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

Marcelo Righi (marcelo.righi@ufrgs.br), Fernanda Maria M\"uller and Marlon Ruoso Moresco

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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.

Date: 2022-08, Revised 2023-05
New Economics Papers: this item is included in nep-rmg and nep-upt
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