Gruss-Type Bounds for the Covariance of Transformed Random Variables
Universidad de la República; University of Montevideo
Luis F. Garcia
University of Lleida
Hong Kong Baptist University (HKBU)
University of Western Ontario - Department of Statistical and Actuarial Sciences
March 16, 2010
A number of problems in economics, finance, information theory, insurance, and generally in decision making under uncertainty rely on estimates of the covariance between (transformed) random variables, which can for example be losses, risks, incomes, financial returns, etc. Several avenues relying on inequalities for analyzing the covariance are available in the literature, bearing the names of Chebyshev, Gruss, Hoeffding, Kantorovich, and others. In the present paper we sharpen the upper bound of a Gruss-type covariance inequality by incorporating a notion of quadrant dependence between random variables and also utilizing the idea of constraining the means of the random variables.
Number of Pages in PDF File: 12
Keywords: Gruss-type Bounds, Covariance, Transformed Random Variables, decision making, uncertainty
JEL Classification: D15, E15working papers series
Date posted: March 24, 2010
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