Multivariate real options valuation

Show simple record

dc.contributor.advisor Dyer, James S.
dc.creator Wang, Tianyang
dc.date.accessioned 2011-06-08T15:25:48Z
dc.date.accessioned 2011-06-08T15:26:08Z
dc.date.available 2011-06-08T15:25:48Z
dc.date.available 2011-06-08T15:26:08Z
dc.date.created 2011-05
dc.date.issued 2011-06-08
dc.date.submitted May 2011
dc.identifier.uri http://hdl.handle.net/2152/ETD-UT-2011-05-2797
dc.description.abstract This dissertation research focuses on modeling and evaluating multivariate uncertainties and the dependency between the uncertainties. Managing risk and making strategic decisions under uncertainty is critically important for both individual and corporate success. In this dissertation research, we present two new methodologies, the implied binomial tree approach and the dependent decision tree approach, to modeling multivariate decision making problems with practical applications in real options valuation. First, we present the implied binomial tree approach to consolidate the representation of multiple sources of uncertainty into univariate uncertainty, while capturing the impact of these uncertainties on the project’s cash flows. This approach provides a nonparametric extension of the approaches in the literature by allowing the project value to follow a generalized diffusion process in which the volatility may vary with time and with the asset prices, therefore offering more modeling flexibility. This approach was motivated by the Implied Binomial Tree (IBT) approach that is widely used to value complex financial options. By constructing the implied recombining binomial tree in a way so as to be consistent with the simulated market information, we extended the finance-based IBT method for real options valuation — when the options are contingent on the value of one or more market related uncertainties that are not traded assets. Further, we present 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.
dc.format.mimetype application/pdf
dc.language.iso eng
dc.subject Real options
dc.subject Decision tree
dc.subject Copulas
dc.subject Dependence
dc.subject Evaluating uncertainties
dc.subject Implied binomial tree
dc.title Multivariate real options valuation
dc.date.updated 2011-06-08T15:26:08Z
dc.contributor.committeeMember Tompaidis, Efstathios
dc.contributor.committeeMember Muthuraman, Kumar
dc.contributor.committeeMember Bickel, J. E.
dc.contributor.committeeMember Butler, John C.
dc.contributor.committeeMember Garlappi, Lorenzo
dc.description.department IROM
dc.type.genre thesis
dc.type.material text
thesis.degree.department IROM
thesis.degree.discipline Information, Risk and Operations Management
thesis.degree.grantor University of Texas at Austin
thesis.degree.level Doctoral
thesis.degree.name Doctor of Philosophy

Files in this work

Download File: WANG-DISSERTATION.pdf
Size: 1.939Mb
Format: application/pdf

This work appears in the following Collection(s)

Show simple record


Advanced Search

Browse

My Account

Statistics

Information