Finance and Economics Discussion Series

Divisions of Research & Statistics and Monetary Affairs

Federal Reserve Board, Washington, D.C.

The Cross Section of Money Market Fund Risks and Financial

Crises

Patrick E. McCabe

2010-51

NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) 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 Finance and Economics Discussion Series (other than

acknowledgement) should be cleared with the author(s) to protect the tentative character of these papers.
The Cross Section of Money Market Fund Risks

and Financial Crises∗

Patrick McCabe

Board of Governors of the Federal Reserve System

September 12, 2010

Abstract

This paper examines the relationship between money market fund (MMF) risks and outcomes during crises, with a focus on the ABCP crisis in 2007 and the run on money funds in

2008. I analyze three broad types of MMF risks: portfolio risks arising from a fund’s assets,

investor risk reflecting the likelihood that a fund’s shareholders will redeem shares disruptively, and sponsor risk due to uncertainty about MMF sponsors’ support for distressed funds.

I find that during the run on MMFs in September and October 2008, outflows were larger for

MMFs that had previously exhibited greater degrees of all three types of risk. In contrast, as

the asset-backed commercial paper (ABCP) crisis unfolded in 2007, many MMFs suffered capital losses, but investor flows were relatively unresponsive to risks, probably because investors

correctly believed that sponsors would absorb the losses. However, the consequences of MMF

risks were quite costly for some sponsors: Using a unique data set of sponsor interventions, I

show that sponsor financial support was more likely for MMFs that previously earned higher

gross yields (a measure of portfolio risk) and funds with bank-affiliated sponsors. Funds’ gross

yields and bank affiliation (but not funds’ ratings) also would have helped forecast holdings of

distressed ABCP. This paper provides some useful lessons for investors and policymakers. The

significance of MMF risks in predicting poor outcomes in past crises highlights the importance

of monitoring such risks, and I offer some useful proxies for doing so. The paper also argues

for greater attention to the systemic risks posed by the industry’s reliance on discretionary

sponsor support.

∗The opinions expressed are mine and do not necessarily reflect those of the Federal Reserve Board or its staff. I thank

Alycia Chin for excellent research assistance. This paper also has benefitted from helpful comments and suggestions

from Steffanie Brady, Lai Ching Chan, Jim Clouse, Joshua Gallin, Peter Holirads, Michael Palumbo, Robert Plaze, Paula

Tkac, and seminar participants at the Federal Reserve Board and the Federal Reserve Bank of Kansas City. Errors and

shortcomings are, of course, mine alone.
1 Introduction

Money market funds (MMFs or “money funds”) have an impressive record of price stability.

From the introduction of the rules specifically governing these funds in 1983 until the Lehman

bankruptcy in September 2008, only one small MMF lost money for investors, and that loss, in

1994, had little broader impact on the industry. Although MMF prospectuses and advertisements

must warn that “it is possible to lose money by investing in the Fund” (U.S. Securities and Exchange Commission, 1998a, 2003), investors virtually never lost anything. Indeed, the perceived

safety of MMFs typically prompted inflows to the funds during periods of heightened uncertainty

and motivated some academic researchers to suggest that money funds might function well as

“narrow banks” that provide liquidity services (Gorton and Pennacchi, 1993; Scott, 1998; Miles,

2001; Pennacchi, 2006).

However, two crises in the MMF industry during the financial turmoil that began in 2007

underlined the importance of money fund risks for MMF investors and sponsors, as well as for

the broader financial system. The meltdown of the market for asset-backed commercial paper

(ABCP) that began in August 2007 caused capital losses for many money funds that held ABCP,

but the losses were absorbed by MMF sponsors (that is, asset management firms and their parents

and affiliates), so MMF investors lost nothing. In contrast, losses on Lehman Brothers debt following that firm’s bankruptcy in September 2008 caused the Reserve Primary Fund to “break the

buck”—its share price fell below $1—and cost its shareholders liquidity as well as principal (as of

this writing, the assets of the fund still had not been completely distributed). Moreover, the damage quickly spread beyond Reserve and its investors amid a broader run on MMFs. Other money

fund investors were put at risk as concerns about the funds’ vulnerabilities prompted a vicious

cycle of redemptions, efforts by MMFs to sell assets, declines in prices for money market instruments, and the possibility of capital losses that motivated further redemptions. A broader liquidity crisis developed as MMF managers, facing enormous redemptions, curtailed their lending to

firms and institutions. The run on MMFs appears to have been slowed only by announcements

on September 19 of unprecedented government interventions to support MMFs and short-term

funding markets.

This paper examines the relationship between MMF risks and outcomes during crises, with

a focus on the ABCP crisis in 2007 and the run on money funds in 2008. I describe three broad types

of MMF risks: (1) portfolio risks arising from the credit, liquidity, and interest-rate risks posed by a

fund’s assets; (2) investor risk due to the composition of an MMF’s investors and the likelihood that

they will suddenly and disruptively redeem shares; and (3) sponsor risk that reflects the possibility

that an MMF sponsor will not provide financial support for an ailing fund. For each type of risk,

I review the relevant academic literature and develop proxies for use in empirical analyses. In

addition, I construct a unique data set of sponsor support actions and MMF holdings of distressed

assets. I employ this data set and the risk proxies in an empirical study of the links between

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MMF risks and the cross section of MMF crisis outcomes, including net redemptions and sponsor

support actions. I find that MMF risks measured well before the unfolding of each crisis had

important predictive power for outcomes during the crisis, and I conclude by discussing some

policy implications of these results.

