Financial Stability Report November 2025
Central bank: fed | Document type: financial_stability_report | Date: 2025-11-07

Financial Stability Report
Note: This report generally reflects information that was available as of October 23, 2025.
Purpose and Framework ................................................. iii
Overview. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
1 Asset Valuations ..................................................... 5
.1. Artificial Intelligence and Algorithmic Trading ............................ 15
2 Borrowing by Businesses and Households .............................. 17
3 Leverage in the Financial Sector ....................................... 27
4 Funding Risks ....................................................... 37
.1. A More Targeted Assessment of Short-Term Funding risk  .................. 40
5 Near-Term Risks to the Financial System ............................... 49
.1. Survey of Salient risk
s to Financial Stability
............................ 51
Appendix: Fi gure Notes ................................................ 53
This report presents the Federal rese rve boar d’s current assessment of the stability of the U.S.
financial system. by pub
lishing this report, the boar
d intends to promote public understand-
ing by increasing transparency around, and creating accountability for, the Federal rese
views on this topic. Financial stability supports the objectives assigned to the Federal rese
including full employment and stable prices, a safe and sound banking system, and an efficient
payments system.
A financial system is considered stable when
banks, other lenders, and financial markets
are able to provide households, communities,
and businesses with the financing they need
to invest, grow, and participate in a well-
ctioning economy—and can do so even
when hit by adverse events, or “shocks.”
Consistent with this view of financial stabil-
ity, the Federal rese
d’s monitoring
framework distinguishes between shocks to,
and vulnerabilities of, the financial system.
Shocks are inherently difficult to predict,
while vulnerabilities, which are the aspects
of the financial system that would exacerbate
stress, can be monitored as they build up or
recede over time. As a result, the framework
focuses primarily on assessing vulnerabilities,
with an emphasis on four broad categories
and how those categories might interact to
amplify stress in the financial system.
1 For a review of the research literature in this area, see Tobias Adrian, Daniel Covitz, and Nellie Liang (2015),
nancial Stability Monitoring,” Annual Review of Financial Economics , vol. 7 (December), pp. 357–95.
1. Valuation pressures ar ise when asset prices are high relative to economic fundamentals or
historical norms. These developments are often driven by an increased willingness of investors
to take on risk. As such, elevated valuation pressures may increase the possibility of outsized
drops in asset prices (see Section 1, Asset
Purpose and Framework
More on the Federal
Reserve’s Monitoring Efforts
See the Financial Stability section  of the
d’s website for more
information on how the Federal rese
monitors the stability of the U.S. and world
financial systems.
The website includes:
e detailed look at our monitoring
framework for assessing risk in each
e data and research on related topics;
ormation on how we coordinate, cooper-
ate, and otherwise take action on financial
system issues; and
lic education resources describing the
importance of our efforts.
iv Financial Stability repor t
2. Excessive borrowing by businesses and households exposes the borrowers to distress if
eir incomes decline or the assets they own fall in value. In these cases, businesses and
households with high debt burdens may need to cut back spending, affecting economic activity
and causing losses for investors (see Section 2,
owing by busine
sses and Households).
ssive leverage within the financial sector increases the risk that financial institutions will
not have the ability to absorb losses without disruptions to their normal business operations
when hit by adverse shocks. In those situations, institutions will be forced to cut back lending,
sell their assets, or even shut down. Such responses can impair credit access for households
and businesses, further weakening economic activity (see Section 3, Leverage in the
Financial Sector).
ding risks expose the financial system to the possibility that investors will rapidly
withdraw their funds from a particular institution or sector, creating strains across markets
or institutions. Many financial institutions raise funds from the public with a commitment
to return their investors’ money on short notice, but those institutions then invest much of
those funds in assets that are hard to sell quickly or have a long maturity. This liquidity and
maturity transformation can create an incentive for investors to withdraw funds quickly in
adverse situations. Facing such withdrawals, financial institutions may need to sell assets
quickly at “fire sale” prices, thereby incurring losses and potentially becoming insolvent, as
well as causing additional price declines that can create stress across markets and at other
institutions (see Section 4, Funding
he Federal rese
rve’s monitoring framework also tracks domestic and international develop-
ments to identify near-term risks—that is, plausible adverse developments or shocks that could
stress the U.S. financial system. The analysis of these risks focuses on assessing how such
potential shocks may spread through the U.S. financial system, given our current assessment of
vulnerabilities.
While this framework provides a systematic way to assess financial stability, some potential
risks may be novel or difficult to quantify and therefore are not captured by the current approach.
Given these complications, we rely on ongoing research by the Federal
rve staff, academ-
ics, and other experts to improve our measurement of existing vulnerabilities and to keep pace
with changes in the financial system that could create new forms of vulnerabilities or add to
existing ones.
Purp ose and Framework  v
Federal Reserve actions to promote the resilience of the
financial system
The assessment of financial vulnerabilities informs Federal reser ve actions to promote the resil-
ience of the financial system. The Federal reser
ve works with other domestic agencies directly
and through the Financial Stability Oversight Council to monitor risks to financial stability and to
undertake supervisory and regulatory efforts to mitigate the risks and consequences of financial
instability.
Actions taken by the Federal
ve to promote the resilience of the financial system include
its supervision and regulation of financial institutions. In the aftermath of the 2007–09 financial
crisis, these actions have included requirements for more and higher-quality capital, an innovative
stress-testing regime, and new liquidity regulations applied to the largest banks in the U.S. In
addition, the Federal
ve’s assessment of financial vulnerabilities informs decisions regard-
ing the countercyclical capital buffer (CCy b). The C
signed to increase the resilience of
large banking organizations when there is an elevated risk of above-normal losses and to promote
a more sustainable supply of credit over the economic cycle.
This report reviews vulnerabilities affecting the stability of the U.S. financial system related to
valuation pressures, borrowing by businesses and households, financial-sector leverage, and
funding risks. It also highlights several near-term risks that, if realized, could interact with these
vulnerabilities. This report reflects market conditions and data as of October 23, 2025.
Overview of financial system vulnerabilities
Leverage in the
ﬁnancial sector Funding risks
Borrowing by businesses
and householdsAsset valuations
• Demand for a broad
range of higher-risk
assets bounced back
after the market declines
in April 2025/uni003B prices for
these assets remained
elevated relative to their
historical relationships
with cash ﬂows.
Liquidity in Treasury
and equity markets
recovered from the
April declines.
• Transaction-based
prices for commercial
proper ties showed
signs of stabilization.
Vacancy rates and rent
growth in the ofﬁce
sector also appeared to
be stabilizing.
• Total business and
household debt
relative to GDP was
stable at 20-year lows.
• Gross leverage of
publicly  traded ﬁrms
remained high and
credit  to privatel y
held ﬁrms continued
to grow. The ability of
most publicly  traded
ﬁrms to service their
debt was robust,
although capacity
for some small
busine sses and risky
privatel y held ﬁrms
continued to decline.
• Househol d debt
was mostly owed
by borrowers
with stro ng credit
scores. Mort gage
delinquency  rates
remained subdue d
due to large home
equity cushion s and
strong underwriting
standards.
• Auto and credit  card
loan delinquenci es
were little changed
and remained
somewhat  above their
average levels over
the past decade.
• Hedge fund leverage
remained high, having
increased across
a range of trading
strategies.  This
increased leverage has
suppor ted signiﬁca nt
positions in key
• Leverage at life
insurers was in the top
quartile of its historical
distribution.
• The banking system
remained sound
and resilient, with
historically high
regulatory capital
ratios, though fair value
losses on ﬁxed-rate
assets were still sizable
for some banks.
• Dealer leverage
remained low, while
their intermediation
activity  increased to
high levels .
• Assets in cash-
manageme nt vehicl es
continued to grow,
primaril y driven by
govern ment money
market funds, which
historically have
been the least fragile
• As a share of general
account assets,
life insurers’ use of
nontraditional liabilities
remained small.
• Most domestic banks
maintained high
levels of liquid assets
and stable funding,
their reliance on
uninsured deposits
remained well below
rece nt peaks.
2 Financial Stability repo rt
A summary of the developments in the four broad categories of vulnerabilities since the
April 2025 Financial Stability Report is as follows:
et valuations. Asset valuations were elevated. Since the market volatility of early April
subsided, the ratio of equity prices to earnings has returned to near the high end of its
historical range. An estimate of the equity premium—the compensation for risk in equity
markets—remained well below average. Spreads between yields on corporate bonds and
those on comparable-maturity Treasury securities also settled to pre-April levels, which were
low compared to their longer-term history. Liquidity in Treasury markets recovered from April’s
trough. In U.S. property markets, home price increases slowed, but the ratio of house prices
to rents continued to be near the highest levels on record. Transaction-based price indexes
(adjusted for inflation) for commercial real estate (C
operties showed some signs of
stabilization following significant declines, though vulnerabilities due to upcoming refinancing
needs remained (see Section 1, Asset
owing by businesses and households. vuln
erabilities from business and household
debt remained moderate. Total debt of businesses and households as a fraction of gross
domestic product (GDP) continued to trend slightly down to its lowest level in the past two
decades. Measures of the leverage of publicly traded firms remained somewhat above the
medians of their historical distributions, and debt owed by privately held firms continued to
grow. While publicly traded firms’ ability to service their debt remained solid in aggregate, the
debt-servicing capacity of small businesses and risky privately held firms declined in recent
years. Household debt relative to GDP has been subdued in recent history. Most household
debt was owed by borrowers with strong credit histories. Mortgage delinquency rates remained
low due to large home equity cushions and strong underwriting standards. Delinquencies on
credit cards and auto loans remained above pre-pandemic levels (see Section 2,
nesses and Households).
erage in the financial sector. vuln
erabilities associated with financial leverage remained
notable. Over the past few years, hedge funds’ leverage has steadily increased across a broad
range of strategies, including those involving Treasury securities, interest rate derivatives,
and equities. Leverage at life insurers was in the top quartile of its historical distribution. The
banking sector remained sound and resilient overall, and most banks continued to report
capital levels well above regulatory requirements. Fair value losses on fixed-rate assets
declined but were still sizable and continued to be sensitive to changes in long-term interest
redit to other financial entities continued to increase, and growth was most
notable in the category of special purpose entities, collateralized loan obligations (CLOs),
and asset-backed securities.
er-dealer leverage remained near historical lows, and
intermediation activity was historically high across a range of markets, including Treasury
markets (see Section 3, Leverage in the Financial Sector).
Overview 3
4. Funding risks. Funding risks have remained moderate. Assets in cash-management vehicles
ntinued to grow; the main contributor to this growth was government money market funds
(MMFs), which historically have been the least susceptible to large-scale investor redemptions.
Assets in more fragile investment vehicles, expressed as a share of GDP, remained near the
median of the historical distribution (discussed in the box “A More Targeted Assessment of
Short-Term Funding
s’ reliance on uninsured deposits, an important component
of their funding risk, was well below the peaks in 2022 and early 2023. Life insurers’
nontraditional liabilities grew further, although they represent only a small share of general
account assets (see Section 4, Funding
This report also discusses potential near-term risks, based in part on topics cited in market
outreach (reported in the box “Survey of Salient
o Financial Stability”). box 5
the most frequently cited risks to U.S. financial stability by a wide range of market contacts who
participated in the Survey of Salient
uring September and October. The most frequently
cited topics from survey respondents were policy uncertainty, geopolitical risks, higher long-term
rates, persistent inflation, and a sharp decline in asset prices, potentially connected to a turn in
artificial intelligence (AI) sentiment.
Survey of salient risks to the financial system
Survey respondents cited several risks to the U.S. financial system and the broader global economy. For more
information, see the box “ Survey of Salient risk
s to Financial Stability .”
of contacts
uncertainty
of contacts
Geopolitical
of contacts
of contacts
long-term rates
of contacts
of contacts
intelligence
of contacts
of contacts
Persistent
of contacts
of contacts
Asset price
of contacts
of contacts
Asset Valuations1
Asset valuations were elevated, with some markets setting new
highs after recovering from April’s declines
Since April, price declines across multiple markets have largely reversed and volatility has
receded. Prices remained high relative to their historical relationship with fundamentals across a
range of markets.
Treasury market liquidity recovered to levels well above the lows seen in April. During that epi-
sode, yields on Treasury securities exhibited considerable volatility, which, in turn, contributed to
April’s deterioration in market liquidity.
Equity markets rebounded from April’s volatility and declines. Corporate bond spreads have nar-
rowed over that same period and stayed well below their historical medians.
Prices and fundamentals in C
rkets showed continued signs of stabilizing, although the
potential for distressed commercial property sales remains if C rE bo
rrowers who need to refi-
nance their mortgages are unable to do so. In residential real estate markets, prices continued
to rise well above their historical relationship with fundamentals but at a lower rate. In the year
ending July 2025, nominal house prices grew between 0.3 and 1.7 percent depending on the
index used.
Table 1.1 shows the sizes of the asset markets discussed in this section. The two largest asset
markets are those for public equities and residential real estate, which are substantially larger
than the next two markets, Treasury securities and C
he table also shows recent and his-
torical growth rates for each asset class. The remainder of this section presents the status of
vulnerabilities across these markets.
Treasury yields declined amid normalizing volatility
Treasury yields across 2- and 10-year maturities declined since the April report and continued
to be well above their average levels over the past 15 years (figure 1.1). Over the same period,
the longer end of the Treasury yield curve has steepened. A model-based estimate of the nom-
inal Treasury term premium—a measure of the compensation that investors require to hold
longer-term Treasury securities rather than shorter-term ones—fell a bit to its historical median,
albeit near the top of its range since 2010 (figure 1.2). Moves in Treasury yields were sizable
in early April. Since the April episode, interest rate volatility implied by interest rate swaps
decreased to just below its long-term median (figure 1.3 ).
6 Financial Stability rep ort
Figure 1.1. Nominal Treasury yields declined and remained above their average levels over the past
1997 2001 2005 2009 2013 2017 2021 2025
Monthly average
Percent, annual rate
Source: Federal res erve boa rd, Statistical rel ease H.15, “Selected Interest rat es.”
Table 1.1. Size of selected asset markets
Item Outstanding
(billions of dollars)
2024:Q2–2025:Q2
Average annual growth,
1997–2025:Q2
Public equities 74,410 15.6 8.9
Residential real estate 61,101 1.4 6.2
Treasury securities 28,518 6.0 8.3
Commercial real estate 20,524 −5.6 5.4
Investment-grade corporate bonds 8,156 4.3 7.8
Farmland 3,558 4.2 5.6
High-yield and unrated corporate bonds 1,724 5.2 6.1
Leveraged loans1 1,494 7.3 12.2
Price growth (real)
Commercial real estate2 −2.2 2.8
Residential real estate3 −1.0 2.6
Note: The data extend through 2025:Q2. Outstanding amounts are in nominal terms. Growth rates are nominal and
are measured from Q2 of the year immediately preceding the period through Q2 of the ﬁnal year of the period. Equi-
ties, real estate, and farmland are at nominal market value; bonds and loans are at nominal book value.
1 The amount outstanding shows institutional leveraged loans and generally excludes loan commitments held by
banks. For example, lines of credit are generally excluded from this measure. Average annual growth of leveraged
loans is from 2001 to 2025:Q2, as this market was fairly small before then.
2 One-year growth of commercial real estate prices is from June 2024 to June 2025, and average annual growth is
from June 1999 to June 2025. Both growth rates are calculated from equal-weighted nominal prices deﬂated using
the consumer price index (CPI).
3 One-year growth of residential real estate prices is from June 2024 to June 2025, and average annual growth is
from June 1998 to June 2025. Nominal prices are deﬂated using the CPI.
