Deep Joint Learning valuation of Bermudan Swaptions

Francisco Gómez Casanova; Álvaro Leitao; Fernando de Lope Contreras; Carlos Vázquez

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arXiv · 2404.11257

Deep Joint Learning valuation of Bermudan Swaptions

Francisco Gómez Casanova·Álvaro Leitao·Fernando de Lope Contreras·Carlos Vázquez

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Abstract

This paper addresses the problem of pricing involved financial derivatives by means of advanced of deep learning techniques. More precisely, we smartly combine several sophisticated neural network-based concepts like differential machine learning, Monte Carlo simulation-like training samples and joint learning to come up with an efficient numerical solution. The application of the latter development represents a novelty in the context of computational finance. We also propose a novel design of interdependent neural networks to price early-exercise products, in this case, Bermudan swaptions. The improvements in efficiency and accuracy provided by the here proposed approach is widely illustrated throughout a range of numerical experiments. Moreover, this novel methodology can be extended to the pricing of other financial derivatives.

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BibTeX RIS

Francisco Gómez Casanova, Álvaro Leitao, Fernando de Lope Contreras, Carlos Vázquez. 2024-04-17. Deep Joint Learning valuation of Bermudan Swaptions. https://arxiv.org/abs/2404.11257

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