Financial Intermediaries and Pressures on International Capital Flows
Linda S. Goldberg and Samantha Hirschhorn

Global factors, like monetary policy rates from advanced economies and risk conditions, drive fluctuations in volumes of international capital flows and put pressure on exchange rates. The components of international capital flows that are described as global liquidity—consisting of cross-border bank lending and financing of issuance of international debt securities—have sensitivities to risk conditions that have evolved considerably over time. This risk sensitivity has been driven, in part, by the composition and business models of the financial institutions involved in funding. In this post, we ask whether these same features have led to changes in the pressures on currency values as risk conditions evolve. Using the Goldberg and Krogstrup (2023) Exchange Market Pressure (EMP) country indices, we show that the features of financial institutions in the source countries for international capital do influence how destination countries experience currency pressures when risk conditions change. Better shock-absorbing capacity in financial institutions moderates the pressures toward depreciation of currencies during adverse global risk events.
The New York Fed DSGE Model Forecast—September 2025
Marco Del Negro, Ibrahima Diagne, Keshav Dogra, Elena Elbarmi, Donggyu Lee, and Michael Pham

This post presents an update of the economic forecasts generated by the Federal Reserve Bank of New York’s dynamic stochastic general equilibrium (DSGE) model. We describe very briefly our forecast and its change since June 2025. To summarize, the model expects growth in 2025 to be stronger, and inflation lower, than in June. Moreover, the model’s predictions for the short-run real natural rate of interest (r*) have increased relative to June throughout the forecast horizon, partly reflecting the strength in the economy and the buoyant financial conditions.
Are Businesses Scaling Back Hiring Due to AI?
Jaison R. Abel, Richard Deitz, Natalia Emanuel, Ben Hyman, and Nick Montalbano

The swift advancement of artificial intelligence (AI) has sparked significant concern that this new technology will replace jobs and stifle hiring. To explore the effects of AI on employment, our August regional business surveys asked firms about their adoption of AI and if they had made any corresponding adjustments to their workforces. Businesses reported a notable increase in AI use over the past year, yet very few firms reported AI-induced layoffs. Indeed, for those already employed, our results indicate AI is more likely to result in retraining than job loss, similar to our findings from last year. That said, AI is influencing recruiting, with some firms scaling back hiring due to AI and some firms adding workers proficient in its use. Looking ahead, however, layoffs and reductions in hiring plans due to AI use are expected to increase, especially for workers with a college degree.
Economic Capital: A New Measure of Bank Solvency
Beverly Hirtle and Matthew Plosser

Bank supervisors, industry analysts, and academic researchers rely on a range of metrics to track the health of both individual banks and the banking system as a whole. Many of these metrics focus on bank solvency—the likelihood that a bank will be able to repay its obligations and thus retain its funding and continue to supply services to consumers, businesses, and other financial institutions. We draw on our recent research to describe a new solvency metric that is more forward-looking, more timely, and more comprehensive in its assessment of solvency than many current measures.
What Is Natural Disaster Clustering—and Why Does It Matter for the Economy?
Jacob Kim-Sherman and Lee Seltzer

Understanding the economic and financial consequences of natural disasters is a major concern for researchers and policymakers. The way in which overlapping natural disaster systems interact, as exemplified by the recent fires in Los Angeles being exacerbated by strong winds, is a major area of study in environmental science but has received comparatively little attention in the economics literature. Examining these potential interactions would likely be important for financial institutions, since such assessments would, in many instances, increase the estimated financial impact of a given natural disaster. In our recent Staff Report, we develop a method of identifying disaster systems in natural disaster data, such as the Spatial Hazard Events and Loss Database (SHELDUS), and use it to argue that the economics and finance literatures may have overlooked some sources of systemic risk.