The Learning, Timing, and Pricing of the Option to Invest With Guaranteed Debt and Asymmetric Information

Abstract

We assume a borrower must borrow from a lender to start a project. The debt is secured by an insurer taking the project and the lender's loss at default. The borrower grants the insurer a fraction of the money borrowed (fee-for-guarantee swap, FGS) or of the project's equity (equity-for-guarantee swap, EGS) as guarantee cost. The borrower knows the project's expected growth rate, but the insurer merely knows it to take a high or low value. Insurers learn about the growth rate and dynamically update their beliefs. We show that asymmetric information makes a high-type borrower pay a higher guarantee cost than usual. The learning accelerates the high-type borrower's investment and reduces the guarantee cost and adverse selection cost. A sufficiently high initial belief on the high-type borrower or a sufficiently large funding gap makes pooling equilibrium Pareto dominate separating equilibrium. FGSs are superior to EGSs.

Keywords: Real options, Secured debt, Asymmetric information, Bayesian learning, Signaling game

JEL Classification: G13, G31, D82, C73