Source: For leveraged loans, PitchBook Data, Leveraged Commentary & Data; for corporate bonds, Mergent, Inc., Fixed
Income Securities Database; for farmland, Department of Agriculture; for residential real estate price growth, Cotality;
for commercial real estate price growth, CoStar Group, Inc., CoStar Commercial Repeat Sale Indices; for all other
items, Federal Reserve Board, Statistical Release Z.1, “Financial Accounts of the United States.”
As set valua tions 7
Equity valuations continued to increase, while volatility declined
Measures of equity valuations rebounded after April’s market episode. The forward price-to-
earnings (P/E) ratio, defined as the ratio of equity prices to expected 12-month earnings,
remained well above its historical median (figure 1.4). The difference between the forward
P/E ratio and the real 10-year Treasury yield—a crude measure of the additional return that inves-
tors require for holding stocks relative to risk-free bonds (the equity premium)—remained well
below its historical median (figure 1.5).
2 Two measures of equity market volatility—option-implied
and realized—rose dramatically in April but have since declined to below their historical medians
(figure 1.6).
2 This estimate is constructed based on expected corporate earnings for 12 months ahead.
Figure 1.3. Interest rate volatility returned to
its median since 2005
2005 2010 2015 2020 20 25
Monthly average
Basis points
Median = 82.67
Source: For data through July 13, 2022, bar clays
and S&P Global; for data from July 14, 2022, onward,
ICAP, Swaptions and Interest rat
e Caps and
Floors Data.
Figure 1.4. The price-to-earnings ratio of S&P 500 firms was once again close to the upper end of its
historical range
1989 1995 2001 2007 2013 2019 2025
Median = 15.87
Source: LSEG, Institutional bro kers’ Estimate System, North American Summary & Detail Estimates, Level 2, Current
& History Data, Adjusted and Unadjusted, https://www.lseg.com/en/data-analytics/financial-data/company-data/
ibes-estimates .
Figure 1.2. An estimate of the nominal
Treasury term premium remained near its
historical median
1997 2004 2011 2018 2025
Monthly average
Percentage points
Median = 0.54
Source: Department of the Treasury; Wolters Kluwer,
e Chip Financial Forecasts; Federal res
of New York; Federal res
rd staff estimates.
8 Financial Stability rep ort
Corporate bond markets have been resilient; spreads in corporate
debt markets narrowed and remained tight
Yields on triple-b-r ated and high-yield corporate bonds were lower than the levels observed in the
April report and below the long-term median (figure 1.7). Spreads relative to comparable-
maturity Treasury securities settled at historically tight levels below those observed before the
April market events—about 0.7 percentage points below the historical median for triple-
and about 1.6 percentage points below the median for high-yield (figure 1.8). The excess bond
premium for all nonfinancial corporate bonds—a measure of the risk premium required by bond
investors after controlling for bond characteristics and credit quality—was below the median of
its historical distribution (figure 1.9).
Figure 1.5. As of October, an estimate of the
equity premium was near a 20-year low
1993 2001 2009 2017 2025
Percentage points
Median = 4.61
Source: LSEG, Institutional bro kers’ Estimate
System, North American Summary & Detail
Estimates, Level 2, Current & History Data, Adjusted
and Unadjusted, https://www.lseg.com/en/
data-analytics/financial-data/company-data/ibes-
estimates.
Figure 1.6. Volatility in equity markets
declined to below the historical median
Option-implie d volatility
Realized volatility
1997 2004 2011 2018 2025
Monthly average
Option-implied
median = 18.74
Source: Cboe vol atility Index® (vIX ® ) accessed via
omberg Finance L.P.; Federal res
estimates.
Figure 1.7. Corporate bond yields fell slightly
but remained near their median for the past
High-yield
1997 2004 2011 2018 2025
Source: ICE Data Indices, LLC, used with permission.
Figure 1.8. Corporate bond spreads fell and
remained at tight levels
Triple-B (left scale)
High-yield (right scale)
1997 2004 2011 2018 2025
Percentage pointsPercentage points
Source: ICE Data Indices, LLC, used with permission.
As set valua tions 9
Issuance in the corporate bond market picked up to a solid pace in August and September, on
par with the average over the past 10 years. Market-based forecasts of one-year-ahead default
probabilities of nonfinancial firms (a forward-looking indicator of credit quality) settled to levels
last seen before April’s market events.
Since the previous report, the average spread on leveraged loans in the secondary market
decreased moderately and remained at the low end of its historical distribution since 2009
(figure 1.10).
Treasury and equity market liquidity was strained in April and has
since recovered
Market liquidity refers to the ease of buying and selling an asset. Low liquidity can amplify the vol-
atility of asset prices and result in larger price moves in response to shocks. Similarly, increased
Figure 1.9. The excess bond premium was below its long-run average
1997 2001 2005 2009 2013 2017 2021 2025
Percentage points
Source: Federal res erve boa rd staff calculations based on Lehman bro thers Fixed Income Database (Warga);
Intercontinental Exchange, Inc., ICE Data Services; Center for res
earch in Security Prices, C rSP
/Compustat Merged
Database, Wharton res
earch Data Services; S&P Global, Compustat.
Figure 1.10. Spreads on leveraged loans decreased moderately to the low end of their distribution
since 2009
1997 2001 2005 2009 2013 2017 2021 2025
Percentage points
Monthly to weekly
Source: Pitchboo k Data, Leveraged Commentary & Data.
10 Financial Stability repo rt
volatility can reduce market liquidity because liquidity providers may become more cautious in
providing quotes. In extreme cases, low liquidity can threaten continued market functioning, lead-
ing to a situation in which participants are unable to trade without incurring a significant cost.
Treasury market liquidity is particularly important because of the key role these securities play in
the financial system. Amid the April volatility, Treasury market liquidity hit historically low levels.
Since then, various measures of Treasury market liquidity, including two different measures of
market depth in the most liquid on-the-run segment, indicated that liquidity increased back to or
above previous levels across all maturities (figures 1.11 and 1.12).
A measure of market liquidity in equity markets stayed below the historical average since 2019
but improved on net compared to April as volatility subsided (figure 1.13). Through September,
liquidity in corporate bond markets remained robust and in line with the average level observed in
recent years. The box “Artificial Intelligence and Algorithmic Trading” explores how the adoption of
AI in algorithmic trading could bring new opportunities and challenges to financial markets.
Figure 1.11. Treasury market depth recovered from April’s low levels
5-year (right scale)
10-year (right scale)
30-year (left scale)
Feb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.
5-day moving average
Millions of dolla rsMillions of dolla rs
2019 2020 20 21 2022 2023 2024 2025
Source: Inter Dealer bro ker Community.
Figure 1.12. While 2-year on-the-run Treasury market depth remained close to historical lows, 10-year
market depth rose to levels last seen in 2021
2-year OTR market depth (right scale)
10-year OTR market depth (left scale)
Oct. Feb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.F eb. JuneO ct.
5-day moving average
Millions of dolla rsMillions of dolla rs
2019 2020 2021 2022 2023 2024 2025
Source: brokerTec; Federal res erve boa rd staff calculations.
As set valua tions 11
Commercial real estate prices showed signs of further stabilization
Aggregate CrE pr ices measured in inflation-
adjusted terms showed signs of further stabi-
lization, following significant declines between
mid-2022 and early 2024 (figure 1.14).
ancy rates and rent growth—fundamental
determinants of prices—also appeared to be
stabilizing for office properties. Capitaliza-
tion rates at the time of property purchase,
which measure the annual income of com-
mercial properties relative to their prices,
were unchanged in aggregate since the April
report but remained below the average of the
historical distribution (figure 1.15). After a
period of tightening from 2022 to 2024, most
banks have left standards on C
ns unchanged over the past two quarters (figure 1.16).3 In
the July survey, banks reported, on net, that the level of credit standards for several types of C rE
ns was still somewhat or significantly tighter than longer-run norms.
A large volume of CrE de
bt is scheduled to mature over the coming year, and forced sales, were
they to occur, would put downward pressure on CrE pr
ices. However, continued willingness by
lenders to mitigate losses via loan modification would alleviate some of that downside risk.
3 The Senior Loan Officer Opinion Survey on ban k Lending Practices (SLOOS) results reported are based on banks’
responses weighted by each bank’s outstanding loans in the respective loan category and might therefore differ from
the results reported in the published SLOOS, which are based on banks’ unweighted responses; SLOOS results are
available on the
rd’s website at https://www.federalreserve.gov/data/sloos.htm .
Figure 1.13. A measure of liquidity in equity markets stayed below average
2019 2020 20 21 2022 2023 2024 2025
5-day moving average
Market depth (number of contracts)
Average = 68.21
Source: LSEG, Tick History; Federal res erve boa rd staff calculations.
Figure 1.14. Inflation-adjusted commercial
real estate prices were little changed
2001 2007 2013 2019 2025
Jan. 2001 = 100
Source: MSCI— rea l Capital Analytics; consumer
price index, bur
eau of Labor Statistics via Haver
Analytics.
12 Financial Stability repo rt
Residential real estate prices remained high relative to their
historical relationship with fundamentals
After posting double-digit gains in 2021 and 2022, house price increases have slowed
(figure 1.17). Model-based measures of housing valuations, which assess their historical relation-
ships with fundamentals, remained high (figure 1.18). Price-to-rent ratios fell in the geographic
areas where they had been the highest, suggesting some cooling in those markets (figure 1.19 ).
Credit standards for borrowers remained tight relative to the early 2000s, suggesting that weak
credit standards are not driving house price growth.
Figure 1.16. Banks reported that lending standards for commercial real estate loans were little
changed in the first half of 2025
1997 2001 2005 2009 2013 2017 2021 2025
Net percentage of banks reporting
Easing Tightening
Source: Federal res erve boa rd, Senior Loan Officer Opinion Survey on ban k Lending Practices; Federal res erve
d staff calculations.
Figure 1.15. Income of commercial properties relative to prices leveled off but remained below the
historical average
2001 2005 2009 2013 2017 2021 2025
Average = 6.88
Source: MSCI— rea l Capital Analytics; Andrew C. Florance, Norm G. Miller, rui jue Peng, and Jay Spivey (2010),
“Slicing, Dicing, and Scoping the Size of the U.S. Commercial rea
l Estate Market,” Journal of Real Estate Portfolio
Management, vol. 16 (May–August), pp. 101–18.
As set valua tions 13
Figure 1.18. Model-based measures of house price valuations cooled from near historically high levels
Owners’ equivalent rent
Market-based rents
1980 1985 1990 1995 2 000 2005 2010 2015 2020 20 25
Source: For house prices, Zillow, Inc., rea l Estate Data; for rent data, bur eau of Labor Statistics.
Figure 1.19. House price-to-rent ratios dropped slightly yet remained elevated across
geographic areas
Middle 80 percent of markets
1997 2001 2005 2009 2013 2017 2021 2025
Jan. 2010 = 100
Source: For house prices, Zillow, Inc., rea l Estate Data; for rent data, bur eau of Labor Statistics.
Figure 1.17. House prices continued to increase in recent months but at a lower rate
Case-S hiller
2005 2009 2013 2017 2021 2025
12-month percent change
Source: Zillow, Inc., rea l Estate Data; Cotality rea l Estate Data; S&P Cotality Case-Shiller Home Price Indices.
14 Financial Stability repo rt
Farmland valuations remained high relative to farm income
U.S. farmland values remained elevated based on annual data as of August 2025, continuing to
rise from historically high levels (figure 1.20), as did price-to-rent ratios (figure 1.21). Prices con-
tinued to be sustained by limited farmland inventory, despite elevated interest rates and higher
operating costs.
Figure 1.20. Inflation-adjusted farmland prices rose further in 2025 from already elevated levels
Midwest index
1969 1977 1985 1993 2001 2009 2017 2025
8000Annual
2024 dollars per acre
Midwest median = $3,725
Source: Department of Agriculture; Federal res erve ban k of Minneapolis staff calculations.
Figure 1.21. Farmland prices relative to rents increased to historical highs in 2025
Midwest index
1969 1977 1985 1993 2001 2009 2017 2025
Midwest median = 18.20
Source: Department of Agriculture; Federal res erve ban k of Minneapolis staff calculations.
As set valua tions 1515
Box 1.1. Artiﬁ  cial Intelligence and Algorithmic Trading
Algorithmic trading refers to automated, computer-driven trading based on predefi  ned trading strat-
egies. Algorithms have long been used by various market participants for market making, optimal
execution, statistical arbitrage, and speculative trading.
1 Traditional algorithms are fast, simple rules
operating at nanosecond frequencies, but they are relatively rigid and hard-coded. Generative AI and
machine learning add self-learning based on historical experience, adaptation based on current mar-
ket conditions, and analysis of unstructured data, such as text. The greater model complexity and the
use of additional information by AI currently come at the cost of reduced speed, and thus the suitabil-
ity of the latest AI models for trading decisions depends on the application. This box examines the
adoption of AI in algorithmic trading and discusses its fi  nancial stability implications. The box leans
on academic research, institutional market outreach, and conversations with key market participants.
The majority of AI applications in trading today seem to be building upon established practices in
machine learning and sophisticated data analysis techniques, rather than representing a signifi  cant
departure from existing methods.
2 Therefore, AI is reportedly viewed as providing effi  ciency gains,
without a fundamental change in the trading process itself, at least for now. Nonetheless, some
policymakers and academics have noted that AI-driven algorithmic trading may generate fi  nancial sta-
bility risks such as correlated trading, collusion, market manipulation, and market concentration. As
we discuss in this box, while the adoption of AI could potentially increase these risks, other factors
often mitigate the potential impact of its use by market participants.
A long-standing concern is that widespread use of trading algorithms with common reaction to market
events has the potential to exacerbate market volatility and lead to rapid price swings, ﬂ  ash crashes,
and market dislocations. That said, the use of AI may also help reduce the likelihood of correlated
trade execution, as it facilitates the use of richer information and more complex logic, potentially
leading to a less uniform response to news and to a greater diversity of trading signals among market
participants.
3 This could, in turn, improve price discovery and market effi  ciency, leading to more accu-
rate and timely reﬂ  ection of information in market prices.
The self-learning nature of generative AI-driven trading algorithms also raises concerns about the
potential for these algorithms to engage in sophisticated market manipulation. 4 Manipulative uses
of AI may be inherently harder to detect than currently known methods such as spoofi  ng and quote
stuffi  ng—submitting a large number of orders to create a false impression of supply or demand—due
to greater design complexity and increased ability to obfuscate manipulative intent.  At the same
time, however, AI has the potential to signifi  cantly enhance market surveillance techniques for inves-
tigators and supervisors. Major electronic market operators are already utilizing advanced machine
learning techniques to detect market manipulation and collusive behaviors.
5 Generative AI could
1 See Andrei Kirilenko and Andrew W. Lo (2013), “Moore’s Law versus Murphy’s Law: Algorithmic Trading and Its Discontents,”
Journal of Economic Perspectives, vol. 27 (Spring), pp. 51–72.
2 See International Monetary Fund (2024), “Advances in Artificial Intelligence: Implications for Capital Market Activities,”
chapter 3 in Financial Stability Report  (Washington: IMF, October), pp. 77–105, https://www.imf.org/en/Publications/GFSr/
Issues/2024/10/22/global-financial-stability-report-october-2024 ; International Organization of Securities Commissions
(2025), Artificial Intelligence in Capital Markets: Use Cases, Risks, and Challenges (Madrid: IOSCO, March), https://www.iosco.
org/library/pubdocs/pdf/IOSCOPD788.pdf .
3 See Anne Lundgaard Hansen and Seung Jung Lee (2025), “Financial Stability Implications of Generative AI: Taming the
System, September), https://doi.org/10.17016/FEDS.2025.090 .
