文章基本信息

标题：A Dynamic Hedging Strategy for Option Transaction Using Artificial Neural Networks
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作者：Hyun Joon Shin ; Jaepil Ryu
期刊名称：International Journal of Software Engineering and Its Applications
印刷版ISSN：1738-9984
出版年度：2012
卷号：6
期号：4
出版社：SERSC
摘要：In order to avoid the risk caused by continuously changing option value, option issuers generally utilize the traditional Dynamic Delta Hedging (DDH) method. DDH tries to maintain risk-neutral position by adjusting hedge position according to the delta by Black- Scholes (BS) model. DDH, however, is not able to guarantee optimal hedging performance due to some impractical assumptions inherent in BS model. Therefore, this study presents a methodology for dynamic option hedging strategy using artificial neural network (ANN) to enhance hedging performance and shows the superiority of the proposed method through computational experiments.
关键词：Risk-neutral Position; Option; Hedging; Artificial Neural Network; Dynamic;Delta Hedging