Deep Learning in Model Risk Neutral Distribution for Option Pricing

Chin Chou, Jhih Chen Liu, Chiao Ting Chen, Szu-Hao Huang

研究成果: Conference contribution同行評審

摘要

Option pricing has been studied extensively in recent years. An important issue in option pricing is the estimation of the risk neutral distribution of an underlying asset. Better estimation of this distribution can lead to a more rational investment, enabling one to earn an equal return with lower risk. To price options precisely and correctly, traditional financial engineering methods make some assumptions for the risk neutral distribution. However, some assumptions of traditional methods have proved inappropriate and insufficient in empirical option pricing analysis. To address these problems in option pricing, this study adopts a data-driven approach. Owing to advances in hardware and software, studies have been using deep learning methods to price options; however, these have not adequately considered the risk neutral distribution. This may cause an uncontrollable risk, thereby preventing the real-world application of the model. To overcome these problems, this study proposes a deep learning method with a mixture distribution model. Further, it generates a rational risk neutral distribution with accurate empirical pricing analysis.

原文American English
主出版物標題Proceedings - 2019 IEEE International Conference on Agents, ICA 2019
發行者Institute of Electrical and Electronics Engineers Inc.
頁面95-98
頁數4
ISBN(電子)9781728140261
DOIs
出版狀態Published - 10月 2019
事件2019 IEEE International Conference on Agents, ICA 2019 - Jinan, China
持續時間: 18 10月 201921 10月 2019

出版系列

名字Proceedings - 2019 IEEE International Conference on Agents, ICA 2019

Conference

Conference2019 IEEE International Conference on Agents, ICA 2019
國家/地區China
城市Jinan
期間18/10/1921/10/19

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