Testing for Stochastic Dominance Efficiency


Book Description

We propose a new test of the stochastic dominance efficiency of a given portfolio over a classof portfolios. We establish its null and alternative asymptotic properties, and define a methodfor consistently estimating critical values. We present some numerical evidence that our testswork well in moderate sized samples.







Empirical Tests for Stochastic Dominance Efficiency


Book Description

We derive empirical tests for the stochastic dominance efficiency of a given portfolio with respect to all possible portfolios constructed from a set of assets. The tests can be computed using straightforward linear programming. Bootstrapping techniques and asymptotic distribution theory can approximate the sampling properties of the test results and allow for statistical inference. Our results could provide a stimulus to the further proliferation of stochastic dominance for the problem of portfolio selection and evaluation. Using our tests, the Fama and French market portfolio is significantly inefficient relative to benchmark portfolios formed on market capitalization and book-to-market equity ratio.







Multivariate Tests for Stochastic Dominance Efficiency of a Given Portfolio


Book Description

We develop empirical tests for stochastic dominance efficiency of a given investment portfolio relative to all possible portfolios formed from a set of assets. Our tests use multivariate statistical methods, which results in good statistical power properties and increases the comparability with existing mean-variance tests. Using our tests, we demonstrate that the mean-variance inefficiency of the CRSP all-share index relative to beta-sorted portfolios can be explained by tail risk not captured by variance.




Extensions on Stochastic Dominance Efficiency Tests


Book Description

We consider second, third, fourth and fifth order stochastic dominance (SSD, TSD, FOSD and FISD, respectively) as well as decreasing absolute risk aversion (DARA) stochastic dominance (DSD). For comparison with DSD we also consider stochastic dominance (ESD) based on CARA utility functions. Their relevance in practice arises from empirical evidence on individual preferences fitting to preference models underlying such stochastic dominance relations. Assuming a known, discrete and finite probability distribution we derive necessary and sufficient efficiency tests under the six types of stochastic dominance. Simple arguments yield well-known SSD and TSD efficiency tests which are subsequently used to develop new FOSD, FISD, DSD and ESD efficiency tests. We provide numerical demonstration using stock market data of the US.




Stochastic Dominance


Book Description




Size and Power of Some Stochastic Dominance Tests


Book Description

Testing for stochastic dominance between distributions is an important issue in the study of asset distribution, income distribution and market efficiency. This paper applies Monte Carlo simulations to examine the size and power of some commonly used stochastic dominance tests when the underlying distributions are correlated, and either homoskedastic or heteroskedastic. Our Monte Carlo study shows that the test suggested by Davidson and Duclos (2000) has a better size- and power-performance than two alternative tests developed by Kaur, Rao and Singh (1994) and Anderson (1996). In addition, when the underlying distributions are heteroskedastic, both the size and power of test developed by Davidson and Duclos (2000) are still superior to those of the two alternative tests.