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Using Genetic Algorithms to Develop a Dynamic Guaranteed Option Hedge System

Author

Listed:
  • Hyounggun Song

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Sung Kwon Han

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Seung Hwan Jeong

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

  • Hee Soo Lee

    (Department of Business Administration, Sejong University, 209 Neungdong-ro, Gwangjin-gu, Seoul 03722, Korea)

  • Kyong Joo Oh

    (Department of Industrial Engineering, Yonsei University, 50 Yonsei-ro, Seodaemun-gu, Seoul 03722, Korea)

Abstract

In this research, we develop a guaranteed option hedge system to protect against capital market risks using a genetic algorithm (GA). We test the hedge effectiveness of our guaranteed option hedge strategy by comparing the performance of our system with those of other strategies. A genetic algorithm heuristic trading method for the optimization of a non-linear problem is applied to each system to improve the hedge effectiveness. The GA dynamic hedge system developed in this research is found to improve hedge effectiveness by reducing the option value volatility and increasing the total profit. Insurance companies are able to make more efficient investment strategies by using our guaranteed option hedge system. It contributes to the investment efficiency of the insurance companies and helps to achieve efficiency for financial markets. In addition, it helps to achieve sustained economic benefits to policyholders. In this sense, the system developed in this paper plays a role in sustaining economic growth.

Suggested Citation

  • Hyounggun Song & Sung Kwon Han & Seung Hwan Jeong & Hee Soo Lee & Kyong Joo Oh, 2019. "Using Genetic Algorithms to Develop a Dynamic Guaranteed Option Hedge System," Sustainability, MDPI, vol. 11(15), pages 1-12, July.
    • Handle: RePEc:gam:jsusta:v:11:y:2019:i:15:p:4100-:d:252785
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    References listed on IDEAS

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    Cited by:

    1. Seung Hwan Jeong & Hee Soo Lee & Hyun Nam & Kyong Joo Oh, 2021. "Using a Genetic Algorithm to Build a Volume Weighted Average Price Model in a Stock Market," Sustainability, MDPI, vol. 13(3), pages 1-16, January.
    2. Jiwoo Kim & Sanghun Shin & Hee Soo Lee & Kyong Joo Oh, 2019. "A Machine Learning Portfolio Allocation System for IPOs in Korean Markets Using GA-Rough Set Theory," Sustainability, MDPI, vol. 11(23), pages 1-15, November.

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