Using genetic algorithms to develop a dynamic guaranteed option hedge system

Hyounggun Song, Sung Kwon Han, Seung Hwan Jeong, Hee Soo Lee, Kyong Joo Oh

Research output: Contribution to journalArticlepeer-review

3 Citations (Scopus)


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.

Original languageEnglish
Article number4100
JournalSustainability (Switzerland)
Issue number15
Publication statusPublished - 2019 Aug 1

Bibliographical note

Publisher Copyright:
© 2019 by the authors.

All Science Journal Classification (ASJC) codes

  • Geography, Planning and Development
  • Renewable Energy, Sustainability and the Environment
  • Environmental Science (miscellaneous)
  • Energy Engineering and Power Technology
  • Management, Monitoring, Policy and Law


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