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A Copulas-Based Approach to Modeling Dependence in Decision Trees

Author

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  • Tianyang Wang

    (College of Business, Colorado State University, Fort Collins, Colorado 80523)

  • James S. Dyer

    (McCombs School of Business, The University of Texas at Austin, Austin, Texas 78712)

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.

Suggested Citation

  • Tianyang Wang & James S. Dyer, 2012. "A Copulas-Based Approach to Modeling Dependence in Decision Trees," Operations Research, INFORMS, vol. 60(1), pages 225-242, February.
    • Handle: RePEc:inm:oropre:v:60:y:2012:i:1:p:225-242
    DOI: 10.1287/opre.1110.1004
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    References listed on IDEAS

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