Using the first reported case of COVID-19 in a given U.S. county as the event day, we find that firms headquartered in an affected county experience, on average, a 27-bps lower return in the 10-day post-event window. This negative effect nearly doubles in magnitude for firms in counties with a higher infection rate (−50 bps). We test a number of transmission channels. Firms belonging to labor-intensive industries and those located in counties with a large mobility decline have worse stock performance. Firms sensitive to COVID-19-induced uncertainty also exhibit more negative returns. Finally, more negative stock returns are associated with downward revisions in earnings forecasts.

We revisit evidence of real effects of uncertainty shocks in the context of interest rate uncertainty. We document that adverse movements in interest rate uncertainty predict significant slowdowns in real activity, both at the aggregate and at the firm levels. To understand how firms cope with interest rate uncertainty, we develop a dynamic model of corporate investment, financing, and risk management and test it using a rich data set on corporate swap usage. We find that interest rate uncertainty depresses financially constrained firms’ investments in spite of hedging opportunities, because risk management by means of swaps is effectively risky. Received December 11, 2016; editorial decision January 26, 2018 by Editor Itay Goldstein.

Fiscal policy matters for bond risk premia. Empirically, government spending level and uncertainty predict bond excess returns, as well as term structure level and slope movements. Shocks to government spending level and uncertainty are also priced in the cross-section of bond and stock portfolios. Theoretically, government spending level shocks raise inflation when marginal utility is high, thus generating positive inflation risk premia (term structure level effect). Uncertainty shocks steepen the yield curve (slope effect), producing positive term premia. These effects are consistent with evidence from a structural vector autoregression. Asset pricing tests using model simulated data corroborate our empirical findings.

The component of the volatility of total factor productivity (TFP) that is orthogonal to the dividend price ratio is shown to have long-run predictive ability for excess market returns. This finding implies that TFP volatility should also predict real cash flows and/or real interest rates: it is found to mainly predict real cash flows through inflation. A model with endogenous growth, Epstein-Zin preferences and price rigidities reconciles both TFP volatility-driven long-run predictability and its real implications. Within the model, we justify the similar (to that of TFP volatility) predictive ability of a low-frequency notion of market volatility as well as the cross-sectional pricing of TFP volatility risk in alternative asset classes.

We propose an equilibrium corporate bond pricing model that accommodates the heterogeneity in institutional investors’ preferences and mandates in an empirically tractable way. Our model, estimated on rich holdings data, quantifies investors’ preferences and demand elasticities, with inelastic insurers focusing on the investment-grade segment, and elastic mutual funds substituting across ratings groups. The model offers a novel quantitative perspective of the effect of recent trends in institutional ownership on equilibrium credit spreads and the funding costs of corporations. Overall, our model emphasizes the composition of institutional demand as an important state variable for corporate bond pricing.