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| Main Authors: | , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2402.12528 |
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| _version_ | 1866911780098277376 |
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| author | Daniluk, Andrzej Lakshtanov, Evgeny Muchorski, Rafal |
| author_facet | Daniluk, Andrzej Lakshtanov, Evgeny Muchorski, Rafal |
| contents | We present a novel technique of Monte Carlo error reduction that finds direct application in option pricing and Greeks estimation. The method is applicable to any LSV modelling framework and concerns a broad class of payoffs, including path-dependent and multi-asset cases. Most importantly, it allows to reduce the Monte Carlo error even by an order of magnitude, which is shown in several numerical examples. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2402_12528 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Denoised Monte Carlo for option pricing and Greeks estimation Daniluk, Andrzej Lakshtanov, Evgeny Muchorski, Rafal Pricing of Securities We present a novel technique of Monte Carlo error reduction that finds direct application in option pricing and Greeks estimation. The method is applicable to any LSV modelling framework and concerns a broad class of payoffs, including path-dependent and multi-asset cases. Most importantly, it allows to reduce the Monte Carlo error even by an order of magnitude, which is shown in several numerical examples. |
| title | Denoised Monte Carlo for option pricing and Greeks estimation |
| topic | Pricing of Securities |
| url | https://arxiv.org/abs/2402.12528 |