4 See Álvaro Cartea, Patrick Chang, and Gabriel García-Arenas (2025), “Spoofing and Manipulating Order books with Learning
Algorithms,” available at SSrN: https://ssrn.com/abstract=4639959  or http://dx.doi.org/10.2139/ssrn.4639959 .
5 See Pedro Gurrola-Perez and Kaitao Lin (2024), “An Analysis of Market Manipulation Definitions around the World,” working
paper (London: World Federation of Exchanges, June).
(continued)
16 Financial Stability repo rt16
further improve this process by identifying suspicious behavior and providing rapid textual descrip-
tions and interpretations of the detected issues. Improved market surveillance capabilities could then
strengthen market integrity and enhance market liquidity.
Academic literature has also identifi  ed the potential for self-learning AI-powered trading algorithms
to autonomously develop collusive behavior, potentially impairing competition and market effi  ciency,
leading to reduced market liquidity and less informative pricing.
6 However, others observe that the
likelihood of collusion is small if traders’ learning processes differ. Furthermore, algorithmic traders
have strong incentives to differentiate their strategies, as non-collusion can be highly profi  table when
others collude, suggesting that algorithmic heterogeneity is a more likely equilibrium outcome.
Finally, some observers have expressed concerns about barriers to entry and increasing concentra-
tion associated with the adoption of AI. The costs of developing and running generative AI models can
be large, discouraging companies from developing proprietary models, potentially leading them to rely
on third-party solutions and thus increasing dependence on common AI models. Common AI models
could also lead to more similar processes through which traders learn, which, as noted previously,
could increase the likelihood of collusion. At the same time, however, market participants observe
that access to technology is being democratized with the development of AI, and wider access to
sophisticated AI-driven trading technology could lower barriers to entry for smaller fi  rms and individual
investors. Increased access and competition could then also contribute to a more diverse range of
market participants and strategies, fostering greater market heterogeneity and, hence, more resilient
market functioning.
In summary, as with many new technologies, AI seems to bring both new dangers and new opportu-
nities for improvements to fi  nancial markets. While the potential for AI to increase correlated trading
and impact market competition cannot be dismissed, historical evidence from algorithmic trading
suggests that correlated trading has not necessarily been detrimental to market quality. Moreover,
strong incentives for algorithmic traders to have differentiated strategies may mitigate the risk of
autonomous collusion, reduce correlated trading, and improve competition. Many exchanges have
also implemented safeguards, such as circuit breakers, which, if deployed simultaneously across
related markets, can help prevent excessive price ﬂ  uctuations. The ability of AI to assist enforcement
of securities laws could also strengthen market integrity. That said, continued monitoring of develop-
ments and further empirical research are warranted to ensure a comprehensive understanding of the
fast-evolving landscape of AI in fi  nancial markets.
6 See Winston Wei Dou, Itay Goldstein, and Yan Ji (2025), “AI-Powered Trading, Algorithmic Collusion, and Price Efficiency,”
NbEr Working Paper Series 34054 (Cambridge, Mass.: National bureau of Economic research, July), https://www.nber.org/
papers/w34054 .
7 See Laura veldkamp (2024), Discussion of “AI-Powered Trading, Algorithmic Collusion, and Price Efficiency” by Winston Wei Dou,
Itay Goldstein, Jan Ji, NbEr Summer Institute, July.
Box 1.1—continued
Borrowing by Businesses and
Households
Vulnerabilities from business and household debt remained moderate
The balance sheet conditions of businesses and households remained stable in aggregate since
the previous report. The level of total private nonfinancial-sector debt continued its moderate
decline relative to GDP, with the debt-to-GDP ratio at its lowest level in two decades (figure 2.1 ).
Trends in both the business and household sectors contributed to the decline in that overall ratio
(figure 2.2).
iness debt-to-GDP (blue line) edged down but remained near the 75th percentile
of its historical range. The household debt-to-GDP ratio (black line) continued to tick downward
and remained at more than 20-year lows.
Figure 2.2. Both business and household debt-to-GDP ratios continued to fall
Nonﬁnancia l business
1980 1985 1990 1995 2 000 2005 2010 2015 2020 20 25
Source: Federal res erve boa rd staff calculations based on bur eau of Economic Analysis, national income and
product accounts, and Federal res
rd, Statistical rel
ease Z.1, “Financial Accounts of the United States.”
Figure 2.1. The total debt of businesses and households relative to GDP remained at its lowest level
in over 20 years
1980 1985 1990 1995 2 000 2005 2010 2015 2020 20 25
Source: Federal res erve boa rd staff calculations based on bur eau of Economic Analysis, national income and
product accounts, and Federal res
rd, Statistical rel
ease Z.1, “Financial Accounts of the United States.”
18 Financial Stability repo rt
For additional context, table 2.1 shows the amounts outstanding and recent historical growth
rates of different forms of debt owed by nonfinancial businesses and households as of the
second quarter of 2025.
Business debt increased slightly; the debt-servicing capacity of
publicly traded firms was generally solid
The growth rate of nonfinancial business debt adjusted for inflation turned slightly positive
to around 1 percent in the first half of 2025 (figure 2.3). Net issuance of risky debt—defined
as issuance of high-yield bonds, unrated bonds, and leveraged loans minus retirements and
repayments—was negative in the second and third quarters of 2025, driven by increased
Table 2.1. Outstanding amounts of nonﬁnancial business and household credit
Item Outstanding
(billions of dollars)
2024:Q2–2025:Q2
Average annual growth,
1997–2025:Q2
Total private nonﬁnancial credit 42,235 1.8 5.3
Total nonﬁnancial business credit 21,863 2.1 5.7
Corporate business credit 13,965 1.7 5.2
Bonds and commercial paper 8,654 2.7 5.5
Bank lending 1,875 −7.8 3.4
Leveraged loans1 1,441 6.4 12.3
Noncorporate business credit 7,897 2.8 6.7
Commercial real estate credit 3,396 2.0 6.0
Total household credit 20,372 1.4 4.9
Mortgages 13,533 2.8 5.0
Consumer credit 4,998 .3 5.0
Student loans 1,814 4.2 7.3
Auto loans 1,563 .3 5.1
Credit cards 1,257 −2.3 3.4
Nominal GDP 30,354 4.6 4.7
Note: The data extend through 2025:Q2. Outstanding amounts are in nominal terms. Growth rates are nominal and
are measured from Q2 of the year immediately preceding the period through Q2 of the ﬁnal year of the period. The
table reports the main components of corporate business credit, total household credit, and consumer credit. Other,
smaller components are not reported. The commercial real estate (CRE) row shows CRE debt owed by both nonﬁnan-
cial corporate and noncorporate businesses as deﬁned in Table L.220: Commercial Mortgages in the “Financial
Accounts of the United States.” Total household-sector credit includes debt owed by other entities, such as nonproﬁt
organizations. GDP is gross domestic product.
1 Leveraged loans included in this table are an estimate of the leveraged loans that are made to nonﬁnancial busi-
nesses only and do not include the small amount of leveraged loans outstanding for ﬁnancial businesses. The
amount outstanding shows institutional leveraged loans and generally excludes loan commitments held by banks.
For example, lines of credit are generally excluded from this measure. Average annual growth of leveraged loans is
from 2001 to 2025:Q2, as this market was fairly small before then.
Source: For leveraged loans, PitchBook Data, Leveraged Commentary & Data; for GDP , Bureau of Economic Analysis,
national income and product accounts; for all other items, Federal Reserve Board, Statistical Release Z.1, “Financial
Accounts of the United States.”
bor rowing by busin esses and Households  1 9
retirements of high-yield and unrated bonds (figure 2.4). Privately held firms account for roughly
60 percent of the total outstanding debt of U.S. nonfinancial firms. These firms tend to have less
access to capital markets and primarily borrow from banks, private credit funds, and other institu-
tional investors.
Gross leverage—the ratio of debt to assets—of all publicly traded nonfinancial firms was flat
through the second quarter of 2025 (figure 2.5). Net leverage—the ratio of debt less cash to
total assets—increased slightly in recent quarters. While both gross and net leverage remained
high relative to history, so did the debt-servicing capacity of publicly traded firms. For publicly
traded firms, where credit quality has been generally sound, interest coverage ratios (IC
little changed since the April report ( figure 2.6).
Debt-to-asset ratios increased on bank commercial and industrial loans but remained below
pre-pandemic levels. This was true for both privately held and publicly traded firms (figure 2.7 ).
Figure 2.3. Business debt adjusted for inflation turned slightly positive
1997 2001 2005 2009 2013 2017 2021 2025
Percent change, annual rate
Source: Federal res erve boa rd, Statistical rel ease Z.1, “Financial Accounts of the United States.”
Figure 2.4. Net issuance of risky debt fell in the middle of 2025
Instituti onal leveraged loans
High-yield and unrated bonds
2004 2007 2010 2013 2016 2019 2022 2025
Billions of dolla rs
Source: Mergent, Inc., Fixed Income Securities Database; Pitch boo k Data, Leveraged Commentary & Data.
20 Financial Stability repo rt
In leveraged loans, the share of newly
issued loans to large corporations with debt
multiples—defined as the ratio of debt to
earnings before interest, taxes, depreciation,
and amortization—greater than 4 increased
moderately to above the historical median
(figure 2.8).
For leveraged loan borrowers, which are
mostly, but not exclusively, privately held
firms, gross and net leverage ratios declined
modestly but remained above their historical
medians since 2016. The median IC
Figure 2.5. Gross leverage of publicly traded
nonfinancial firms leveled off but was still high
by historical standards
75th percentil e
2000 2005 2010 2015 2020 20 25
Source: Federal res erve boa rd staff calculations
based on S&P Global, Compustat.
Figure 2.6. Interest coverage ratios, which
indicate firms’ ability to service their debt,
were largely unchanged
Median for all public ﬁrms
Median for all non-inve stment-grade public ﬁrms
2000 2005 2010 2015 2020 20 25
Source: Federal res erve boa rd staff calculations
based on S&P Global, Compustat.
Figure 2.7. Firms with commercial and
industrial bank loans increased their
leverage slightly
Privatel y held ﬁrms
Publicly traded ﬁrms
2013 2016 2019 2022 2025
Debt as percentage of assets
Source: Federal res erve boa rd, Form Fr Y -14Q
(Schedule H.1), Capital Assessments and Stress
Figure 2.8. Newly issued leveraged loans with debt multiples greater than 4 increased moderately to
above the historical median
Debt multiples ≥ 6x
Debt multiples 5x–5.99x
Debt multiples 4x–4.99x
Debt multiples < 4x
2001 2005 2009 2013 2017 2021 2025
Source: Mergent, Inc., Fixed Income Securities Database; Pitch boo k Data, Leveraged Commentary & Data.
bor rowing by busin esses and Households  21
leveraged loan borrowers stayed near its historical lows. IC rs of s maller and riskier firms, includ-
ing leveraged loan borrowers, are sensitive to interest rate changes due to their high leverage,
high use of floating-rate loans, and short-term debt maturity structure. The volume-weighted
default rate on leveraged loans stayed well below its historical median (figure 2.9, black line).
However, defaults including distressed exchanges, which reflect the number of defaults and dis-
tressed loans that have been renegotiated between the borrower and the lender, continued to be
elevated relative to history (figure 2.9, blue line).
Private credit remains a small fraction of outstanding nonfinancial business debt, and growth
seemed to have slowed somewhat this year.
ed on available data for privately held firms that
have borrowing activities from large banks, the IC r f
or the median firm continued its downward
trend over the previous few years, as higher interest rates have contributed to reduced earnings
and increased the cost of debt servicing. The average IC
t issuance for private credit borrowers
increased but remained low at a value of around 2. Aggregate leverage of privately held firms was
similar to the previous report and remained near its historical median. The recent bankruptcies
of two privately held firms, an auto parts supplier and a subprime auto lender, so far appear to
be isolated events. However, these examples highlight that unexpected losses could arise from
opaque off-balance-sheet funding arrangements that may be used by certain privately held firms.
Credit availability to small businesses tightened, and delinquencies
remained above pre-pandemic levels
According to the August 2025 National Federation of Independent bus iness’s Small bus iness
Economic Trends Survey, the share of firms that borrow regularly has trended down since
November 2021.4 Measures of small business loan originations were level through the first half
of 2025. Data from the Small bus
iness Lending Survey showed that banks continued to tighten
4 This survey’s data are available on the National Federation of Independent bus iness’s website at https://www.nfib.
com/surveys/small-business-economic-trends .
Figure 2.9. The realized default rate on leveraged loans remained well below its previous peaks
Realized default rate
Realized default rate incl. distresse d exchange s
2000 2005 2010 2015 2020 2025
Source: Pitchboo k Data, Leveraged Commentary & Data.
22 Financial Stability repo rt
credit standards.5 Interest rates on small business loans have been largely stable in recent
months and remained near the top of the range observed since 2008. Short-term (up to 90 days)
delinquency rates ticked up but were still substantially lower than during the pandemic or the
ession. Long-term (more than 90 days) delinquency rates have levelled off recently but
remained above their pre-pandemic levels.
Outstanding household debt adjusted for inflation was little changed
Outstanding household debt adjusted for
inflation has been little changed over the
past two years. The share of that debt that is
currently owed by households with a subprime
credit rating has risen somewhat, reflecting
in part the rise in consumer delinquencies
and a related deterioration of those borrow-
ers’ credit scores (figure 2.10). The ratio of
total required household debt payments to
total disposable income (the household debt
service ratio) was little changed since the
last report. Most household debt has fixed
interest rates, and the higher interest rate
environment of the past few years has only
partially passed through to household interest
Mortgage credit risk remained low
Mortgage debt accounted for roughly three-fourths of total household debt. Housing leverage—
measured as outstanding mortgage loan balances relative to home values—remained subdued
(figure 2.11). When measured relative to market prices (blue line), outstanding mortgage bal-
ances continued to sit well below previous peaks. Outstanding mortgage loan balances relative to
an estimate of home values from a model using rents and other market fundamentals were some -
what higher but remained far below earlier peaks (black line). The overall mortgage delinquency
rate remained at the lower end of its historical distribution in the first half of 2025 (figure 2.12 ).
Delinquency rates remained subdued due to large home equity cushions (figure 2.13) and strong
underwriting standards.
5 This survey’s data are available on the Federal res erve ban k of Kansas City’s website at https://www.kansascityfed.
org/surveys/small-business-lending-survey/ .
Figure 2.10. Inflation-adjusted household debt
was largely unchanged
Near prime
2000 2005 2010 2015 2020 20 25
Trillions of dollar s (real)
Source: Federal res erve ban k of New York Consumer
Credit Panel/Equifax; consumer price index, bur
of Labor Statistics via Haver Analytics.
bor rowing by busin esses and Households  23
New mortgage extensions declined slightly for
borrowers with a prime credit score (the group
with the largest share) and for borrowers
with near-prime credit scores but increased
slightly for borrowers with subprime credit
scores over the past year (figure 2.14). As of
the fourth quarter of 2024, the early payment
delinquency rate—the share of balances
becoming delinquent within one year of mort-
gage origination—remained somewhat above
the median of its historical distribution.
Figure 2.11. Measures of housing leverage
stayed significantly below their peak levels
Relative to model-implie d values
Relative to market value
2000 2005 2010 2015 2020 20 25
1999:Q1 = 100
Source: Federal res erve ban k of New York Consumer
Credit Panel/Equifax; Zillow, Inc., rea
l Estate Data;
eau of Labor Statistics via Haver Analytics.
Figure 2.12. Mortgage delinquency rates
edged down and remained close to the low
end of their historical distribution
Mortgages in loss mitigation
All mortgages
2001 2007 2013 2019 2025
Percent of mortgages
Source: Federal res erve ban k of New York Consumer
Credit Panel/Equifax.
