OptionNet: A multiscale residual deep learning model with confidence interval to predict option price
Option is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult to extract multi-scale temporal features from option data, which greatly limits their performan...
| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
KeAi Communications Co., Ltd.
2023-11-01
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| Series: | Journal of Finance and Data Science |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405918823000211 |
| _version_ | 1797656115687718912 |
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| author | Luwei Lin Meiqing Wang Hang Cheng Rong Liu Fei Chen |
| author_facet | Luwei Lin Meiqing Wang Hang Cheng Rong Liu Fei Chen |
| author_sort | Luwei Lin |
| collection | DOAJ |
| description | Option is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult to extract multi-scale temporal features from option data, which greatly limits their performance. To solve this problem, we propose a novel deep learning model named as MRC-LSTM-CI. It contains three modules, including Multi-scale Residual CNN module (MRC), Long Short-Term Memory neural network module (LSTM) and confidence interval output module (CI). The proposed model can effectively extract multi-scale features from real market option data, and make interval prediction to provide more information to the decision maker. In addition, the proposed model is further improved using the residual prediction strategy, where the output value is chosen as the residual value between BS theory price and actual market price. Experimental results show that our model has better prediction accuracy than other deep learning models and achieves the state-of-the-art performance. |
| first_indexed | 2024-03-11T17:24:41Z |
| format | Article |
| id | doaj.art-542583f9c00b471ca43af700483916b7 |
| institution | Directory Open Access Journal |
| issn | 2405-9188 |
| language | English |
| last_indexed | 2024-03-11T17:24:41Z |
| publishDate | 2023-11-01 |
| publisher | KeAi Communications Co., Ltd. |
| record_format | Article |
| series | Journal of Finance and Data Science |
| spelling | doaj.art-542583f9c00b471ca43af700483916b72023-10-19T04:22:36ZengKeAi Communications Co., Ltd.Journal of Finance and Data Science2405-91882023-11-019100105OptionNet: A multiscale residual deep learning model with confidence interval to predict option priceLuwei Lin0Meiqing Wang1Hang Cheng2Rong Liu3Fei Chen4College of Mathematics and Statistics, Fuzhou University, Fuzhou, ChinaCollege of Mathematics and Statistics, Fuzhou University, Fuzhou, China; Corresponding author.College of Mathematics and Statistics, Fuzhou University, Fuzhou, ChinaCollege of Mathematics and Statistics, Fuzhou University, Fuzhou, ChinaCollege of Computer Science and Data Science, Fuzhou University, Fuzhou, ChinaOption is an important financial derivative. Accurate option pricing is essential to the development of financial markets. For option pricing, existing time series models and neural networks are difficult to extract multi-scale temporal features from option data, which greatly limits their performance. To solve this problem, we propose a novel deep learning model named as MRC-LSTM-CI. It contains three modules, including Multi-scale Residual CNN module (MRC), Long Short-Term Memory neural network module (LSTM) and confidence interval output module (CI). The proposed model can effectively extract multi-scale features from real market option data, and make interval prediction to provide more information to the decision maker. In addition, the proposed model is further improved using the residual prediction strategy, where the output value is chosen as the residual value between BS theory price and actual market price. Experimental results show that our model has better prediction accuracy than other deep learning models and achieves the state-of-the-art performance.http://www.sciencedirect.com/science/article/pii/S2405918823000211Option pricingDeep learningMulti-scale time seriesConfidence interval |
| spellingShingle | Luwei Lin Meiqing Wang Hang Cheng Rong Liu Fei Chen OptionNet: A multiscale residual deep learning model with confidence interval to predict option price Journal of Finance and Data Science Option pricing Deep learning Multi-scale time series Confidence interval |
| title | OptionNet: A multiscale residual deep learning model with confidence interval to predict option price |
| title_full | OptionNet: A multiscale residual deep learning model with confidence interval to predict option price |
| title_fullStr | OptionNet: A multiscale residual deep learning model with confidence interval to predict option price |
| title_full_unstemmed | OptionNet: A multiscale residual deep learning model with confidence interval to predict option price |
| title_short | OptionNet: A multiscale residual deep learning model with confidence interval to predict option price |
| title_sort | optionnet a multiscale residual deep learning model with confidence interval to predict option price |
| topic | Option pricing Deep learning Multi-scale time series Confidence interval |
| url | http://www.sciencedirect.com/science/article/pii/S2405918823000211 |
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