Valuing Real Options in the Volatile Real World

Abstract

Motivated by the real‐world challenges of real options evaluation faced by many companies when commodity prices exhibit dramatic volatility and project values can become negative, this study presents a framework for solving a multifactor real options problem by approximating the underlying stochastic process of project value with a generalized implied binomial tree. The proposed approach allows a flexible structure for stochastic processes with fat tail distributions, such as jump diffusion, regime switch or mean reversion and provides a more accurate estimate of the extreme downside risk by allowing negative values for the underlying project values. Our illustrative example shows that the value of a real option estimated by the proposed approach is more accurate and stable than the alternative lattice‐based approaches in the literature under a wide variety of underlying commodity process, which makes this a more robust approach for valuing complex real options under multiple sources of uncertainty in the volatile real world.