The case for supporting liquidity supply in (some corners of) non-bank intermediation

Board of Governors of the Federal Reserve System International Finance Discussion Papers ISSN 1073-2500 (Print) ISSN 2767-4509 (Online) Number 1425 October 2025 The case for supporting liquidity supply in (some corners of) non-bank intermediation Sirio Aramonte Please cite this paper as: Aramonte, Sirio(2025). “Thecaseforsupportingliquiditysupplyin(somecornersof)nonbank intermediation,” International Finance Discussion Papers 1425. Washington: Board of Governors of the Federal Reserve System, https://doi.org/10.17016/IFDP.2025.1425. NOTE: International Finance Discussion Papers (IFDPs) are preliminary materials circulated to stimulate discussion and critical comment. The analysis and conclusions set forth are those of the authors and do not indicate concurrence by other members of the research staff or the Board of Governors. References in publications to the International Finance Discussion Papers Series (other than acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers. Recent IFDPs are available on the Web at www.federalreserve.gov/pubs/ifdp/. This paper can be downloaded without charge from the Social Science Research Network electronic library at www.ssrn.com.

The case for supporting liquidity supply in (some corners of) non-bank intermediation Sirio Aramonte Federal Reserve Board September 26, 2025 Abstract After the Global Financial Crisis, the liquidity-supply ecosystem that underpins nonbank intermediation shifted away from traditional dealers. Instead, it started to rely more on intermediaries with fragile funding structures and opportunistic investment strategies. Over the years, stress episodes saw the sudden retrenchment of these intermediaries, which amplified liquidity imbalances and market malfunction. Efforts to reduce the risk and magnitude of liquidity imbalances have mostly focused on reducing liquidity transformation and on constraining liquidity demand. This paper highlights the importance of strengthening liquidity supply in certain non-bank segments, particularly those that allow households to conduct long-term consumption smoothing. The main argument is that the rise of non-bank intermediation, and the ensuing risk of spikes in liquidity demand, partly reflects structural changes in how households can meet fundamental financial needs. In addition, the risk-taking channel of monetary policy can affect liquidity-demand dynamics, including for some intermediaries that facilitate household consumption smoothing. Keywords: Drivers of liquidity demand; Liquidity supply; NBFIs JEL classification: G14; G28; G38 Sirio.aramonte@frb.gov. I would like to thank Daniel Beltran, Stephanie Curcuru, Torsten Ehlers, Falko Fecht,LindaGoldberg,LucaGuerrieri,JuanLondono-Yarce,MariaPerozek,ViktorsStebunovs,andseminar participantsattheInternationalBankingResearchNetworkandInvestmentCompanyInstituteforcomments and suggestions. This article represents the views of the author and not those of the Federal Reserve Board, Federal Reserve System, their principals, or other members of their staff. 1

1 Introduction The growth of non-bank financial intermediaries (NBFIs) has led to rising liquidity demand across financial markets. In fact, the business models of non-banks are often predicated on investing in marketable securities that trade relatively often. These trading flows, which can be amplified by forced deleveraging or disruptions in liquidity transformation, need reliable liquidity supply, including at times of market distress. The liquidity-provision ecosystem on which NBFIs rely experienced significant structural changes over time. In particular, the traditional dealer-centric model that emerged in the 1980s became more diffuse after the Global Financial Crisis (GFC). It now depends on intermediariesthat often have fragile funding structures and can exhibitopportunistic behavior. Given the broad-based effects of liquidity imbalances, which propagate shocks through price externalities, academics and policymakers have extensively studied how to assess the risk of liquidity disruptions and what remedial actions are feasible. This paper reviews structural developments that have shaped the size and composition of the NBFI sector in the United States over the past fifty years. It then highlights how some of these developments are useful to devise strategies for containing liquidity imbalances. Specifically, the paper discusses (i) how long-term consumption smoothing is increasingly the sole responsibility of individuals, which boosts demand for delegated asset management and for liquidity; and (ii) how search for yield can generate portfolio reallocation—and liquidity demand—including by intermediaries that support household consumption smoothing. The paper comprises three parts, in addition to a concluding section that reviews the key considerations. Section 2 includes a shorty summary of how NBFIs evolved over time, of what factors drove non-banks growth, and of how these developments affected key features of the financial system. Section 3 is a deeper dive that emphasizes certain changes in how households can achieve long-term consumption smoothing, including their implications for non-bank intermediation and liquidity demand. Section 4 studies the effect of selected macroeconomic factors on asset reallocation within the NBFI sector, which naturally generates liquidity demand. Search for yield, as a manifestation of the risk-taking channel 2

of monetary policy, is especially relevant, including for select intermediaries that facilitate household consumption smoothing. In particular, a shift toward riskier and less liquid securities may generate heightened liquidity demand when the policy stance changes and investors re-optimize their portfolios. The concluding section highlights that, since market liquidity is instrumental to meeting fundamental financial needs and sometimes reflects how investors adjusted to certain policies, tackling liquidity imbalances primarily with demand restrictions may be suboptimal compared to a framework that also includes fostering supply. 2 The rise of NBFIs and its implications The share of bank-held assets in the U.S. financial system declined steadily between the late 1970s and the mid-1990s (Figure 1). This drop coincided with sustained growth in mortgagebacked securities issued by government-sponsored enterprises (or GSEs; teal area) and pooled investment vehicles, such as mutual funds and exchange-traded funds (ETFs) (yellow and purple areas). Over time, the share of defined-benefit (DB) pension funds declined in favor of defined-contribution (DC) funds (light blue and dark red areas), while the footprint of insurance companies contracted slightly (orange area). In addition, large non-financial firms alsoprogressivelyincreasedtheirholdingsoffinancialassets(Figure2,shadedarea),including relatively illiquid securities such as corporate bonds (red line; see Duchin et al., 2017 and Mota and Darmouni, 2023). The overall shift toward marketable securities raised the need for liquidity supply, which dealers met by expanding their balance sheets (Figure 1, red area). After the 2008 financial crisis, dealer assets shrank due to changes in business models and regulation. The corresponding decline in stable dealer-provided liquidity (see, among others, Bao, O’Hara, and Zhou, 2018 and Bessembinder et al., 2018) opened the door to more opportunistic liquidity supply by other non-banks, such as mutual funds (Anand et al., 2013 and Wang et al., 2020) and insurance companies (Aramonte and Mano, 2022), especially at times of market stress (O’Hara et al., 2025). In relatively deep markets, such as those for U.S. Treasuries, certain intermediaries that provide significant amount of liquidity, including 3

