Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model
This study focuses on the volatility prediction and option volatility investment. By investigating the traditional Volatility Prediction Model and machine learning algorithms, this study tries to merge these two aspects together. This work setup a bridge of previous financial studies and machine lea...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2022-01-01
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| Series: | Discrete Dynamics in Nature and Society |
| Online Access: | http://dx.doi.org/10.1155/2022/8952996 |
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| _version_ | 1832562384337633280 |
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| author | Yuanyang Teng Yicun Li Xiaobo Wu |
| author_facet | Yuanyang Teng Yicun Li Xiaobo Wu |
| author_sort | Yuanyang Teng |
| collection | DOAJ |
| description | This study focuses on the volatility prediction and option volatility investment. By investigating the traditional Volatility Prediction Model and machine learning algorithms, this study tries to merge these two aspects together. This work setup a bridge of previous financial studies and machine learning studies by proposing an algorithm integrating neural network and three traditional volatility models, called “Quantile based neural network and model integration combination algorithm.” The algorithm effectively lowers the volatility prediction error (measured by root of mean square error, shorted for RMSE: 0.319724) and beat the Wavenet (RMSE: 0.44) which is the benchmark and surpasses integrated model (RMSE: 0.348346) in test set. In terms of option investment strategy, this paper constructs a CSI 300 index option portfolio which hedges the underlying asset price risk and exposes the volatility risk. Then propose the “Option strategy of volatility prediction with dynamic thresholds.” With the new algorithm above, the strategy improves the return-risk ratio in test set (measured by Sharpe ratio: 1.99–2.07). |
| format | Article |
| id | doaj-art-1d9947e261cf41a6a819dadc4611ef96 |
| institution | Kabale University |
| issn | 1607-887X |
| language | English |
| publishDate | 2022-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Discrete Dynamics in Nature and Society |
| spelling | doaj-art-1d9947e261cf41a6a819dadc4611ef962025-02-03T01:22:46ZengWileyDiscrete Dynamics in Nature and Society1607-887X2022-01-01202210.1155/2022/8952996Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction ModelYuanyang Teng0Yicun Li1Xiaobo Wu2School of ManagementSchool of ManagementSchool of ManagementThis study focuses on the volatility prediction and option volatility investment. By investigating the traditional Volatility Prediction Model and machine learning algorithms, this study tries to merge these two aspects together. This work setup a bridge of previous financial studies and machine learning studies by proposing an algorithm integrating neural network and three traditional volatility models, called “Quantile based neural network and model integration combination algorithm.” The algorithm effectively lowers the volatility prediction error (measured by root of mean square error, shorted for RMSE: 0.319724) and beat the Wavenet (RMSE: 0.44) which is the benchmark and surpasses integrated model (RMSE: 0.348346) in test set. In terms of option investment strategy, this paper constructs a CSI 300 index option portfolio which hedges the underlying asset price risk and exposes the volatility risk. Then propose the “Option strategy of volatility prediction with dynamic thresholds.” With the new algorithm above, the strategy improves the return-risk ratio in test set (measured by Sharpe ratio: 1.99–2.07).http://dx.doi.org/10.1155/2022/8952996 |
| spellingShingle | Yuanyang Teng Yicun Li Xiaobo Wu Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model Discrete Dynamics in Nature and Society |
| title | Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model |
| title_full | Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model |
| title_fullStr | Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model |
| title_full_unstemmed | Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model |
| title_short | Option Volatility Investment Strategy: The Combination of Neural Network and Classical Volatility Prediction Model |
| title_sort | option volatility investment strategy the combination of neural network and classical volatility prediction model |
| url | http://dx.doi.org/10.1155/2022/8952996 |
| work_keys_str_mv | AT yuanyangteng optionvolatilityinvestmentstrategythecombinationofneuralnetworkandclassicalvolatilitypredictionmodel AT yicunli optionvolatilityinvestmentstrategythecombinationofneuralnetworkandclassicalvolatilitypredictionmodel AT xiaobowu optionvolatilityinvestmentstrategythecombinationofneuralnetworkandclassicalvolatilitypredictionmodel |