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

The Korea Academia-Industrial cooperation Society (한국산학기술학회)
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A Methodology for Hedging Equity Linked Warrant Using Artificial Neural Network

인공신경망을 이용한 주식워런트증권(ELW)의 헤징 방안

  • Ryu, Jae-Pil (Dept. of Management Engineering, Sangmyung University) ;
  • Shin, Hyun-Joon (Dept. of Management Engineering, Sangmyung University)
  • 유재필 (상명대학교 경영공학과) ;
  • 신현준 (상명대학교 경영공학과)
  • Received : 2011.11.18
  • Accepted : 2012.03.08
  • Published : 2012.03.31
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Abstract

From the perspective of risk management, financial organization that have issued ELW require an efficient hedging methodology due to recently increased trade volume of ELW. This study presents an ELW hedging methodology using artificial neural network(ANN) to minimize hedging costs. The performance of the presented methodology in this study is examined by analysis utilizing the prices and volatilities of underlying assets, risk free interest rates, and maturities and computational experiments show that the proposed method is superior to existing dynamic delta hedging(DDH) technique in terms of hedging costs ranged from 25% to 250%.

최근 주식 워런트 증권(ELW)의 시장 규모가 급격하게 증가하면서 ELW를 발행한 금융기관들에는 리스크 관리 측면에서 효율적인 헤징 방안에 대한 필요성이 대두되고 있다. 본 연구는 인공신경망 학습 기법을 이용하여 ELW를 헤징하는 데 소요되는 비용을 최소화하는 방안을 제시하고자 하며, 기초자산의 현재가격, 변동성, 무위험이자율, 만기 등의 시장 상황 변화에 따른 다양한 시나리오에 대한 실험을 통해 본 연구에서 제시하는 방법론의 성능을 기존의 동적 델타 헤징 방법론과 비교 실험하였다. 그 결과 만기 행사가 안 된 상품의 경우 본 연구에서 제시하는 헤징 방법론이 동적 델타 헤징에 비해 최종 비용이 약 250% 이상 개선되었으며, 행사한 상품은 최종 비용에 있어서 약 25%의 개선 율을 보이는 것을 알 수 있었다.

Keywords

References

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