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| DC 欄位 | 值 | 語言 |
|---|---|---|
| dc.contributor.advisor | 呂育道 | zh_TW |
| dc.contributor.advisor | Yuh-Dauh Lyuu | en |
| dc.contributor.author | 林哲毅 | zh_TW |
| dc.contributor.author | Che-Yi Lin | en |
| dc.date.accessioned | 2024-08-08T16:17:56Z | - |
| dc.date.available | 2024-08-09 | - |
| dc.date.copyright | 2024-08-08 | - |
| dc.date.issued | 2024 | - |
| dc.date.submitted | 2024-07-24 | - |
| dc.identifier.citation | Anderson, D., & Ulrych, U. (2023). Accelerated American option pricing with deep neural networks. Quantitative Finance and Economics, 7(2), 207--228.
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An overview of gradient descent optimization algorithms. ArXiv. https://doi.org/10.48550/arXiv.1609.04747 Ruder, S. (2017). An overview of multi-task learning in deep neural networks. ArXiv. https://doi.org/10.48550/arXiv.1706.05098 Sutskever, I., Martens, J., Dahl, G., & Hinton, G. (2013). On the importance of initialization and momentum in deep learning. Proceedings of the 30th International Conference on Machine Learning, Atlanta, 16--21 June, 1139--1147. Tseng, P. (2001). Convergence of a block coordinate descent method for nondifferentiable minimization. Journal of Optimization Theory and Applications, 109(3), 475--494. Zoph, B., & Le, Q. V. (2017). Neural architecture search with reinforcement learning. Proceedings of the 5th International Conference on Learning Representations, Toulon, France, 24--26 April, 2841--2856. | - |
| dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/93803 | - |
| dc.description.abstract | 傳統美式選擇權定價方法的計算效率不足。本論文使用類神經網路推理來定價美式選擇權，並提出多任務學習以提升準確度。此外，我們採用神經架構搜索技術來自動選擇有效的網路架構。研究發現，我們的方法能在推理階段減少CPU時間，且提供跟傳統方法相當的結果。 | zh_TW |
| dc.description.abstract | Conventional methods for pricing American options can be computationally inefficient. This thesis uses neural network inference to price American options. We propose multi-task learning to enhance accuracy. Furthermore, neural architecture search is adopted to automate choosing a competitive neural network architecture. This thesis finds that our methods not only provide results comparable to conventional ones but also require less CPU time during inference. | en |
| dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2024-08-08T16:17:56Z No. of bitstreams: 0 | en |
| dc.description.provenance | Made available in DSpace on 2024-08-08T16:17:56Z (GMT). No. of bitstreams: 0 | en |
| dc.description.tableofcontents | Acknowledgements i
Abstract (Chinese) ii Abstract iii Contents iv List of Figures vi List of Tables vii Chapter 1 Introduction 1 Chapter 2 Preliminaries 3 2.1 Neural Networks 3 2.1.1 Neural Network Basics 3 2.1.2 Neural Network Architectures 4 2.1.3 Training Neural Networks 4 2.2 American Options 6 2.2.1 American Option Basics 6 2.2.2 Pricing American Options 6 2.2.3 Asset Price Models 7 Chapter 3 Methodology 10 3.1 Data Generation 10 3.2 Multi-task Learning 12 3.3 Neural Architecture Search 15 Chapter 4 Numerical Results 16 4.1 Experimental Setup 16 4.2 Convergence Analysis 19 4.3 Error Analysis 23 4.4 Timing Analysis 26 4.5 Space Analysis 28 Chapter 5 Conclusions 29 Bibliography 30 | - |
| dc.language.iso | en | - |
| dc.subject | 選擇權定價 | zh_TW |
| dc.subject | 美式選擇權 | zh_TW |
| dc.subject | 神經架構搜索 | zh_TW |
| dc.subject | 多任務學習 | zh_TW |
| dc.subject | 類神經網路 | zh_TW |
| dc.subject | neural networks | en |
| dc.subject | multi-task learning | en |
| dc.subject | neural architecture search | en |
| dc.subject | option pricing | en |
| dc.subject | American options | en |
| dc.title | 多任務類神經網路於高效的美式選擇權定價 | zh_TW |
| dc.title | Efficient American Option Pricing with Multi-task Neural Networks | en |
| dc.type | Thesis | - |
| dc.date.schoolyear | 112-2 | - |
| dc.description.degree | 碩士 | - |
| dc.contributor.oralexamcommittee | 金國興;陸裕豪;張經略 | zh_TW |
| dc.contributor.oralexamcommittee | Gow-Hsing King;U-Hou Lok;Ching-Lueh Chang | en |
| dc.subject.keyword | 美式選擇權,選擇權定價,類神經網路,多任務學習,神經架構搜索, | zh_TW |
| dc.subject.keyword | American options,option pricing,neural networks,multi-task learning,neural architecture search, | en |
| dc.relation.page | 34 | - |
| dc.identifier.doi | 10.6342/NTU202401974 | - |
| dc.rights.note | 未授權 | - |
| dc.date.accepted | 2024-07-26 | - |
| dc.contributor.author-college | 電機資訊學院 | - |
| dc.contributor.author-dept | 資訊工程學系 | - |
| 顯示於系所單位： | 資訊工程學系 | |
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