The two crises I study provide different perspectives on the importance of MMF risks. The

run on MMFs in 2008 was not indiscriminate; I find that redemptions from prime MMFs marketed

to institutional investors were correlated significantly with ex ante indicators for each of the three

types of risk. For example, outflows were larger for MMFs that had paid higher gross yields in the

previous year and thus were likely carrying greater portfolio risks, for funds with larger pre-crisis

flow volatility that signified greater investor risk, and for funds that had sponsors with wider

credit default swap (CDS) spreads and hence greater sponsor risk. Meanwhile, net redemptions

from retail prime MMFs during the run varied with investor risk proxies but not significantly with

portfolio or sponsor risk measures, perhaps because retail investors—who generally did not redeem shares en masse—were less cognizant of MMF vulnerabilities and posed lower investor risk

for the funds. Indeed, one lesson from the distinction between institutional and retail investors’

behavior during the run is the interactions among fund risks: MMFs with greater investor risks

were also more sensitive to portfolio and sponsor risks.

Interactions among fund risks were also consequential during the ABCP crisis, as widespread sponsor support absorbed funds’ losses. With sponsor risks apparently dormant, other

MMF risks—at least as perceived by fund shareholders—also remained latent, and the funds saw

only modest net outflows that exhibited little cross-sectional correlation with ex ante risks. However, MMF risks were consequential for money fund sponsors; their financial support for their

funds reflected concerns about actual or expected losses in funds’ portfolios as well as concerns

about investors’ potential responses to those risks. Using a unique data set of sponsor support

actions in the wake of the ABCP crisis, I show that portfolio risks, as measured by gross yields

in the year prior to the crisis, are useful for predicting whether sponsors intervened to support

their funds. A separate analysis of MMFs’ holdings of distressed ABCP corroborates this result.

Interestingly, sponsor risk played a more complex role in ABCP crisis than during the run in 2008.

MMFs with bank-affiliated sponsors, which presumably had particularly deep pockets with which

to support ailing money funds, were more likely both to hold troubled ABCP and to receive financial support to absorb losses. However, controlling for bank affiliation, riskier sponsors (those

with higher pre-crisis CDS spreads) were more likely to experience problems.

My findings provide some useful lessons for MMF shareholders and policymakers alike.

The significance of MMF risks in predicting poor outcomes in past crises underscores the importance of monitoring these risks, and this paper offers some useful proxies for doing so. For example, shareholders and regulators might track funds’ gross yields for early signs of undue portfolio

risks, particularly in light of asset managers’ incentives to take on risks to boost yields. This paper

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also shows that MMF risks are broader than the portfolio risks that are the focus of the current regulatory framework for money funds. The importance of investor risk during the run in 2008 lends

some support for the Securities and Exchange Commission’s (SEC’s) 2009 proposals to require additional liquidity for funds that are marketed to riskier investors, such as institutional investors,

and the proxies for investor risk that I employ may be useful for identifying funds with riskier

clienteles. The link between sponsor risk and holdings of distressed paper during the ABCP crisis

indicates that the sponsor-support option may distort incentives for portfolio managers, and the

role of sponsor risk in channeling concerns about financial institutions to their off-balance-sheet

MMFs during the 2008 run suggests that expectations for such support may contribute to transmission of financial shocks. These concerns at least warrant greater attention to the systemic risks

posed by the MMF industry’s reliance on sponsor support.

Importantly, the analysis in this paper focuses on risks that explain cross-sectional variation

in money funds’ experiences during episodes of financial turmoil. As such, I do not focus on some

of the generic features of MMFs that make them structurally vulnerable to runs—such as MMFs’

practice of rounding their net asset values (NAVs) to the nearest penny—or analyze the types of

events that can trigger runs. My results do suggest that fund-specific risks are important during

crises and that such risks may interact with the structural vulnerabilities of MMFs to contribute to

broader risks. For example, incentives for investors to redeem shares in funds with rounded NAVs

are strengthened when portfolio risks are larger, when other MMF investors are more risk averse

and more sophisticated (and hence more likely to redeem shares quickly), and when sponsors

are less likely to bail out troubled MMFs. The aggregate importance of cross-sectional variation

was also evident in the example of the Reserve Primary Fund, which demonstrated that even a

single poorly run fund may impose significant costs on the rest of the industry as well as on the

broader financial system. Hence, by highlighting some ex ante risk measures that predicted poor

outcomes for individual MMFs during past crises, this paper provides guidance on monitoring—

and perhaps mitigating—MMF risks to reduce the likelihood of another crisis.

Section 2 of this paper describes data sources. Section 3 reviews the historical significance

of MMF risks, academic literature relevant to their study, and the two crises that I examine in this

paper. Section 4 introduces proxies for portfolio, investor, and sponsor risks. Section 5 describes

my empirical analysis of the run in 2008, and section 6 turns to an analysis of the ABCP crisis in

2007. Section 7 concludes the paper and discusses policy implications.

2 Data sources

The U.S. MMF industry comprises three basic types of funds: (i) prime funds, which chiefly invest in short-term private debt instruments such as commercial paper, bank certificates of deposit

(CDs), and floating-rate notes issued by private firms; (ii) government-only MMFs, which typically hold only obligations of the Treasury, U.S. government agencies, and government-sponsored

3
enterprises, as well as repurchase agreements collateralized with such instruments; and (iii) taxexempt MMFs, which generally hold municipal securities. This paper focuses on prime MMFs,

which bore the brunt of the strains during the MMF crises in 2007 and 2008. Prime funds account

for most MMF assets; Investment Company Institute (ICI) weekly data show that assets under

management in U.S. MMFs totaled $3.58 trillion as of September 10, 2008 (just before the Lehman

bankruptcy and the run on MMFs): $2.18 trillion in prime MMFs, $0.89 trillion in governmentonly funds, and $0.52 trillion in tax-exempt funds.1

The fund-level data used in this paper mostly come from iMoneyNet, a data vendor that

provides weekly and monthly data on each MMF’s assets under management, yields, expense

ratios, portfolio composition, and weighted average maturity (WAM). The iMoneyNet data are

survivorship-bias free, as they include defunct funds. iMoneyNet also classifies MMFs by fund

type and investor type—MMFs are considered either “institutional” or “retail,” depending on the

types of investors to whom the funds are marketed. I make several adjustments and corrections

to the iMoneyNet data. For example, I analyze only MMFs with at least 12 months of pre-crisis

data and funds with at least $100 million in assets, and I make several corrections to MMF assets

recorded during the run in 2008. These adjustments are described in section A.1 of the appendix.

