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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 李存修 | |
| dc.contributor.author | Wei-Di Wang | en |
| dc.contributor.author | 王偉地 | zh_TW |
| dc.date.accessioned | 2021-06-17T03:26:02Z | - |
| dc.date.available | 2019-05-17 | |
| dc.date.copyright | 2018-05-17 | |
| dc.date.issued | 2018 | |
| dc.date.submitted | 2018-05-15 | |
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| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/69742 | - |
| dc.description.abstract | 過去相關研究表明，使用歷史數據訓練後的神經網絡模型可以對選擇權進行定價，並且由於其不需要對模型做任何前提假設，在實際定價精度中要優於Black-Scholes等傳統參數模型。過去使用的神經網絡模型有兩個特徵，一是模型本身選用的是淺層的神經網絡模型，二是在設計輸入變量時，僅波動率被作為描述標的物波動情況的變量。
隨著近幾年人工智慧相關技術的發展，一些基於深度學習的神經網絡模型被應用於社會各個領域。本文就嘗試使用了基於深度學習的循環神經網絡模型對選擇權進行定價研究，同時直接將標的物的時間序列作為輸入變量直接放入模型中，希望能夠相對於之前的淺層神經網絡模型有更好的定價精度。 中國大陸的上證50ETF選擇權被選為本次研究的對象。經過對神經網絡模型結構的調整和選擇後，本文選取了一個淺層神經網絡模型、兩個循環神經網絡模型和一個Black-Scholes模型共4個模型，分別使用這4個模型對選擇權進行定價，並比較其定價精度的表現。最終結果表明，在樣本期間內，循環神經網絡模型在定價精度方面要顯著優於Black-Scholes模型和淺層神經網絡模型。 | zh_TW |
| dc.description.abstract | Previous studies have shown that neural network models which are trained by historical data can be used to price options. Because of no assumption needed, neural network has better performance than those traditional parameter models such as Black-Scholes in pricing. Those neural network models used in the past have two characteristics. One is that those models have only several hidden layers, and the other is that only the volatility is used as a variable to describe the fluctuation of the underlying security when designing the input variables.
With the development of artificial intelligence-related technologies in recent years, some neural network models based on deep learning are applied to many fields of society. In this paper, we try to use the recurrent neural network to study the pricing of options, and directly put the time series data of the underlying security as input variables into the model, hoping to hoping to get a neural network model which has better performance than the formers do. Shanghai Stock Exchange 50ETF option is selected as the object of this study. After the adjustment and selection of neural network model structure, this paper selects a neural network model with one hidden layer, two recurrent neural network models and a Black-Scholes model. We use these four models to price options and compare the performance in pricing accuracy. The final results show that in terms of the pricing accuracy, the recurrent neural network model is significantly superior to the Black-Scholes model and the neural network model with one hidden layer during the selected data samples. | en |
| dc.description.provenance | Made available in DSpace on 2021-06-17T03:26:02Z (GMT). No. of bitstreams: 1 ntu-107-R05723072-1.pdf: 5085691 bytes, checksum: 6853711b204a8e3f67fb70d359d8358d (MD5) Previous issue date: 2018 | en |
| dc.description.tableofcontents | 目 錄
謝辭 i 中文摘要 ii Abstract iii 第壹章、緒論 1 第一節、研究背景與動機 1 第二節、研究目的 3 第三節、研究方法 3 第四節、本文架構 5 第貳章、文獻回顧 6 第一節、神經網絡模型相關方法 6 第二節、相關方法在金融領域的應用研究 11 第三節、使用神經網絡對選擇權定價研究 13 第四節、本章小結 15 第三章、研究方法 17 第一節、數據選擇 17 第二節、模型設計 21 第三節、試驗過程設計 29 第四節、評價方法 30 第四章、實證結果 31 第一節、損失函數值比較 31 第二節、假設檢定 34 第三節、本章小結 34 第五章、結論與建議 36 第一節、文章結論 36 第二節、未來研究建議 37 參考文獻 38 附錄 41 | |
| dc.language.iso | zh-TW | |
| dc.subject | 循環神經網絡 | zh_TW |
| dc.subject | 深度學習 | zh_TW |
| dc.subject | 選擇權定價 | zh_TW |
| dc.subject | Recurrent Neural Network | en |
| dc.subject | Option pricing | en |
| dc.subject | Deep learning | en |
| dc.title | 基於循環神經網路模型的選擇權定價研究 | zh_TW |
| dc.title | The Study of Option Pricing——Based on the Recurrent Neural Network | en |
| dc.type | Thesis | |
| dc.date.schoolyear | 106-2 | |
| dc.description.degree | 碩士 | |
| dc.contributor.oralexamcommittee | 王耀輝,石百達 | |
| dc.subject.keyword | 選擇權定價,深度學習,循環神經網絡, | zh_TW |
| dc.subject.keyword | Option pricing,Deep learning,Recurrent Neural Network, | en |
| dc.relation.page | 41 | |
| dc.identifier.doi | 10.6342/NTU201800813 | |
| dc.rights.note | 有償授權 | |
| dc.date.accepted | 2018-05-16 | |
| dc.contributor.author-college | 管理學院 | zh_TW |
| dc.contributor.author-dept | 財務金融學研究所 | zh_TW |
| 顯示於系所單位： | 財務金融學系 | |
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