We propose linear programming tests for spanning and intersection based on stochastic dominance rather than mean-variance analysis. An empirical application investigates the diversification benefits to US investors from emerging equity markets.

We derive empirical tests for stochastic dominance that allow for diversification between choice alternatives. The tests can be computed using straightforward linear programming. Bootstrapping techniques and asymptotic distribution theory can approximate the sampling properties of the test...

Sensitivity analysis is used to quantify the impact of changes in the initial data of linear programs on the optimal value. In particular, parametric sensitivity analysis involves a perturbation analysis in which the effects of small changes of some or all of the initial data on an optimal...

We derive empirical tests for the mean-variance efficiency of a given portfolio. The tests can be computed using straightforward linear programming, and they give substantial flexibility in modeling the investment possibilities. Using this test, we can reject the hypothesis that the S&P 500...

We develop a Stochastic Dominance methodology to analyze if new assets expand the investment possibilities for rational nonsatiable and risk-averse investors. This methodology avoids the simplifying assumptions underlying the traditional mean-variance approach to spanning. The methodology is...

We derive an empirical test for third-order stochastic dominance that allows for diversification between choice alternatives. The test can be computed using straightforward linear programming. Bootstrapping techniques and asymptotic distribution theory can approximate the sampling properties of...