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R&D is often a highly uncertain venture where experiments achieve successful outcomes on an extraordinarily rare basis. Just one successful product could change the future of a company; the discovery stage can often be an invaluable or disastrous experience. We develop a real R&D option model based on extreme distributions, where cash flows for R&D follows a standard stochastic process. Then, we allow the arrival frequency of ?wildcard' discoveries to follow some extreme densities. We provide analytical solutions of project values for perpetual American options and optimal stopping triggers. We examine the roles of tail-shape parameters of discovery distributions, and compare values as well as triggers to invest with models governed by distributions with other types of heavy tails. Conventional real option models have higher trigger values for higher option premiums. In contrast, we find that effective premium for options and their trigger values behave differently when we based extreme discoveries on heaviness of the tails of the discovery distributions. Distributions that are upperbounded provide higher option values (lower trigger values) at lower number of extreme discoveries than distributions that have fat-tails characteristics. As the number of discoveries increases, the reverse results occur. Depending on extreme distribution selected, the most extreme frequency of breakthroughs can lead to the most valuable R&D discovery project. We include an empirical example of a biotechnology where blockbuster drugs data are simulated to extreme discovery distributions and draw further analyses on extreme prospects of its pipeline.

Auteurs :Koh Wilson , Paxson Dean
Extrait de la revue BMI 86