Figure 2.13. Very few homeowners had
negative equity in their homes
2013 2016 2019 2022 2025
Percent of mortgages
Source: Cotality rea l Estate Data.
Figure 2.14. New mortgage extensions increased for subprime borrowers
Near prime
2000 2005 2010 2015 2020 2025
1800Annual
Billions of dollar s (real)
Source: Federal res erve ban k of New York Consumer Credit Panel/Equifax; consumer price index, bur eau of Labor
Statistics via Haver Analytics.
24 Financial Stability repo rt
Consumer delinquencies remained high by historical standards
Consumer debt accounted for the remaining
one-fourth of household debt and consisted
primarily of student, auto, and credit card
ances were broadly unchanged in
inflation-adjusted terms relative to the previ-
ous report (figure 2.15).
The average maturity of auto loans at origina-
tion for used cars was near historical highs
for borrowers with a nonprime credit score
(figure 2.16). On balance, longer-maturity
loans tend to have higher default risks, partly
because such loans have a higher risk of fall-
ing deep into a negative equity position, which
can lead to consumer defaults. The share of
auto loans in delinquent status was largely unchanged from the previous report and stood at a
level somewhat above its historical median (figure 2.17).
The stock of outstanding credit card debt shifted slightly to subprime borrowers over the first
half of 2025 (figure 2.18). Credit card delinquency rates remained flat in the first half of 2025
after reaching their highest level since 2010 in the previous year (figure 2.19). The stabilization
of credit performance has been broad based, with delinquency rates leveling off across credit
Figure 2.15. Consumer debt balances were
largely unchanged for student and auto loans
and for credit cards
Student loans
Auto loans
Credit cards
2000 2005 2010 2015 2020 20 25
Billions of dollar s (real)
Source: Federal res erve ban k of New York Consumer
Credit Panel/Equifax; consumer price index, bur
of Labor Statistics via Haver Analytics.
Figure 2.16. The average maturity of loans at
origination for used cars remained elevated for
nonprime borrowers
Near prime
2015 2017 2019 2021 2023 2025
Source: Experian vel ocity.
Figure 2.17. Auto loan delinquencies remained
above the historical median
2000 2005 2010 2015 2020 20 25
Median = 3.09
Source: Federal res erve ban k of New York Consumer
Credit Panel/Equifax.
bor rowing by busin esses and Households  25
score and income groups.6 The overall increase in credit card delinquencies since early 2022 was
attributable primarily to elevated delinquencies among borrowers with a nonprime credit score
and reflected in large part looser underwriting standards and large growth in inflation-adjusted
revolving credit over the pandemic period.
Delinquencies on student loan debt increased significantly in the first half of 2025, reflecting
the resumption of student loan repayments and reporting of delinquent loans to credit bureaus.
However, student loan borrowers have not yet shown much greater difficulty in meeting their non-
student loan debt payments relative to the overall population.
6 Income and credit score are not strongly correlated; see rac hael bee r, Felicia Ionescu, and Geng Li (2018), “Are
Income and Credit Scores Highly Correlated?” FEDS Notes (Washington: boa
rd of Governors of the Federal res
System, August 13), https://doi.org/10.17016/2380-7172.2235.
Figure 2.19. Credit card delinquencies remained
slightly above their long-term median
2000 2005 2010 2015 2020 20 25
Median = 3.93
Source: Federal res erve ban k of New York Consumer
Credit Panel/Equifax.
Figure 2.18. Inflation-adjusted credit card
balances for subprime borrowers were up
Near prime
2000 2005 2010 2015 2020 20 25
Billions of dollar s (real)
Source: Federal res erve ban k of New York Consumer
Credit Panel/Equifax; consumer price index, bur
of Labor Statistics via Haver Analytics.
Leverage in the Financial Sector3
Vulnerabilities associated with financial leverage remained notable
While banks and broker-dealers have maintained solid capital positions, leverage for some
other types of financial entities—such as hedge funds and life insurers—was elevated relative to
historical standards. When taken together, the overall level of vulnerability due to financial-sector
leverage was notable.
In the first quarter of 2025, hedge fund leverage was as high as it has been since comprehensive
data have been collected. Hedge funds’ use of leverage increased across a range of trading strat-
egies supporting large positions in Treasury securities, interest-rate derivatives, and equities. Life
insurers’ leverage was in the upper quartile of its historical distribution.
The banking system remained sound and resilient, but many banks continued to carry fair value
losses that are not reflected in their regulatory capital ratios. Leverage at broker-dealers stayed
near historically low levels. However, the potential for strains on the willingness of dealers to
intermediate during periods of market stress remained a vulnerability to Treasury markets.
Table 3.1 shows the sizes and growth rates of assets of financial institutions discussed in
this section.
Banks maintained historically high levels of regulatory capital, but
their fair value losses and exposure to interest rate risk remained
robust capital positions allow banks to pursue growth opportunities while providing a cushion
gainst unexpected losses. The common equity Tier 1 (CET1) ratio, a regulatory risk-based mea-
sure of bank capital adequacy, remained at historically high levels across bank types (figure 3.1).
The income-generating capacity of banks is an additional potential source of resiliency, as banks
can accrete capital to buffer against future losses by retaining a portion of their current earnings.
ks’ return on equity—a measure of profitability—remained within recent historical ranges
through the second quarter of 2025 (figure 3.2).7
A decline in interest rates caused the fair value of banks’ fixed-rate assets to increase over the
first half of 2025, but fair value losses remained sizable. As of June 30, 2025, the fair values
7 The return on equity for large non–G-SI bs (g lobal systemically important banks) as a group fluctuated in the first half
of 2025 due to one-off effects stemming from acquisitions involving two banks. Third-quarter earnings calls through
the data close showed a sizable increase in the return on equity for large banks relative to the third quarter of 2024.
28 Financial Stability repo rt
Table 3.1. Size of selected sectors of the ﬁnancial system, by types of institutions and vehicles
Item Total assets
(billions of dollars)
2024:Q2–2025:Q2
Average annual growth,
1997–2025:Q2
Banks and credit unions 28,576 3.7 5.5
Mutual funds 22,686 8.1 8.3
Insurance companies 14,388 7.6 5.5
Life 10,719 7.2 5.5
Property and casualty 3,669 8.9 5.7
Hedge funds1 12,465 13.8 8.7
Broker-dealers2 6,843 15.4 5.2
Outstanding
(billions of dollars)
Securitization 14,122 4.0 5.3
Agency 12,418 3.2 5.7
Non-agency3 1,704 10.7 3.9
Note: The data extend through 2025:Q2 unless otherwise noted. Outstanding amounts are in nominal terms. Growth
rates are nominal and are measured from Q2 of the year immediately preceding the period through Q2 of the ﬁnal year
of the period. Life insurance companies’ assets include both general and separate account assets.
1 Hedge fund data start in 2012:Q4 and are updated through 2025:Q1. Growth rates for the hedge fund data are
measured from Q1 of the year immediately preceding the period through Q1 of the ﬁnal year of the period.
2 Broker-dealer assets are calculated as unnetted values.
3 Non-agency securitization excludes securitized credit held on balance sheets of banks and ﬁnance companies.
Source: Federal Reserve Board, Statistical Release Z.1, “Financial Accounts of the United States”; Federal Reserve
Board, “Enhanced Financial Accounts of the United States.”
of banks’ available-for-sale (AFS) and held-to-maturity (HTM) portfolios were below their book
values by $143 billion and $251 billion, respectively (figure 3.3). The duration of banks’ securities
portfolios—a measure of the sensitivity of the market value of assets to changes in interest
rates—remained elevated, although it has decreased significantly from its peak level in 2022.
Figure 3.1. Banks’ average risk-based capital ratios remained near previous peaks
Large non–G-SIBs
Other BHCs
2001 2005 2009 2013 2017 2021 2025
Q2Quarterly
Percent of risk-weighted assets
Source: Federal res erve boa rd, Form Fr Y -9C, Consolidated Financial Statements for Holding Companies.
Le verage in the Financial Sector  29
An alternative measure of bank capital—the ratio of tangible common equity to total tangible
assets, which, unlike the CET1 ratio, does not factor in the riskiness of assets but does include fair
value declines on AFS securities for all banks—increased for large non–G-SI
nd regional banks
but remained below its median level over the past decade for all bank categories (figure 3.4 ).
Credit quality at banks remained sound
recent responses from the Senior Loan Officer Opinion Survey on ban k Lending Practices indi-
cated that overall bank lending standards showed some signs of easing (figure 3.5). At the same
time, delinquency rates on bank loans declined across key categories (figure 3.6).
Delinquencies of loans backed by commercial properties were stable or decreased over the first
half of 2025. Larger banks, where these delinquencies are concentrated, tend to have more
Figure 3.2. Returns on equity for banks were at typical levels
Large non–G-SIBs
2016 2017 2018 2019 2020 20 21 2022 2023 2024 2025
Source: Federal Financial Institutions Examination Council, Consolidated rep orts of Condition and Income (Call
ort) Form FFIEC 031; Federal res
rd, Form Fr Y
-9C, Consolidated Financial Statements for Holding
Companies.
Figure 3.3. The fair value losses of banks’ securities portfolios decreased but remained sizable
Available-for-sale securities
Held-to-maturity securities
2017 2018 2019 2020 20 21 2022 2023 2024 2025
Billions of dolla rs
Source: Federal Financial Institutions Examination Council, Consolidated rep orts of Condition and Income (Call
ort) Form FFIEC 031; Federal res
rd, Form Fr Y
-9C, Consolidated Financial Statements for Holding
Companies.
30 Financial Stability repo rt
substantial loan loss allowances and appear to be positioned to manage potential portfolio
losses. ban
ks also continued to actively manage their CrE ex
posures by modifying loan terms,
such as by requiring additional collateral from some borrowers.
Broker-dealers’ leverage remained low
The ratio of broker-dealers’ assets to equity was at the lower end of its historical distribution
through the first half of 2025 (figure 3.7). Smoothing through seasonal factors, trading profits
continued to increase, and the distribution of trading profits remained balanced across equities;
fixed income, rates, and credit; and other business lines (figures 3.8 and 3.9).
Figure 3.4. The ratio of tangible common equity to tangible assets remained below its median over
the past decade
Large non–G-SIBs
Other BHCs
1985 1990 1995 2 000 2005 2010 2015 2020 20 25
Percent of tangible assets
Source: For data through 1996, Federal Financial Institutions Examination Council, Consolidated rep orts of
Condition and Income (Call rep
ort) Form FFIEC 031. For data from 1997 onward, Federal res
-9C, Consolidated Financial Statements for Holding Companies; Federal Financial Institutions Examination
Council, Consolidated rep
orts of Condition and Income (Call rep
ort) Form FFIEC 031.
Figure 3.5. Bank lending standards showed
some signs of easing
1997 2004 2011 2018 2025
Net percentage of banks reporting
Easing Tightening
Source: Federal res erve boa rd, Senior Loan
Officer Opinion Survey on ban
k Lending Practices;
Federal res
rd staff calculations.
Figure 3.6. Delinquencies on bank loans
Multifamily
2007 2013 2019 2025
Percent of loans
Source: Federal res erve boa rd, Form Fr Y -9C,
Consolidated Financial Statements for Holding
Companies; Federal Financial Institutions
Examination Council, Consolidated
Condition and Income (Call rep
ort) Form FFIEC 031.
Le verage in the Financial Sector  31
Dealers are important intermediaries in Treasury markets, serving in key roles that support orderly
market functioning. Measures of dealer intermediation activity in Treasury markets increased
further due to growth in secured lending, particularly repurchase agreement (repo) lending to hedge
fund clients. While dealers’ intermediation capacity remains adequate for market functioning in
normal times, their willingness and ability to intermediate can be tested during periods of market
stress due to internal risk limits as well as regulatory requirements. The Federal
Office of the Comptroller of the Currency, and the Federal Deposit Insurance Corporation issued
proposed rules in June to recalibrate G-SI
nhanced supplemental leverage ratio. Among
other things, the proposed rules are intended to reduce regulatory disincentives for U.S. G-SI bs’
Figure 3.7. Leverage at broker-dealers
remained low
1995 2001 2007 2013 2019 2025
Ratio of assets to equity
Source: Federal res erve boa rd, Statistical rel ease Z.1,
“Financial Accounts of the United States.”
Figure 3.8. Broker-dealers’ trading profits
were within their seasonally adjusted range of
the past 5 years
2019 2021 2023 2025
Millions of dolla rs
Source: Federal res erve boa rd, rep orting,
rdkeeping, and Disclosure req
Associated with reg
Proprietary Trading
and Certain Interests in and rel
ationships with
Covered Funds, 12 C.F.r. pt
Figure 3.9. The distribution of the sources of broker-dealers’ trading profits was in line with recent
Fixed income, rates, and credit
Other business lines
2018 2019 2020 20 21 2022 2023 2024 2025
Source: Federal res erve boa rd, rep orting, rec ordkeeping, and Disclosure req uirements Associated with reg ulation v v
roprietary Trading and Certain Interests in and rel
ationships with Covered Funds, 12 C.F.r. pt
32 Financial Stability repo rt
broker- dealer subsidiaries to engage in certain low-risk activities such as intermediating in
easury markets.8
The September 2025 Senior Credit Officer Opinion Survey on Dealer Financing Terms (SCOOS)
focused on recent trends in dealers accepting securities in lieu of cash as collateral to satisfy
variation margin (
bligations for over-the-counter derivatives transactions. 9 While allowing
clients to post securities as collateral instead of cash for margin payments can help counter -
rties avoid selling securities in order to raise cash during periods of stress, it exposes dealers
to the interest rate and credit risk of the securities. One-third of SCOOS respondents reported
an increase since January 2023 in the share of
ing the form of securities, primarily in
response to client demand coupled with more aggressive competition from other dealers. One-
third of respondents expect the share of the volume of securities delivered as
somewhat over the next 12 months.
Leverage at life insurance companies was in the upper quartile of its
historical distribution
Leverage at life insurers remained in the upper quartile of its historical distribution over the first
half of 2025, while leverage at property and casualty insurers remained at historically low levels
(figure 3.10).
the Comptroller of the Currency (2025), “Agencies req
uest Comment on Proposal to Modify Certain reg
Capital Standards,” joint press release, June 27, https://www.federalreserve.gov/newsevents/pressreleases/
bcreg20250627a.htm.
9 The SCOOS is available on the Federal res erve boa rd’s website at https://www.federalreserve.gov/data/scoos.htm .
Figure 3.10. Leverage at life insurance companies was in the upper quartile of its historical
distribution
Property and casualt y
2001 2005 2009 2013 2017 2021 2025
Ratio of assets to equity
Source: Generally accepted accounting principles data from 10-Q and 10-K filings accessed via S&P Global,
Capital IQ Pro.
Le verage in the Financial Sector  33
Hedge funds’ leverage was elevated and continued to grow
In the first quarter of 2025, the most recent quarter for which comprehensive data from the
Securities and Exchange Commission’s Form PF data are available, measures of hedge funds’
leverage were at their highest levels since the adoption of Form PF in 2013 (figure 3.11). The use
of leverage over the past couple of years has increased across a range of strategies and sup-
ported significant positions in key markets, such as Treasury securities, interest rate derivatives,
and equities. Looking across strategies, the largest funds generally continued to be the most
leveraged (figure 3.12). According to data from the SCOOS, dealers reported that hedge funds’
use of financial leverage pulled back a bit in April, possibly because some hedge funds unwound
leveraged positions during that period (figure 3.13).
