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A risk measurement approach from risk-averse stochastic optimization of score functions

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  • Righi, Marcelo Brutti
  • Müller, Fernanda Maria
  • Moresco, Marlon Ruoso

Abstract

We propose a risk measurement approach for a risk-averse stochastic problem. We provide results that guarantee the existence of a solution to our problem. We characterize and explore the properties of the argmin as a risk measure and the minimum as a generalized deviation measure. We provide an example to demonstrate a specific application of our approach. Additionally, we present a numerical example of the problem's solution to illustrate the usefulness of our approach in risk management analysis.

Suggested Citation

  • Righi, Marcelo Brutti & Müller, Fernanda Maria & Moresco, Marlon Ruoso, 2025. "A risk measurement approach from risk-averse stochastic optimization of score functions," Insurance: Mathematics and Economics, Elsevier, vol. 120(C), pages 42-50.
    • Handle: RePEc:eee:insuma:v:120:y:2025:i:c:p:42-50
    DOI: 10.1016/j.insmatheco.2024.11.005
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    1. Shanyu Han & Yang Liu & Xiang Yu, 2025. "Risk-sensitive Reinforcement Learning Based on Convex Scoring Functions," Papers 2505.04553, arXiv.org, revised May 2025.

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