Quasi-Monte Carlo methods in portfolio selection with many constraints

Alexander Brunhuemer; Gerhard Larcher

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

Quasi-Monte Carlo methods in portfolio selection with many constraints

Alexander Brunhuemer
, Gerhard Larcher^*

^*Corresponding author for this work

Institute of Financial Mathematics and Applied Number Theory

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

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.

Original language	English
Title of host publication	Advances in Modeling and Simulation
Subtitle of host publication	Festschrift for Pierre L'Ecuyer
Editors	Zdravko Botev, Alexander Keller, Christiane Lemieux, Bruno Tuffin
Publisher	Springer, Cham
Pages	89-109
Number of pages	21
ISBN (Electronic)	978-3-031-10193-9
ISBN (Print)	978-3-031-10192-2, 978-3-031-10195-3
DOIs	https://doi.org/10.1007/978-3-031-10193-9_5
Publication status	Published - 2022

Fields of science

101 Mathematics
101019 Stochastics
101025 Number theory
101007 Financial mathematics

JKU Focus areas

Digital Transformation

Access to Document

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

Cite this

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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.",

author = "Alexander Brunhuemer and Gerhard Larcher",

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isbn = "978-3-031-10192-2",

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Quasi-Monte Carlo methods in portfolio selection with many constraints. / Brunhuemer, Alexander; Larcher, Gerhard.
Advances in Modeling and Simulation: Festschrift for Pierre L'Ecuyer. ed. / Zdravko Botev; Alexander Keller; Christiane Lemieux; Bruno Tuffin. Springer, Cham, 2022. p. 89-109.

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Quasi-Monte Carlo methods in portfolio selection with many constraints

AU - Brunhuemer, Alexander

AU - Larcher, Gerhard

PY - 2022

Y1 - 2022

N2 - 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.

AB - 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.

UR - https://www.scopus.com/pages/publications/85162727853

U2 - 10.1007/978-3-031-10193-9_5

DO - 10.1007/978-3-031-10193-9_5

M3 - Chapter

SN - 978-3-031-10192-2

SN - 978-3-031-10195-3

SP - 89

EP - 109

BT - Advances in Modeling and Simulation

A2 - Botev, Zdravko

A2 - Keller, Alexander

A2 - Lemieux, Christiane

A2 - Tuffin, Bruno

PB - Springer, Cham

ER -

Quasi-Monte Carlo methods in portfolio selection with many constraints

Abstract

Fields of science

JKU Focus areas

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Cite this