An Option Hedge Strategy Using Machine Learning and Dynamic Delta Hedging

기계학습과 동적델타헤징을 이용한 옵션 헤지 전략

  • Ru, Jae-Pil (Dept. of Management Engineering, Sangmyung University) ;
  • Shin, Hyun-Joon (Dept. of Management Engineering, Sangmyung University)
  • 유재필 (상명대학교 경영공학과) ;
  • 신현준 (상명대학교 경영공학과)
  • Received : 2010.12.13
  • Accepted : 2011.02.10
  • Published : 2011.02.28


Option issuers generally utilize Dynamic Delta Hedging(DDH) technique to avoid the risk resulting from continuously changing option value. DDH duplicates payoff of option position by adjusting hedge position according to the delta value from Black-Scholes(BS) model in order to maintain risk neutral state. DDH, however, is not able to guarantee optimal hedging performance because of the weaknesses caused by impractical assumptions inherent in BS model. Therefore, this study presents a methodology for dynamic option hedge using artificial neural network(ANN) to enhance hedging performance and show the superiority of the proposed method using various computational experiments.


Dynamic Delta Hedging;Option Hedge Cost;Hedging Performance;Black-Scholes;Machine Learning;Artificial Neural Networks


Supported by : 상명대학교


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