Quasi-Monte Carlo Methods in Portfolio Selection with Many Constraints

Alexander Brunhuemer () and Gerhard Larcher ()
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Alexander Brunhuemer: JKU Linz, Institute for Financial Mathematics and Applied Number Theory
Gerhard Larcher: JKU Linz, Institute for Financial Mathematics and Applied Number Theory

A chapter in Advances in Modeling and Simulation, 2022, pp 89-109 from Springer

Abstract: 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.

Keywords: Portfolio-optimization; Quasi-Monte Carlo methods; Dispersion (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-031-10193-9_5

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DOI: 10.1007/978-3-031-10193-9_5

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