principal trading firms, tend to focus on trading speed and have short holding periods and high turnover (Eren and Wooldridge, 2021), heightening the fragility of liquidity supply. 2.1 Drivers of NBFI growth Many factors contributed to the expansion of non-bank intermediation. Besides changes to how investors conduct consumptions smoothing, which are the focus of Section 3, here I review three broad driving forces: regulation, evolving bank business models, and shifts in corporate governance. Starting with regulation, certain policy actions had the stated intent of developing selected non-bank intermediation channels. Government-sponsored enterprises (GSEs) are a prime example, starting with the Farm Credit System and culminating with Fannie Mae and Freddie Mac, which were established to support residential mortgage finance. The GSEs were pivotal to the growth of debt securitizations.1 In other instances, regulation only played an indirectrole. Formanyyearsstartingin1933, forinstance, RegulationQlimitedtheinterests that banks could pay to depositors. As short-term market rates rose above interest-rate ceilings in the late 1960s (Gilbert, 1986), savers had an incentive to invest in money-market mutual funds (MMFs), which have since grown to manage about $7 trillion in assets. As a more recent example, adjustments to securities regulations in the mid-1990s improved the ability of late-stage start-ups to raise private capital (Ewens and Farre-Mensa, 2020), which has contributed to the declining relative size of public markets. Turning to evolving bank business models, the early rise of NBFIs put pressure on banks’ balance sheets, both on the asset side (eg, firms could issue bonds instead of taking loans) and on the liability side (eg, MMF shares were an alternative to deposits); see Berger, Kashyap, and Scalise (1995) and Gorton and Metrick (2013). The rollback of restrictions on the ability of banks to expand geographically increased competition within the sector, leading to consolidation (Berger, Demsetz, and Strahan, 1999 and Jayaratne and Strahan, 1 GSE-sponsoredmortgage-backedsecurities(MBS)areperceivedasrelativelyclosesubstitutestoTreasuries intermsofcreditrisk,andgrewrapidlyinthe15yearspriortotheGreatFinancialCrisis. Between1992and 2007, mortgages guaranteed by Fannie Mae and Freddie Mac rose from about $700 billion to roughly $3.5 trillion (Richardson et al., 2017). At present, outstanding agency MBS amount to nearly $9 trillion (Ginnie Mae, “Global Markets Analysis Report,” April 2024) and are widely held by fixed-income investment funds. 4

1998). In turn, consolidation allowed banks to raise efficiency as demanded by shareholders (G10, 2001), both by improving operations (Hughes et al., 1996) and by enhancing their risk profiles through portfolio diversification (McAllister and McManus, 1993). Balancesheet efficiency was also facilitated by loan securitizations and by the development of the originate-to-distribute model (Berger, Kashyap, and Scalise, 1995 and Buchak et al., 2024). Furthermore, advances in data processing paved the way for automated credit scoring and, at the margin, reduced credit flows to smaller firms (G10, 2001 and Berger, Kashyap, and Scalise, 1995), which are less easily scored using quantitative models. Switching to corporate governance, the rise of institutional investors lowered coordination costs among shareholders, allowing them to be more assertive in seeking improvements that would boost stock prices (Donaldson, 1994). Investors used leverage, also through corporate-bond issuance, to align the incentives of corporate managers with their own, given that debt absorbs free cash flows and reduces the scope for wasteful spending (see Grossman and Hart, 1982 and Jensen, 1986 for early contributions, and Shleifer and Vishny, 1997b for a review).2 2.2 Effects of NBFI growth Besides re-routing capital flows through a variety of non-bank channels, the rise of NBFIs had significant implications for the breadth of available funding sources, for the cyclicality of credit, and for financial stability. Starting with broader funding availability, the presence of NBFIs can increase the numberandtypesofavailablelenders. Inpractice, NBFIscanactasa“sparetire”thatmakesthe economy and the financial system more resilient to unexpected shocks (Greenspan, 1999a,b). Fischer (2015) notes that long-term investors, such as mutual funds and insurance companies, normally tend to offset changes in credit extended by banks. For instance, firms can use corporate bonds to replace syndicated loans when the supply of bank credit contracts (Benveniste, Singh, and Wilhelm, 1993 and Becker and Ivashina, 2014). 2 As a percent of GDP, corporate bonds nearly doubled between the early 1980s and the late 1990s; see: Federal Reserve’s Financial Accounts of the United States (https://www.federalreserve.gov/releases/ z1/dataviz/z1/nonfinancial_debt/chart/#series:corporate;units:percent-of-gdp). 5

Turning to cyclicality in capital availability, investor flows into NBFIs affect the amount offundsthatnon-bankscandeploy. Indeed, inflowsareakeydeterminantoftheparticipation ofnon-banklenderstoloansyndicates(Fleckensteinetal.,2024). Flowsintomutualfundsare also important drivers of financial conditions for companies that issue bonds widely held by mutualfunds(Zhu,2021). AmongvariousNBFIs, DCpensionplansareparticularlysensitive to macroeconomic conditions in their allocation to equities and bonds (Sialm, Starks, and Zhang, 2015). In terms of financial stability, structural features of the NBFI sector – chiefly, leverage and liquidity transformation – create vulnerabilities that generate systemic risk, as summarized by Erel and Inozemtsev (2024). The effects of these vulnerabilities can be amplified by the intrinsic dependence of NBFIs on reliable market liquidity; see Aramonte, Schrimpf, and Shin (2023) for an overview of the implications of liquidity imbalances. As a consequence, it becomes crucial to understand the factors that could make liquidity supply insufficient to match demand. The balance-sheet capacity of key intermediaries, such as dealers and market makers, is a paramount driver of liquidity supply (see, for instance, Comerton-Forde et al., 2010 and Goldberg and Nozawa, 2021) and can generate liquidity co-movement across asset classes (Aramonte and Szerszen, 2020). Besides capital shortages, risk-management strategies can be a key source of constrained balance sheets and reduced liquidity supply (Shin, 2010). Intermediary leverage is also a very important driver of liquidity supply (Adrian and Shin, 2010 and Adrian and Shin, 2014), including when taken through margins in cleared markets (Aramonte, Schrimpf, and Shin, 2023). The financial-market volatility that characterized the onset of the Covid-19 pandemic is a prime example of how a rapid contraction in the balance-sheet capacity of key intermediaries can lead to severe liquidity disruptions. Most notably, hedge funds active in Treasury markets retrenched quickly (Kruttli et al., 2023) and the resulting illiquidity spiral (Schrimpf et al., 2020) was so pronounced that it led to calls for changes in the trading architecture of Treasury markets (Duffie, 2020). Corporate-bond markets also faced unusually poor liquidity during the Covid-19 crisis (Kargar et al., 2021 and O’Hara and Zhou, 2021), partly due to interruptions in liquidity transformation by mutual funds, which hold large amounts of 6

