Real Option Games with R&D and Learning Spillovers

We model pre-investment R&D decisions in the presence of spillover effects in an option pricing framework with analytic tractability. Two firms face two decisions that are solved for interdependently in a two-stage game. The first-stage decision is: what is the optimal level of coordination (optimal policy/technology choice)? The second-stage decision is: what is the optimal effort for a given level of the spillover effects and the cost of information acquisition? The framework is extended to a two-period stochastic game with (path-dependency inducing) switching costs that make strategy revisions harder. Strategy shifts are easier to observe in more volatile environments.Benefit Analysis; Real Options; Coordination Games; R&D

Real Option Games with R&D and Learning Spillovers

We model pre-investment R&D decisions in the presence of spillover effects in an option pricing framework with analytic tractability. Two firms face two decisions that are solved for interdependently in a two-stage game. The first-stage decision is: what is the optimal level of coordination (optimal policy/technology choice)? The second-stage decision is: what is the optimal effort for a given level of the spillover effects and the cost of information acquisition? The framework is extended to a two-period stochastic game with (path-dependency inducing) switching costs that make strategy revisions harder. Strategy shifts are easier to observe in more volatile environments

Real Option Games with R&D and Learning Spillovers

We model pre-investment R&D decisions in the presence of spillover effects in an option pricing framework with analytic tractability. Two firms face two decisions that are solved for interdependently in a two-stage game. The first-stage decision is: what is the optimal level of coordination (optimal policy/technology choice)? The second-stage decision is: what is the optimal effort for a given level of the spillover effects and the cost of information acquisition? The framework is extended to a two-period stochastic game with (path-dependency inducing) switching costs that make strategy revisions harder. Strategy shifts are easier to observe in more volatile environments

Artificial Neural Network Enhanced Parametric Option Pricing

In this paper we explore ways that alleviate problems of nonparametric (artificial neural networks) and parametric option pricing models by combining the two. The resulting enhanced network model is compared to standard artificial neural networks and to parametric models with several historical and implied parameters. Empirical results using S\&P 500 index call options strongly support our approach.Option pricing, implied volatilities, implied parameters, artificial neural networks, optimization