Option Pricing Using LSTM: A Perspective of Realized Skewness
Deep learning has drawn great attention in the financial field due to its powerful ability in nonlinear fitting, especially in the studies of asset pricing. In this paper, we proposed a long short-term memory option pricing model with realized skewness by fully considering the asymmetry of asset ret...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-01-01
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| Series: | Mathematics |
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| Online Access: | https://www.mdpi.com/2227-7390/11/2/314 |
| _version_ | 1797438988153257984 |
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| author | Yan Liu Xiong Zhang |
| author_facet | Yan Liu Xiong Zhang |
| author_sort | Yan Liu |
| collection | DOAJ |
| description | Deep learning has drawn great attention in the financial field due to its powerful ability in nonlinear fitting, especially in the studies of asset pricing. In this paper, we proposed a long short-term memory option pricing model with realized skewness by fully considering the asymmetry of asset return in emerging markets. It was applied to price the ETF50 options of China. In order to emphasize the improvement of this model, a comparison with a parametric method, such as Black-Scholes (BS), and machine learning methods, such as support vector machine (SVM), random forests and recurrent neural network (RNN), was conducted. Moreover, we also took the characteristic of heavy tail into consideration and studied the effect of realized kurtosis on pricing to prove the robustness of the skewness. The empirical results indicate that realized skewness significantly improves the pricing performance of LSTM among moneyness states except for in-the-money call options. Specifically, the LSTM model with realized skewness outperforms the classical method and other machine learning methods in all metrics. |
| first_indexed | 2024-03-09T11:46:21Z |
| format | Article |
| id | doaj.art-3aa2910e5c6c4a3682e2f0e4559e0863 |
| institution | Directory Open Access Journal |
| issn | 2227-7390 |
| language | English |
| last_indexed | 2024-03-09T11:46:21Z |
| publishDate | 2023-01-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Mathematics |
| spelling | doaj.art-3aa2910e5c6c4a3682e2f0e4559e08632023-11-30T23:20:29ZengMDPI AGMathematics2227-73902023-01-0111231410.3390/math11020314Option Pricing Using LSTM: A Perspective of Realized SkewnessYan Liu0Xiong Zhang1School of Economics, Ocean University of China, Qingdao 266100, ChinaSchool of Economics, Ocean University of China, Qingdao 266100, ChinaDeep learning has drawn great attention in the financial field due to its powerful ability in nonlinear fitting, especially in the studies of asset pricing. In this paper, we proposed a long short-term memory option pricing model with realized skewness by fully considering the asymmetry of asset return in emerging markets. It was applied to price the ETF50 options of China. In order to emphasize the improvement of this model, a comparison with a parametric method, such as Black-Scholes (BS), and machine learning methods, such as support vector machine (SVM), random forests and recurrent neural network (RNN), was conducted. Moreover, we also took the characteristic of heavy tail into consideration and studied the effect of realized kurtosis on pricing to prove the robustness of the skewness. The empirical results indicate that realized skewness significantly improves the pricing performance of LSTM among moneyness states except for in-the-money call options. Specifically, the LSTM model with realized skewness outperforms the classical method and other machine learning methods in all metrics.https://www.mdpi.com/2227-7390/11/2/314deep learningOption pricingLSTMrealized skewness |
| spellingShingle | Yan Liu Xiong Zhang Option Pricing Using LSTM: A Perspective of Realized Skewness Mathematics deep learning Option pricing LSTM realized skewness |
| title | Option Pricing Using LSTM: A Perspective of Realized Skewness |
| title_full | Option Pricing Using LSTM: A Perspective of Realized Skewness |
| title_fullStr | Option Pricing Using LSTM: A Perspective of Realized Skewness |
| title_full_unstemmed | Option Pricing Using LSTM: A Perspective of Realized Skewness |
| title_short | Option Pricing Using LSTM: A Perspective of Realized Skewness |
| title_sort | option pricing using lstm a perspective of realized skewness |
| topic | deep learning Option pricing LSTM realized skewness |
| url | https://www.mdpi.com/2227-7390/11/2/314 |
| work_keys_str_mv | AT yanliu optionpricingusinglstmaperspectiveofrealizedskewness AT xiongzhang optionpricingusinglstmaperspectiveofrealizedskewness |