bonds (Ma et al., 2022). Lack of liquidity became so detrimental to market functioning that policymakers were forced to intervene as liquidity providers of last resort (Gilchrist et al., 2024, Boyarchenko, Kovner, and Shachar, 2022, and Haddad, Moreira, and Muir, 2021). 3 The shifting burden of consumption smoothing Over the past fifty years, the funding of certain large life-cycle expenses, such as retirement, has increasingly become the responsibility of individuals, contributing to the accumulation of households savings. Income risk, which is a key driver of precautionary savings, has also inched up over the same period. How households invest these savings has helped shape some corners of non-bank intermediation, in particular facilitating the rise of investment vehicles—like fixed-income mutual funds—that have been at the center of the academic and policy discussion on liquidity disruptions (Chen et al., 2010, Choi et al., 2020, Goldstein et al., 2017, and FSB, 2023). This section starts be reviewing changes to how individuals provide for their retirement, before considering healthcare expenses and income risk.3 To the extent possible, the section also reviews how households invest their savings, which are typically entrusted to asset managers. The section concludes with a discussion of why potential conflicts of interests in delegated portfolio management generate a deep-seated preference for liquidity. 3.1 Retirement savings In the United States, individuals fund their retirement with a mix of Social Security benefits, corporate pensions, and personal savings, including those accumulated in tax-advantaged accounts. The share of old-age income provided by Social Security and corporate pensions has generally declined over the past 50 years. For Social Security, earnings replacement rates 3 Risingcostsforhighereducationarenotdiscussedinthissection,buttheyalsoshiftconsumptionsmoothingtoindividualsinasimilarwayaschangestopensionavailabilityandincomereplacementratesdo. Assets held in tax-advantaged 529 education savings accounts increased together with tuition costs (Hannon et al., 2016) and amounted to about $500 billion (ICI, 2025) in 2024. 7

(benefits received at 65 as a percent of career-average earnings) trended up after the program was established and peaked for those retiring in 1980 at 67.5% (low earners) and 48.5% (high earners). For those retiring in 2020, the figures were 53.0% and 32.5%, respectively (Burkhalter and Rose, 2025, Table D). At the same time, normal retirement age increased to 67 years. These changes aimed at stabilizing the overall cost of the program, which had roughly doubled to 5% of GDP between the early 1960s and the early 1980s (Martin and Weaver, 2005). There is also perceived uncertainty about the future path of benefits, which, should the Social Security Trust Funds continue to deplete at current trends, could decline to 81% of scheduled benefits (Social Security Trustees, 2025). Turning to corporate pensions, and focusing on workers covered by a retirement plan, the share of employees with access only to DC options—without DB pensions—rose from about 12% in 1983 to roughly 73% in 2019 (CRR, 2024). Headline coverage numbers can overestimatethesignificanceofDB-planavailabilityforretirementincome,becauseemployers may have an incentive to fire workers just before vesting (Schiller and Weiss, 1979) and inflation may erode the value of vested participation after job separation. Still, legislation sets statutory vesting periods (Ippolito, 1988) and, after vesting, workers often have the option to cash out pensions (Burman et al., 1999 and Hurd and Panis, 2006) and roll them into accounts that offer investments able to hedge inflation. DecliningeligibilityforDBcorporatepensionscoincidedwithrapidgrowthinDCplans, including employer-sponsored tax-deferred accounts (Salisbury, 1990). These changes partly reflected demand for portable retirement savings in industries with high employee turnover (Aaronson and Coronado, 2005), but also offered meaningful cost reductions to employers (Cocco, 2014 and Rauh, Stefanescu, and Zeldes, 2020). Reliance on DC plans increases uncertainty about future income, since these plans can provide higher wealth accumulation but also raise the risk of poor outcomes (Poterba et al., 2007). Understanding how changes in retirement-funding options can affect non-bank intermediation hinges on gauging their impact on savings rates. Researchers generally conclude that public pensions reduce private savings (Bernheim and Levin, 1989 and Gokhale, Kotlikoff, and Sebelhaus, 1996; see Feldstein and Liebman, 2002 for a comprehensive summary). Based 8

on the literature, CBO (1998) reckons that private savings decline by about 25% to 50% of the payouts of public pensions. These estimates imply that declining income replacement rates from public and corporate pensions have meaningfully increased private savings rates, which are also boosted by uncertainty about future pension benefits (Jappelli et al., 2021). While most research focuses on the United States, international evidence also points to a significant inverse link between government-provided DB pensions and savings rates. This is the case for country-specific studies that use data from the United Kingdom (Attanasio and Rohwedder, 2003) and Italy (Attanasio and Brugiavini, 2003), as well as for research based on cross-country comparisons (Samwick, 2000). 3.2 Health expenses Healthcare costs paid for by patients—even those with insurance—are known as out-ofpocket expenses. In the United States, these costs averaged $1,400 in 2022.4 Considerable asymmetry in their distribution highlights the risk that patients may shoulder high costs: in 2021, about 70% of the population spent less than $500, while 9% spent more than $2,500. Expenses differ markedly across age (2.6 times higher for patients older than 65 compared to those in the 19-34 bracket) and health conditions (3.3 times higher for patients in poor rather than excellent conditions). In advanced old age, expenses increase exponentially into the tens of thousands of dollars, especially for higher-income households (De Nardi et al., 2016). Out-of-pocket expenses tend to grow more rapidly than inflation. For instance, they increased by 1.1% per year between 1996 and 2016, adjusted for inflation and for the size and age composition of the population (Dieleman, Cao, and Chapin, 2020). Healthcare-driven precautionary savings are a meaningful fraction of overall household savings, and shift asset allocation toward safer assets (Guiso et al., 1996). Capatina (2015) finds that the need to cover medical expenses in old age increases savings by about 25% for 4 Unless otherwise noted, the statistics reported in this section are from the Peterson-KFF Health System Tracker (www.healthsystemtracker.org/indicator/access-affordability/ out-of-pocket-spending). 9

