This article provides a unified framework for analyzing a wide variety of real option problems. These problems include the frequently studied, simple real option problems, as described in Dixit and Pindyck [1994. Investment Under Uncertainty. Princeton University Press, Princeton] for example, but also problems with more complicated and realistic assumptions. We reveal that all the real option problems belonging to the more general class considered in this study are described by the same mathematical structure, which can be solved by applying a computational algorithm developed in the field of mathematical programming. More specifically, all of the present real option problems can be directly solved by reformulating their optimality condition as a dynamical system of generalized linear complementarity problems (GLCPs). This enables us to develop an efficient and robust algorithm for solving a broad range of real option problems in a unified manner, exploiting recent advances in the theory of complementarity problems.
- Generalized complementarity problem
- Real options
- Smoothing function-based algorithm