Mortgage Rate Intelligence & Market Updates

More than $30 trillion U.S. Treasury debt is outstanding. Less than 4 percent of this amount, which is associated with the most recently issued Treasuries, called on-the-run securities, accounts for 65 percent of average daily trading volume. The remaining portion of the amount outstanding is accounted for by seasoned issues that have been replaced by newer benchmarks, which are referred to as off-the-run securities. In this post, we review the key results in our paper that uses transaction-level Treasury TRACE data to study how trading activity and liquidity evolve as securities move from on-the-run to off-the-run. We show three main patterns. First, off-the-run notes and bonds rely much more on dealer-to-customer intermediation than benchmark securities. Second, trading activity falls sharply and transaction costs increase as securities age. Third, securities that are cheapest to deliver into Treasury futures are an important exception: they trade more actively than other off-the-run bonds of similar age.

The cross-border spillover effects of shifts in U.S. monetary policy have long been a focus of academics and policymakers alike. A common finding in the literature is that changes in the stance of U.S. monetary policy have sizable effects on economic activity and financial markets in emerging market economies (EMEs). In this post, we analyze one specific aspect of these spillovers: how EMEs fared through the U.S. monetary policy tightening cycle of 2022-23 relative to the predictions of a model, which was calibrated to capture empirically relevant features of these economies based on historical data. We find that more vulnerable EMEs fared better in both financial market and growth outcomes than would be expected from our model, while the relatively less vulnerable fared a bit better than the model predictions for financial outcomes but substantially worse for growth outcomes.

The labor share of income in the U.S. is currently at its lowest-ever level in the post-war period. The labor share measures the fraction of economic output paid to workers as wages and salaries. As such, it is a useful benchmark for wage growth: when the labor share falls, it means that productivity, prices, or both are growing faster than wages. After much-studied drops in the 2000s, the labor share fell sharply again after the COVID pandemic. In this post, we compare the dynamics of the labor share post-COVID to earlier periods to understand whether the recent decline represents the continuation of a trend or a new and distinct phenomenon. We find that both the cyclicality of the labor share and the contribution of reallocation to the labor share post-COVID are similar to earlier periods.

On October 10, 2025, the announcement of a potential additional 100 percent tariff on Chinese goods drove risk-off moves across equities, Treasuries, credit spreads, and digital assets. Digital asset prices fell sharply, trading volumes surged, and liquidity vanished from key exchanges. In this post, we show how the price shock in digital assets was transmitted and amplified through a class of instruments called synthetic stablecoins—crypto assets whose structural design turned an external shock into a self-reinforcing deleveraging spiral within the crypto ecosystem.

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 March 2026. To summarize, inflation forecasts are higher in 2026 than predicted in March. Projections for the short-run real natural rate of interest (r*) increased slightly relative to March.

Several states have recently capped consumer loan rates with the stated purpose of protecting borrowers. In a recent Staff Report, we study how these interventions have played out in three states. In our first post about that study, we showed that rate caps lead riskier borrowers to face rationing in the credit market. One question that naturally arises is what lenders do with the credit they used to provide to high-risk borrowers before the caps were imposed. Lenders that lend exclusively to high-risk borrowers (at rates above the cap) may decide to stop lending to high-risk borrowers in that state. Others, however, may try to change their “credit box” by lending more to somewhat safer borrowers. In this post, we will try to understand how lenders reallocate credit after usury limits are implemented.

In imperial China, 3 percent was the maximum legal monthly loan rate; charging more was punishable by 40 to 100 blows with the “light cane.” (Rockoff 2003) Centuries later, many U.S. states are imposing the same cap (without corporal penalties) on alternative credit providers, such as payday, installment, and auto-title lenders, with the goal of lowering credit costs and delinquency for the high-risk borrowers that rely on these funding sources. A concern, however, is that lenders will simply refuse to lend to these borrowers at lower interest rates. Our recent Staff Report studies how interest rate caps have played out in several states that recently adopted them. Using household-level data from a major credit bureau, we find that loan balances for the riskiest borrowers declined substantially relative to counterparts in states without caps. Despite taking on less debt, these borrowers did not experience an improvement in delinquencies.

We recently updated the suite of indicators describing the performance of small businesses in the Second District (defined, for the purpose of this study, as New York, New Jersey, and Connecticut) and nationally with data from the 2025 edition of the Small Business Credit Survey (SBCS). In this post, we find that regional small businesses reported severe declines in employment and revenue growth in 2025 and became more pessimistic about growth in 2026. In contrast, small firms in the rest of the nation enjoyed stable revenues and employment in 2025 and, while they also had lower expectations of future growth, the decline was smaller in magnitude. Given the importance of small businesses in employment generation, analyzing such data helps to inform the design of effective monetary policy and to understand trends in the regional economy.

Youth unemployment has risen dramatically since the pandemic—as has the prevalence of remote work. Our analysis suggests that these trends are related, with remote work making it more difficult for managers to train and mentor new employees. Accordingly, companies may be reluctant to hire less-experienced workers in distributed work arrangements. We estimate that remote work can explain 64 percent of the recent increase in unemployment among young college graduates. Further, the timing of this surge suggests that remote work—not generative AI—explains the bulk of the rise in youth unemployment.