people in or close to retirement, and by approximately 10% for younger cohorts.5 By itself, the risk of out-of-pocket expenses for nursing homes accounts for 3% of aggregate wealth, and it slows savings drawdown in old age (Kopecky and Koreshkova, 2014). 3.3 Income risk The risk that labor income may fluctuate can increase savings rates, as individuals hedge against possible future earnings shortfalls. Income risk has increased over time for male workers, especially when considering transitory shocks rather than permanent changes to income (Gottschalk and Moffitt, 2009 and Moffitt and Zhang, 2018). The possibility of very large income drops is an important component of income risk (Moffitt et al., 2022) and it is key for explaining its cyclical variation (Guvenen et al., 2014). Theoretical research that built on life-cycle models indicated that precautionary motives linked to income risk could potentially explain up to about half of household savings (Skinner, 1988 and Caballero, 1991). However, establishing the causal effect of income risk on consumption and savings is not straightforward, chiefly because measures of income variability are often noisy and career choices are endogenous (Lusardi, 1998), meaning that workers with higher risk tolerance can self-select into professions with more volatile earnings. Indeed, the early empirical literature found conflicting results (see the discussion in Carroll, Dynan, and Krane, 2003 and Lugilde et al., 2019). Over time, consensus emerged that income risk affects consumption (Carroll, 1994 and Hahm and Steigerwald, 1999) and savings, and that the possibility of very large income declineshasaparticularlypersistenteffectonsavings(MalmendierandShen,2024). Notably, Carroll andSamwick(1998) find thatdifferences in exposureto income riskacross households can explain between 30% an 50% of wealth accumulation. Engen and Gruber (2001) use changes in unemployment insurance for identification, and find that halving the income replacement rate offered by insurance raises household financial assets by about 15%. Besides increasing savings rates, income risk pushes households towards safer assets (Guiso, Jappelli, 5 Thefiguresarewithin-agegroupaveragesofestimatesreportedonline“Medicalexpenditures=0,ages>65” of Table 8 in Capatina (2015). 10

and Terlizzese, 1996), especially if their income falls when stocks experience negative returns (Viceira, 2001 and Bonaparte et al., 2014). Thestrengthofprecautionarymotivesarisingfromincomeriskvariesacrossindividuals. Kazarosian (1997) studies differences across professions and reports that those in managerial positions tend to save more for a given level of income uncertainty. Across income levels, Carroll, Dynan, and Krane (2003) note that higher-income households are more sensitive to increases in the probability of job loss. 3.4 How are savings invested? The need to save for retirement facilitates the development of non-bank capital markets and increases the size of the overall financial sector (Scharfstein, 2018). As Viceira (2008, pg. 3) writes, “one of the major beneficiaries of this change [theshiftfromDBtoDCplans]has been the mutual fund industry.” In the United States, DC plans invest through mutual funds more often than DB ones do (Broadbent, Palumbo, and Woodman, 2006), partly because mutual funds offer cost advantages (Hubbard et al., 2010). Globally, the mutual funds industry is more developed in countries with a larger share of DC plans (Khorana, Servaes, and Tufano, 2005). Of total U.S. retirement assets, 64% are held in 401(k) accounts, individual retirement accounts (IRAs), and annuities (up from 41% in 1995; these figures are from ICI, 2025, as are those reported below unless noted otherwise). Of 401(k) assets, 60% are invested in mutual funds, as are 38% of IRA assets. Within 401(k) plans, savers change asset allocation with age. Exposure to equities is 90% for individuals in their 20s, and declines to 57% for those in their 60s. At this age, bonds-only funds represent 10% of assets, while target-date and balanced funds—both of which have significant shares of bonds—represent a combined 37% of assets.6 On balance, these figures suggest that mutual funds that engage in liquidity transformation hold a meaningful share of retirement assets for older savers. 6 At the time of writing, two of the largest target-date funds for investors approaching retirement (2030 target-date funds managed by Fidelity and Vanguard) reported a 39% average allocation to fixed income securities. In turn, this allocation consisted of government bonds (about half on average), corporate bonds (15%), securitizations (14%) and other instruments, including derivatives. 11

Households wishing to save for retirement can indirectly hold registered funds through accounts at life insurers that issue annuities, in particular variable annuities. About 6% of U.S. retirement assets are in the form of annuities. Savers make recurring deposits that are invested in financial assets, which are eventually used to back a stream of periodic payments made by the insurer. Strong demand for annuities has turned them into the main business line of life insurers (Ellul et al., 2022). About 75% of the assets held in annuity accounts are invested in stocks, often through mutual funds (Drexler et al., 2017). Mutual funds and other registered funds, including ETFs and closed-end funds, represent a large share of U.S. household wealth outside tax-advantaged retirement accounts. Overall, households held 24% of their financial wealth in registered funds.7 For comparison, 10% of their financial wealth is in registered funds held in retirement accounts. Conversely, households are the largest investor type in mutual funds, holding 88% of U.S. mutual funds net assets and 68% of MMF assets. These observations are important for two reasons. First, households savings outside of retirement assets are bound to support the growth of registered funds.8 Second, liquidity disruptions, togetherwithprovisionsthatconstrainliquiditydemand, directlyaffecthouseholds’ ability to achieve the desired portfolio allocation. 3.5 A deep-seated preference for liquidity Delegated portfolio management is typically characterized by potential agency conflicts (Bebchuk, Cohen, and Hirst, 2017), which arise from portfolio managers’ incentives to maximize their own expected benefits at the expense of value accrued to investors. These incentives typically pertain to compensation or job security. For instance, managers tend to take more 7 ETFs are increasingly the investment vehicle of choice for households. The share creation/redemption mechanism of ETFs reduces the risk of liquidity disruptions because traded share prices internalize liquidity costs. However, ETFs can still transmit significant selling pressure to the reference assets, depending on certaincharacteristicsoftheintermediariesinvolvedincreationsandredemptions(ShimandTodorov,2023). 8 Savings for healthcare reasons are also likely to increase assets held in registered funds. For instance, taxadvantaged savings accounts for healthcare expenses offer similar investment options as retirement accounts. However,relativelylowstatutorycontributionlimitsmeanthatassetsinhealth-savingsaccountsareunlikely tocovermorethanafractionofexpectedout-of-pockethealthcosts(Fronstin,2010). Asaresult,healthcarerelated savings are bound to be held in general-purpose accounts that likely invest in registered funds, including mutual funds. 12

risk to compensate for poor returns—unless employment risk is high, in which case they reduce portfolio risk to avoid trailing peers by wide margins (Kempf, Ruenzi, and Thiele, 2009). In general, researchers have long highlighted that, even in the relatively standardized mutual fund industry, compensation packages can be more directly tied to the profitability of the asset management company than to investor returns (Farnsworth and Taylor, 2006). Besides well designed fund governance (Clifford, Ellis, and Gerken, 2018), the threat of capitalwithdrawalisthemaindiscipliningdevicethatinvestorscanusetoaligntheincentives of portfolio managers with their own. Such threat is so powerful that portfolio managers may forgo profitable arbitrage if it encumbers capital for too long (Shleifer and Vishny, 1997a). Consistent with investors’ preference for capital redeemability, the discount of closed-end funds’ prices to the underlying investments is partly driven by measures of agency conflicts, and turning these funds into open-ended vehicles substantially reduces discounts (Bradley, Brav, Goldstein, and Jiang, 2010). Given that agency conflicts are intrinsic to delegated portfolio management, demand for open-ended vehicles is an ingrained feature of non-bank intermediation. The structural nature of investors’ preferences for liquidity does not subtract from the vulnerabilities that arise from liquidity transformation and spikes in liquidity demand. Rather, it emphasizes that promoting market stability by suppressing access to liquidity is costly for investors. 4 The effect of macroeconomic and financial conditions Thus far, the discussion has covered key structural factors that underpinned the long-term growth of non-bank intermediation. I now take a different perspective, focusing on cyclical asset reallocation in response to changing macroeconomic and financial conditions. Naturally, reallocation creates liquidity demand. I first consider possible reallocation between banks and non-banks, before studying reallocation across certain non-banks (Section 4.1). I then characterize how certain intermediaries with search-for-yield incentives invest in corporate bonds—an example of illiquid securities at the center of the discussion on liquidity transformation—when interest rates rates are low, and when market stress spikes (Section 13

