We present two complementing selection procedures for comparing simulated systems based on the stochastic dominance relationship of a performance metric of interest. The decision maker specifies an output quantile set representing a section of the distribution of the metric, e.g., downside or upside risks or central tendencies, as the basis for comparison. The first procedure compares systems o...

Traditional stochastic dominance rules are so strict and qualitative conditions that generally a stochastic dominance relation between two alternatives does not exist. To solve the problem, we firstly supplement the definitions of almost stochastic dominance (ASD). Then, we propose a new definition of stochastic dominance degree (SDD) that is based on the idea of ASD. The new definition takes b...

We analyze relations between two methods frequently used for modeling the choice among uncertain outcomes: stochastic dominance and mean–risk approaches. New necessary conditions for stochastic dominance are developed. These conditions compare values of a certain functional, which contains two components: the expected value of a random outcome and a risk term represented by the central semidevi...

This paper re-examines the performance of REITs, stocks, and fixed-income assets based on the preferences of risk-averse and risk-seeking investors using mean-variance and stochastic dominance approaches. Our findings indicate no first-order stochastic dominance and no arbitrage opportunity among these assets. However, our stochastic dominance results reveal that in order to maximize their expe...

We develop an optimization method for constructing investment portfolios that dominate a given benchmark portfolio in terms of third-degree stochastic dominance. Our approach relies on the properties of the semivariance function, a refinement of an existing ‘super-convex’ dominance condition and quadratic constrained programming. We apply our method to historical stock market data using an indu...

Considering first-order stochastic dominance constraints for random variables arising as optimal values of stochastic programs with linear recourse, verifiable sufficient conditions for metric regularity are presented. A growth condition developed in [22] has a crucial role in the analysis of the present paper. Implications regarding stability and sensitivity of optimal values and optimal solut...

We consider truthfulness concepts for auctions with payments based on firstand second-order stochastic dominance. We assume bidders consider wealth in standard quasi-linear form as valuation minus payments. Additionally, they are sensitive to risk in the distribution of wealth stemming from randomized mechanisms. Firstand second-order stochastic dominance are well-known to capture risk-sensitiv...

Abstract: This paper deals with nonparametric inference for second order stochastic dominance of two random variables. If their distribution functions are unknown they have to be inferred from observed realizations. Thus, any results on stochastic dominance are in uenced by sampling errors. We establish two methods to take the sampling error into account. The rst one is based on the asymptotic ...

Farinelli and Tibiletti (F-T) ratio, a general risk-reward performance measurement ratio, is popular due to its simplicity and yet generality that both Omega ratio and upside potential ratio are its special cases. The F-T ratios are ratios of average gains to average losses with respect to a target, each raised by a power index, p and q. In this paper, we establish the consistency of F-T ratios...