Relative portfolio optimization via a value at risk based constraint

Abstract In this paper, we consider n agents who invest in a general financial market that is free of arbitrage and complete. The aim of each investor is to maximize her expected utility while ensuring, with a specified probability, that her terminal wealth exceeds a benchmark defined by her competitors’ performance. This setup introduces an interdependence between agents, leading to a search for Nash equilibria. In the case of two agents and CRRA utility, we are able to derive all Nash equilibria in terms of terminal wealth. For $$n&gt;2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> agents and logarithmic utility we distinguish two cases. In the first case, the probabilities in the constraint are small and we can characterize all Nash equilibria. In the second case, the probabilities are larger and we look for Nash equilibria in a certain set. We also discuss the impact of the competition using some numerical examples. As a by-product, we solve some portfolio optimization problems with probability constraints.

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Publication Details

Journal
Decisions in Economics and Finance
Published
2025-09-23
DOI
https://doi.org/10.1007/s10203-025-00541-w
Citations
1
Primary Topic
Risk and Portfolio Optimization
Type
article
Field-Weighted Citation Impact
2.11
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article

Relative portfolio optimization via a value at risk based constraint

Nicole Bäuerle, Tamara Göll
1 citations
Decisions in Economics and Finance
Risk and Portfolio Optimization
2.11
article

Relative portfolio optimization via a value at risk based constraint

Nicole Bäuerle, Tamara Göll
article en
1 citations

Abstract

Abstract In this paper, we consider n agents who invest in a general financial market that is free of arbitrage and complete. The aim of each investor is to maximize her expected utility while ensuring, with a specified probability, that her terminal wealth exceeds a benchmark defined by her competitors’ performance. This setup introduces an interdependence between agents, leading to a search for Nash equilibria. In the case of two agents and CRRA utility, we are able to derive all Nash equilibria in terms of terminal wealth. For $$n&gt;2$$ <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML"> <mml:mrow> <mml:mi>n</mml:mi> <mml:mo>&gt;</mml:mo> <mml:mn>2</mml:mn> </mml:mrow> </mml:math> agents and logarithmic utility we distinguish two cases. In the first case, the probabilities in the constraint are small and we can characterize all Nash equilibria. In the second case, the probabilities are larger and we look for Nash equilibria in a certain set. We also discuss the impact of the competition using some numerical examples. As a by-product, we solve some portfolio optimization problems with probability constraints.

Decisions in Economics and Finance
Karlsruhe Institute of Technology (DE)
Openalex Percentile: Top 10%
Risk and Portfolio Optimization
2.11
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