4.2). The results indicate that macroeconomic variables have little explanatory power, with the exception of interest rates: changes in rates anticipate growth in MMFs relative to longer-duration investment funds, and rate levels anticipate corporate-bond investments by intermediaries with known search-for-yield incentives. In turn, these intermediaries retrench quickly at times of stress. Search for yield is particularly significant when discussing the risk of liquidity disruptions, fortwomainreasons. First, itfacilitatestheaccumulationofriskyandilliquidpositions that, as noted above, can be unwound rapidly and overwhelm liquidity provision (similar to the reversals mechanism outlined in Feroli et al., 2014). Second, search for yield is a reflection of the risk-taking channel of monetary policy (Borio and Zhu, 2012), meaning that surges in liquidity demand from rapid derisking can, to some extent, be an aftereffect of policy choices. 4.1 The macroeconomic cycle This section starts by investigating whether selected macroeconomic variables have explanatory power for differences in the growth rate of the banking sector vs. the growth rate of non-banks as a whole. Growth rates are computed from annual transactions (net of valuation effects) and year-end assets, as reported in the Financial Accounts of the United States. The explanatory variables are lagged annual changes in 10-year Treasury yields (∆Treas ), y−1 percent growth in gross domestic product (∆GDP) and in the consumer price index (∆CPI), changes in the private non-financial credit-to-GDP gap (∆Gap), and changes in the personal savings rate (∆Savings). These data are from the FRED database of the Federal Reserve Bank of St. Louis, except for the credit-to-GDP gap, which is from the Bank for International Settlements. All variables are standardized, so that regression coefficients can be interpreted as the effect of a one-standard deviation change in the independent variable in terms of standard deviations of the dependent variable. As shown in Table 1, there is little evidence that changes macroeconomic variables, including interest rates, anticipate growth differences between banks and non-banks. Indeed, 14

not one of the fifteen coefficients is statistically significant at the 10% level. Furthermore, R2s are in the mid to low single digits, indicating poor fit. In contrast, interest rates do affect the composition of the non-bank sector, as proxied by differences in the growth rates of assets held by various NBFIs. I consider NBFIs that, exante, should be relatively more or less attractive to yield-focused investors depending on the evolution of interest rates. In particular, MMFs lose allure as rates decline, while Treasuries become relatively more appealing due to the higher yields that come from longer duration. The positive coefficient in the first column in Panel A of Table 2 indicates that, following rate declines, MMFs grow less than Treasuries held by mutual funds. Similarly, as shown by the coefficient in the second column, falling rates anticipate lower MMF growth compared to mortgage real-estate investment trusts (mREITs), which are funds that largely invest in mortgage-backed securities and employ high leverage to boost yields. There is also some evidence that MMFs grow less relative to equities held by mutual funds (third column), but the coefficient is marginally statistically insignificant. Within assets held by mutual funds, falling rates anticipate faster growth for equities compared to Treasuries (fourth column), while there is no appreciable effect for corporate bonds relative to Treasuries (fifth column). Like changes in interest rates, inflation also anticipates growth-rate differences between certain NBFI types. As shown in Panel B of Table 2, the effect of inflation is limited to MMF growth vs. growth in (i) Treasuries and (ii) equities held by mutual funds (first and third columns), and it is generally weaker in terms of both economic and statistical significance. 4.2 Search for yield There is ample evidence that many NBFIs tend to invest, within the limits of their mandates, in relatively higher-yielding and riskier securities, especially when interest rates are low. Researcher have studied MMFs (Di Maggio and Kacperczyk, 2017 and La Spada, 2018), pension funds (Andonov et al., 2017), insurers (Becker and Ivashina, 2015), in addition to mutual funds and securitizations (Choi and Kronlund, 2018 and Aramonte et al., 2022). In this section, I focus on intermediaries that can invest in a broad array of assets 15

and analyze their holdings of corporate bonds,9 which are relatively illiquid and are often at the center of concerns about the fragility of liquidity transformation (see, among others, Goldstein et al., 2017). The intermediaries in question, which also help support household consumption smoothing, are life insurers and DB and DC pension funds. Figure 3 provides insights into their balance sheets. Corporate bonds represent the largest asset class held by lifeinsurers. Theirinvestmentsinmutualfunds,whichhaverisensharplysincethe1980s,also include corporate bond funds.10 Turning to pension funds, DB funds hold mostly equities, although the share of corporate bonds has increased since the 2008 financial crisis. As for DC funds, the large majority of their assets consists of mutual funds, which have progressively crowded out direct holdings of equities and especially of corporate bonds. Just as for life insurers, DC funds’ holdings of mutual funds likely add significant indirect investments in corporate bonds. The search-for-yield incentives of these three NBFI types arise different types of institutional frictions and behavioral traits. In general, life insurers tend to “attempt to increase the yield in their bond portfolio by taking on extra priced risk, while leaving capital requirements unaffected” (Becker and Ivashina, 2015, p.1865). Furthermore, Garavito et al. (2024) and Kaufmann, Leyva, and Storz (2024) find evidence of search for yield by life insurers after monetary policy loosening. As for DB funds, they have nominal liabilities that must be met by investing contributions, and underfunding can lead to a shift toward riskier investments (Giesecke and Rauh, 2023). Turning to DC funds, individual investors—who often self-direct their investments in these plans—tend to search for yield, especially if young and less wealthy (Lian et al., 2019 and Gomes et al., 2025). In addition, the investment funds included in DC plans also can engage in search for yield, for instance due to career concerns faced by fund managers (Guerrieri and Kondor, 2012). 9 The Financial Accounts of the United States group together corporate and foreign bonds. For brevity, I will refer to this category as “corporate bonds.” At the end of 2022, the Financial Accounts of the United States recorded holdings of $15.11 trillion in corporate and foreign bonds (https://www.federalreserve. gov/releases/z1/20250612/html/l208.htm). In the same time period, the U.S. Treasury reported that U.S. residents held $3.24 trillion in long-term foreign-government and foreign-corporate bonds (https:// ticdata.treasury.gov/resource-center/data-chart-center/tic/Documents/slt_table2.html). 10 Theshareofcorporatebondsinmutual-fundassetshasincreasedovertime, roughlydoublingbetweenthe 1950s and the 2020s to about 13%. 16