I obtained some fund and sponsor data from other sources. For example, data on creditdefault swap (CDS) spreads for MMF sponsors came from Markit, and I used Moody’s data to

corroborate iMoneyNet MMF ratings information.

An important contribution of this paper is my compilation of a unique data set of sponsorsupport actions and the securities that prompted such actions. The data come both from public

sources and from confidential SEC records. Many of the sponsor-support records are public because sponsors’ actions required some relief from prohibitions under the Investment Company

Act of 1940 (ICA). That relief typically came in the form of a “no-action” letter from SEC staff,

and the SEC has published these no-action letters on its website.2 An additional source of public

information on sponsor support is the financial statements of asset management companies and

their parent firms.3

The SEC also obtains data on sponsor support arrangements that remain confidential. For

example, SEC rule 2a-7 requires a fund to notify the Commission in the event of a default of any security representing more than 0.5 percent of the fund’s assets, and the fund must also describe how

it intends to respond to the default. In other cases, funds have voluntarily apprised SEC staff of

support actions that did not trigger notification requirements. SEC staff provided me with a complete list of the sponsor support actions for MMFs for which the Commission had been notified

1See http://www.ici.org/pdf/mm_data_2010.pdf.

2The ICA restricts transactions between mutual funds and their sponsors. In particular, no-action relief was needed

when advisers or their affiliates purchased from money funds securities which were still technically eligible as MMF

portfolio securities. See http://www.sec.gov/divisions/investment/im-noaction.shtml#money.

3Peter Crane has compiled a useful listing of support information collected from publicly available SEC data and

from financial statements (Crane, 2009).

4
between August 2007 and March 2009. These include support arrangements to protect MMFs from

losses or runs in the aftermath of both the ABCP crisis and the Lehman bankruptcy. Together with

the public information described above, these data represent the most complete available record

of sponsor support for MMFs affected by the financial turmoil that began in 2007—although the

fact that some support can occur without notification of the SEC suggests that the data still may

not reflect every instance of support.

Information about MMFs’ holdings of specific securities (such as individual ABCP issues

and Lehman debt securities) comes from two sources. The first is each fund’s closest previous

quarterly portfolio-holdings filing with the SEC, which is available on the SEC’s EDGAR database.

Where appropriate, I supplement those data with information garnered from records of sponsor

support and other SEC notifications described above, since these records typically name the distressed securities at issue.

The unit of observation in my analysis is the MMF. One money market fund may comprise

multiple share classes, each of which is a claim on the same portfolio assets. All share classes in

an MMF have the same gross yield—that is, the yield earned on portfolio instruments before fees.

However, different share classes have different fee structures (expense ratios) and pay investors

different net yields (net yield is gross yield less the expense ratio).4

3 Money market fund risks: history and literature

The financial turmoil that began in 2007 heightened awareness of MMF risks among investors, academics, and policymakers. Prior to the ABCP crisis and the run in 2008, MMF risks—particularly

investor and sponsor risks—appear to have been viewed as unimportant or latent. MMF assets

grew at an average annual rate of 13 percent over the quarter century from the end of 1983 to the

eve of the Lehman crisis (Investment Company Institute, 2010, p. 160), and the funds’ perceived

safety was undoubtedly part of their appeal to investors. MMFs’ long record of stability also impressed some academics, who advocated MMFs as alternatives to banks as providers of liquidity

in the economy. This section provides a brief overview of the perceptions of MMF risks and how

these views have been shaped by the crises that I study in this paper.

3.1 Perspectives on MMF risks prior to the financial turmoil in 2007

The importance of governing MMF portfolio risks always has been central to money fund regulation, operations, and monitoring. The provisions of the SEC’s rule 2a-7, which applies only

4To derive a single observation for a fund with multiple share classes, I cumulate the assets for all of its share classes

and compute asset-weighted measures of other fund characteristics (for example, expense ratios) that vary across share

classes. For money market funds with both retail and institutional share classes, I created two fund observations for

each month by cumulating the retail and institutional share classes separately.

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to MMFs and hence distinguishes them from other types of mutual funds, are primarily limitations on MMFs’ credit, interest-rate, and liquidity risks consistent with the funds’ maintenance of

a stable NAV. As financial innovation has introduced new risks to MMFs, the SEC has tightened

portfolio restrictions. For example, the Commission adopted new diversification requirements,

new limits on holdings of lower-rated (“second-tier”) assets, and a more stringent WAM limit in

1991, and it issued guidance to clarify that certain derivative instruments were inappropriate for

MMF portfolios in 1994. MMFs that receive “AAA” (or equivalent) ratings from ratings organizations must satisfy additional restrictions, beside those in rule 2a-7, that are primarily constraints

on portfolio risks (see, for example, Moody’s Investors Service, 2005; Standard & Poor’s, 2007; and

Fitch Ratings, 2009).

