[Paper Review] Real Options Technique as a Tool of Strategic Risk Management

В. Й. Савчук|arXiv (Cornell University)|Mar 16, 2023

Capital Investment and Risk AnalysisEconomics, Econometrics and Finance3 citations

TL;DR

This paper proposes a practical real options methodology using binomial trees to evaluate strategic investment projects under uncertainty, focusing on two-stage investment decisions with random cash flows. It introduces a Binomial-Random-Cash-Flow Real Options Model with Gaussian-distributed outcomes and defines Project Value at Risk as a feasibility criterion, enabling analytical solutions without Monte Carlo simulation for tractable business applications.

ABSTRACT

The real options approach is now considered an effective alternative to the corporate DCF model for a feasibility study. The current paper offers a practical methodology employing binomial trees and real options techniques for evaluating investment projects. A general computation procedure is suggested for the decision tree with two active stages of real options, which correspond to additional investments. The suggested technique can be used for most real options, which are practically essential regarding enterprise strategy. The special case named Binomial-Random-Cash-Flow Real Options Model with random outcomes is developed as the next step of real options modelling. Project Value at Risk is introduced and used as a criterion of investment project feasibility under the assumption regarding random outcomes. In particular, the Gaussian probability distribution is used for modelling option outcomes uncertainty. The choice of the Gaussian distribution is caused by the desire to obtain estimates in the final analytical form. Choosing another distribution for random outcomes leads to using Monte Carlo simulation, for which a general framework is developed by demonstrating some instances. The author could avoid the computational complexity that makes these solutions feasible for business practice.

Motivation & Objective

• To develop a practical real options framework for strategic risk management in corporate investment decisions.
• To model two-stage investment projects using binomial trees with additional capital expenditures at each stage.
• To introduce Project Value at Risk as a risk-based criterion for project feasibility under uncertainty.
• To enable analytical solutions via Gaussian distribution assumptions, avoiding computationally intensive Monte Carlo simulations.
• To provide a general modeling framework applicable to most real options in enterprise strategy.

Proposed method

• The paper employs binomial trees to model the evolution of project value over time, incorporating two active stages of real options with additional investments.
• It introduces a Binomial-Random-Cash-Flow Real Options Model where option outcomes follow a Gaussian probability distribution for analytical tractability.
• The model computes Project Value at Risk using the cumulative distribution function of the normal distribution to assess downside risk.
• The methodology avoids Monte Carlo simulation by assuming normality, enabling closed-form analytical solutions.
• A general framework for alternative distributions is outlined, with illustrative instances provided for non-Gaussian cases.
• The approach integrates risk management into strategic investment evaluation by quantifying downside exposure through Value at Risk.

Experimental results

Research questions

• RQ1 How can real options be applied to two-stage investment projects with sequential capital expenditures?
• RQ2 What is the impact of assuming normally distributed cash flows on the analytical feasibility of real options models?
• RQ3 Can Project Value at Risk serve as a reliable criterion for investment feasibility under uncertainty?
• RQ4 How does the proposed model compare to traditional DCF and Monte Carlo-based real options approaches in terms of computational complexity?
• RQ5 What is the role of binomial trees in modeling strategic flexibility and risk in long-term investment decisions?

Key findings

• The Binomial-Random-Cash-Flow Real Options Model enables analytical computation of project value and risk under Gaussian assumptions.
• Project Value at Risk is successfully defined and used as a feasibility criterion, allowing risk-aware investment decisions.
• The use of the Gaussian distribution allows for closed-form solutions, significantly reducing computational burden compared to Monte Carlo simulation.
• The framework remains generalizable, with a clear path to adapting to other probability distributions when needed.
• The model supports practical application in business settings by balancing analytical precision with computational efficiency.
• The methodology provides a structured approach to evaluating strategic investments with embedded options and uncertainty.