Multiple Volatility Real Options Approach to Investment Decisions Under Uncertainty

Abstract

We present a novel multiple volatility real options approach (MVR) to value investment projects with embedded flexibility and affected by multiple uncertainties. A core innovation is the MVR decision tree composed of MVR synthetic tree components, each reflecting a unique binomial process that approximates a geometric Brownian motion of project value induced by one uncertainty source. MVR uses Monte Carlo simulation to generate volatility of project value log-returns arising from each uncertainty source. MVR produces a multidimensional surface, which is hidden in other approaches, representing enhanced net present value (ENPV) as a function of each uncertainty. It allows the impact of each uncertainty’s volatility on ENPV to be measured through three MVR sensitivity analysis levers. To illustrate MVR, it is applied to a real-world investment project, revealing that MVR provides a more accurate valuation than alternative approaches that do not account for separate impacts of each uncertainty. MVR with its greater veracity, provides robust investment decisions through MVR decision rules.