Xiaowei Chen, Jianxin Liu and Fuzhe Huang
520  ‎$a‎This article develops a unified framework for the pricing of catastrophe bonds that integrates distortion operator theory with recurrent neural networks. It introduces a peer-adjusted distortion factor that incorporates market information, investor sentiment, and reinsurance capacity. The jump-diffusionbased model outperforms the Wang transform in both explanatory and predictive terms. The RNN methodology enables more efficient and stable estimation of structural parameters. Empirical results confirm significant improvements in both the primary and secondary CAT bond markets
650 4‎$0‎MAPA20080552367‎$a‎Reaseguro
650 4‎$0‎MAPA20080615673‎$a‎Transferencia de riesgos
650 4‎$0‎MAPA20130005317‎$a‎Pérdidas máximas por siniestros
650 4‎$0‎MAPA20260001562‎$a‎Riesgos catastróficos
650 4‎$0‎MAPA20260000794‎$a‎Mercado de capitales
7001 ‎$0‎MAPA20260008011‎$a‎Liu, Jianxin
7001 ‎$0‎MAPA20260008028‎$a‎Huang, Fuzhe
7102 ‎$0‎MAPA20100017661‎$a‎International Actuarial Association
7730 ‎$w‎MAP20077000420‎$g‎20/04/2026 Volumen 56 Número 2 - abril 2026 , 23 p.‎$x‎0515-0361‎$t‎Astin bulletin‎$d‎Belgium : ASTIN and AFIR Sections of the International Actuarial Association