A Copulas-Based Approach to Modeling Dependence in Decision Trees

• 成果类型: Article
• 署名作者: Wang, Tianyang; Dyer, James S.
• 署名单位: Colorado State University System; Colorado State University Fort Collins; University of Texas System; University of Texas Austin
• 刊物名称: OPERATIONS RESEARCH
• ISSN/ISSBN: 0030-364X
• DOI: 10.1287/opre.1110.1004
• 发表日期: 2012
• 页码: 225-242
• 关键词: specified marginals; random vectors; distributions; uncertainty; generation

摘要

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.