On Modeling Correlated Random Variables in Risk Assessment

Charles Nathan Haas

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Financial Risk and Volatility Modeling Risk and Safety Analysis Simulation Techniques and Applications

Abstract

Monte Carlo methods in risk assessment are finding increasingly widespread application. With the recognition that inputs may be correlated, the incorporation of such correlations into the simulation has become important. Most implementations rely upon the method of Iman and Conover for generating correlated random variables. In this work, alternative methods using copulas are presented for deriving correlated random variables. It is further shown that the particular algorithm or assumption used may have a substantial effect on the output results, due to differences in higher order bivariate moments.

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References

1982A distribution-free approach to inducing rank correlation among input variables 1998Understanding Relationships Using Copulas 1993Statistical Inference Procedures for Bivariate Archimedean Copulas 1986The Joy of Copulas: Bivariate Distributions with Uniform Marginals 1996Quantitative Risk Analysis: A Guide to Monte Carlo Simulation Modelling 1999Correlations and Copulas for Decision and Risk Analysis 1994Principles of Good Practice for the Use of Monte Carlo Techniques in Human Health and Ecological Risk Assessments 1994Assessment of Variability and Uncertainty Distributions for Practical Risk Analyses

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