When, despite the risk-limiting provisions of rule 2a-7, MMF assets have lost value, fund

sponsors historically have absorbed the losses to prevent funds’ NAVs from declining. For example, sponsors picked up losses on three commercial paper issues in 1989 and 1990, and, in 1994, 25

MMF sponsors intervened to support their funds when a jump in short-term interest rates caused

assets held by the funds to lose value (Investment Company Institute, 2009, pp. 175-176). Nothing required these sponsors to provide support, but because allowing a fund to break the buck

would have been destructive to a sponsor’s reputation and franchise, sponsors backstopped their

funds voluntarily. Asset managers’ incentive to preserve reputation and their record of support

for MMFs led industry observers to view sponsors as a source of stability—not risk—for MMFs

and to regard sponsor support as almost a given. Stigum’s Money Market, a classic reference guide

to money markets, notes that “. . . a money fund run by an entity with deep pockets, while it may

not have federal insurance, certainly has something akin to private insurance . . . [and] that insurance is likely to prove adequate to cover any losses sustained by the fund” (Stigum and Crescenzi,

2007, p. 1117).

Because of the relative safety of their portfolios and sponsors’ practice of absorbing losses

when they have occurred, MMFs are usually recipients of flight-to-quality inflows during periods

of high uncertainty and market turmoil. For example, Baba et al. (2009) showed that MMFs typically attracted inflows when stock market implied volatility, as measured by the VIX, was high.5

The largest weekly inflow to MMFs (as a fraction of assets) recorded by ICI in the past decade

came in the week following the September 11, 2001 terrorist attacks, when MMF assets grew 3.8

percent.

Perhaps because MMFs historically have been so safe, MMF risks have attracted only limited attention in the academic literature on MMFs, particularly before the financial turmoil erupted

in 2007. Portfolio risks have garnered most of the discussion, but even these have often been

viewed as tangential or benign. Domian (1992) and DeGennaro and Domian (1996) found that

MMF portfolio managers could not earn extra-normal returns by anticipating interest-rate changes,

5More generally, Carpenter and Lange (2003) found that heightened equity market volatility boosted total M2

assets, which include retail MMFs.

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and DeGennaro and Domian concluded that “MMFs in general cannot outperform similar investments without taking commensurately higher risks.” Although they added that they could not

rule out the possibility that individual managers might outperform the market, they did not study

risk taking. Domian and Reichenstein (1998), analyzing the variation in MMFs’ net yields, emphasized the importance of the expense ratios charged by the funds, rather than the risk and return of

MMF portfolio assets. These authors concluded that, controlling for investment objective, MMFs

with more than $300 million in assets “are essentially commodities” with little meaningful differentiation in portfolios. Advocates of MMFs as “narrow banks” have pointed to the transparency

and liquidity of MMF portfolios as evidence that runs on the funds should be unlikely (Gorton

and Pennacchi, 1993; Scott, 1998).

However, MMF portfolio risks were central in Collins and Mack (1994), who examined

changes in aggregate MMF portfolio risk in an event study of the SEC’s adoption in 1991 of amendments to rule 2a-7 that further restricted funds’ exposures to credit and interest-rate risks. Collins

and Mack estimated the time-series relationship between funds’ yields and market risk premiums and concluded that risk-taking did indeed decline. Koppenhaver (1999) examined the crosssection of MMF net and gross yields and found that both varied positively with MMFs’ holdings

of agency securities and commercial paper and with funds’ WAMs.

With MMFs typically attracting inflows during crises, investor risk—the risk reflecting the

composition of a fund’s shareholders and the likelihood that they will redeem shares suddenly—

does not appear to have been a concern among academicians. Indeed, research focusing on MMFs’

suitability as “narrow banks” suggested that MMF investors’ behavior might mitigate broader

risks. Gorton and Pennacchi (1993) noted that MMF investors did not run from funds following commercial paper defaults, Scott (1998) emphasized that the transparency of MMF portfolios

would prevent an “ignorance-driven panic,” and Miles (2001) showed that, following monetary

policy shocks, MMFs attracted net inflows and increased lending to private borrowers. Miles

concluded that MMFs “are, if anything, perceived as safer than commercial banks and certainly

less risky than smaller depository institutions.” Similarly, Pennacchi (2006) showed that MMFs

attracted net inflows following commercial paper-Treasury bill spread increases (which he interpreted as liquidity shocks) and used this evidence to argue that a system of insurance for MMF

shares, rather than bank deposits, might improve money market liquidity following such a shock.

3.2 The ABCP crisis in 2007

The ABCP crisis, which caused substantial losses for many MMFs, nonetheless appears to have

reinforced prior notions about MMF safety and the stabilizing effect of sponsor support. The crisis

began in earnest on August 6, 2007, when the American Home Mortgage Investment Corporation

filed for bankruptcy and its Broadhollow ABCP program exercised the option to extend its maturity. Another ABCP program, Ottimo Funding Ltd., was unable to roll its paper on the same day

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and began to liquidate (Standard & Poor’s, 2008b). Amid heightened concerns about exposures of

some ABCP to distressed securities, including subprime mortgages, many ABCP programs began

to encounter difficulties rolling their paper, and two ABCP programs defaulted in August. Covitz

et al. (2009) show evidence that an indiscriminate run on ABCP began that month, as investors

became unwilling to purchase reissued paper regardless of program characteristics.

As large investors in the ABCP market, MMFs were vulnerable to the credit and liquidity

risks posed by distressed programs, and many MMF portfolio holdings lost value. However, MMF

sponsors absorbed these losses by purchasing securities out of their funds at above-market prices

and by entering capital support agreements to guarantee securities still in the funds. The SEC

reported that sponsors intervened to support at least 44 MMFs because of exposures to distressed

ABCP, and no MMF broke the buck (U.S. Securities and Exchange Commission, 2009a, note 38).

