Abhik Bhatt, Anthony M. Diercks, Benjamin Eyal, and Arsenios SkaperdasThis paper uses a natural experiment to measure the causal effect of an expected debt-financed fiscal stimulus on interest rates. We find that a 1 percentage point increase in the expected US debt-to-GDP ratio leads to an increase of about 1-2 basis points in the longer-run neutral rate (r∗) and of about 2–3 basis points in the 10-year Treasury term premium.
Celso Brunetti, Christoph FreiWe study the rise of nonbank financial intermediation and its implications for systemic risk. We develop a structural network model of banks and nonbank financial institutions (NBFIs) that decomposes intermediation into a capacity channel, driven by bank balance-sheet constraints, and a reliance channel, reflecting NBFI funding reliance. Using U.S. banking confidential supervisory data, we estimate key structural parameters and quantify both channels.
Statistical Notices update the definitions and guidance contained in the Banking Statistics Yellow Folder
This paper develops a sequential deep learning algorithm for solving dynamic stochastic general equilibrium (DSGE) models. The algorithm trains a deep neural network to approximate the model’s policy functions across four progressive phases: steady-state anchoring, exploration around the steady state, simulation on the ergodic set, and Monte Carlo integration of stochastic expectations.
This paper studies the employment effects of carbon pricing under the European Union’s Emissions Trading System (EU-ETS). I refer to standard methods from the literature to define and measure the environmental properties of jobs along two dimensions: how “green” a job is, and how polluting it is. I then leverage a series of shocks to EU-ETS prices to estimate their dynamic impacts on employment. The panel local projections estimates reveal that an exogenous 1% increase in EU-ETS prices leads to a roughly 0.2% decline in employment after one and a half years.
This paper develops a sequential deep learning algorithm for solving dynamic stochastic general equilibrium (DSGE) models. The algorithm trains a deep neural network to approximate the model’s policy functions across four progressive phases: steady-state anchoring, exploration around the steady state, simulation on the ergodic set, and Monte Carlo integration of stochastic expectations.
This paper studies the employment effects of carbon pricing under the European Union’s Emissions Trading System (EU-ETS). I refer to standard methods from the literature to define and measure the environmental properties of jobs along two dimensions: how “green” a job is, and how polluting it is. I then leverage a series of shocks to EU-ETS prices to estimate their dynamic impacts on employment. The panel local projections estimates reveal that an exogenous 1% increase in EU-ETS prices leads to a roughly 0.2% decline in employment after one and a half years.
Recent stress in parts of the US private credit market − including concerns about exposures in the software sector and redemption pressure in semi-liquid vehicles − has led to renewed focus on possible financial stability risks stemming from private credit and the potential relevance of such risks for the euro area. This special feature looks at the exposure of the euro area financial system to private credit. Using available commercial, public and proprietary data, it finds that euro area financial institutions appear to have limited direct exposure to private credit.
Ryan Byun, Bennett Fees, Margaret M. Jacobson, and Todd B. WalkerCentral bank communications, particularly FOMC statements and press conferences, play a crucial role in shaping financial market expectations. Using large language models to quantify central bank content, this paper demonstrates how sentiment aligns with traditional market-based monetary policy measures. We show that press conferences correlate with future policy to a greater extent than other communications.
Artificial intelligence (AI) is rapidly transforming financial decision-making. To explore the implications for financial stability we ran simulation-based experiments on two different AI architectures. We found that Q-learning algorithms, a form of reinforcement learning, achieved a high degree of coordination, but were prone to bank run-like dynamics. In contrast, large language models , which rely on contextual reasoning, were less prone to such runs but generated heterogeneous and unpredictable behaviour.
