The Choice of Stochastic Process in Real Option Valuation II: Selecting Multiple Factor Models

The stochastic process choice plays a central role in real option valuation and it can have an impact not only on the project value but also on the investment rule. The first works written on real options used one factor models – more specifically Geometric Brownian Motion (GBM) and Mean Reversion Models (MRM) – to represent the uncertainties in the valuation modeling. Selecting the most appropriate model is not always a trivial issue, and besides statistical tools, in general, theoretical considerations are taken for this task. In order to generate more realistic models, in the last decades many authors have presented papers proposing the combination of different kinds of stochastic processes creating multiple factor models. Although these models can be more realistic, the task of selecting among many multiple factor models is even harder than in case of one factor models, which implies a new set of tools to make the analysis. This work discusses the choice of multiple factor models in real options valuation, and the main statistical tools and theoretical considerations that can be used for this task.