We present a new dynamic bank run model for liquidity risk where a financial institution finances its risky assets by a mixture of short- and long-term debt. The financial institution is exposed to insolvency risk at any time until maturity and to illiquidity risk at a finite number of rollover dates. We compute both insolvency and illiquidity default probabilities in this multiperiod setting using a structural credit risk model approach. Firesale rates can be determined endogenously as expected debt value over current asset value. Numerical results illustrate the impact of various input parameters on the default probabilities.

This paper proposes a class of optimal tests for the constancy of parameters in random coefficients models. Our testing procedure covers the class of Hamilton's models, where the parameters vary according to an unobservable Markov chain, but also applies to nonlinear models where the random coefficients need not be Markov. We show that the contiguous alternatives converge to the null hypothesis at a rate that is slower than the standard rate. Therefore, standard approaches do not apply. We use Bartlett-type identities for the construction of the test statistics. This has several desirable properties. First, it only requires estimating the model under the null hypothesis where the parameters are constant. Second, the proposed test is asymptotically optimal in the sense that it maximizes a weighted power function. We derive the asymptotic distribution of our test under the null and local alternatives. Asymptotically valid bootstrap critical values are also proposed.

This paper examines how the reporting model for a firm's operating assets affects analyst forecast accuracy. We contrast U.K. and U.S. investment property firms having real estate as their primary operating asset, exploiting that U.K. (U.S.) firms report these assets at fair value (historical cost). We assess the accuracy of a balance-sheet-based forecast (net asset value, or NAV) and an income-statement-based forecast (earnings per share, or EPS). We predict and find higher NAV forecast accuracy for U.K. relative to U.S. firms, consistent with the fair value reporting model revealing private information that is incorporated into analysts' balance sheet forecasts. We find this difference is attenuated when the fair value and historical cost models are more likely to converge: during recessionary periods. Finally, we predict and find lower EPS forecast accuracy for U.K. firms when reporting under the full fair value model of IFRS, in which unrealized fair value gains and losses are included in net income. This is consistent with the full fair value model increasing the difficulty of forecasting net income through the inclusion of non-serially correlated elements such as these gains/losses. Information content analyses provide further support for these inferences. Overall, the results indicate that the fair value reporting model enhances analysts' ability to forecast the balance sheet, but the full fair value model reduces their ability to forecast net income.

A model of racial discrimination provides testable implications for two features of statistical discriminators: differential treatment of signals by race and heterogeneous experience that shapes perception. We construct an experiment in the U.S. rental apartment market that distinguishes statistical discrimination from taste-based discrimination. Responses from over 14,000 rental inquiries with varying applicant quality show that landlords treat identical information from applicants with African American– and white-sounding names differently. This differential treatment varies by neighborhood racial composition and signal type in a manner consistent with statistical discrimination and in contrast to patterns predicted by a model of taste-based discrimination.