We have all created options. We create a simple option when we make a nonrefundable deposit to hold a hotel room for the first night of a four-night ski vacation or to save a seat in the entering class of a college. Did our option premium give us good value? That depends on the chances that we would actually exercise the option to stay in the hotel, how much we would value the room, and how we value the alternative scenario where we don't have the room. What about much more complicated real (as distinct from market-traded financial) business options? The option structure and the unfolding of uncertainty would be more involved, yet the valuation would follow similar thinking. This note discusses how to use decision trees and Monte Carlo simulations to structure the analysis of option decisions and to value them. The note starts with simple examples and moves on to very rich examples, limited only by students' ability to model the underlying uncertainties and clarify the nature of the option. Part of the challenge is to anticipate how the future exercise of an option would be triggered. Regression helps identify triggering variables or functions. By the end of the note, students will have developed generally applicable tools for widely ranging real options using accessible methodology.
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