Option pricing mechanisms driven by backward stochastic differential equations
Abstract This study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning. We adopted a data-driven approach to find a market-appropriate generator of the backward stochastic differential equation, which is achieved by...
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| Format: | Article |
| Language: | English |
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SpringerOpen
2025-04-01
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| Series: | Financial Innovation |
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| Online Access: | https://doi.org/10.1186/s40854-024-00714-3 |
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| _version_ | 1850153520788930560 |
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| author | Yufeng Shi Bin Teng Sicong Wang |
| author_facet | Yufeng Shi Bin Teng Sicong Wang |
| author_sort | Yufeng Shi |
| collection | DOAJ |
| description | Abstract This study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning. We adopted a data-driven approach to find a market-appropriate generator of the backward stochastic differential equation, which is achieved by leveraging the universal approximation capabilities of neural networks. Option pricing, which is the solution to the equation, is approximated using a recursive procedure. The empirical results for the S&P 500 index options show that the proposed deep learning g-pricing model has lower absolute errors than the classical Black–Scholes–Merton model for the same forward stochastic differential equations. The g-pricing mechanism has potential applications in option pricing. |
| format | Article |
| id | doaj-art-be331292e0454844b353b659caede3b6 |
| institution | OA Journals |
| issn | 2199-4730 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | SpringerOpen |
| record_format | Article |
| series | Financial Innovation |
| spelling | doaj-art-be331292e0454844b353b659caede3b62025-08-20T02:25:41ZengSpringerOpenFinancial Innovation2199-47302025-04-0111111910.1186/s40854-024-00714-3Option pricing mechanisms driven by backward stochastic differential equationsYufeng Shi0Bin Teng1Sicong Wang2Institute for Financial Studies and School of Mathematics, Shandong UniversityInstitute for Financial Studies and School of Mathematics, Shandong UniversityInstitute for Financial Studies and School of Mathematics, Shandong UniversityAbstract This study investigates an option pricing method called g-pricing based on backward stochastic differential equations combined with deep learning. We adopted a data-driven approach to find a market-appropriate generator of the backward stochastic differential equation, which is achieved by leveraging the universal approximation capabilities of neural networks. Option pricing, which is the solution to the equation, is approximated using a recursive procedure. The empirical results for the S&P 500 index options show that the proposed deep learning g-pricing model has lower absolute errors than the classical Black–Scholes–Merton model for the same forward stochastic differential equations. The g-pricing mechanism has potential applications in option pricing.https://doi.org/10.1186/s40854-024-00714-3Option pricingBackward stochastic differential equationNumerical methodDeep learning |
| spellingShingle | Yufeng Shi Bin Teng Sicong Wang Option pricing mechanisms driven by backward stochastic differential equations Financial Innovation Option pricing Backward stochastic differential equation Numerical method Deep learning |
| title | Option pricing mechanisms driven by backward stochastic differential equations |
| title_full | Option pricing mechanisms driven by backward stochastic differential equations |
| title_fullStr | Option pricing mechanisms driven by backward stochastic differential equations |
| title_full_unstemmed | Option pricing mechanisms driven by backward stochastic differential equations |
| title_short | Option pricing mechanisms driven by backward stochastic differential equations |
| title_sort | option pricing mechanisms driven by backward stochastic differential equations |
| topic | Option pricing Backward stochastic differential equation Numerical method Deep learning |
| url | https://doi.org/10.1186/s40854-024-00714-3 |
| work_keys_str_mv | AT yufengshi optionpricingmechanismsdrivenbybackwardstochasticdifferentialequations AT binteng optionpricingmechanismsdrivenbybackwardstochasticdifferentialequations AT sicongwang optionpricingmechanismsdrivenbybackwardstochasticdifferentialequations |