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

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<title>Multifactor cat bond pricing using distortion operator models with recurrent neural networks</title>
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<namePart>Liu, Jianxin</namePart>
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<namePart>Huang, Fuzhe</namePart>
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<namePart>International Actuarial Association</namePart>
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<abstract displayLabel="Summary">This 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</abstract>
<note type="statement of responsibility">Xiaowei Chen, Jianxin Liu and Fuzhe Huang</note>
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<topic>Reaseguro</topic>
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<topic>Transferencia de riesgos</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20130005317">
<topic>Pérdidas máximas por siniestros</topic>
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<subject xmlns:xlink="http://www.w3.org/1999/xlink" xlink:href="MAPA20260001562">
<topic>Riesgos catastróficos</topic>
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<topic>Mercado de capitales</topic>
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<title>Astin bulletin</title>
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<publisher>Belgium : ASTIN and AFIR Sections of the International Actuarial Association</publisher>
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<identifier type="issn">0515-0361</identifier>
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<text>20/04/2026 Volumen 56 Número 2 - abril 2026 , 23 p.</text>
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