Testing for Stochastic Dominance Efficiency

Testing for Stochastic Dominance Efficiency
Author: Oliver B. Linton
Publisher:
Total Pages: 29
Release: 2012
Genre:
ISBN:

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

Empirical Tests for Stochastic Dominance Efficiency
Author: Thierry Post
Publisher:
Total Pages: 0
Release: 2012
Genre:
ISBN:

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.

Extensions on Stochastic Dominance Efficiency Tests

Extensions on Stochastic Dominance Efficiency Tests
Author: Markku Kallio
Publisher:
Total Pages: 23
Release: 2018
Genre:
ISBN:

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.

Topics in Microeconomics

Topics in Microeconomics
Author: Elmar Wolfstetter
Publisher: Cambridge University Press
Total Pages: 394
Release: 1999-10-28
Genre: Business & Economics
ISBN: 9780521645348

This book in microeconomics focuses on the strategic analysis of markets under imperfect competition, incomplete information, and incentives. Part I of the book covers imperfect competition, from monopoly and regulation to the strategic analysis of oligopolistic markets. Part II explains the analytics of risk, stochastic dominance, and risk aversion, supplemented with a variety of applications from different areas in economics. Part III focuses on markets and incentives under incomplete information, including a comprehensive introduction to the theory of auctions, which plays an important role in modern economics.

Panel Stochastic Dominance Test and Panel Informational Efficiency LR Test

Panel Stochastic Dominance Test and Panel Informational Efficiency LR Test
Author: Christian de Peretti
Publisher:
Total Pages: 18
Release: 2015
Genre:
ISBN:

This paper propose a new panel stochastic dominance (SD) test-PDD test, the asymptotic properties are derived, which extends Davidson and Duclos (DD) SD test to a panel context. The PDD test also contributes to settle one of the demerits while working with financial derivatives time series: that the standard individual tests for Stochastic Dominance in time series are unsatisfactory in terms of power when the sample size is too small, and typically the financial derivatives have a limited life, in particular, stock options and covered warrants. This is because the pairwise SD tests are nonparametric, and nonparametric tests require large sample size, in this case, the individual tests for financial derivative time series may not distinguish between the null and the alternative hypotheses for each series, and lead to retain the null hypothesis, even if the alternative is true. Hence the PDD test would improve the power of individual SD tests: a panel test gathers all the information of all the series, and then increases the power compared to its corresponding individual test. This paper also extends the classical likelihood ratio (LR) information efficiency test to a panel framework to get more powerful new tests. A bootstrap methodology is developed to correct the size distortion of the LR test.