Multifactor cat bond pricing using distortion operator models with recurrent neural networks
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<rdf:RDF xmlns:rdf="http://www.w3.org/1999/02/22-rdf-syntax-ns#" xmlns:dc="http://purl.org/dc/elements/1.1/" xmlns:xsi="http://www.w3.org/2001/XMLSchema-instance">
<rdf:Description>
<dc:creator>Chen, Xiaowei</dc:creator>
<dc:creator>Liu, Jianxin</dc:creator>
<dc:creator>Huang, Fuzhe</dc:creator>
<dc:creator>International Actuarial Association</dc:creator>
<dc:date>2026-04-20</dc:date>
<dc:description xml:lang="es">Sumario: 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</dc:description>
<dc:identifier>https://documentacion.fundacionmapfre.org/documentacion/publico/es/bib/190607.do</dc:identifier>
<dc:language>eng</dc:language>
<dc:rights xml:lang="es">InC - http://rightsstatements.org/vocab/InC/1.0/</dc:rights>
<dc:subject xml:lang="es">Reaseguro</dc:subject>
<dc:subject xml:lang="es">Transferencia de riesgos</dc:subject>
<dc:subject xml:lang="es">Pérdidas máximas por siniestros</dc:subject>
<dc:subject xml:lang="es">Riesgos catastróficos</dc:subject>
<dc:subject xml:lang="es">Mercado de capitales</dc:subject>
<dc:type xml:lang="es">Artículos y capítulos</dc:type>
<dc:title xml:lang="es">Multifactor cat bond pricing using distortion operator models with recurrent neural networks</dc:title>
<dc:relation xml:lang="es">En: Astin bulletin. - Belgium : ASTIN and AFIR Sections of the International Actuarial Association = ISSN 0515-0361. - 20/04/2026 Volumen 56 Número 2 - abril 2026 , 23 p.</dc:relation>
</rdf:Description>
</rdf:RDF>