Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method
The option butterfly portfolio is the commonly option arbitrage strategy. In reality, because the distribution of the option state price density (SPD) function is not normal and unknown, so the nonparametric deep learning methods to estimate option butterfly portfolio returns are proposed. This pape...
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| Main Authors: | , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Wiley
2023-01-01
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| Series: | Journal of Function Spaces |
| Online Access: | http://dx.doi.org/10.1155/2023/4989036 |
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| _version_ | 1832546596447846400 |
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| author | Xiangyu Ge Xia Zhu Gang Bi Hao Zheng Qing Li |
| author_facet | Xiangyu Ge Xia Zhu Gang Bi Hao Zheng Qing Li |
| author_sort | Xiangyu Ge |
| collection | DOAJ |
| description | The option butterfly portfolio is the commonly option arbitrage strategy. In reality, because the distribution of the option state price density (SPD) function is not normal and unknown, so the nonparametric deep learning methods to estimate option butterfly portfolio returns are proposed. This paper constructs the single-index nonparametric option pricing model which contains multiple influencing factors and presents the nonparametric estimation form for option butterfly portfolio returns. The empirical analysis shows that the SPD function estimated by using single-index nonparametric option model can effectively calculate the option butterfly portfolio returns with the minimum option strike price interval and provide an effective reference tool for risk-averse investors with limited risk preferences. |
| format | Article |
| id | doaj-art-4b2bb87f4b9a484f9674ceffc59404a2 |
| institution | Kabale University |
| issn | 2314-8888 |
| language | English |
| publishDate | 2023-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | Journal of Function Spaces |
| spelling | doaj-art-4b2bb87f4b9a484f9674ceffc59404a22025-02-03T06:47:40ZengWileyJournal of Function Spaces2314-88882023-01-01202310.1155/2023/4989036Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning MethodXiangyu Ge0Xia Zhu1Gang Bi2Hao Zheng3Qing Li4Department of FinanceSchool of Statistics and MathematicsSchool of Statistics and MathematicsSchool of ManagementSchool of Statistics and MathematicsThe option butterfly portfolio is the commonly option arbitrage strategy. In reality, because the distribution of the option state price density (SPD) function is not normal and unknown, so the nonparametric deep learning methods to estimate option butterfly portfolio returns are proposed. This paper constructs the single-index nonparametric option pricing model which contains multiple influencing factors and presents the nonparametric estimation form for option butterfly portfolio returns. The empirical analysis shows that the SPD function estimated by using single-index nonparametric option model can effectively calculate the option butterfly portfolio returns with the minimum option strike price interval and provide an effective reference tool for risk-averse investors with limited risk preferences.http://dx.doi.org/10.1155/2023/4989036 |
| spellingShingle | Xiangyu Ge Xia Zhu Gang Bi Hao Zheng Qing Li Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method Journal of Function Spaces |
| title | Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method |
| title_full | Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method |
| title_fullStr | Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method |
| title_full_unstemmed | Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method |
| title_short | Analysis of Option Butterfly Portfolio Models Based on Nonparametric Estimation Deep Learning Method |
| title_sort | analysis of option butterfly portfolio models based on nonparametric estimation deep learning method |
| url | http://dx.doi.org/10.1155/2023/4989036 |
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