Real Options Luke Miller

Development of a Bayesian Real Options Framework: And Its Application to Capital Budgeting Problems

A Bayesian Real Options framework for capital budgeting decisions defined by irreversibility, uncertainty, and the opportunity to gather information before committing.

Bayesian inference real options capital budgeting information acquisition investment under uncertainty

This research explores combining real options theory with Bayesian decision theory to enhance capital budgeting decisions. The work specifically addresses investment decisions defined by partial or full irreversibility of capital outlays, uncertainty, and the opportunity to gather information before committing.

Methodology

The methodology merges option pricing theory with Bayesian revision processes. Rather than treating parameters as fixed, the framework updates beliefs about project value as new information arrives — allowing firms to refine their investment thresholds dynamically.

Key Contributions

  • Identifying thresholds for better decision-making under uncertainty
  • Formally defining information’s impact on downstream investment decisions
  • Developing project activation policies that account for learning
  • Creating new modeling approaches for delayed investment scenarios with sequential information acquisition

Application

The Bayesian Learning Real Options (BLRO) methodology was tested using real industry data from aerospace maintenance, repair, and overhaul firms. It was applied to contingent investment and license valuation scenarios, demonstrating meaningful deviations from classical static frameworks.