Higher order measures of risk and stochastic dominance

ArXiv ID: 2402.15387 “View on arXiv”

Authors: Unknown

Abstract

Higher order risk measures are stochastic optimization problems by design, and for this reason they enjoy valuable properties in optimization under uncertainties. They nicely integrate with stochastic optimization problems, as has been observed by the intriguing concept of the risk quadrangles, for example. Stochastic dominance is a binary relation for random variables to compare random outcomes. It is demonstrated that the concepts of higher order risk measures and stochastic dominance are equivalent, they can be employed to characterize the other. The paper explores these relations and connects stochastic orders, higher order risk measures and the risk quadrangle. Expectiles are employed to exemplify the relations obtained.

Keywords: Higher Order Risk Measures, Stochastic Dominance, Risk Quadrangles, Expectiles, Stochastic Optimization

Complexity vs Empirical Score

• Math Complexity: 9.0/10
• Empirical Rigor: 2.0/10
• Quadrant: Lab Rats
• Why: The paper is dense with advanced mathematical analysis, featuring stochastic optimization, duality theorems, and explicit derivations of spectral risk measures; however, it contains no data, backtests, or implementation details, focusing solely on theoretical equivalences and characterizations.

  flowchart TD
    A["Research Goal:<br>Explore relationship between<br>Higher Order Risk Measures and<br>Stochastic Dominance"] --> B["Key Methodology:<br>Analyze equivalence properties<br>and Risk Quadrangles"]
    B --> C["Input Data:<br>General probability spaces<br>Random variables X, Y"]
    C --> D["Computational Process:<br>Derive proofs connecting<br>Stochastic Orders to<br>Expectiles and Risk Measures"]
    D --> E["Key Outcomes:<br>Equivalence established<br>Integration with<br>Stochastic Optimization"]
    E --> F["Application:<br>Risk quadrangles provide<br>unified framework for<br>optimization under uncertainty"]