Grüss-Type Bounds for the Covariance of Transformed Random Variables
1 Department of Economics, University of Montevideo, Montevideo 11600, Uruguay
2 Accounting and Finance Department, Norte Construcciones, Punta del Este 20100, Uruguay
3 Departamento de Métodos Matemáticos e de Representación, Escola Técnica Superior de Enxeñeiros de Camiños, Canais e Portos, Universidade da Coruña, 15001 A Coruña, Spain
4 Department of Economics, Institute for Computational Mathematics, Hong Kong Baptist University, Kowloon Tong, Hong Kong
5 Department of Statistical and Actuarial Sciences, University of Western Ontario, London, ON, Canada, N6A 5B7
Journal of Inequalities and Applications 2010, 2010:619423 doi:10.1155/2010/619423Published: 28 March 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, and so forth. Several avenues relying on inequalities for analyzing the covariance are available in the literature, bearing the names of Chebyshev, Grüss, Hoeffding, Kantorovich, and others. In the present paper we sharpen the upper bound of a Grüss-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.