I characterize the search for yield of life insurers and DB and DC pension funds by analyzing the link between their investment in corporate bonds and lagged interest rates. The analysis focuses on the low interest-rate period that followed the GFC, when rates remained low for extended periods. The dependent variable is the difference between the growth rate of corporate-bond holdings and the growth rate of safe-assets holdings (Treasury and Agencies securities), to which I refer as the growth spread. Both growth rates are computed from valuation-unadjusted quarterly holdings in the Financial Accounts of the United States, but I include the contemporaneous changes in the BAA spread control for changes in valuations. The main independent variable is the lagged level of 10-year Treasury rates. Other explanatory variables are lagged values of: GDP growth, four quarter-ahead GDP-growth expectations (from the Survey of Professional Forecasters), and the VIX implied volatility index. Some specifications also include lagged values of the dependent variable. Controls are mostly lagged twice, in an effort to avoid potential endogeneity with once-lagged interest rates. Starting with life insurers, the negative coefficients on lagged Treasury rates reported in Table 3 indicate that lower interest rates anticipate a wider growth spread between corporate bonds and safe securities. Since regressions are based on standardized variables, the coefficients imply that one-standard deviation lower rates correspond to growth spreads wider by about 0.3 standard deviation. When including lagged growth spreads as controls, the standard errors are adjusted for persistence with the Newey-West correction (last three columns), and the results are broadly unchanged. Rates also correlate with wider future growth spreads for DB pension funds. The coefficients imply a roughly 0.45-standard deviation increase in the growth spread for onestandard deviation lower Treasury yields. The results are quite similar for DC funds, as shown in Table 5: the coefficients are smaller in absolute value than for DB funds, but are larger than for life insurers. The sample analyzed so far includes the Covid-19 pandemic, which caused widespread turmoil in financial markets and might have upended normal investment patterns, possibly influencing the results. To gauge the possible effects of these disruptions, Table 6 shows 17

regression coefficients on Treasury rates for the 2010-2019 period. The main difference relative to the full sample is that, for life insurers, the coefficients are both economically and statistically insignificant. In contrast, the coefficients are broadly similar for DB pension funds, and larger for DC funds. The outsize effect exerted by the pandemic on life insurers’ corporate-bond investments is consistent with the findings in Aramonte and Mano (2022). In early 2020, these intermediaries were unusually active in the investment-grade segment of corporate bond markets, providing liquidity in the face of large bond sales by other market participants. The combination of low rates and large purchases by life insurers during the pandemic likely contributed to the statistically significant coefficients reported in Table 3. While the incentives to search for yield are different for life insurers and pension funds, theresultsinTables3to5indicatethattheeffectofTreasuryratesonrisktakingisrelatively similar. Indeed, formal tests show that, between 2010 and 2023, the coefficients on Treasury yields are statistically indistinguishable across intermediaries (Table 7, left panel). Over the shorter sample (right panel), the coefficients for DB and DC funds are statistically the same, but they are different than those on life insurers—recall that, when excluding the pandemic, there is no evidence that life insurers replace safer securities with corporate bonds (Table 6). 4.3 The effect of market distress on corporate-bond holdings A portfolio tilt in favor of riskier securities can unwind rapidly when market stress spikes, possibly straining the liquidity-provision ecosystem. In general, periods of elevated volatility tend to coincide with portfolio rebalancing toward safe assets (see Vayanos, 2004, among others) and with liquidity disruptions (Nagel, 2012). The accumulation of riskier securities that comes from prolonged search for yield can amplify these dynamics, in line with the risk-taking reversal described in Feroli et al. (2014). In line with these considerations, the investments patterns of life insures and pension fundspointtotheirrapidretrenchmentfromcorporatebondmarketsduringperiodsofstress. I define episodes of market stress as quarters in which the highest VIX value is in the top quintile of the distribution across all quarters. As shown in the upper panel of Table 8, the corporate-bond positions of life insurers and pension funds tend to grow by about 1.5% to 2% 18

on a quarterly basis, gross of valuation effects. During episodes of market stress, the growth rate becomes slightly negative for life insurers and stalls for both DB and DC pension funds. On balance, the decline in growth rates is quite sizable, and turns these intermediaries from liquidityproviderstoliquiditydemandersorneutralmarketparticipants. Valuationeffectsdo not drive the results, which change little when growth rates are orthogonalized with respect to changes in BAA corporate-bond yields (bottom panel). 5 How to avoid liquidity disruptions? Imbalances between liquidity demand and liquidity supply can unsettle financial markets by hindering portfolio reallocation and by rapidly transmitting shocks via price externalities (Morris and Shin, 2008). These disruptions can result from surges in liquidity demand, as was the case at the onset of the Covid-19 pandemic, or from rapid declines in liquidity supply, which typically arise when key financial intermediaries face binding balance-sheet constraints (Morris and Shin, 2004 and Adrian and Shin, 2010, among others). The risk and severity of disruptions can be reduced by curbing liquidity demand or by supporting liquidity supply. Demandcanbecurbedafterimbalancesemerged,forinstancebyhaltingredemptionsor by forcing redeeming investors to pay for liquidity costs through swing pricing, thus reducing theexternalitiesimposedonnon-redeemers.11 Demandcanalsobeconstrainedpre-emptively, by reducing liquidity transformation through holdings of liquidity buffers (see, for instance, Aramonte, Scotti, and Zer, 2020 and Chernenko and Sunderam, 2020), or by promoting structures that better embed liquidity costs in traded prices (like ETFs, see Pan and Zeng, 2019 and Shim and Todorov, 2023). 11 Provisions meant to internalize the liquidity costs can be difficult to calibrate. Lewrick and Schanz (2023) find that swing pricing did not prevent outflows from fixed-income funds during the 2013 Taper Tantrum, and conclude that the size of the swing factor did not fully reflect the externalities created by running on funds. In addition, the non-linearity of liquidity restrictions based on preset thresholds can catalyze runs when these thresholds are approached (Fecht and Wedow, 2014 and Li et al., 2021). Even arrangements that fully internalize liquidity costs, such as redemptions in kind (RIK) may have unintended consequences. About 40% of U.S. mutual funds reserve the right to use RIK, and, of these, more than 10% have used RIK in practice (Agarwal et al., 2023). RIK shift the burden of liquidation on investors, who may have poorer market access than funds. Furthermore, funds may behave strategically and deliver the most illiquid securities among those eligible. 19

