Valuing Real Options in the Volatile Real World

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.

Automated summary

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, allowing for flexible structures for stochastic processes with fat tail distributions. The proposed approach provides a more accurate estimate of extreme downside risk by allowing negative values for the underlying project values, making it a more robust method for valuing complex real options under multiple sources of uncertainty in the volatile real world.