A market resilient data-driven approach to option pricing
Fuente:
arXiv
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| Autores principales: | , |
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| Formato: | Preprint |
| Publicado: |
2024
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| Materias: | |
| Acceso en línea: | |
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| _version_ | 1866910044268789760 |
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| author | Goswami, Anindya Rana, Nimit |
| author_facet | Goswami, Anindya Rana, Nimit |
| contents | In this paper, we present a data-driven ensemble approach for option price prediction whose derivation is based on the no-arbitrage theory of option pricing. Using the theoretical treatment, we derive a common representation space for achieving domain adaptation. The success of an implementation of this idea is shown using some real data. Then we report several experimental results for critically examining the performance of the derived pricing models. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2409_08205 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | A market resilient data-driven approach to option pricing Goswami, Anindya Rana, Nimit Mathematical Finance In this paper, we present a data-driven ensemble approach for option price prediction whose derivation is based on the no-arbitrage theory of option pricing. Using the theoretical treatment, we derive a common representation space for achieving domain adaptation. The success of an implementation of this idea is shown using some real data. Then we report several experimental results for critically examining the performance of the derived pricing models. |
| title | A market resilient data-driven approach to option pricing |
| topic | Mathematical Finance |
| url | https://arxiv.org/abs/2409.08205 |