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PMA license valuation: A Bayesian learning real options approach
Author(s) -
Miller Luke T.
Publication year - 2010
Publication title -
review of financial economics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.347
H-Index - 41
eISSN - 1873-5924
pISSN - 1058-3300
DOI - 10.1016/j.rfe.2009.10.004
Subject(s) - license , valuation (finance) , context (archaeology) , bayesian probability , actuarial science , value of information , time horizon , influence diagram , decision analysis , computer science , valuation of options , bayesian inference , economics , operations research , econometrics , decision tree , finance , engineering , machine learning , artificial intelligence , paleontology , mathematical economics , biology , operating system
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.