한국산학기술학회논문지 (Journal of the Korea Academia-Industrial cooperation Society)

  • 제12권2호
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  • Pages.712-717
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  • 2011
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  • 1975-4701(pISSN)
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  • 2288-4688(eISSN)
한국산학기술학회 (The Korea Academia-Industrial cooperation Society)
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기계학습과 동적델타헤징을 이용한 옵션 헤지 전략

An Option Hedge Strategy Using Machine Learning and Dynamic Delta Hedging

  • 유재필 (상명대학교 경영공학과) ;
  • 신현준 (상명대학교 경영공학과)
  • Ru, Jae-Pil (Dept. of Management Engineering, Sangmyung University) ;
  • Shin, Hyun-Joon (Dept. of Management Engineering, Sangmyung University)
  • 투고 : 2010.12.13
  • 심사 : 2011.02.10
  • 발행 : 2011.02.28
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초록

동적 델타 헤징(Dynamic Delta Hedging)이란 옵션 발행자가 옵션의 만기정산금액(payoff)을 지급하기 위해 주기적으로 델타에 근거한 헤지 포지션을 조절함으로써 옵션의 payoff를 복제하고 옵션 가치변화에 따른 위험을 회피하는 방법이다. 본 연구에서는 헤지에 있어서 주요 변수인 블랙-숄즈의 모형에 의해 산출된 델타의 대체 값을 찾기 위해 기계학습의 일종인 인공신경망 학습을 적용하여 옵션의 만기 시 헤지 비용의 최소화 및 차익 실현을 위한 방법론을 제시하고자 한다. 기초자산의 현재가격, 변동성, 무위험이자율, 만기 등의 시장 상황 변화에 따른 다양한 시나리오에 대한 실험을 통해 본 연구에서 제시하는 방법론의 성능을 분석하고 그 우수성을 보인다.

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.

키워드

참고문헌

  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.

피인용 문헌

  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