Real Extreme R&D Discovery Options

Abstract

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 or blockbuster products per company 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 and higher option premiums for similar parameters. In contrast, we find that model premium for options and their trigger values behave differently when we base extreme discoveries on heaviness of the tails of the discovery distributions. Extreme distributions that are upper-bounded provide higher option values (lower trigger values) at lower number of extreme discoveries than extreme distributions that have heavier tails. As the number of discoveries increases, the reverse results occur. Depending on the extreme distribution selected, the most extreme frequency of breakthroughs can lead to the most valuable R&D discovery project. We apply the model to an empirical example of a pharmagenomic company where we simulate the project option values of a number of possible blockbuster candidates that may emerge from its pipeline.

Keywords: Research and development, real options, extreme value theory

JEL Classification: D81, G12, G13, G31, G32