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Portfolio Insurance and Intelligent Algorithms

Part of the Modeling and Optimization in Science and Technologies book series (MOST,volume 18)

Abstract

Minimizing portfolio insurance (PI) costs is an investment strategy of great importance. In this chapter, by converting the classical minimum-cost PI (MCPI) problem to a multi-period MCPI (MPMCPI) problem, we define and investigate the MPMCPI under transaction costs (MPMCPITC) problem as a nonlinear programming (NLP) problem. The problem of MCPI gets more genuine in this way. Given the fact that such NLP problems are widely handled by intelligent algorithms, we are introducing a well-tuned approach that can solve the challenging MPMCPITC problem. In our portfolios’ applications, we use real-world data and, along with some of the best memetic meta-heuristic and commercial methods, we provide a solution to the MPMCPITC problem, and we compare their solutions to each other.

Keywords

  • Portfolio selection
  • Multi-period portfolio insurance
  • Transaction costs
  • Nonlinear programming
  • Meta-heuristic optimization

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Correspondence to Vasilios N. Katsikis .

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Katsikis, V.N., Mourtas, S.D. (2021). Portfolio Insurance and Intelligent Algorithms. In: Patnaik, S., Tajeddini, K., Jain, V. (eds) Computational Management. Modeling and Optimization in Science and Technologies, vol 18. Springer, Cham. https://doi.org/10.1007/978-3-030-72929-5_14

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