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Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints

In: Advances in Modeling and Simulation

Author

Listed:
  • Alexander Brunhuemer

    (JKU Linz, Institute for Financial Mathematics and Applied Number Theory)

  • Gerhard Larcher

    (JKU Linz, Institute for Financial Mathematics and Applied Number Theory)

Abstract

We describe a concrete on-going industry project on advanced portfolio optimization based on machine-learning techniques, and we report on attempts and results of successful and advantageous application of QMC methods in this project. We are also carrying out an approach to determine a measure for dispersion in an opportunity set, which cannot trivially be found, because of the uncertainty of the shape of an opportunity set. Finally, we state some still open problems and questions in this context.

Suggested Citation

  • Alexander Brunhuemer & Gerhard Larcher, 2022. "Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints," Springer Books, in: Zdravko Botev & Alexander Keller & Christiane Lemieux & Bruno Tuffin (ed.), Advances in Modeling and Simulation, pages 89-109, Springer.
    • Handle: RePEc:spr:sprchp:978-3-031-10193-9_5
    DOI: 10.1007/978-3-031-10193-9_5
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