Deep Joint Learning valuation of Bermudan Swaptions

Francisco G\'omez Casanova, \'Alvaro Leitao, Fernando de Lope Contreras and Carlos V\'azquez

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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.

Date: 2024-04
New Economics Papers: this item is included in nep-big and nep-cmp
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