PMA license valuation: A Bayesian learning real options approach

This paper develops a Bayesian learning real option (BLRO) by merging the Bayesian decision-making process with the real options framework. The BLRO approach is then used to value a parts manufacturing approval (PMA) license for an aerospace firm in the maintenance, repair, and overhaul industry. The model combines statistical decision theory with options pricing to evaluate strategic capital expenditures assuming a decision time horizon and posturing costs. Real option attributes are discussed in a decision analytic context and thresholds are identified for improved decision-making. In contrast to other models in the real options literature in which new information is passively introduced during the delay period, our approach encourages active information acquisition and quantifies its impact on the decision.