Machine learning methods for American-style path-dependent contracts

Matteo Gambara, Giulia Livieri and Andrea Pallavicini

Papers from arXiv.org

Abstract: In the present work, we introduce and compare state-of-the-art algorithms, that are now classified under the name of machine learning, to price Asian and look-back products with early-termination features. These include randomized feed-forward neural networks, randomized recurrent neural networks, and a novel method based on signatures of the underlying price process. Additionally, we explore potential applications on callable certificates. Furthermore, we present an innovative approach for calculating sensitivities, specifically Delta and Gamma, leveraging Chebyshev interpolation techniques.

Date: 2023-11
New Economics Papers: this item is included in nep-big and nep-cmp
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