Abstract: Mortgage structure matters not only for monetary policy transmission, but also for financial stability. In an adjustable-rate mortgage (ARM) regime, interest rate rises cause higher default rates due to increases in mortgage payments. In a fixed-rate mortgage (FRM) regime, households are protected, but banks are potentially more exposed to rate rises. To evaluate these competing mechanisms under different mortgage regimes, we build a quantitative model with flexible mortgage contract structures, borrowers, and an intermediary sector. Our approach captures borrowers’ endogenous default decisions and intermediaries’ equilibrium pricing effects on mortgage rates and risk premia, reflecting the interaction between interest rate and credit risks, and intermediary net worth. We find that financial stability risks are “U-shaped” in mortgage structure: while ARM payments are more sensitive to interest rates, defaults happen in states when intermediary net worth is high, resulting in lower risk premia in constrained states of the world compared to the benchmark FRM economy. As a result, an intermediate mortgage fixation length minimizes the volatility of intermediary net worth and improves the sharing of aggregate risks. Our findings have implications for mortgage design, macroprudential, and monetary policy.
Discussant: Isha Agarwal, University of British Columbia
Stavros Panageas, University of California-Los Angeles
Abstract: Macro-finance models featuring an infinitely-lived, representative agent typically imply that (a) the equity premium reflects compensation for aggregate risk and (b) the long-run, risk-adjusted growth rate of consumption is smaller than the risk-free rate (``transversality condition''). The international historical experience with growth-indexed bonds suggests that these bonds, which isolate the risk premium of aggregate fluctuations, command only a moderate risk premium. Equity investments that that are hedged against aggregate fluctuations still command a sizable equity premium, suggesting that the equity premium is not just compensation for aggregate risk. In addition, the risk-adjusted GDP-growth rate is roughly the same (and slightly higher) than the risk-free rate, which calls into question one of the basic tenets of standard macro-finance models. The findings have potential implications for some recent puzzles pertaining to the pricing of government debt.
Discussant: Mindy Xiaolan, University of Texas-Austin
Abstract: This paper investigates how data technology affects firms' market power and asset prices. Using a novel dataset tracking firms' employment of data scientists, we document three key empirical findings: firms with higher proportions of data scientists exhibit larger markups, have higher information quality proxied by lower sales forecast errors, and earn higher stock returns. Specifically, a long-short portfolio strategy based on firms' data scientist ratios generates significant annual excess returns of approximately 4%. To quantitatively rationalize these empirical findings, we develop a heterogeneous firm model in which firms optimally hire data scientists to learn about unobserved consumer tastes. The model demonstrates how data enables firms to improve demand forecasting accuracy and extract higher markups. Importantly, supply-constrained firms have stronger incentives to hire data scientists, leading to countercyclical data scientist hiring that amplifies their exposures to aggregate risk through an operating leverage channel. We provide empirical evidence supporting our model mechanism.
