Machine-learning regression methods for American-style path-dependent contracts
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arXiv
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| Hauptverfasser: | , , |
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| Format: | Preprint |
| Veröffentlicht: |
2023
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| _version_ | 1866918096603709440 |
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| author | Gambara, Matteo Livieri, Giulia Pallavicini, Andrea |
| author_facet | Gambara, Matteo Livieri, Giulia Pallavicini, Andrea |
| contents | Evaluating financial products with early-termination clauses, in particular 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2311_16762 |
| institution | arXiv |
| publishDate | 2023 |
| record_format | arxiv |
| spellingShingle | Machine-learning regression methods for American-style path-dependent contracts Gambara, Matteo Livieri, Giulia Pallavicini, Andrea Pricing of Securities Computational Finance 65C05, 91G20, 91G60 Evaluating financial products with early-termination clauses, in particular 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. |
| title | Machine-learning regression methods for American-style path-dependent contracts |
| topic | Pricing of Securities Computational Finance 65C05, 91G20, 91G60 |
| url | https://arxiv.org/abs/2311.16762 |