Figure 3.11. As of the first quarter of 2025,
hedge funds’ leverage was at its highest level
since data became availableMean gross leverage
Mean balance sheet leverage
2013 2016 2019 2022 2025
Source: Securities and Exchange Commission,
Form PF, rep
orting Form for Investment Advisers to
Private Funds and Certain Commodity Pool Operators
and Commodity Trading Advisors.
Figure 3.12. Balance sheet leverage at the
15 largest hedge funds increased further
through the first quarter of 2025
Top 15, by GAV
16–50, by GAV
51+, by GAV
2013 2016 2019 2022 2025
Source: Securities and Exchange Commission,
Form PF, rep
orting Form for Investment Advisers to
Private Funds and Certain Commodity Pool Operators
and Commodity Trading Advisors.
Figure 3.13. Dealers indicated that the use of leverage by hedge funds declined around April
Hedge funds
Trading REITs
Insurance companies
Mutual funds
2013 2015 2017 2019 2021 2023 2025
Net percentage
Source: Federal res erve boa rd, Senior Credit Officer Opinion Survey on Dealer Financing Terms.
34 Financial Stability repo rt
Issuance of non-agency securities remained strong
Issuance of non-agency securities remained robust through June (figure 3.14).10 Credit spreads on
most major securitized products have narrowed notably after widening in April. Credit performance
across a range of securitized products was stable or modestly improved since the last report.
Bank lending to other financial entities continued to grow at a
robust pace
bank credit commitments to other financial entities grew appreciably in the first half of 2025 to
2.5 trillion, reflecting the growth in market-based finance and other forms of private nonbank
lending (figure 3.15). ban
k lending to other financial entities is not significantly concentrated in
any one sector, but recent growth has been notably robust for the category of special purpose
entities, CLOs, and asset-backed securities, followed by the category of other financial vehicles
and the category of private equity, business development companies (
s), and private credit
(figure 3.16).
10 Securitization allows financial institutions to bundle loans or other financial assets and sell claims on the cash flows
enerated by these assets as tradable securities, much like bonds. by fu
nding assets with debt issued by invest-
ment funds known as special purpose entities (SPEs), securitization can add leverage to the financial system, in part
because SPEs are generally subject to regulatory regimes, such as risk retention rules, that are less stringent than
banks’ regulatory capital requirements. Examples of the resulting securities include CLOs (predominantly backed by
leveraged loans), asset-backed securities (often backed by credit card and auto debt), commercial mortgage-backed
securities, and residential mortgage-backed securities.
Figure 3.14. The pace of issuance of securitized products remained robust through June
Private- label RMBS
Non-agenc y CMBS
Auto loan/lease ABS
CDOs (including CLOs and ABS CDOs)
2001 2005 2009 2013 2017 2021 2025
3200Annual
Billions of dollar s (real)
Source: Green Street, Commercial Mortgage Alert’s CM bS Da tabase and Asset- bac ked Alert’s AbS Da tabase;
consumer price index, bur
eau of Labor Statistics via Haver Analytics.
Le verage in the Financial Sector  35
Figure 3.15. Bank credit commitments to other financial entities continued to grow
1. Financial transactions processing
2. Private equity, BDCs, and private credit
3. Broker-dealer s
4. Insurance companies
6. Open- end investment funds
7. Special purpose entities , CLOs, and ABS
8. Other ﬁnancial vehicles
9. Real estate lenders and lessors
2018 2019 2020 20 21 2022 2023 2024 2025
2750Q2Quarterly
Billions of dolla rs
10. Consumer lenders, other lenders, and lessors
Source: Federal res erve boa rd, Form Fr Y -14Q (Schedule H.1), Capital Assessments and Stress Testing.
Figure 3.16. Bank credit growth was strongest for special purpose entities, collateralized loan
obligations, and asset-backed securities between 2024:Q2 and 2025:Q2
Committed amounts
Utilized amounts
REITs Total
transactions
processing
investment
Source: Federal res
rd, Form Fr Y
-14Q (Schedule H.1), Capital Assessments and Stress Testing.
Funding Risks4
Vulnerabilities from funding risks were at levels roughly in line with
historical norms
Funding risks for most banks remained near historical norms. As a share of assets, uninsured
deposits, an important component of most banks’ funding risk, stabilized at levels signifi-
cantly below their 2022 peaks. Large banks also maintained sound levels of high quality liquid
assets (HQLA).
Assets in cash-management vehicles continued to grow, primarily driven by government MMFs,
which have historically proved the least susceptible to large-scale investor redemptions among
cash-management vehicles.
Some open-end bond and loan mutual funds remained exposed to liquidity transformation risks
that could cause asset fire sales in market downturns, as they allow daily redemptions while
holding assets that might become illiquid in times of stress. Meanwhile, life insurers’ use of non
ditional liabilities increased at a greater rate than their assets.
Table 4.1 gives the outstanding amounts of runnable money-like liabilities, and figure 4.1 shows
the total relative to GDP. The box “A More Targeted Assessment of Short-Term Funding risk”
ows how accounting for the varying degrees of susceptibility of money-like liabilities, such as
government MMFs, uninsured deposits, and repo, can provide additional insights regarding aggre-
gate funding risk.
Most banks maintained high levels of liquidity, and their funding
sources stabilized further over the past year
Aggregate liquidity in the banking system measured by the ratio of HQLA to total assets ticked
down somewhat since the last report but has remained at the higher end of the historical distri-
bution for all bank groups (figure 4.2). Many U.S. G-SI
tinued to hold a significant portion of
their HQLA in HTM securities, primarily long-duration agency mortgage-backed securities, whose
market values continued to be well below their book values. Any need to monetize these assets
would likely rely on repo market access rather than asset sales.
11 Securities held in HTM accounts are accounted at fair value for liquidity coverage ratio (LC r) pur poses but at book
value for regulatory capital purposes. Selling HTM securities (rather than holding them to maturity) could “taint” the
entire HTM investment portfolio, requiring it to be marked to market. This could result in the selling bank recognizing a
significant mark-to-market loss and reduction in regulatory capital.
s with access to repo markets can raise cash
by pledging securities in a repo transaction without tainting their HTM portfolio.
38 Financial Stability repor t
Figure 4.1. The ratio of runnable money-like liabilities to GDP was around 80 percent
2. Securities lending
3. Commercial paper
4. Domestic money market funds
5. Repurchase agreements
6. Uninsured deposit s
2004 2007 2010 2013 2016 2019 2022 2025
Percent of GDP
Source: Securities and Exchange Commission, Private Fund Statistics; iMoneyNet, Inc., Offshore Money Fund
Analyzer; bloo
mberg Finance L.P.; Securities Industry and Financial Markets Association: U.S. Municipal vari
emand Obligation Update; DTCC Solutions LLC, an affiliate of the Depository Trust & Clearing Corporation:
commercial paper and negotiable certificates of deposit data; Federal rese
d staff calculations based on
anagement Association, Securities Lending repo
rt; S&P Securities Finance; Investment Company Institute;
Federal rese
d, Statistical rel eas
e Z.1, “Financial Accounts of the United States”; Federal Financial
Institutions Examination Council, Consolidated repo
rts of Condition and Income (Call repo
rt) Form FFIEC 031; gross
domestic product, bure
au of Economic Analysis via Haver Analytics; Llama Corp, DeFiLlama.
Table 4.1. Size of selected instruments and institutions
Item Outstanding/total assets
(billions of dollars)
2024:Q2–2025:Q2
Average annual growth,
1997–2025:Q2
Total runnable money-like liabilities1 25,049 12.6 5.2
Uninsured deposits 7,314 8.8 10.7
Domestic money market funds2 7,024 15.3 6.6
Government 5,723 16.4 15.2
Prime 1,163 11.3 3.5
Tax exempt 138 6.9 −.6
Repurchase agreements 5,813 12.6 6.0
Commercial paper 1,390 14.1 2.7
Securities lending3 1,164 13.5 7.4
Bond mutual funds 5,032 7.7 8.0
Note: The data extend through 2025:Q2 unless otherwise noted. Outstanding amounts are in nominal terms. Growth
rates are nominal and are measured from Q2 of the year immediately preceding the period through Q2 of the ﬁnal year
of the period. Total runnable money-like liabilities exceed the sum of listed components. Unlisted components of run-
nable money-like liabilities include variable-rate demand obligations, federal funds, funding-agreement-backed securi-
ties, private liquidity funds, offshore money market funds, short-term investment funds, local government investment
pools, and stablecoins. Bond mutual funds are not part of the total runnable money-like liabilities.
1 Average annual growth is from 2003:Q1 to 2025:Q2.
2 Average annual growth is from 2001:Q1 to 2025:Q2.
3 Average annual growth is from 2000:Q1 to 2025:Q1. Securities lending includes only lending collateralized by cash.
Source: Securities and Exchange Commission, Private Fund Statistics; iMoneyNet, Inc., Offshore Money Fund Analyzer;
Bloomberg Finance L.P .; Securities Industry and Financial Markets Association: U.S. Municipal Variable-Rate Demand
Obligation Update; DTCC Solutions LLC, an afﬁliate of the Depository Trust & Clearing Corporation: commercial paper
and negotiable certiﬁcates of deposit data; Federal Reserve Board staff calculations based on Risk Management
Association, Securities Lending Report; S&P Securities Finance; Investment Company Institute; Federal Reserve
Board, Statistical Release Z.1, “Financial Accounts of the United States”; Federal Financial Institutions Examination
Council, Consolidated Reports of Condition and Income (Call Report) Form FFIEC 031; Morningstar, Inc., Morningstar
Direct; Llama Corp, DeFiLlama.
Fun ding risks 39
banks’ funding structures were little changed in the aggregate through the first half of 2025
gure 4.3). The share of uninsured deposits relative to total bank assets remained well below
the elevated levels seen in 2022 and early 2023 and near the levels seen in the latter half of
the 2010s. Large banks, in lowering their uninsured deposits, increased their reliance on short-
term nondeposit wholesale funding sources, such as repos.
nal and community banks, by
contrast, generally relied more on brokered and reciprocal deposits. While a majority of brokered
deposits and all reciprocal deposits are fully insured, they are more expensive than traditional
core insured deposits and may not be as stable during times of stress.
Figure 4.2. The share of high-quality liquid assets to short-term debt ticked down in the first half of
2025 but remained at the higher end of the historical distribution
Large non–G-SIBs
1997 2001 2005 2009 2013 2017 2021 2025
Percent of short-term debt
Source: Federal rese rve boar d, Form Fr Y- 9C, Consolidated Financial Statements for Holding Companies; Federal
Financial Institutions Examination Council, Consolidated repo
rts of Condition and Income (Call repo
FFIEC 031.
Figure 4.3. Banks’ reliance on uninsured deposits and short-term wholesale funding stabilized to
levels more typical of the longer history
Uninsured deposit s
Short-term wholesale funding
2004 2007 2010 2013 2016 2019 2022 2025
Percent of total assets
Source: Federal rese rve boar d, Form Fr Y- 9C, Consolidated Financial Statements for Holding Companies; Federal
Financial Institutions Examination Council, Consolidated repo
rts of Condition and Income (Call repo
FFIEC 031.
40 Financial Stability repor t40
Box 4.1. A More Targeted Assessment of Short-Term
Funding Risk
The volume and composition of short-term uninsured fi  nancial liabilities that are potentially sus-
ceptible to disruptive withdrawals or redemptions, referred to as “runnables” for short, are key
indicators of the aggregate level of funding risk in the fi  nancial system.
1 runnables can be either
short-term investment vehicles, like MMFs, or short-term funding instruments, like repos. The total
outstanding volume of runnables is now equivalent in size to 85 percent of GDP, which exceeds the
pre-pandemic level of this ratio and is approaching levels reached just before the 2007–09 fi  nancial
crisis (fi  gure 4.1 and fi g u r e  A). Yet not all components of the aggregate are equally susceptible, and
since 2007 some of the riskiest components have shrunk substantially. This box provides more tar-
geted assessments of run-related funding risk in the fi  nancial system by sorting runnables according
to their historical run propensity.
With this approach, the riskiest group of runnables, defi  ned as short-term investment vehicles and
funding instruments that have experienced either industry-wide runs or market freezes, have trended
down since 2007. Figure A shows that the GDP-scaled volume of this group stands at about half its
level just before the 2007–09 fi  nancial crisis and slightly below its pre-pandemic level. A broader set
of runnables, which also includes vehicles and instruments that have experienced notable stress
events—such as sizable redemptions or more acute but isolated strains—is also well below its level
before the fi  nancial crisis. The widening gaps over the past decade between aggregate runnables and
these two categories of risky runnables highlight that components that historically have been more
stable account for much of the recent growth in the aggregate measure. Hence, a simple sorting of
the runnables sharpens the assessment of funding risk and suggests lower vulnerabilities than the
aggregate indicator on its own.
1 For a broader introduction to runnables, see the box “runnables: An Indicator of Aggregate run-related vulnerabilities in the
Governors, April), pp. 39–40, https://www.federalreserve.gov/publications/files/financial-stability-report-20250425.pdf .
(continued)
Figure A. Runnable vehicles and instruments, by historical run propensity
Total runnables
Experienced runs or
notable stres s
Experienced
industry-wide runs or
market freezes
1985 1990 1995 2 000 2005 2010 2015 2020 20 25
Source: Securities and Exchange Commission, Private Fund Statistics; iMoneyNet, Inc., Offshore Money
Fund Analyzer; bloomberg Finance L.P.; Investment Company Institute; DTCC Solutions LLC, an affiliate
of the Depository Trust & Clearing Corporation: commercial paper and negotiable certificates of deposit
data; Federal reserve bank of New York; Securities Industry and Financial Markets Association;
J.P. Morgan Chase & Co.; Llama Corp, DeFiLlama; Federal Financial Institutions Examination Council,
Consolidated reports of Condition and Income (Call report) Form FFIEC 031; Securities and Exchange
Commission, Form N-POrT, Monthly Portfolio Investments report; Morningstar, Inc., Morningstar Direct;
risk Management Association, Securities Lending report; Federal reserve board, Statistical release Z.1,
“Financial Accounts of the United States”; Federal reserve board staff calculations.
Fun ding risks 4141
Figure b focuses on short-term investment vehicles and sorts them by their historical fragility. The
riskiest category, those that have experienced industry-wide runs (dark red area, currently equivalent
to 2.6 percent of GDP), consists of domestic institutional prime MMFs and dollar-denominated off-
shore prime MMFs. These vehicles’ susceptibility to runs arises from a conﬂ  uence of structural
vulnerabilities—such as substantial liquidity transformation—and highly risk-averse institutional
investors. both types of MMFs experienced severe and widespread runs during the 2007–09 fi  nancial
crisis and the pandemic.
A broader category of vehicles have experienced notable stress incidents (orange area, equivalent to
9 percent of GDP). For instance, retail prime MMFs faced notable redemptions during the
2007–09 fi  nancial crisis and the pandemic, but those redemptions were less severe than the runs
on their institutional counterparts. Ultrashort bond funds with signifi  cant exposures to credit risk
experi enced heavy redemptions during both the 2007–09 fi  nancial crisis and the pandemic. Some
local government investment pools, including those used by Orange County, California, in 1994 and
by the state of Florida in 2007, have encountered notable but localized stress. A few private liquidity
funds suffered losses and serious stress during the 2007–09 fi  nancial crisis that led some to freeze
redemptions. Some bank-sponsored short-term investment funds (STIFs) came under stress during
the 2007–09 fi  nancial crisis, with one bank abruptly liquidating a STIF in September 2008 and sev-
eral other banks providing support for their funds. Some stablecoins have also experienced notable
stress in the past.
