Abstract: Mortgage structure matters not only for monetary policy transmission, but also for financial stability. Adjustable-rate mortgages (ARMs) expose households to rising rates, increasing default risk through higher payments, while fixed-rate mortgages (FRMs) protect households but potentially expose banks to greater interest rate risk. To evaluate these competing forces, we develop a quantitative model with flexible mortgage contracts, liquidity- and net worth-driven household default, and a banking sector with sticky deposits and occasionally binding constraints. We find financial stability risks exhibit a U-shaped relationship with mortgage fixation length. FRMs benefit from deposit rate stickiness, reducing volatility, whereas ARMs provide net worth hedging by concentrating defaults when intermediary net worth is high, thus lowering risk premia. An intermediate fixation length balances these effects, minimizing banking sector volatility and improving aggregate risk-sharing. Our model explains observed differences in delinquencies, house prices, and bank equity prices between ARM and FRM countries during 2022–2023, with implications for mortgage design, macroprudential regulation, and monetary policy.
Discussant: Isha Agarwal, University of British Columbia
Stavros Panageas, University of California-Los Angeles
Abstract: This paper infers the risk compensation for bearing pure GDP risk using data from a historical episode where government bonds were indexed to aggregate growth. Two findings stand out: First, the risk compensation for bearing aggregate risk is moderate. Second, the risk-adjusted growth rate (the growth rate under the ``risk-neutral'' measure) exceeds the interest rate ($E^Q(g)>r$). The first finding implies that GDP-hedged equity investments still command a sizable equity premium, implying that the equity market rewards risks that are orthogonal to aggregate risk. The second finding calls into question the validity of the ``transversality condition'' that is imposed by infinitely-lived, representative-agent models. From a practical perspective, this historical episode illustrates the potential of GDP-indexed bonds to provide an ex-ante measure on whether financial markets are willing to accept negative yields (using GDP as a numeraire), which in turn allows deterministic predictions about the path of the debt-to-GDP ratio for a given primary deficit.
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.
