Abstract
The article is devoted to the development of the method for evaluation and control of investment projects efficiency in conditions of high uncertainty. There are marked the limitations of the traditional evaluation methods of investment projects efficiency and proposed more relevant approach based on the application of real option theory, which allows evaluating projects with consideration of their multivariate and opportunity of decision making at every stage of their development. The article shows that, despite the high relevance of real option theory, its practical use is very difficult for a number of reasons. The main problem in estimating the value of real options is the need to use a sufficient number of a priori statistics regarding the project, which in practice is difficult. The authors propose a method in which the probabilistic characteristics of the model project are replaced with the correct expert ratings, named the modified ROV – method (MROV). The method is based on the binomial model of real option value assessing, based on the building of a decision tree, at each node of which there is possible the development according to the “optimistic” or “pessimistic” forecast. Since the development of the project under any scenario depends on a number of criteria environments, the task of assessing the value of real options becomes a multicriteria task. One of the most reasonable and practically relevant methods of solving such problems is the method of analytic hierarchy process (AHP), which is used in the basis of the developed MROV-method. The article also details the algorithm of the method application to determine quantitative estimates of the relative probabilities of the development of the project on “optimistic” or “pessimistic” scenario in each node of the binomial tree. Obtained estimates are used as weights in the calculation of the net present value of the optional investment project for a decision on the feasibility of its realization and inclusion in the project evaluated real options.
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