Extension of stochastic dominance theory to random variables
Extension of stochastic dominance theory to random variables
In this paper, we develop some stochastic dominance theorems for the location and scale family and linear combinations of random variables and for risk lovers as well as risk averters that extend results in Hadar and Russell (1971) and Tesfatsion (1976). The results are discussed and applied to decision-making.
Grüss-type bounds for covariances and the notion of quadrant dependence in expectation
We show that Grüss-type probabilistic inequalities for covariances can be considerably sharpened when the underlying random variables are quadrant dependent in expectation (QDE). The herein established covariance bounds not only sharpen the classical Grüss inequality but also improve upon recently derived Grüss-type bounds under the assumption of quadrant dependency (QD), which is stronger than QDE. We illustrate our general results with examples based on specially devised bivariate distributions...
Stochastic dominance theory for location-scale family.
Robust estimation in capital asset pricing model.
Segregation and integration: a study of the behaviors of investors with extended value functions.
Grüss-type bounds for the covariance of transformed random variables.
New variance ratio tests to identify random walk from the general mean reversion model.
The relationship between stock markets of major developed countries and Asian emerging markets.
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