Risk Budgeting Allocation for Dynamic Risk Measures

Time-Consistent Diversified Portfolio Allocations Portfolio allocation problems typically involve diversification of risks, which can be achieved by risk budgeting strategies. Of additional importance is being time consistent—that is, ensuring that decisions about what to do in the future under certain outcomes remain optimal when those future outcomes are realized. In “Risk Budgeting Allocation for Dynamic Risk Measures,” Pesenti, Jaimungal, Saporito, and Targino develop time-consistent portfolio strategies, termed dynamic risk budgeting strategies, where the diversification is on the level of risk contribution of assets. The authors formalize the dynamic setting of risk budgeting strategies, recast dynamic risk budgeting strategies as solutions of sequences of convex optimization problems, and develop an efficient actor-critic deep reinforcement learning algorithm for estimating dynamic risk budgeting strategies. The algorithm and the dynamic risk budgeting strategies are illustrated on a complex simulation case study involving five assets and 12 time steps.

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Publication Details

Journal
Operations Research
Published
2024-12-23
DOI
https://doi.org/10.1287/opre.2023.0299
Citations
3
Primary Topic
Risk and Portfolio Optimization
Type
article
Field-Weighted Citation Impact
1.47
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article

Risk Budgeting Allocation for Dynamic Risk Measures

Silvana M. Pesenti, Sebastian Jaimungal, Rodrigo S. Targino, Yuri F. Saporito
3 citations
Operations Research
Risk and Portfolio Optimization
1.47
article

Risk Budgeting Allocation for Dynamic Risk Measures

Silvana M. Pesenti, Sebastian Jaimungal, Rodrigo S. Targino, Yuri F. Saporito
article en
3 citations

Abstract

Time-Consistent Diversified Portfolio Allocations Portfolio allocation problems typically involve diversification of risks, which can be achieved by risk budgeting strategies. Of additional importance is being time consistent—that is, ensuring that decisions about what to do in the future under certain outcomes remain optimal when those future outcomes are realized. In “Risk Budgeting Allocation for Dynamic Risk Measures,” Pesenti, Jaimungal, Saporito, and Targino develop time-consistent portfolio strategies, termed dynamic risk budgeting strategies, where the diversification is on the level of risk contribution of assets. The authors formalize the dynamic setting of risk budgeting strategies, recast dynamic risk budgeting strategies as solutions of sequences of convex optimization problems, and develop an efficient actor-critic deep reinforcement learning algorithm for estimating dynamic risk budgeting strategies. The algorithm and the dynamic risk budgeting strategies are illustrated on a complex simulation case study involving five assets and 12 time steps.

Operations ResearchVol. 73(3)
University of Toronto (CA), University of Oxford (GB), Fundação Getulio Vargas (BR)
Openalex Percentile: Top 15%
Risk and Portfolio Optimization
1.47
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