Real Options Technique as a Tool of Strategic Risk Management

Paper: arXiv 2303.09176

Authors: Volodymyr Savchuk

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.

Complexity vs Empirical Score

• Math Complexity: 5.5/10
• Empirical Rigor: 3.0/10
• Quadrant: Lab Rats — theoretically deep, empirically untested

Why this score: The paper provides a analytical framework combining binomial trees, real options, and Project Value at Risk with Gaussian assumptions to lower computational complexity. However, it lacks robust empirical testing on historical capital budgeting datasets, relying primarily on theoretical modeling and algorithmic setups.

Research Flowchart

  flowchart TD
    A[Research Goal: Strategic Risk Management via Real Options] --> B[Data Inputs: Project Cash Flows & Volatility Parameters]
    B --> C[Methodology: Binomial Lattice & Decision Trees]
    C --> D{Model Path Selection}
    D -->|Gaussian Outcomes| E[Analytical Model: Project Value at Risk]
    D -->|Complex Outcomes| F[Monte Carlo Simulation Framework]
    E --> G[Outcomes: Practical Valuation & Feasibility Decision]
    F --> G