DOI QR코드

DOI QR Code

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

Abstract

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.

Keywords

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

Acknowledgement

Supported by : 상명대학교

References

  1. 이경수, 권영은, 신진호, "파생상품 Modeling I: MATLAB 활용", 도서출판아진, pp. 35-184, 2008.
  2. Figlewki, S., "Hedging Performance and Basis Risk in Stock Index Futures", Journal of Finance, 39 , pp. 657-669, 1984. https://doi.org/10.2307/2327924
  3. Figlewki, S., "Hedging With Stock Index Futures : Theory and Applicatios in a New Market", Journal of Futures Markets, 5, pp. 183-199, 1985. https://doi.org/10.1002/fut.3990050204
  4. Ghosh, A., "Hedging with Stock Index futures: Estimation and Forecasting with Error Correction Model", Journal of Futures Markets, 13, pp. 743-752, 1993. https://doi.org/10.1002/fut.3990130703
  5. Grammatikos, Theoharry and Anthony Saunders, "Stability and the Hedging Performance of Foreign Currency Futures", The Journal of Futures Markets, 3(3), pp. 295-305, 1983. https://doi.org/10.1002/fut.3990030305
  6. Gencay R. and Qi M., "Pricing and hedging derivative securities with neural networks: Bayesian regulation, early stopping, and bagging", IEEE Transactions on Neural Networks, 12, pp. 726-734, 2001. https://doi.org/10.1109/72.935086
  7. Haykin S., "Neural networks A comprehensive foundation", Prentice-Hall of India, pp. 25-99, 1999.
  8. Hutchinson J. M., Lo A.W., and Poggio T., "A nonparametric approach to pricing and hedging derivative securities via learning networks", Journal of Finance, 49, pp. 851-889, 1994. https://doi.org/10.2307/2329209
  9. John C. Hull, "Options, futures and other derivatives", Person Prentice Hall, pp. 115-280, 2006.
  10. Marmer, H.S., "Portfolio Model Hedging with Canadian Dollar Futures: A Framework for Analysis", The Journal of Futures Markets, 6(1), pp. 83-92, 1986. https://doi.org/10.1002/fut.3990060108
  11. Myers, R.J., "Estimating Time-Varying Optimal Hedge Ratios on Futures Markets", The Journal of Futures Markets 11(1), pp. 39-53, 1991. https://doi.org/10.1002/fut.3990110105
  12. Peters, E., "Hedged Equity Portfolios: Components of Risk and Return", Advances in Futures and Options Research 1, pp. 75-91, 1986.

Cited by

  1. A Methodology for Hedging Equity Linked Warrant Using Artificial Neural Network vol.13, pp.3, 2012, https://doi.org/10.5762/KAIS.2012.13.3.1091