New Research Directions in Engineering Economics—Modeling Dependencies with Copulas

Abstract

Understanding and quantifying dependencies among variables often arises in stochastic capital investment and real option analysis. In such modeling situations, Pearson product moment linear correlation is widely used as a dependence measure. Linear correlation has several limitations. More recently, copulas have been used in financial economics and insurance to model dependencies. We contribute to the engineering economy literature by introducing copulas to model dependent risks that will enhance research and practice. We demonstrate the usefulness of copula-based sampling in simulation of project risk and regression analysis for forecasting. We also discuss potential copula research in engineering economic analysis.

ACKNOWLEDGEMENTS

Hemantha Herath acknowledges research support from the Social Sciences and Humanities Research Council (SSHRC) of Canada. We are grateful to the Real Option session participants at the IIE-Industrial Engineering Research Conference (IERC) 2006 and Financial Engineering and Risk Management session participants at the IIE-IERC 2007 for their valuable comments and suggestions.

Notes

¹More generally, the marginal distributions and joint distribution of the random variables must be elliptical distributions. (such as multivariate normal, multivariate t-distribution, logistic distribution, and Laplace distribution).

²A nice summary of sampling procedure with regression can be found in Park and Sharp-Bette (1990, pages 520–524).

³Notice that although we have fitted an empirical continuous distribution, a better method is to fit a theoretical distribution (gamma, uniform, Pareto, etc.). Then one can use built-in computer functions for inverting the distribution.