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 studies the implications of data technology for firm dynamics and asset prices. We develop a heterogeneous firm model in which firms optimally hire data scientists to learn about unobserved consumer preferences. Data enhances firms' demand forecasting accuracy, enabling them to charge higher markups. Firms that are constrained in expanding production capacity have stronger incentives to hire data scientists. This results in countercyclical data scientist hiring, which amplifies firms' exposure to aggregate risk via the operating leverage channel. Using a novel dataset that tracks firms' employment of data scientists, we document three key empirical findings that support the model's main mechanisms: firms with a higher share of data scientists exhibit larger markups, higher information quality, and higher stock returns.