Asforsupportingsupply, broadeningmarketparticipationlowerstheriskthatmanyliquidity providers retrench simultaneously. Specific options include facilitating central clearing and optimizing liquidity regulations (Duffie, 2020 and Liang and Parkinson, 2020), improving risk management (King et al., 2023), and reducing pro-cyclicality in regulatory requirements (Murphy and Vause, 2021). In addition, during episodes of widespread market stress such as the Covid-19 crisis, the use of central-bank balance sheets has proved instrumental to restoring market functioning (Gilchrist et al., 2024, Boyarchenko, Kovner, and Shachar, 2022, and Haddad, Moreira, and Muir, 2021). As standard, codified recourse to public resources would entail regulatory oversight to limit moral hazard (see, among others, Tucker, 2014, Adrian and Boyarchenko, 2015, and Metrick and Tarullo, 2021). Devising an encompassing strategy to manage liquidity disruptions is ultimately a trade off between constraining demand and bolstering supply. The optimal balance—and, notably, to what extent public resources should be made available—partly depends on the drivers behind the growth of NBFIs and of the attending demand for liquidity. This paper highlights that certain non-bank segments—including investment funds and especially mutual funds—expanded as a result of structural changes to how households can achieve long-term consumption smoothing. In addition, the paper highlights how certain intermediaries that support household consumption smoothing tend to accumulate riskier securities when interest rates are low, and also rapidly reduce purchases of riskier securities, or outright sell them, when market stress is elevated. That is, rapid portfolio rebalancing by these intermediaries, and the potential associated shifts in liquidity, can be the reflection of policy choices that investors had to adapt to. 20

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Figure 1: Composition of the U.S. financial system over time. The chart shows the share of financial assets held by various types of intermediaries. The data are from the Financial Accounts of the United States. The sample covers 1952 to 2022. For defined-benefit pension funds, the chart shows total financial assets minus claims on sponsors (unfunded pension entitlements). The acronymsshowninthelegendstandfor: real-estateinvestmenttrusts(REITs),asset-backedsecurities(ABS), government-sponsoredenterprises(GSEs),agencypools(Ag. Pools),defined-contributionanddefined-benefit pensionfunds(DCandDBpensions), exchange-tradedfunds(ETFs), mutualfunds(MFs), closed-endfunds (CEFs), and money-market mutual funds (MMFs). 100 80 60 40 20 )%( stessa latot fo erahS Assets of U.S. financial intermediaries Other REITs/ABS GSEs/Ag.Pools DC pensions DB pensions Insurers ETFs MFs/CEFs MMFs Broker-dealers Banks 1952 1962 1972 1982 1992 2002 2012 2022 30

Figure 2: Financial assets held by large non-financial corporations. TheshadedareashowsfinancialassetsheldbyaselectionoflargeU.S.non-financialcorporations,asdefined by Darmouni and Mota (2023). The red line represents the share of financial assets invested in corporate bonds. The sample covers 2000 to 2021. 25 20 15 10 5 0 )%( erahs dnob .proC 2 1.5 1 0.5 0 )nrt $( stessa laicnanif latoT Financial assets of non-financial firms 2000 2005 2010 2015 2020 Total financial assets ($ trn) Corp. bond share (%) 31

Figure 3: The asset mix of select types of U.S. financial intermediaries. The three charts report the composition of the balance sheets of three types of U.S. financial intermediaries: lifeinsurers(top), defined-benefit(DB)pensionfunds(middle)anddefined-contribution(DC)pensionfunds (bottom). MMFs and CP stands for money-market mutual funds and commercial paper, respectively. The data are from the Financial Accounts of the United States. The sample covers 1952 to 2022. 100 80 60 40 20 0 )%( stessa laicnanif latot fo erahS Life insurers Other Mutual funds Equities Corp. bonds Mortgages Treas./Agency MMFs Cash/CP 1952 1962 1972 1982 1992 2002 2012 2022 100 80 60 40 20 0 )%( stessa laicnanif latot fo erahS DB pension funds Other Mutual funds Equities Corp. bonds Mortgages Treas./Agency MMFs Cash/CP 1952 1962 1972 1982 1992 2002 2012 2022 100 80 60 40 20 0 )%( stessa laicnanif latot fo erahS DC pension funds Other Mutual funds Equities Corp. bonds Mortgages Treas./Agency MMFs Cash/CP 1952 1962 1972 1982 1992 2002 2012 2022 32

Table 1: Macro drivers of growth-rate differences between banks and non-banks. The table shows the link between lagged changes in various macroeconomic variables and growth-rate differences (net of valuation effects) between assets held by banks and non-banks. The macroeconomic variables are changes in 10-year Treasury yields (∆Treas), percent growth in gross domestic product (∆GDP) and in the consumer price index (∆CPI), changes in the private non-financial credit-to-GDP gap (∆Gap), and changes in the personal savings rate (∆Savings). t-statistics are shown in parentheses. The data are annual and are from the Financial Accounts of the United States, the FRED database of the Federal Reserve Bank of St. Louis, and the Bank for International Settlements. The sample covers 1965 (or later, based on data availability)to2022. Allvariablesarestandardized,hencethecoefficientscanbeinterpretedastheeffectofa one-standarddeviationchangeintheindependentvariablesintermsofstandarddeviationsofthedependent variable. Dep. Var.: Growth-rate differences Predictor: ∆Treas ∆GDP ∆CPI ∆Gap ∆Savings Lag 1 -0.025 0.074 -0.164 0.246 0.012 (-0.20) (0.51) (-1.05) (1.58) (0.09) Lag 2 -0.125 -0.196 -0.110 0.067 0.155 (-1.01) (-1.32) (-0.46) (0.49) (0.93) Lag 3 0.133 -0.167 0.031 -0.236 0.180 (1.05) (-1.06) (0.17) (-1.33) (0.88) Obs. 54 55 55 54 54 R2 0.035 0.080 0.056 0.072 0.029 33

Table 2: Drivers of growth-rate differences across select investment funds. Thetableshowsthelinkbetweengrowth-ratedifferencesacrossvarioustypesofinvestmentfundsandlagged interest rates (Panel A) or lagged inflation (Panel B). MMFs are money-market mutual funds and mREITs aremortgagereal-estateinvestmenttrusts. Thevariables“Treas. inMFs,”“Eq. inMFs,”and“Corp. Bonds in MFs” refer to the growth rates of Treasuries, equities, and corporate and foreign bonds held by mutual funds. t-statistics are shown in parentheses. The data are annual and are from the Financial Accounts of the United States. The sample covers 1965 (or later, based on data availability) to 2022. All variables are standardized, and the coefficients can be interpreted as the effect of a one-standard deviation change in the independent variables in terms of standard deviations of the dependent variable. Growth-rate MMFs vs. MMFs vs. MMFs vs. Eq. in MFs vs. Corp. Bonds in MFs difference b/w: Treas. in MFs mREITs Eq. in MFs Treas. in MFs vs. Treas. in MFs Panel A: Effect of lagged rate changes ∆Treas 0.376** 0.345** 0.151 -0.108* -0.061 y−1 (2.51) (2.40) (1.47) (-1.74) (-0.46) Obs. 48 48 48 56 56 R2 0.145 0.122 0.023 0.011 0.003 Panel B: Effect of lagged inflation ∆CPI 0.304** 0.231 0.173* -0.035 0.132 y−1 (2.01) (1.62) (1.70) (-0.54) (0.92) Obs. 48 48 48 56 56 R2 0.092 0.053 0.030 0.001 0.018 34

