Using Binomial Decision Trees to Solve Real-Option Valuation Problems

TLDR

Traditional decision analysis methods provide an intuitive approach to valuing projects with managerial flexibility or real options, and the discrete‑time real‑option valuation has typically been implemented in finance using a binomial lattice framework. The study proposes using a binomial decision tree with risk‑neutral probabilities to approximate uncertainty in project value changes over time. The authors solve the binomial decision tree via dynamic programming, offering a computationally intensive yet simpler and more intuitive solution compared to traditional lattice methods. The approach yields greater modeling flexibility, enabling inclusion of multiple underlying uncertainties and concurrent options with complex payoff characteristics.

Abstract

Traditional decision analysis methods can provide an intuitive approach to valuing projects with managerial flexibility or real options. The discrete-time approach to real-option valuation has typically been implemented in the finance literature using a binomial lattice framework. Instead, we use a binomial decision tree with risk-neutral probabilities to approximate the uncertainty associated with the changes in the value of a project over time. Both methods are based on the same principles, but we use dynamic programming to solve the binomial decision tree, thereby providing a computationally intensive but simpler and more intuitive solution. This approach also provides greater flexibility in the modeling of problems, including the ability to include multiple underlying uncertainties and concurrent options with complex payoff characteristics.