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Competitive real options under private information

Leandro Gorno and Felipe Iachan ()

Journal of Economic Theory, 2020, vol. 185, issue C

Abstract: We study a research and development race by extending the standard investment under uncertainty framework. Each firm observes the stochastic evolution of a new product's expected profitability and chooses the optimal time to release it. Firms are imperfectly informed about the state of their opponents, who could move first and capture the market. We characterize a family of priors for which the game admits a stationary equilibrium. In this case, the equilibrium is unique and can be explicitly constructed. Across games with priors in this family, there is a maximal intensity of competition that can be supported, which is a simple function of the environment's parameters. Away from this family, we offer sufficient conditions for convergence of a non-stationary equilibrium. When these hold, the intensity of competition tends to the maximal possible value. Furthermore, we develop methods that can be useful for other applications, including a modified Kolmogorov forward equation for tracking posterior beliefs and an algorithm for computing non-stationary equilibria.

Keywords: Real options; Uncertainty; Investment; Learning; Competition; Private information (search for similar items in EconPapers)
JEL-codes: C73 D92 G31 (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1016/j.jet.2019.104945

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