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.sdnuf noisnep noitubirtnoc-denfieD :sgnidloh dnob etaroproc fo etar htworg eht dna setar tseretnI :5 elbaT -denfied ta ”stessa efas“ fo )ii( dna sdnob ngierof dna etaroproc fo )i( sgnidloh eht ni etar htworg eht neewteb ecnereffid eht si daerpShtworG laicnaniFehtmorfselbatlevelgnisudetaluclacerasetarhtworG .seitirucesseicnegAdnaseirusaerTedulcni”stessaefaS“ .sdnufnoisnepnoitubirtnoc hcihw ,sdaerps dnob etaroproc AAB ni segnahc sedulcni setairavoc fo tes ehT .seulav deggal gnidnopserroc yb dedivid setatS detinU eht fo stnuoccA eerht ehT .snoitinfied elbairav rof 4 noitceS eeS .detroper ton era stpecretnI .sesehtnerap ni nwohs era scitsitats-t .stceffe noitaulav rof tnuocca -yticitsadeksoretehhtiwsnoissergerSLOnodesaberasnmulocrehto ;sgalowthtiwsnoissergertseW-yeweNmorfstluserwohssnoitacfiicepstsomthgir ,dezidradnats era selbairav llA .3202 fo retrauq dnoces eht ot 0102 fo retrauq tsrfi eht morf snur elpmas ylretrauq ehT .srorre dradnats tnetsisnoc snoitaived dradnats fo smret ni selbairav tnednepedni eht ni egnahc noitaived dradnats-eno a fo tceffe eht sa deterpretni eb nac stneicffieoc eht ecneh .elbairav tnedneped eht fo sdnuf noisnep CD rof daerpShtworG :.raV .peD q )8( )7( )6( )5( )4( )3( )2( )1( :noitacfiicepS ***733.0- ***214.0- ***114.0- ***973.0- **283.0- ***153.0- ***214.0- ***114.0saerT 1−q )37.2-( )67.3-( )08.3-( )96.3-( )66.2-( )84.3-( )01.4-( )60.4-( **694.0- **264.0- **954.0- **754.0- **054.0- **434.0- **264.0- **954.0- AAB∆ q )02.2-( )41.2-( )52.2-( )65.2-( )53.2-( )91.2-( )05.2-( )85.2-( 070.0 710.0 710.0 PDG∆ 1−q )80.1( )52.0( )42.0( 570.0- PDG∆ 2−q )21.1-( 840.0- 4+qpxePDG∆ 2−q )13.0-( 401.0- XIV 2−q )72.1-( 302.0 daerpShtworG 1−q )60.1( 35 35 35 25 25 25 35 35 .sbO 733.0 623.0 033.0 543.0 543.0 2R 37

Table 6: Interest rates and the growth rate of corporate-bond holdings: Excluding the Covid-19 pandemic. The table shows the coefficients on changes in Treasury yields from regressions similar to those in Tables 3, 4, and 5, with the quarterly sample running from 2010 to 2019. Dep. Var.: GrowthSpread q Specification: (1) (2) (3) (4) (5) (6) (7) (8) Life insurers Treas -0.034 -0.010 -0.038 0.036 -0.038 -0.034 -0.010 0.020 q−1 (-0.26) (-0.07) (-0.24) (0.21) (-0.22) (-0.20) (-0.06) (0.14) Defined-benefit pension funds Treas -0.424*** -0.422*** -0.396** -0.451*** -0.396*** -0.424** -0.422** -0.170 q−1 (-3.58) (-3.46) (-2.63) (-3.34) (-2.88) (-2.65) (-2.50) (-1.33) Defined-contribution pension funds Treas -0.561*** -0.532*** -0.414** -0.559*** -0.444** -0.561** -0.532** -0.172 q−1 (-3.33) (-3.01) (-2.45) (-3.17) (-2.65) (-2.65) (-2.45) (-1.63) 38

Table 7: Differences in search-for-yield coefficients across intermediary types. The table shows statistical-significance levels for tests that search-for-yield coefficients (β ) are equal Treasq−1 for different types of intermediaries. Tests are conducted with the seemingly unrelated estimation of Weesie (1999). Dots indicate that coefficients are not statistically different, while asterisks indicate that coefficients are statistically different at the 1% (***), 5% (**), and 10% (*) levels. The regressions are similar to those in Tables 3 to 6. Rows show results from specifications with different sets of controls. The quarterly sample runs from the first quarter of 2010 to either the second quarter of 2023 (left panel) or the fourth quarter of 2019 (right panel). Sample 2010-2023 2010-2019 Null tested (by column): Null tested (by column): βIC βIC βDB βIC βIC βDB Treasq−1 Treasq−1 Treasq−1 Treasq−1 Treasq−1 Treasq−1 = = = = = = βDB βDC βDC βDB βDC βDC Treasq−1 Treasq−1 Treasq−1 Treasq−1 Treasq−1 Treasq−1 Controls ∆BAA . . . ** ** . q ∆BAA ; ∆GDP . . . ** ** . q q−1 ∆BAA ; ∆GDP . . . * . . q q−2 ∆BAA ; ∆GDPexpq+4 ** . . ** ** . q q−2 ∆BAA ; VIX . . . * . . q q−2 39

Table 8: Changes in corporate-bond holdings in times of financial-market stress. Changes are quarter-on-quarter percent growth rates of corporate and foreign bonds held by life insurance companies (IC), defined-benefit pension funds (DB PF) and defined-contribution pension funds (DC PF). OrthogonalizedholdingschangesarecomputedwithregressionsofgrowthratesonchangesinBAAcorporate bond yields. Quarters with high stress are those in which the maximum within-quarter value of the impliedvolatility index VIX is in the top quintile of its distribution across quarters. Asterisks in the ”Difference” rows indicate statistical significance at the 10% (*), 5% (**), or 1% (***) levels, respectively. The quarterly sample runs from the first quarter of 2010 to the second quarter of 2023. Variable Sample IC DB PF DC PF High-stress quarters -0.20 0.19 0.14 Holdings changes Other quarters 1.45 1.89 2.16 Difference -1.64* -1.71* -2.02** High-stress quarters -0.09 0.21 0.62 Holdings changes (orth.) Other quarters 1.41 1.89 2.02 Difference -1.50* -1.67* -1.40* 40