Sensitivity Analysis of Model Input Dependencies Using Copulas

34 Pages Posted: 21 Oct 2015 Last revised: 22 Jan 2016

See all articles by Tianyang Wang

Tianyang Wang

Colorado State University - Department of Finance & Real Estate

James Dyer

University of Texas at Austin

Warren Hahn

University of Texas at Austin - Red McCombs School of Business

Date Written: October 19, 2015

Abstract

Many important decision and risk analysis problems are complicated by dependencies between input variables. In such cases, standard one-variable-at-a-time sensitivity analysis methods are typically eschewed in favor of fully probabilistic, or n-way, analysis techniques which simultaneously model all n input variables and their interdependencies. Unfortunately, much of the intuition provided by one-way sensitivity analysis and its associated graphical output displays such as Tornado diagrams may not be available in fully probabilistic methods. It is also difficult or impossible to isolate the marginal effects of variables in an n-way analysis. In this paper, we present a dependence-adjusted approach for identifying and analyzing the impact of the input variables in a model through the use of probabilistic sensitivity analysis based on copulas. This approach provides insights about the influence of both the input variables and the dependence relationships between the input variables. A key contribution of this approach is that it facilitates assessment of the relative marginal influence of variables for the purpose of determining which variables should be modeled in applications where computational efficiency is a concern, such as in decision tree analysis of large scale problems. In addition, we also investigate the sensitivity to the magnitude of correlation in the inputs.

Keywords: Decision Analysis, Sensitivity Analysis, Correlations, Dependence, Copulas

Suggested Citation

Wang, Tianyang and Dyer, James and Hahn, Warren, Sensitivity Analysis of Model Input Dependencies Using Copulas (October 19, 2015). Available at SSRN: https://ssrn.com/abstract=2676316 or http://dx.doi.org/10.2139/ssrn.2676316

Tianyang Wang (Contact Author)

Colorado State University - Department of Finance & Real Estate ( email )

Finance and Real Estate Department
1272 Campus Delivery
Fort Collins, CO 80523
United States

James Dyer

University of Texas at Austin ( email )

2317 Speedway
Austin, TX 78712
United States

Warren Hahn

University of Texas at Austin - Red McCombs School of Business ( email )

Austin, TX 78712
United States

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