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Rating Under Asymmetric Information

Rating Under Asymmetric Information

July 26th, 2018


09.21 (Blücherstr.)
11:45 am

(deviating place!)


University of Southern Denmark


Stefan Hirth

We study a dynamic signaling game where a firm, by its decision to stay
solvent, signals its quality to a rating agency with the rating feeding back
into the firm’s cost of capital. Observing the firm’s true cash flow blurred
by a persistent measurement error, the error-minimizing rating agency learns
dynamically through the firm’s solvency decision. Firms observed with higher
measurement error default earlier, inducing directional learning by successively
eliminating measurement errors which are too high to be feasible. In a partially
separating perfect Bayesian equilibrium in Markov strategies, the firm employs
a measurement-error dependent cut-off strategy. We discuss the extensive
economic consequences of such a learning mechanism.