The sizes and powers of some stochastic dominance tests: A Monte Carlo study for correlated and heteroskedastic distributions

Testing for stochastic dominance among distributions is an important issue in the study of asset management, income inequality, and market efficiency. This paper conducts Monte Carlo simulations to examine the sizes and powers of several commonly used stochastic dominance tests when the underlying distributions are correlated or heteroskedastic. Our Monte Carlo study shows that the test developed by Davidson and Duclos [9] has better size and power performances than two alternative tests developed by Kaur et al. [18] and Anderson [1]. In addition, we find that when the underlying distributions are heteroskedastic, both the size and power of the test developed by Davidson and Duclos [9] are superior to those of the two alternative tests.