A Copulas-Based Approach to Modeling Dependence in Decision Trees

Tianyang, W., & Dyer, J. S. (2012). A Copulas-Based Approach to Modeling Dependence in Decision Trees. Operations research, (1), 225-242.

Posted: 20 Apr 2015

See all articles by Tianyang Wang

Tianyang Wang

Colorado State University - Department of Finance & Real Estate

James Dyer

University of Texas at Austin

Date Written: April 18, 2012

Abstract

This paper presents a general framework based on copulas for modeling dependent multivariate uncertainties through the use of a decision tree. The proposed dependent decision tree model allows multiple dependent uncertainties with arbitrary marginal distributions to be represented in a decision tree with a sequence of conditional probability distributions. This general framework could be naturally applied in decision analysis and real options valuations, as well as in more general applications of dependent probability trees. While this approach to modeling dependencies can be based on several popular copula families as we illustrate, we focus on the use of the normal copula and present an efficient computational method for multivariate decision and risk analysis that can be standardized for convenient application.

Keywords: correlation; copulas; multivariate decision and risk analysis

JEL Classification: G13

Suggested Citation

Wang, Tianyang and Dyer, James, A Copulas-Based Approach to Modeling Dependence in Decision Trees (April 18, 2012). Tianyang, W., & Dyer, J. S. (2012). A Copulas-Based Approach to Modeling Dependence in Decision Trees. Operations research, (1), 225-242.. Available at SSRN: https://ssrn.com/abstract=2596083

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

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