Sponsors’ actions evidently allayed investors’ concerns; despite the exposures of many

MMFs to troubled ABCP, MMF investors responded with only a modest pullback from prime

MMFs in August 2007. As shown by the solid black line in figure 1, panel A, prime MMFs, which

mainly invest in private debt instruments such as ABCP, saw only very small net outflows (about

$14 billion, or 0.8 percent of assets) in the three weeks ending August 29, 2007.6 To be sure, some

individual funds suffered more significant outflows; the Wall Street Journal reported that one very

large Credit Suisse MMF saw redemptions of more than half its assets beginning in August 2007,

and the authors cited the fund’s exposure to distressed ABCP and its unstable investor base as

factors behind the large outflows (Smith and Lauricella, 2007). But section 6.1 of this paper shows

that net flows of individual funds during this episode generally did not vary significantly with

proxies for MMF portfolio risk. Surprisingly, given the role of sponsor support in shoring up

MMFs during this crisis, net flows also did not vary significantly with sponsor risk.

In any case, prime funds’ small aggregate outflows were short-lived, as assets began growing rapidly in September 2007. As illustrated in panel A of figure 1, prime MMF assets increased

$425 billion (24 percent) from August 2007 to August 2008, as investors moved cash into vehicles

that were seen as safe.7 The poor performance of alternative cash-management vehicles, such as

“enhanced cash” funds and auction-rate securities, likely boosted MMF inflows over this period

(see Investment Company Institute, 2009, pp. 46-50 and Baba et al., 2009).

One MMF sponsor that appears to have exploited investors’ confidence in MMFs and the

funds’ growth following the ABCP crisis was Reserve Management, Inc., which operated the Reserve Primary Fund. Reserve’s cofounders introduced the first MMF in 1970 and for decades

reportedly maintained a relatively conservative investment policy, even among MMFs (Nocera,

1994; U.S. Securities and Exchange Commission, 2009b). As shown by the thin solid black line in

6More notable than the outflows from prime MMFs in August 2007 were the simultaneous flight-to-quality inflows

to government-only MMFs, which—as illustrated by the dashed blue line in panel A—grew $115 billion (23 percent) in

the three weeks following the American Home Mortgage bankruptcy.

7Government-only MMFs grew even faster over this period; their assets expaned 46 percent.

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panel B of figure 1, in the decade before mid-2007, the Primary Fund typically earned a gross yield

below that of its average competitor. But the ICI has documented a change in Reserve’s portfolio

management beginning in the summer of 2007, when the Primary Fund began purchasing commercial paper—including Lehman debt securities—and its gross yield jumped relative to that of

its peers (Investment Company Institute, 2009, pp. 53-57; see also U.S. Securities and Exchange

Commission, 2009b). Net yield, the dashed black line, shot up, too, and investors took notice: Between August 2007 and August 2008, the Primary Fund’s market share surged as its assets more

than tripled (panels B and C).

3.3 The run on MMFs in 2008

The Reserve Primary Fund was holding Lehman Brothers debt valued at $785 million (at amortized cost) when Lehman declared bankruptcy early on Monday September 15, 2008. Losses on

this exposure caused the Primary Fund to break the buck. The run that followed underlined the

importance of all three types of MMF risks: portfolio, investor, and sponsor risks.

Clearly, portfolio risk was central to Reserve’s demise. Moreover, aggregate flows to different types of MMFs reflected broader concerns about portfolio risks. As shown in panel A of

figure 1, nearly all of the massive net redemptions from MMFs during the run in 2008 came from

prime MMFs (the black line), which—unlike government-only and tax-exempt funds—mainly

held the debt instruments of private issuers such as Lehman Brothers. Prime funds’ aggregate

assets dropped $450 billion (21 percent) over the four weeks beginning on September 10. Amid a

general flight to quality, government-only funds (the blue dashed line) attracted very large inflows

over the same period. Tax-exempt funds (the green dotted line) had moderate net outflows.

In addition, the striking distinction between institutional and retail investors’ behavior during the crisis highlighted the importance of investor risks. As of September 10, 2008, institutional

funds accounted for 63 percent ($2.17 trillion) of total assets under management in MMFs and 63

percent of prime MMF assets ($1.32 trillion). As illustrated in figure 2, panel A, prime funds marketed to institutional investors suffered the brunt of the run, with net redemptions of $410 billion

(30 percent of assets under management) in the four weeks beginning September 10. Prime MMFs

for retail investors saw outflows of just $40 billion (5 percent of assets) over the same period.

Sponsor risk also played a critical role in the run. The Primary Fund was not unique in

holding Lehman’s debt at the time of its bankruptcy, but other MMFs with Lehman exposures

obtained sufficient sponsor support to avoid breaking the buck (Investment Company Institute,

2009, pp. 60-62). What distinguished the Primary Fund was the inability of Reserve to provide the

capital needed to absorb the MMF’s losses.8Indeed, sponsor support burgeoned during the run:

8The crucial importance of sponsor support apparently was not lost on Reserve’s principals, who, according to an

SEC complaint on the matter, falsely assured investors, ratings organizations, and the press immediately after Lehman’s

bankruptcy on September 15 that sponsor support would protect the Primary Fund’s $1 NAV. Such support was not

forthcoming, however, and these assurances ultimately failed to prevent massive redemptions requests. Reserve an9
The SEC reported that almost 20 percent of all MMFs received support in September and October

2008 (U.S. Securities and Exchange Commission, 2009a, p. 20).

Reserve’s inability to support its Primary Fund after the Lehman Brothers bankruptcy

clearly undermined investor confidence in sponsor support. Panel B of figure 2 depicts daily net

flows to prime funds during the crisis.9 Heavy redemptions—at least from institutional MMFs—

began immediately following Lehman’s bankruptcy on September 15, but outflows surged after

Reserve’s announcement late on September 16 that the Primary Fund had broken the buck.

