Deep joint learning valuation of Bermudan swaptions

Abstract

[Abstract]: This paper addresses the problem of pricing involved financial derivatives by means of advanced deep learning techniques. More precisely, we methodically integrate 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 approach proposed here are widely illustrated throughout a range of numerical experiments. Moreover, this novel methodology can be extended to the pricing of other financial derivatives.