Gambara, Matteo, Livieri, Giulia 
ORCID: 0000-0002-3777-7329 and Pallavicini, Andrea
(2025)
Machine-learning regression methods for American-style path-dependent contracts.
Quantitative Finance.
ISSN 1469-7688
Abstract
Evaluating financial products with early-termination clauses, particularly those with path-dependent structures, is challenging. This paper focuses on Asian options, look-back options, and callable certificates. We will compare regression methods for pricing and computing sensitivities, highlighting modern machine learning techniques against traditional polynomial basis functions. Specifically, we will analyze randomized recurrent and feed-forward neural networks, along with a novel approach using signatures of the underlying price process. For option sensitivities like Delta and Gamma, we will incorporate Chebyshev interpolation. Our findings show that machine learning algorithms often match the accuracy and efficiency of traditional methods for Asian and look-back options, while randomized neural networks are best for callable certificates. Furthermore, we apply Chebyshev interpolation for Delta and Gamma calculations for the first time in Asian options and callable certificates.
| Item Type: | Article |
|---|---|
| Additional Information: | © 2025 Informa UK Limited, trading as Taylor & Francis Group |
| Divisions: | Statistics |
| Subjects: | Q Science > QA Mathematics |
| JEL classification: | C - Mathematical and Quantitative Methods > C6 - Mathematical Methods and Programming > C63 - Computational Techniques G - Financial Economics > G1 - General Financial Markets > G13 - Contingent Pricing; Futures Pricing |
| Date Deposited: | 01 Jul 2025 09:06 |
| Last Modified: | 01 Jul 2025 18:21 |
| URI: | http://eprints.lse.ac.uk/id/eprint/128600 |
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