Orateur
Description
Stochastic dominance is essential in decision-making under uncertainty and quantitative finance, providing a rigorous method for comparing random variables through their distribution functions.
Despite its importance in decision-making under uncertainty, (higher-order) stochastic dominance is computationally intractable due to infinitely many constraints.
Our research addresses this by reducing these constraints to a finite number, enabling algorithms that optimize returns while satisfying (higher-order) stochastic dominance conditions.
This contribution introduces StochasticDominance.jl, an open-source Julia package.
It is designed for verifying higher-order stochastic dominance between random variables and optimizing under higher-order stochastic dominance constraints.
As a black-box solution, it allows users to input data and obtain results seamlessly, making the analysis of higher-order stochastic dominance accessible with minimal technical expertise.