The fi  nal category, investment vehicles that have not experienced notable stress (beige area, equiv-
alent to about 20 percent of GDP), includes domestic and offshore government MMFs. They account
for about 60 percent of the total assets of runnable vehicles and for much of their growth over the
past decade.
Figure C focuses on short-term funding instruments and sorts them by historical fragility. The instru-
ments that have experienced market-wide freezes (dark red area, equivalent to 27 percent of GDP)
include commercial paper (CP), negotiable certifi  cates of deposit (NCDs), variable-rate demand
obligations (vrDOs), and repo. During both the 2007–09 fi  nancial crisis and the pandemic, issuance
of CP almost froze, particularly at maturities beyond overnight, and NCD issuance also plummeted in
Box 4.1—continued
(continued)
Figure B. Runnable vehicles, by risk category, as a percentage of nominal GDP
1. No notable stres s
2. Notable stres s incident s
3. Experienced industr y-wide runs
2000 2005 2010 2015 2020 2025
35Q2Quarterly
Source: iMoneyNet, Inc., Offshore Money Fund Analyzer; Investment Company Institute; bloomberg Finance L.P.;
Federal Financial Institutions Examination Council, Consolidated reports of Condition and Income (Call report)
Form FFIEC 031; Securities and Exchange Commission, Private Fund Statistics; Securities and Exchange
Commission, Form N-POrT, Monthly Portfolio Investments report; Morningstar, Inc., Morningstar Direct;
Llama Corp, DeFiLlama; Federal reserve board staff calculations.
42 Financial Stability repor t42
March 2020. Amid the 2007–09 fi  nancial crisis, the market for vrDOs, variable-rate municipal bonds
that typically can be sold to a bank at par on short notice, effectively froze as investors rushed to sell,
and the vrDO market has never fully recovered.
2 The repo market also came under severe stress
during the 2007–09 fi  nancial crisis, as concerns over counterparty and collateral risks prompted
lenders to suddenly curtail funding and caused freezes in certain market segments.
The instruments that have experienced notable stress incidents (orange area, equivalent to
28 percent of GDP) include uninsured deposits, federal funds, securities lending, and funding-
agreement-backed securities (FAbS). Uninsured deposits, the largest of these components,
have been susceptible to rapid withdrawals during multiple periods of stress. The federal funds
market, an overnight unsecured interbank lending market, came under notable stress during the
2007–09 fi  nancial crisis as liquidity dried up in the banking system. Firms that engage in securities
lending typically reinvest the cash collateral they receive, and some of these reinvestments soured
during the 2007–09 fi  nancial crisis and left lenders unable to return collateral promptly. FAbS, which
are wholesale funding instruments issued by insurance companies, experienced severe stress during
the 2007–09 fi  nancial crisis as investors pulled back from opaque credit exposures.
To be sure, sorting runnables based on historical stress events may not fully capture current resil-
ience. For example, Securities and Exchange Commission reforms for MMFs implemented in 2023
likely reduced the run susceptibility of institutional prime MMFs relative to their past, and a require-
ment for expanded central clearing of Treasury repo could also mitigate vulnerabilities. On the other
hand, some emerging vehicles may have no record of stress events or serious runs simply because
they are new. The methodology also does not fully account for heterogeneity within components. For
instance, vulnerabilities of stablecoins likely depend substantially on their pegging mechanisms and
reserve compositions, and the GENIUS Act’s requirements will mitigate vulnerabilities in payment
stable coins. Nonetheless, historical experience provides a systematic means of sorting runnables that
offers new insights into the fragilities of funding markets and enhances assessments of funding risk.
2 vrDOs are long-term municipal bonds with short-term interest rate resets. Investors typically can “put” (sell the bonds at par)
on short notice, such as weekly, to a bank. In early 2008, investors began exercising their put options en masse. banks were
forced to repurchase vrDOs they could not resell, and rates on vrDOs rose significantly.
Box 4.1—continued
Figure C. Runnable instruments, by risk category, as a percentage of nominal GDP
1. Notable stres s incident s
2. Experienced market freeze
2000 2005 2010 2015 2020 2025
Source: Federal Financial Institutions Examination Council, Consolidated reports of Condition and Income
(Call report) Form FFIEC 031; DTCC Solutions LLC, an affiliate of the Depository Trust & Clearing Corporation:
commercial paper and negotiable certificates of deposit data; risk Management Association, Securities
Lending report; Federal reserve bank of New York; bloomberg Finance L.P.; Securities Industry and Financial
Markets Association; J.P. Morgan Chase & Co.; Federal reserve board, Statistical release Z.1, “Financial
Accounts of the United States”; Federal reserve board staff calculations.
Fun ding risks 43
Assets in cash-management vehicles continued to grow, primarily
driven by government MMFs
As of July 2025, total MMF assets had risen to $7.1 trillion from $6.3 trillion in July 2024,
likely because MMFs continued to provide more attractive yields relative to most bank deposits
(figure 4.4). The main contributor to this growth was government funds, which account for
more than 80 percent of MMF assets and are less susceptible to runs because they only hold
U.S. government and agency securities as well as repos backed by them. Assets under manage-
ment (AUM) in institutional prime MMFs—historically, the most vulnerable segment—shrank by
almost 18 percent over that period.
Other cash-management vehicles, such as dollar-denominated offshore MMFs and STIFs, also
invest in money market instruments and engage in liquidity transformation. Estimated aggregate
AUM of these vehicles has remained around $2.2 trillion for the past year. Many of these vehicles
have portfolios similar to prime MMFs. Estimates of the size of these vehicles that are most like
prime MMFs are limited by information gaps and range from $1 trillion to $2 trillion.
The GENIUS Act provided a regulatory framework for payment
stablecoins
Stablecoin assets—digital assets designed to maintain a stable value relative to a national
currency or another reference asset—have grown more than 70 percent in the past 12 months. 13
In mid-October, the total market capitalization of stablecoins reached an all-time high around
12 Cash-management vehicles included in this total are dollar-denominated offshore MMFs, STIFs, private liquidity funds,
trashort bond mutual funds, and local government investment pools.
13 Stablecoins are typically backed by a pool of “reserve” assets that include Treasury bills and other short-term instru-
nts, but some stablecoin reserve assets also include loans and other digital assets.
Figure 4.4. Assets under management at money market funds remained high
1. Government
2. Tax exempt
3. Retail prime
4. Instituti onal prime
2000 2005 2010 2015 2020 2025
JulyMonthly
Trillions of dollar s (real)
Source: Federal rese rve boar d staff calculations based on Investment Company Institute data; consumer price index,
au of Labor Statistics via Haver Analytics.
44 Financial Stability repor t
$300 billion (figure 4.5). On July 18, 2025, the Guiding and Establishing National Innovation for
U.S. Stablecoins Act (GENIUS Act) was signed into law. The GENIUS Act established a new regula-
tory framework for the issuance and transaction of “payment stablecoins.” Among its provisions
are requirements that federal regulators issue rules regarding reserve requirements and redemp -
tions, which will help mitigate run risks and likely encourage further growth of this asset class.
Bond and loan mutual funds weathered short-lived outflows in April
without amplifying market disruptions
As of the second quarter of 2025, mutual funds held approximately $1.5 trillion in U.S. corporate
bonds—accounting for around 13 percent of U.S. corporate bonds outstanding (figure 4.6). AUM
in mutual funds with holdings that are concentrated in high-yield bonds and bank loans—which
are riskier and less liquid forms of debt—were around $366 billion in August 2025, about 20 per-
cent below levels in 2021 (figure 4.7). During the period of volatility in April, corporate bond and
bank loan mutual funds all experienced appreciable outflows, but the outflows were short lived
and orderly (figure 4.8).
Central counterparties’ initial margin levels and other prefunded
resources remained high
Central counterparties’ (CCPs) initial margin levels remained high through the first half of 2025.
Initial margin requirements for some products were increased further due to the April volatility,
during which CCPs operated normally as transaction volumes increased. CCPs as a group also
14 The regulatory framework specified in the GENIUS Act also includes provisions regarding capital and risk management
ell as illicit finance, among other areas. The act takes effect on the earlier of 18 months following enactment or
120 days after federal regulators issue final regulations implementing the act.
Figure 4.5. Market capitalization of major stablecoins experienced accelerated growth
2. TerraUSD
4. Binance USD
5. USD Coin
Jan. Apr. JulyO ct.J an.A pr. JulyO ct.J an.A pr. JulyO ct.J an.A pr. JulyO ct.J an.A pr. JulyO ct.
Billions of dolla rs
2021 2022 2023 2024 2025
Source: Llama Corp, DeFiLlama.
Fun ding risks 45
Figure 4.6. Corporate bond holdings of mutual funds were stable in the first half of 2025
2000 2005 2010 2015 2020 2025
Billions of dollar s (real)
Source: Federal rese rve boar d staff estimates based on Federal rese rve boar d, Statistical rele ase Z.1, “Financial
Accounts of the United States”; consumer price index, bure
au of Labor Statistics via Haver Analytics.
Figure 4.7. Bank loan and high-yield mutual fund assets remained steady at levels far below their
2021 peaks
Bank loan mutual funds
High-yield bond mutual funds
2000 2005 2010 2015 2020 2025
Billions of dollar s (real)
Source: Investment Company Institute; consumer price index, bure au of Labor Statistics via Haver Analytics.
Figure 4.8. April’s outflows stabilized
Investment-grade bond mutual funds
Bank loan mutual funds
High-yield bond mutual funds
Feb. Aug. Feb. Aug. Feb. Aug. Feb. Aug. Feb. Aug. Feb. Aug. Feb. Aug. Feb. Aug. Feb. Aug.
Billions of dolla rs
2017 2018 2019 2020 20 21 2022 2023 2024 2025
Source: Investment Company Institute.
46 Financial Stability repor t
continued to increase prefunded mutualized resources from already high levels. 15 Elevated initial
margins and ample overall prefunded resources lower the risk faced by CCPs to the potential
default by a clearing member or market participant. This, in turn, reduces the possibility of large
liquidity demands from a CCP to its clearing members (usually banks). However, client collateral is
heavily concentrated at the largest clearing members, presenting challenges in transferring client
positions to other clearing members if it were ever necessary.
Life insurers’ nontraditional liabilities increased further
Life insurers continued to increase their reliance on nontraditional liabilities, including FA bS,
deral Home Loan bank a
dvances, and cash received through securities lending and repo
transactions (figure 4.9). The total amount of these liabilities grew by around 20 percent from
2024:Q2 to 2025:Q2, although they remain small relative to general account assets. Measures of
the share of illiquid assets to total assets for life insurers and for property and casualty insurers
were around 37 percent and 14 percent, respectively, in 2024 (figure 4.10).
15 Prefunded resources represent financial assets, including cash and securities, transferred by the clearing members
he CCP to cover that CCP’s potential credit exposure in case of default by one or more clearing members. These
prefunded resources are held as initial margin and prefunded mutualized resources, which builds the resilience of
CCPs to the possible default of a clearing member or market participant.
Figure 4.9. Life insurers’ use of nontraditional liabilities increased further
Repurchase agreements
Securities lending cash collateral
FHLB advances
Funding-ag reement-backed securities
2007 2009 2011 20132 0152 0172 0192 021 2023 2025
Billions of dollar s (real)
Source: Consumer price index, bure au of Labor Statistics via Haver Analytics; Moody’s Analytics, Inc., Credit view ,
Asset-backe
d Commercial Paper Program Index; Securities and Exchange Commission, Forms 10-Q and 10-K;
National Association of Insurance Commissioners, quarterly and annual statutory filings accessed via S&P Global,
Capital IQ Pro; bloo
mberg Finance L.P.
Fun ding risks 47
Figure 4.10. Life insurers continued to hold a significant share of illiquid assets on their
balance sheets
1. Other ABS
2. CRE loans
3. CRE loans, securitize d
4. Alternative investments
5. Illiquid corporate debt
6. Illiquid corporate debt,
Share of life insurer assets (left scale)
Share of P&C insurer assets (left scale)
2006 2008 2010 2012 2014 2016 2018 2020 20 22 2024
3500Billions of dollar s (real)Percent share
securitized
Source: Consumer price index, bure au of Labor Statistics via Haver Analytics; Federal rese rve boar d staff
estimates based on data from bloo
mberg Finance L.P. and National Association of Insurance Commissioners Annual
Statutory Filings.
Near-Term Risks to the Financial
The Federal res erve routinely engages in discussions with domestic and international policy -
ers, academics, community groups, and others to gauge the set of risk events that, should
they occur, would be of greatest concern to these groups. As captured in the box “ Survey of
Salient risk
s to Financial Stability,” fewer respondents in recent outreach noted risks associated
with fiscal sustainability than had done so in the spring survey, while more participants cited risks
related to high interest rates or geopolitical developments.
The following discussion considers possible interactions of existing domestic vulnerabilities with
three potential near-term risks.
A further increase in term premiums leading to higher-than-
anticipated long-term interest rates, particularly if accompanied by
persistent inflation, could pose risks for both borrowers and lenders
Higher interest rates and inflation could have significant financial and economic effects, including
declines in asset prices. In the near term, higher interest rates,  as well as weaker balance sheets
resulting from asset price declines, could raise consumer borrowing costs and, along with infla-
tion, strain household budgets, increasing the potential for delinquencies. Debt-servicing costs
for governments and businesses would similarly increase, which, for businesses, could amplify
existing vulnerabilities linked to high leverage and upcoming refinancing needs. In this context,
reduced spending likely would lead to slower economic growth. Collectively, these factors could
lead to fair value losses on fixed-rate securities among financial intermediaries, which, in turn,
could reduce the supply of credit to the economy and further weigh on economic activity.
A marked slowdown in global economic growth could exacerbate
existing financial vulnerabilities
A pronounced economic slowdown in the U.S. and other economies could weigh on investor, busi-
ness, and consumer sentiment and prompt a broader pullback from riskier assets or those with
elevated valuations, increasing volatility in financial markets and raising the potential for market
dislocations. Tighter funding market conditions could also result from weaker investor sentiment,
leading to reduced dollar credit from non-U.S. banks and sales of dollar debt securities by inter-
national investors that rely on less stable wholesale sources for dollar funding or for hedging
50 Financial Stability repo rt
exchange rate risk.16 Weaker-than-expected economic activity could also erode the fundamentals
of some businesses and households by broadly reducing the outlook for revenue and income
growth, impairing their ability to service debt and raising the potential for defaults and delinquen-
cies. These increased credit risks could strain the balance sheets of financial intermediaries,
which may restrict the supply of credit as a result. In addition, concerns about elevated public
debt levels and fiscal sustainability in many advanced economies may limit governments’ ability
to respond to weaker growth.
Cyberattacks and other cyber events could disrupt market
functioning and the provision of financial services
Over recent years, cyber events, and the risks they pose to the financial system, have been a
recurring concern for participants in the Federal res
erve’s market outreach surveys. In other
venues, industry experts have suggested that new technologies like AI could introduce new pos-
sibilities for cyber events. In addition to malicious cyberattacks and costly heists, non-malicious
cyber events, such as software malfunctions, have caused disruptions to the provision of finan-
cial services. Shocks caused by cyber events may propagate through complex interdependencies
among financial institutions and market infrastructures as well as service providers and can be
further amplified by existing financial vulnerabilities. For example, a cyber event at a financial
market utility may disrupt core infrastructure that supports clearing and settlement, degrading
market liquidity. An attack on a large financial institution could impair its ability to access or verify
data, complete transactions, or meet obligations, posing risks for funding and depositor runs as
well as fire sales. Attacks on critical third-party providers could affect multiple institutions, with
the effects of such disruptions likely to be further amplified when there is limited substitutability
for the affected services. Through continued interagency coordination and information sharing,
U.S. government agencies and financial regulators are advancing efforts to further protect the
financial system and financial infrastructure from cyber risks.
