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

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      <subfield code="a">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</subfield>
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