The run hit the MMF industry unevenly. As noted above, outflows were concentrated in

institutional prime funds, but even among these funds, there was much cross-sectional disparity

in net flows. The red bars in panel C of figure 2 show the distribution of outflows from institutional

prime MMFs from September 9 to October 7. (Retail fund flows, the blue bars, exhibited much

less dispersion.) Of the 116 institutional prime funds in my sample, ten saw assets shrink by more

than 50 percent during this period and another twelve lost between 40 and 50 percent of assets,

but 20 had net inflows over the same period. This heterogeneity provides an opportunity to test

hypotheses about the links between individual MMFs’ risks and outcomes during the crisis.

3.4 Post-crisis perspectives

All three types of MMF risk have attracted new attention since the beginning of the financial turmoil in 2007. Researchers and policymakers have focused on portfolio risk, in particular. Jank

and Wedow (2008) examined the performance of German MMFs before and during the subprime

crisis that began in 2007.10 They found that MMFs that took on more portfolio risk earned higher

returns than their less risky counterparts during periods of normal money market liquidity but

that the riskier MMFs fared relatively worse during periods of low liquidity. As noted in section

3.2, ICI documented a substantial increase in the portfolio risk of the Reserve Primary Fund beginning in mid-2007, just over a year before that fund broke the buck (Investment Company Institute,

2009). In a study of the effectiveness of the Federal Reserve’s Asset-Backed Commercial Paper

Money Market Mutual Fund Liquidity Facility (AMLF), Duygan-Bump et al. (2010) found that

MMFs with greater ABCP exposures initially suffered larger outflows during the run in 2008 but

recovered quickly with the announcement of the AMLF. Furthermore, the SEC in 2010 adopted

amendments to rule 2a-7 that impose further constraints on portfolio risk, including new liquidity

nounced that the Primary Fund had broken the buck at 4 pm on Tuesday, September 16 (U.S. Securities and Exchange

Commission, 2009b).

9Net flows depicted in this panel reflect my adjustments to daily assets data for several MMFs. I describe the

adjustments in section A.1 of the appendix.

10German MMFs, unlike US funds, report mark-to-market (accumulating) NAVs, so individual MMF returns were a

useful measure of outcomes during the crisis. As a proxy for portfolio risk, Jank and Wedow used each fund’s share of

assets held in “debt securities,” a residual category that excludes German Treasury bills, bank deposits, CP, and “other

assets.” (The authors argue that the share of debt securities in an MMF’s portfolio is a measure of the MMF’s liquidity

risk, but this measure likely comprises broader portfolio risks, including credit risk.)

10
requirements for MMF assets, “to make money market funds more resilient and less likely to break

a buck as a result of disruptions such as those that occurred in the fall of 2008” (U.S. Securities and

Exchange Commission, 2010).

Investor risk also has received greater attention, especially in the aftermath of the run in

2008. Disparities in outflows from institutional and retail funds prompted the SEC to propose

new liquidity requirements for MMFs that would have been more stringent for institutional funds

than for retail funds.11 In addition, ratings organizations, the SEC, and the ICI have highlighted

the investor risks arising from “hot money” that flows into and out of MMFs in response to even

small yield changes (Fitch Ratings, 2009; U.S. Securities and Exchange Commission, 2009a, pp.

56, 66-68; Investment Company Institute, 2009, pp. 83-84). For example, investors’ growing use

of MMF portals, which facilitate comparisons of MMF yields and the transfer of monies from

fund to fund, may have heightened investor risks (Baba et al., 2009). Fitch’s MMF rating criteria,

published a year after the run, note that a fund with “an over-reliance on hot money sources such

as portals may need an additional liquidity buffer” (Fitch Ratings, 2009).

The MMF crises at issue in this paper have also brought an increased awareness of the importance of sponsor risk. Baba et al. (2009) reported that during the run in 2008, “[t]he largest

redemptions occurred at institutional prime funds managed by the remaining securities firms and

small independent managers, which investors doubted could support their funds.” In assigning

MMF ratings, ratings agencies began focusing more specifically on sponsors’ resources and abilities to support their MMFs (Moody’s Investors Service, 2008; Fitch Ratings, 2009; Standard &

Poor’s, 2007, 2008a). The SEC’s new rules for MMFs, adopted in 2010, facilitate sponsor support

for funds during emergencies (U.S. Securities and Exchange Commission, 2010, p. 94-97).12

4 Money market fund risks and proxies

Prime MMFs are financial intermediaries that provide both maturity and credit transformation.

The funds hold risky assets that may mature in a year or more but issue shares that are redeemable

11The SEC proposed new requirements for holdings of “daily liquid assets” and “weekly liquid assets” and suggested that the liquidity requirements for institutional funds be double those for retail funds. The Commission’s rationale for the distinction between institutional and retail funds was based on the greater historical liquidity needs of

institutional funds, as reflected, for example, in their higher flow volatility and larger outflows during the run in 2008

(see U.S. Securities and Exchange Commission, 2009a, pp. 55-65). The SEC ultimately adopted new liquidity requirements for MMFs that were the same for institutional and retail funds, but included a “general liquidity requirement”

that an MMF “hold securities that are sufficiently liquid to meet reasonably foreseeable shareholder redemptions,”

which presumably would cause institutional funds to hold more liquid assets on average than retail funds (U.S. Securities and Exchange Commission, 2010, pp. 51-54, 56-67).

12Specifically, the new rules allow an MMF sponsor to purchase from a fund securities that have defaulted or are

“distressed,” but which are still “eligible” as MMF portfolio holdings under rule 2a-7. Purchases of such securities

are generally restricted under section 17 of the ICA because of concerns that a transaction between an MMF and an

affiliated party might not be in the interests of the fund. In the past, the SEC had routinely provided no-action relief

for MMF sponsors that purchased such securities from MMFs to protect the funds, but the new rules were intended to

prevent delays in sponsor support by allowing a sponsor to intervene without getting no-action relief first.

