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

Author

Listed:
  • Marcelo Brutti Righi
  • Fernanda Maria Muller
  • Marlon Ruoso Moresco
Abstract
We propose a risk measurement approach for a risk-averse stochastic problem. We provide results that guarantee that our problem has a solution. We characterize and explore the properties of the argmin as a risk measure and the minimum as a deviation measure. We provide a connection between linear regression models and our framework. Based on this conception, we consider conditional risk and provide a connection between the minimum deviation portfolio and linear regression. Moreover, we also link the optimal replication hedging to our framework.

Suggested Citation

  • Marcelo Brutti Righi & Fernanda Maria Muller & Marlon Ruoso Moresco, 2022. "A risk measurement approach from risk-averse stochastic optimization of score functions," Papers 2208.14809, arXiv.org, revised May 2023.
    • Handle: RePEc:arx:papers:2208.14809
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