Neural Network Learning of Black-Scholes Equation for Option Pricing
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
| Veröffentlicht: |
2024
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| _version_ | 1866910440691335168 |
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| author | Santos, Daniel de Souza Ferreira, Tiago Alessandro Espinola |
| author_facet | Santos, Daniel de Souza Ferreira, Tiago Alessandro Espinola |
| contents | One of the most discussed problems in the financial world is stock option pricing. The Black-Scholes Equation is a Parabolic Partial Differential Equation which provides an option pricing model. The present work proposes an approach based on Neural Networks to solve the Black-Scholes Equations. Real-world data from the stock options market were used as the initial boundary to solve the Black-Scholes Equation. In particular, times series of call options prices of Brazilian companies Petrobras and Vale were employed. The results indicate that the network can learn to solve the Black-Sholes Equation for a specific real-world stock options time series. The experimental results showed that the Neural network option pricing based on the Black-Sholes Equation solution can reach an option pricing forecasting more accurate than the traditional Black-Sholes analytical solutions. The experimental results making it possible to use this methodology to make short-term call option price forecasts in options markets. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2405_05780 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Neural Network Learning of Black-Scholes Equation for Option Pricing Santos, Daniel de Souza Ferreira, Tiago Alessandro Espinola Machine Learning Computational Finance Pricing of Securities One of the most discussed problems in the financial world is stock option pricing. The Black-Scholes Equation is a Parabolic Partial Differential Equation which provides an option pricing model. The present work proposes an approach based on Neural Networks to solve the Black-Scholes Equations. Real-world data from the stock options market were used as the initial boundary to solve the Black-Scholes Equation. In particular, times series of call options prices of Brazilian companies Petrobras and Vale were employed. The results indicate that the network can learn to solve the Black-Sholes Equation for a specific real-world stock options time series. The experimental results showed that the Neural network option pricing based on the Black-Sholes Equation solution can reach an option pricing forecasting more accurate than the traditional Black-Sholes analytical solutions. The experimental results making it possible to use this methodology to make short-term call option price forecasts in options markets. |
| title | Neural Network Learning of Black-Scholes Equation for Option Pricing |
| topic | Machine Learning Computational Finance Pricing of Securities |
| url | https://arxiv.org/abs/2405.05780 |