16 Non-U.S. banks’ large role in dollar-denominated financial intermediation and their dollar funding vulnerabilities are
ocumented in the box “ vul
nerabilities in Global U.S. Dollar Funding Markets” in boa
rd of Governors of the
Federal res
erve System (2021), Financial Stability Report  (Washington: boa
rd of Governors, May), pp. 55–58,
https://www.federalreserve.gov/publications/files/financial-stability-report-20210506.pdf . The sale of dollar securi-
ties by international investors during a period of strained liquidity is documented in the box “The rol
e of Foreign Inves-
tors in the March 2020 Turmoil in the U.S. Treasury Market” in boa
rd of Governors of the Federal res
erve System
(2021), Financial Stability Report  (Washington: boa
rd of Governors, November), pp. 22–25, https://www.
federalreserve.gov/publications/files/financial-stability-report-20211108.pdf .
Ne ar-Term ris ks to the Financial System  5151
Box 5.1. Survey of Salient Risks to Financial Stability
As part of its market intelligence gathering, staff from the Federal reserve bank of New York solic-
ited views from a wide range of contacts on risks to U.S. fi  nancial stability. During September and
October, the staff surveyed 23 contacts, including professionals at broker-dealers, banks, invest-
ment funds, and advisory fi  rms. This section is a summary of the views provided by survey respon-
dents and should not be interpreted as representing the views of the Federal reserve board or the
Federal reserve bank of New York.
Policy uncertainty was the most cited risk in this survey ( fi g u r e  A), similar to the previous survey
(fi g u r e  b). A number of geopolitical risks and the prospect of higher long-term interest rates were also
frequently cited this cycle. Persistent inﬂ  ation was again one of the most cited risks, along with con-
cerns over private credit. Concerns about AI, a depreciating U.S. dollar, and a sharp decline in asset
prices were also frequently cited this round. The prospect of a successful cyberattack continued to
be ﬂ  agged as having the most severe potential consequences.
Policy uncertainty
respondents continued to highlight concerns about policy uncertainty, including trade policy, central
bank independence, and the availability of economic data.
Geopolitical risks
Contacts cited a range of geopolitical risks and are monitoring for the potential broadening of existing
tensions. respondents also noted that fi  nancial market indicators may not currently be reﬂ  ecting geo-
political risks.
Persistent inflation
respondents continued to note the risk of persistent inﬂ  ation, though not as frequently as some sur-
veys over the past several years. One difference from many of those previous surveys is that respon-
dents noted the risk of high inﬂ  ation alongside a weakening labor market.
Higher long-term rates
respondents highlighted the potential for higher long-term interest rates, which could be driven by ris-
ing term premia, elevated inﬂ  ation expectations, or weak demand for U.S. Treasury securities. Some
noted that higher rates would likely increase unrealized losses in the banking sector and could force
fi  xed-income investors to take mark-to-market losses.
Artificial intelligence
respondents noted that a turn in the prevailing sentiment toward AI, which has been viewed as a
main driver of recent U.S. equity performance, could lead to a correction in risk assets. Participants
noted that such a turn could lead to large losses in private and public markets and, if the declines
were large enough, drive a further slowdown in the labor market and tighten fi  nancial conditions.
(continued)
52 Financial Stability repo rt52
Private credit
Private credit markets were cited as a concern more frequently than in the previous survey. respon-
dents noted the opacity of private credit as contributing to uncertainties over potential negative spill-
overs, which could include impacts on banks in the event of credit stress or the failure of a nonbank
fi  nancial institution.
Box 5.1—continued
Figure A. Fall 2025: Most cited potential shocks over the next 12 to 18 months
Policy uncertainty
Geopolit ical risks
Persistent inﬂation/uni003B monetary tightening
Higher long-ter m rates
Artiﬁcial intelligence
Asset price declines
Fiscal debt sustainability
U.S. dollar depreciation
Private credit
Foreign divestment from U.S. assets
Money market stres s
Cyberattacks
01 02 03 04 05 06 07 0
Percentage of respondents
Source: Federal reserve bank of New York survey of 23 market contacts from September through October.
Figure B. Spring 2025: Most cited potential shocks over the next 12 to 18 months
Risks to global trade
Policy uncertainty
U.S. ﬁscal debt sustainability
Persistent inﬂation/uni003B monetary tightening
Asset price declines
Treasury market functioning
U.S. recession
Banking-sector stres s
Geopolit ical risks
Foreign divestment from U.S. assets
Private credit stres s
Nonbank ﬁnancial instituti on stres s
Value of U.S. dollar
Corporate credit stres s
01 02 03 04 05 06 07 08 0
Percentage of respondents
Source: Federal reserve bank of New York survey of 22 market contacts from February through April.
Figure Notes
Figure 1.1. Nominal Treasury yields declined and remained above their average levels over the
past 15 years
Treasury rates are the 2-year and 10-year constant-maturity yields based on the most actively
traded securities.
es are averaged within a calendar month, except for the value of the last
month of the series, which is averaged through the data close date.
Figure 1.2. An estimate of the nominal Treasury term premium remained near its historical median
Term premiums are estimated from a 3-factor term structure model using Treasury yields and
hip interest rate forecasts. valu
es are averaged within a calendar month, except for the
value of the last month of the series, which is averaged through the data close date.
Figure 1.3. Interest rate volatility returned to its median since 2005
The data begin in April 2005. Implied volatility on the 10-year swap rate, 1 month ahead, is
derived from swaptions.
es are averaged within a calendar month, except for the value of the
last month of the series, which is averaged through the data close date.
Figure 1.4. The price-to-earnings ratio of S&P 500 firms was once again close to the upper end of
its historical range
The figure shows the aggregate forward price-to-earnings ratio of Standard & Poor’s (S&P) 500
firms, based on expected earnings for 12 months ahead.
es are reported as of month-end,
except for the value of the last month of the series, which is reported as of the data close date.
Figure 1.5. As of October, an estimate of the equity premium was near a 20-year low
The data begin in October 1991. The figure shows the difference between the aggregate forward
earnings-to-price ratio of Standard & Poor’s 500 firms and the expected real Treasury yields,
based on expected earnings for 12 months ahead. Expected real Treasury yields are calculated
from the 10-year consumer price index inflation forecast, and the smoothed nominal yield curve is
estimated from off-the-run securities.
es are reported as of month-end, except for the value
of the last month of the series, which is reported as of the data close date.
Figure 1.6. vola
tility in equity markets declined to below the historical median
ized volatility is computed from an exponentially weighted moving average of 5-minute daily
realized variances with 75 percent of the weight distributed over the past 20 business days.
es are averaged within a calendar month, except for the value of the last month of the series,
which is averaged through the data close date.
Figure 1.7. Corporate bond yields fell slightly but remained near their median for the past
The triple-
ries reflects the effective yield of the ICE bank o
f America Merrill Lynch (bofA
triple-b U.
S. Corporate Index (C0A4), and the high-yield series reflects the effective yield of the
54 Financial Stability repor t
ICE bofAML U.S. High Yield Index (H0A0). valu es are reported as of month-end, except for the
value of the last month of the series, which is reported as of the data close date.
Figure 1.8. Corporate bond spreads fell and remained at tight levels
The triple-b se
ries reflects the option-adjusted spread of the ICE bank o
f America Merrill
Lynch (bofA
ML) triple-b U.
S. Corporate Index (C0A4), and the high-yield series reflects the
option-adjusted spread of the ICE bofA
ML U.S. High Yield Index (H0A0). valu
es are reported as of
month-end, except for the value of the last month of the series, which is reported as of the data
close date.
Figure 1.9. The excess bond premium was below its long-run average
The excess bond premium (E
bP) is a m
easure of bond market investors’ risk sentiment. It is
derived as the residual of a regression that models corporate bond spreads after controlling for
expected default losses.
struction, its historical mean is 0. Positive (negative) EbP val
indicate that investors’ risk appetite is below (above) its historical mean.
Figure 1.10. Spreads on leveraged loans decreased moderately to the low end of their distribution
since 2009
The data show secondary-market discounted spreads to maturity. Spreads are the constant
spread used to equate discounted loan cash flows to the current market price.
ted spreads
begin in July 1997. The black dashed line represents the data transitioning from monthly to
weekly in November 2013.
Figure 1.11. Treasury market depth recovered from April’s low levels
Market depth is defined as the average top 3 bid and ask quote sizes for on-the-run Treasury
securities.
Figure 1.12. While 2-year on-the-run Treasury market depth remained close to historical lows,
10-year market depth rose to levels last seen in 2021
The data show the time-weighted average market depth at the best quoted prices to buy and sell,
for 2-year and 10-year Treasury notes. OT
n-the-run.
Figure 1.13. A measure of liquidity in equity markets stayed below average
The data show the depth at the best quoted prices to buy and sell, defined as the ask size plus
the bid size divided by 2, for E-mini Standard & Poor’s 500 futures.
Figure 1.14. Inflation-adjusted commercial real estate prices were little changed
The data are deflated using the consumer price index. The dashed line at 100 indicates the index
to January 2001 values.
Figure 1.15. Income of commercial properties relative to prices leveled off but remained below
the historical average
The data are a 12-month moving average of weighted capitalization rates in the industrial, retail,
office, and multifamily sectors, based on national square footage in 2009.
Figu re Notes 55
Figure 1.16. bank s reported that lending standards for commercial real estate loans were little
changed in the first half of 2025
s’ responses are weighted by their commercial real estate loan market shares. Survey
respondents to the Senior Loan Officer Opinion Survey on bank L
ending Practices are asked
about the changes over the quarter. The shaded bars with top caps indicate periods of
business recession as defined by the National bure
au of Economic rese
arch: March 2001–
November 2001, December 2007–June 2009, and February 2020–April 2020.
Figure 1.17. House prices continued to increase in recent months but at a lower rate
The data extend through September 2025 for Zillow, August 2025 for Cotality, and July 2025 for
Case-Shiller.
Figure 1.18. Model-based measures of house price valuations cooled from near historically
high levels
The owners’ equivalent rent value for 2025:Q2 is based on monthly data through August 2025.
The data for the market-based rents model begin in 2004:Q1.
ation is measured as the
deviation from the long-run relationship between the price-to-rent ratio and the real 10-year
Treasury yield.
Figure 1.19. House price-to-rent ratios dropped slightly yet remained elevated across
geographic areas
The data are seasonally adjusted. Percentiles are based on 19 large metropolitan statistical areas.
Figure 1.20. Inflation-adjusted farmland prices rose further in 2025 from already elevated levels
The data for the U.S. begin in 1997. Midwest index is a weighted average of Corn
Plains states derived from staff calculations. valu
es are given in real terms. The value for 2025 is
based on monthly data through July 2025.
Figure 1.21. Farmland prices relative to rents increased to historical highs in 2025
The data for the U.S. begin in 1998. Midwest index is a weighted average of Corn belt a
Plains states derived from staff calculations. The value for 2025 is based on monthly data
through July 2025.
Figure 2.1. The total debt of businesses and households relative to GDP remained at its lowest
level in over 20 years
The shaded bars with top caps indicate periods of business recession as defined by the National
au of Economic rese
arch: January 1980–July 1980, July 1981–November 1982, July 1990–
March 1991, March 2001–November 2001, December 2007–June 2009, and February 2020–
April 2020. GDP is gross domestic product.
Figure 2.2.
usiness and household debt-to-GDP ratios continued to fall
The shaded bars with top caps indicate periods of business recession as defined by the National
au of Economic rese
arch: January 1980–July 1980, July 1981–November 1982, July 1990–
56 Financial Stability repor t
March 1991, March 2001–November 2001, December 2007–June 2009, and February 2020–
April 2020. GDP is gross domestic product.
Figure 2.3. busi
ness debt adjusted for inflation turned slightly positive
Nominal debt growth is seasonally adjusted and is translated into real terms after subtracting the
growth rate of the price deflator for the core personal consumption expenditures price index.
Figure 2.4. Net issuance of risky debt fell in the middle of 2025
The data begin in 2004:Q2. Institutional leveraged loans generally exclude loan commitments
held by banks. The key identifies bars in order from top to bottom (except for some bars with at
least one negative value). For 2025:Q3, the value corresponds to preliminary data.
Figure 2.5. Gross leverage of publicly traded nonfinancial firms leveled off but was still high by
historical standards
Gross leverage is an asset-weighted average of the ratio of firms’ book value of total debt to book
value of total assets. The 75th percentile is calculated from a sample of the 2,500 largest firms
by assets. The dashed sections of the lines in 2019:Q1 reflect the structural break in the series
due to the 2019 compliance deadline for Financial Accounting Standards
d rule Accounting
Standards Update 2016-02. The accounting standard requires operating leases, previously con-
sidered off-balance-sheet activities, to be included in measures of debt and assets.
Figure 2.6. Interest coverage ratios, which indicate firms’ ability to service their debt, were largely
The interest coverage ratio is earnings before interest and taxes divided by interest payments.
Firms with leverage less than 5 percent and interest payments less than $500,000 are excluded.
Figure 2.7. Firms with commercial and industrial bank loans increased their leverage slightly
The figure shows the weighted median leverage of nonfinancial firms that borrow using commer-
cial and industrial loans from the 23 banks that have filed in every quarter since 2013:Q1. Lever-
age is measured as the ratio of the book value of total debt to the book value of total assets of
the borrower, as reported by the lender, and the median is weighted by committed amounts.
Figure 2.8. Newly issued leveraged loans with debt multiples greater than 4 increased moderately
to above the historical median
mes are for large corporations with earnings before interest, taxes, depreciation, and amor-
tization greater than $50 million and exclude existing tranches of add-ons and amendments as
well as restatements with no new money. The key identifies bars in order from top to bottom.
Figure 2.9. The realized default rate on leveraged loans remained well below its previous peaks
The data begin in December 1998 for the realized default rate and in December 2016 for the
default rate including distressed exchanges. The default rate is calculated as the amount in
default over the past 12 months divided by the total outstanding volume of loans that are not in
default at the beginning of the 12-month period. The default rate including distressed exchanges
is calculated as the number of issuers in default or distressed exchange over the past 12 months
Figu re Notes 57
divided by the total number of issuers that are not in default at the beginning of the 12-month
period. The shaded bars with top caps indicate periods of business recession as defined by
the National
au of Economic rese
arch: March 2001–November 2001, December 2007–
June 2009, and February 2020–April 2020.
Figure 2.10. Inflation-adjusted household debt was largely unchanged
Subprime are borrowers with an Equifax risk Sc
ore less than 620; near prime are from 620 to
719; prime are greater than 719. Scores are measured contemporaneously. Student loan bal-
ances before 2004 are estimated using average growth from 2004 to 2007, by risk score. The
data are converted to constant 2025 dollars using the consumer price index.
Figure 2.11. Measures of housing leverage stayed significantly below their peak levels
Housing leverage is estimated as the ratio of the average outstanding mortgage loan balance
for owner-occupied homes with a mortgage to (1) current home values using the Zillow national
house price index and (2) model-implied house prices estimated by a staff model based on rents,
interest rates, and a time trend.
Figure 2.12. Mortgage delinquency rates edged down and remained close to the low end of their
historical distribution
Loss mitigation includes tradelines that have a narrative code of forbearance, natural disaster,
payment deferral (including partial), loan modification (including federal government plans), or
loans with no scheduled payment and a nonzero balance. Delinquent loans in both series are
loans reported to the credit bureau as at least 30 days past due.
Figure 2.14. New mortgage extensions increased for subprime borrowers
The figure plots the year-over-year change in balances for the second quarter of each year among
those households whose balance increased over this window. Subprime are those with an Equifax
ore less than 620; near prime are from 620 to 719; prime are greater than 719. Scores
were measured 1 year ago. The data are converted to constant 2025 dollars using the consumer
price index. The key identifies bars in order from left to right.