11
on demand, and MMF shares historically have been safer than many of the assets the funds hold,

even though MMFs (unlike some other financial intermediaries) do not have capital cushions or

insurance to guarantee the stable value of their liabilities. Instead, MMFs’ record of safety reflects

regulatory constraints on portfolio composition, conservative asset management, broad investor

bases, and financial support from sponsors. Yet, none of these is fail-safe, so MMFs are subject to

the portfolio, investor, and sponsor risks that are the focus of this paper. In this section, I discuss

these risks and describe proxies that I use for them in my empirical analyses.

4.1 Portfolio risk

Portfolio risks comprise credit, liquidity, and interest-rate risks, all of which have played significant roles in past episodes of MMF stress. Credit risks that turned out badly triggered substantial

sponsor support to absorb MMF losses in 1989 and 1990 as well as during the ABCP crisis nearly

two decades later, and Reserve’s poor management of credit risks in the Primary Fund’s portfolio helped to start the run on MMFs in 2008. Liquidity risks came into the spotlight during the

run, as MMFs sought to sell securities to meet redemption requests but encountered unreceptive

secondary markets for instruments such as term commercial paper—in part, because the dealers

who typically make those markets were themselves short of liquidity. At least a couple of MMFs

that reportedly had no exposures to Lehman Brothers or other distressed issuers nonetheless had

to take extraordinary steps (one closed and the other began paying redemptions in-kind) because

sales of securities into illiquid markets would have caused capital losses (Investment Company

Institute, 2009, p. 62). Finally, interest-rate risk was responsible for the first instance of an MMF

breaking the buck, in 1994, when the Community Bankers Mutual Fund, a small institutional

MMF, incurred losses on a sizable position in interest-sensitive structured notes as rates rose suddenly (U.S. Securities and Exchange Commission, 1998b).13 Although all three types of portfolio

risk are important in assessing MMF risks, these risks blur during crises, when unusual discounts

on securities (and hence, higher interest rates) may reflect impaired credit quality, impaired liquidity, or both. Thus, I generally do not attempt to distinguish among different types of portfolio

risks in this paper.

4.1.1 Gross yield as a proxy for portfolio risk

MMFs’ history of maintaining stable NAVs precludes the use of price variation as a measure of

portfolio risk. However, given the typical relationship between risk and return, one alternative

proxy for a fund’s portfolio risk is the gross yield that it earns on its assets. MMF gross yields

do not vary much, in part because of rule 2a-7’s constraints on fund portfolios. As shown in the

second column of table 1, the cross-sectional standard deviation of annual gross yields for prime

13Poorly managed interest-rate risk also caused an MMF to break the buck in 1977 (Gorton and Pennacchi, 1993,

note 13), but this event occurred before the SEC adopted rule 2a-7.

12
funds (line 1) was only 12 basis points in the five years from 2004 to 2008. The much larger 29

basis point cross-sectional standard deviation in the net yields that the funds paid to shareholders

(line 2) primarily reflected variation in expense ratios (line 3).14

Even so, gross yields at prime funds varied with the composition and maturity of their

assets in ways that suggest that gross yields are a useful proxy for portfolio risk. Section A.2 of the

appendix shows that, over a sample period from 2004 to 2008, holdings of relatively safe assets,

such as Treasury and agency securities and repurchase agreements, tended to reduce MMF gross

yields. At the same time, larger holdings of riskier assets, such as ABCP, floating-rate notes, and

other bank obligations, boosted funds’ gross yields. Longer WAM also increased gross yields.

Why would MMF sponsors take on greater portfolio risk? Higher portfolio risks increase

the likelihood that an MMF will experience capital losses and shortfalls in liquidity, but—for a

given level of fees charged—a higher gross yield attracts cash inflows to a fund and boosts its

expected future fee revenue (see, for example, Christoffersen, 2001; Christoffersen and Musto,

2002; Jank and Wedow, 2008).15 In section A.3 of the appendix, I analyze the effects of gross

yields on subsequent net flows and find that, over a sample period extending from January 1997

to August 2008, an institutional fund that, all else equal, maintained for a year a gross yield that

was one standard deviation above average would have expected to attract net flows that were

6 percentage points (of assets) larger than average. For retail MMFs, the analogous effect was

a smaller 2 percentage point gain in net flows.16 How MMF sponsors and portfolio managers

chose to balance the potential costs and benefits of greater portfolio risk is beyond the scope of

this paper, but there is evidence that some sponsors—including, notably, Reserve Management

Company—elected to take on greater risk than their peers in order to attract greater inflows.

Tables 2a and 2b list means, standard deviations, and the 10th and 90th percentiles for the

risk proxies used in my empirical analyses. As shown on line 1 of table 2a, the distributions of

gross yields for institutional and retail funds in the year prior to the run in 2008 were very similar.17

Gross yields for both types averaged 3.88 percent (columns 1 and 5), and standard deviations

(columns 2 and 6) were also similar. (Statistics for expense ratios, which appear on line 4 of the

14The fact that variation in MMFs’ net yields was primarily due to variation in expense ratios, rather than gross

yields, prompted Domian and Reichenstein’s (1998) conclusion that MMFs “are essentially commodities.”

15The relationship between mutual fund performance and subsequent inflows is the subject of an extensive literature

that mostly analyzes long-term mutual funds (see, for example, Wharton School, University of Pennsylvania, 1962;

Friend et al., 1970; Smith, 1978; Ippolito, 1992; Chevalier and Ellison, 1997; Sirri and Tufano, 1998; Del Guercio and

Tkac, 2002).

