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Permanent URI for this collectionhttps://hdl.handle.net/10217/206604

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Browsing Publications by Author "Dyer, James, author"

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    A copulas-based approach to modeling dependence in decision trees
    (Colorado State University. Libraries, 2012-01) Wang, Tianyang, author; Dyer, James, author; Operations Research, publisher
    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.
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    ItemOpen Access
    Valuing multifactor real options using an implied binomial tree
    (Colorado State University. Libraries, 2010-06) Wang, Tianyang, author; Dyer, James, author; Decision Analysis, publisher
    This paper proposes an approach for solving a multifactor real options problem by approximating the underlying stochastic process with an implied binomial tree. The implied binomial tree is constructed to be consistent with simulated market information. By simulating European option prices as artificial market information, we apply the implied binomial tree method for real options valuation when the options are contingent on the value of market uncertainties that are not traded assets. Compared to the discrete approximations suggested in the current literature, this method offers a more flexible distribution assumption for project values and therefore provides an alternative approach to estimating the value of high-dimensional real options. For risk managers, it serves as a capital budgeting method for projects with managerial flexibility.