We use an exogenous, forward-looking measure of price uncertainty to examine the effect of uncertainty on the use of covenants in private and public debt contracts. The effect of uncertainty differs significantly in loans versus bonds, suggesting that private and public lenders face different incentives when contracting under high uncertainty. In loans, uncertainty increases the use of performance-based covenants. In contrast, covenant usage decreases in bonds, but firms with low agency conflicts mainly drive this effect. However, bondholders require higher spreads to offset the lack of protection with fewer covenants. Hedging does not affect the covenant-uncertainty relationship for debt contracts.

We develop a dynamic capital structure model in which interest rates are stochastic and driven by three state variables: level, slope, and curvature of the yield curve in an arbitrage-free Nelson-Siegel model. Our analysis suggests that the yield-curve factors are critical determinants of the capital structure of firms and that an increase in any of the three factors is followed by an increase of the firm’s leverage and a shortening of its debt maturity. We perform a two-stage least squares system of equations that accounts for the joint determination of leverage and debt maturity and confirms our model’s predictions.

We show that when investors suffer from endogenous asymmetric money illusion, the usual proportionality between money supply and nominal prices commonly present in frictionless economies is eliminated. This drives changes in the money supply to cause real price fluctuations. Nevertheless, the combined effect on the real state price density and the price of money leads the nominal state price density, and consequently nominal bond prices, to be independent of money illusion. This article thus provides a theoretical foundation for Modigliani-Cohn’s conjecture that money illusion impacts stock markets but not bond markets.

We develop an algorithm for solving a large class of nonlinear high-dimensional continuous-time models in finance. We approximate value and policy functions using deep learning and show that a combination of automatic differentiation and Ito’s lemma allows for the computation of exact expectations, resulting in a negligible computational cost that is independent of the number of state variables. We illustrate the applicability of our method to problems in asset pricing, corporate finance, and portfolio choice and show that the ability to solve high-dimensional problems allows us to derive new economic insights.