Figure 2.15. Consumer debt balances were largely unchanged for student and auto loans and for
credit cards
The data are converted to constant 2025 dollars using the consumer price index. Student loan
data begin in 2005:Q1.
Figure 2.16. The average maturity of loans at origination for used cars remained elevated for non-
prime borrowers
The data are seasonally adjusted. Loans are for used auto vehicles only. Subprime are those with
ageScore less than 601; near prime are from 601 to 660; prime are greater than 660.
Figure 2.17. Auto loan delinquencies remained above the historical median
Delinquent includes loans reported to the credit bureau as at least 30 days past due. The data
for auto loans are reported semiannually by the
sessment, Data Analysis, and rese
58 Financial Stability repor t
Data Warehouse until 2017, after which they are reported quarterly. The data are seasonally
Figure 2.18. Inflation-adjusted credit card balances for subprime borrowers were up slightly
Subprime are borrowers with an Equifax risk Sc
ore less than 620; near prime are from 620 to
719; prime are greater than 719. Scores are measured contemporaneously. The data are con-
verted to constant 2025 dollars using the consumer price index.
Figure 2.19. Credit card delinquencies remained slightly above their long-term median
Delinquency measures the fraction of balances that are at least 30 days past due, excluding
severe derogatory loans, which are delinquent and have been charged off, foreclosed, or repos-
sessed by the lender. The data are seasonally adjusted.
Figure 3.1.
s’ average risk-based capital ratios remained near previous peaks
The sample consists of domestic bank holding companies ( bHCs
) and intermediate hold-
ing companies (IHCs) with a substantial U.S. commercial banking presence. G-SI bs are
lobal system ica
lly important banks. Large non–G-SI bs are bHCs a
nd IHCs with greater
than $100 billion in total assets that are not G-SIbs. befo
re 2014:Q1 (advanced-approaches
, for additional information see https://www.federalreserve.gov/supervisionreg/
basel/advanced-approaches-capital-framework-implementation.htm) or before 2015:Q1
(non-advanced-approaches bHCs
), the numerator of the common equity Tier 1 ratio is Tier 1
common capital. Afterward, the numerator is common equity Tier 1 capital. The denominator
is risk-weighted assets. The shaded bars with top caps indicate periods of business reces-
sion as defined by the National
au of Economic rese
arch: March 2001–November 2001,
December 2007–June 2009, and February 2020–April 2020. The data are seasonally adjusted by
Figure 3.2. retu
rns on equity for banks were at typical levels
rn on equity is equal to net income divided by average equity. The net income of banks that
acquired failed banks was adjusted for the one-off gains from the acquisitions. Calculations for
2023:Q4 exclude Federal Deposit Insurance Corporation special assessment costs. G-SI
lobal systemically important banks. Large non–G-SI bs are b
ank holding companies and inter-
mediate holding companies with greater than $100 billion in total assets that are not G-SI bs.
e shaded bar with top cap indicates a period of business recession as defined by the National
au of Economic rese
arch: February 2020–April 2020.
Figure 3.3. The fair value losses of banks’ securities portfolios decreased but remained sizable
The figure plots the difference between the fair and amortized cost values of the securities. The
sample consists of all bank holding companies and commercial banks.
Figure 3.4. The ratio of tangible common equity to tangible assets remained below its median
over the past decade
The sample consists of domestic bank holding companies (
), intermediate holding compa-
nies (IHCs) with a substantial U.S. commercial banking presence, and commercial banks. G-SI bs
Figu re Notes 59
are global systemically important banks. Large non–G-SI bs are bHCs a nd IHCs with greater than
$100 billion in total assets that are not G-SIbs. bank e
quity is total equity capital net of preferred
equity and intangible assets. bank a
ssets are total assets net of intangible assets. The shaded
bars with top caps indicate periods of business recession as defined by the National bure
Economic rese
arch: July 1990–March 1991, March 2001–November 2001, December 2007–
June 2009, and February 2020–April 2020. The data are seasonally adjusted by Federal rese
Figure 3.5. bank l
ending standards showed some signs of easing
s’ responses are weighted by their loans. Survey respondents to the Senior Loan Officer
Opinion Survey on bank L
ending Practices are asked about the changes over the quarter. The
shaded bars with top caps indicate periods of business recession as defined by the National
au of Economic rese
arch: March 2001–November 2001, December 2007–June 2009, and
February 2020–April 2020.
Figure 3.6. Delinquencies on bank loans declined
The figure shows banks with total assets greater than or equal to $10 billion. C&I is commercial
and industrial; NFN
s nonfarm nonresidential. The shaded bars with top caps indicate periods of
business recession as defined by the National bure
au of Economic rese
arch: December 2007–
June 2009 and February 2020–April 2020.
Figure 3.7. Leverage at broker-dealers remained low
Leverage is calculated by dividing total assets by equity.
Figure 3.8. brok
er-dealers’ trading profits were within their seasonally adjusted range of the past
The sample includes all trading desks of bank holding companies subject to the volc
eporting requirement.
Figure 3.9. The distribution of the sources of broker-dealers’ trading profits was in line with recent
The sample includes all trading desks of bank holding companies subject to the
eporting requirement. The “other business lines” category comprises desks trading in municipal
securities, foreign exchange, and commodities, as well as any unclassified desks. The key identi-
fies series in order from top to bottom.
Figure 3.10. Leverage at life insurance companies was in the upper quartile of its historical
distribution
io is calculated as (total assets – separate account assets)/(total capital – accumulated other
comprehensive income) using generally accepted accounting principles. The largest 10 publicly
traded life and property and casualty insurers are represented.
60 Financial Stability repor t
Figure 3.11. As of the first quarter of 2025, hedge funds’ leverage was at its highest level since
data became available
Means are weighted by net asset value (NAv). On
-balance-sheet leverage is the ratio of gross
asset value to NAv. Gros
s leverage is the ratio of gross notional exposure to NAv. Gros
s notional
exposure includes both on-balance-sheet exposures and off-balance-sheet derivative notional
exposures. Options are delta adjusted, and interest rate derivatives are reported at 10-year bond
equivalent values. The data are reported on a 2-quarter lag beginning in 2013:Q1.
Figure 3.12.
nce sheet leverage at the 15 largest hedge funds increased further through the
first quarter of 2025
Leverage is measured by gross asset value (GAv) div
ided by net asset value (NAv). Fu
sorted into cohorts based on GA v. Ave
rage leverage is computed as the NAv-we
ighted mean. The
data are reported on a 2-quarter lag beginning in 2013:Q1.
Figure 3.13. Dealers indicated that the use of leverage by hedge funds declined around April
Net percentage equals the percentage of institutions that reported increased use of financial
leverage over the past 3 months minus the percentage of institutions that reported decreased
use of financial leverage over the past 3 months.
s real estate investment trust.
Figure 3.14. The pace of issuance of securitized products remained robust through June
The data from the first and second quarters of 2025 are annualized to create the 2025 bar.
rMbS is re
sidential mortgage-backed securities; CM bS is co
mmercial mortgage-backed
securities; CDO is collateralized debt obligation; CLO is collateralized loan obligation. The “other”
category consists of other asset-backed securities (A
cked by credit card debt, student
loans, equipment, floor plans, and miscellaneous receivables; resecuritized real estate mortgage
investment conduit (
C) rMbS; an
C CMbS. Th
e data are converted to constant
2025 dollars using the consumer price index. The key identifies bars in order from top to bottom.
Figure 3.15. bank c
redit commitments to other financial entities continued to grow
Committed amounts on credit lines and term loans extended to nonbank financial institutions.
Nonbank financial institutions are identified based on reported North American Industry Classifi-
cation System (NAICS) codes. In addition to NAICS codes, a name-matching algorithm is applied
to identify specific entities such as real estate investment trusts (
s), special purpose enti-
ties, collateralized loan obligations (CLOs), asset-backed securities (AbS), p
rivate equity, business
development companies (bDCs
), and private credit. rEIT
s incorporate both mortgage (trading)
s and equity rEIT
er-dealers also include commodity contracts dealers and broker-
ages and other securities and commodity exchanges. Other financial vehicles include closed-end
investment and mutual funds.
Figure 3.16.
redit growth was strongest for special purpose entities, collateralized loan
obligations, and asset-backed securities between 2024:Q2 and 2025:Q2
The figure shows 2025:Q2-over-2024:Q2 growth rates as of the end of the second quarter of
s real estate investment trust; PE is private equity; bDC is b
usiness development
Figu re Notes 61
company; SPE is special purpose entity; CLO is collateralized loan obligation; A bS is as set-backed
securities. The key identifies bars in order from left to right.
Figure 4.1. The ratio of runnable money-like liabilities to GDP was around 80 percent
The black striped area denotes the period from 2008:Q4 to 2012:Q4, when insured deposits
increased because of the Transaction Account Guarantee program. The “other” category consists
of variable-rate demand obligations (
), federal funds, funding-agreement-backed securities,
private liquidity funds, offshore money market funds, short-term investment funds, local govern-
ment investment pools, and stablecoins. Securities lending includes only lending collateralized
by cash. GDP is gross domestic product.
es for vrDOs c
ome from bloom
berg beginning
in 2019:Q1. See Jack bao, J
osh David, and Song Han (2015), “The runn
ables,” FEDS Notes
(Washington: boar
d of Governors of the Federal rese
rve System, September 3), https:/ /www.
federalreserve.gov/econresdata/notes/feds-notes/2015/the-runnables-20150903.html.
Figure 4.2. The share of high-quality liquid assets to short-term debt ticked down in the first half
of 2025 but remained at the higher end of the historical distribution
The figure shows banks with total assets greater than or equal to $10 billion. The sample con-
sists of domestic bank holding companies (
), intermediate holding companies (IHCs) with a
substantial U.S. commercial banking presence, and commercial banks. G-SI bs are g
lobal system-
lly important banks. Large non–G-SI bs are bHCs a
nd IHCs with greater than $100 billion in
total assets that are not G-SIbs. Sh
ort-term debt is total liabilities minus long-term debt. The
shaded bars with top caps indicate periods of business recession as defined by the National
au of Economic rese
arch: March 2001–November 2001, December 2007–June 2009, and
February 2020–April 2020.
Figure 4.3. bank
s’ reliance on uninsured deposits and short-term wholesale funding stabilized to
levels more typical of the longer history
Short-term wholesale funding is defined as the sum of large time deposits with maturity less than
1 year, federal funds purchased and securities sold under agreements to repurchase, deposits
in foreign offices with maturity less than 1 year, trading liabilities (excluding revaluation losses
on derivatives), and other borrowed money with maturity less than 1 year. The shaded bars with
top caps indicate periods of business recession as defined by the National
au of Economic
arch: December 2007–June 2009 and February 2020–April 2020.
Box 4.1. A More Targeted Assessment of Short-Term Funding Risk
Figure A. runn
able vehicles and instruments, by historical run propensity
The “experienced industry-wide runs or market freezes” category includes domestic prime
institutional money market funds (MMFs), offshore prime MMFs, commercial paper, negotia-
ble certificates of deposit, variable-rate demand obligations, and repurchase agreements.
The “experienced runs or notable stress” category includes all components in “experienced
industry-wide runs or market freezes” plus domestic prime retail MMFs, local government invest-
ment pools, short-term investment funds, ultrashort bond funds, private liquidity funds, stable
62 Financial Stability repor t
coins, uninsured deposits, securities lending, federal funds, and funding-agreement-backed
securities. The “total runnables” category includes all components in “experienced runs or
notable stress” plus domestic and offshore government MMFs. GDP is gross domestic prod-
uct. The shaded bars with top caps indicate periods of business recession as defined by the
au of Economic rese
arch: July 1990–March 1991, March 2001–November 2001,
December 2007–June 2009, and February 2020–April 2020.
Figure b. runn
able vehicles, by risk category, as a percentage of nominal GDP
The “no notable stress” category includes domestic government money market funds (MMFs) and
offshore government MMFs. The “notable stress incidents” category includes domestic prime
retail MMFs, local government investment pools, short-term investment funds, ultrashort bond
funds, private liquidity funds, and stablecoins. The “experienced industry-wide runs” category
includes domestic prime institutional MMFs and offshore prime MMFs. GDP is gross domestic
product. The shaded bars with top caps indicate periods of business recession as defined by
the National
au of Economic rese
arch: March 2001–November 2001, December 2007–
June 2009, and February 2020–April 2020.
Figure C. runn
able instruments, by risk category, as a percentage of nominal GDP
The “notable stress incidents” category includes uninsured deposits, securities lending, federal
funds, and funding-agreement-backed securities. The “experienced market freeze” category
includes commercial paper, negotiable certificates of deposit, variable-rate demand obligations,
and repurchase agreements. None of the runnable instruments are classified as having expe-
rienced “no notable stress.” GDP is gross domestic product. The shaded bars with top caps
indicate periods of business recession as defined by the National
au of Economic rese
March 2001–November 2001, December 2007–June 2009, and February 2020–April 2020.
Figure 4.4. Assets under management at money market funds remained high
The data are converted to constant 2025 dollars using the consumer price index.
Figure 4.5. Market capitalization of major stablecoins experienced accelerated growth
The key identifies series in order from top to bottom. USD is U.S. dollar.
Figure 4.6. Corporate bond holdings of mutual funds were stable in the first half of 2025
The data show holdings of all U.S. corporate bonds by all U.S.-domiciled mutual funds (holdings of
foreign bonds are excluded). The data are converted to constant 2025 dollars using the consumer
price index.
Figure 4.7.
an and high-yield mutual fund assets remained steady at levels far below their
2021 peaks
The data are converted to constant 2025 dollars using the consumer price index. The key
identifies series in order from top to bottom.
Figu re Notes 63
Figure 4.8. April’s outflows stabilized
Mutual fund assets under management as of August 2025 included $2,450 billion in investment-
grade bond mutual funds, $280 billion in high-yield bond mutual funds, and $82 billion in bank
loan mutual funds.
an mutual funds, also known as floating-rate bond funds, are excluded
from high-yield bond mutual funds. Curved line segments on the y-axis and bar indicate a scale
break to accommodate high values observed in March 2020.
Figure 4.9. Life insurers’ use of nontraditional liabilities increased further
The data are converted to constant 2025 dollars using the consumer price index. FHL
Home Loan bank
. The data are annual from 2006 to 2010 and quarterly thereafter. The key iden-
tifies bars in order from top to bottom.
Figure 4.10. Life insurers continued to hold a significant share of illiquid assets on their
balance sheets
The data are converted to constant 2024 dollars using the consumer price index. Securitized
products include collateralized loan obligations for corporate debt, private-label commercial
mortgage-backed securities for commercial real estate (C
nd private-label residential
mortgage-backed securities and asset-backed securities (A bS) ba
cked by autos, credit cards,
consumer loans, and student loans for other AbS. Il
liquid corporate debt includes private place-
ments, bank and syndicated loans, and high-yield bonds. Alternative investments include assets
filed under Schedule
C is property and casualty. The key identifies bars in order from top
to bottom.
Box 5.1. Survey of Salient Risks to Financial Stability
Figure A. Fall 2025: Most cited potential shocks over the next 12 to 18 months
onses are to the following question: “Over the next 12–18 months, which shocks, if realized,
do you think would have the greatest negative impact on the functioning of the U.S. financial
Figure b. Spr
ing 2025: Most cited potential shocks over the next 12 to 18 months
onses are to the following question: “Over the next 12–18 months, which shocks, if realized,
do you think would have the greatest negative impact on the functioning of the U.S. financial
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