16Because higher gross yields attract new cash from performance-sensitive investors, a fund’s gross yield may also

reflect investor risk. However, the empirical evidence suggests that gross yield, especially leading up to the run in

2008, was predominantly an indicator of portfolio risk. Simple correlations between gross yield and several measures

of investor risk are all insignificant. Moreover, my analyses include multiple controls for investor risk. See sections 4.4

and 5.2.

17Gross yields, like several other risk proxies reported on tables 2a and 2a, are computed over the year ending just

prior to the onset of each crisis. A year’s data are useful in computing several of these proxies (such as the sensitivity of

flow to yield, line 7), and may help smooth through some of the seasonal variation in MMF yields and flows described

in Farinella and Koch (2000).

13
table, indicate that retail MMFs charged much higher fees than institutional funds, so retail funds

earned smaller net yields.) As shown in columns 1 and 5 on the first line of table 2b, gross yields

for both institutional and retail funds averaged 5.37 percent in the year before the ABCP crisis.

The distribution of gross yields for MMFs was very tight during this period: Standard deviations

for institutional and retail MMFs were just 2 and 3 basis points, respectively (columns 2 and 6).

4.1.2 MMF ratings as a proxy for portfolio risk

Many MMF carry “AAA” or similar top ratings from ratings organizations, particularly S&P,

Moody’s, and Fitch. For example, Fitch’s “AAAmmf” rating represents its judgment that an MMF

has “[e]xtremely strong capacity to achieve fund’s investment objective of preserving principal

and providing shareholder liquidity through limiting credit, market, and liquidity risk” (Fitch

Ratings, 2009). Thus, AAA ratings may be viewed as rating agency opinions that MMF portfolio

risks are relatively low.18 I include, as a binary proxy for (low) portfolio risk, the existence of a

AAA rating from at least one of the three major ratings organizations. As shown on line 2 of table

2a, 53 percent of institutional MMFs had triple-A ratings on the eve of the run in 2008, but only 18

percent of retail funds had such ratings.

4.1.3 Controlling for portfolio holdings of specific distressed securities

Money funds’ exposures to several specific distressed securities, such as Lehman Brothers’ obligations and troubled ABCP issues, played central roles in the two MMF crises that I study in

this paper. Past holdings of problematic securities are not promising predictors of future MMF

strains—funds are, for example, unlikely to begin purchasing Lehman paper again. However,

controlling for such exposures may be important in understanding the cross-sectional variation in

outcomes (particularly net flows) of MMFs during past crises.

The critical role of the Lehman Brothers bankruptcy in the run in 2008 motivates controlling

for MMFs’ holdings of Lehman debt in analyzing redemptions during that crisis. To do so, I use

the sources described in section 2 to identify MMFs that held Lehman’s obligations at the time

of its bankruptcy. As shown on line 3 of table 2a, those sources indicate that 12 percent of both

institutional funds (column 1) and retail funds (column 5) held Lehman debt on the eve of the run.

Multiple ABCP issues caused problems for MMFs during the ABCP crisis. As a broad proxy

for exposures to distressed securities in this episode, I use an indicator variable equal to one for

18Increasingly, ratings organizations are considering investor and sponsor risks in assigning ratings to MMFs

(Moody’s Investors Service, 2008; Fitch Ratings, 2009). Thus, particularly in the aftermath of the 2008 run, top MMF

ratings may be viewed as opinions on each MMF’s combination of portfolio, investor, and sponsor risks, although

portfolio risks appear to remain central to the ratings organizations’ analysis of MMFs. For the period covered by my

analysis, however, ratings agencies seem to have focused almost exclusively on portfolio risks. (The exact nature of the

risks analyzed by the agencies matters little for the interpretation of my results, as I find that AAA ratings generally

had little predictive power for the outcomes for MMFs during the two crises that I study.)

14
MMFs that held ABCP issues that ultimately prompted sponsor-support actions for at least one

MMF. Line 3 of table 2b shows that 57 percent of institutional funds (column 1) and 44 percent of

retail funds (column 5) held problematic paper as the crisis unfolded.

4.1.4 Alternative proxies for portfolio risk

Gross yields may be more than compensation for risk; a higher gross yield may reflect a portfolio

manager’s ability to obtain superior risk-adjusted yield. One approach to capturing portfolio risk

more directly would be to use portfolio characteristics, such as WAM and shares of assets held in

Treasury securities and ABCP (and other characteristics discussed in section A.2 of the appendix)

as proxies. Indeed, Jank and Wedow (2008) used the share of individual German MMFs’ assets

in one apparently risky asset class as a proxy for these funds’ liquidity risk. Instead of analyzing

every conceivable permutation of fund characteristics in my analyses, I employ—as an alternative

to my main specification—the fund portfolio measures in an instrumental variables framework.

That is, I use portfolio characteristics to instrument for gross yields and hence capture, as a proxy

for portfolio risk, the component of gross yields that is measured by portfolio attributes. Section

5.2 discusses this approach in more detail.

Another approach to measuring portfolio risk in MMFs would be to estimate risk-factor

loadings using an arbitrage-pricing model applied to MMF gross yields. Collins and Mack (1994)

used such an approach in an event-study analysis of changes in the aggregate riskiness of MMFs

following the adoption of amendments to rule 2a-7 in 1991. One hurdle to using this approach to

pinpoint cross-sectional MMF risks, however, is that amortized-cost accounting prevents funds’

yields from fluctuating directly with the market yields of the assets they hold, so MMFs’ factor

loadings would be biased down (and their “alphas” biased up). Even if yields did move with

market rates, the riskiness of individual funds that invested in more exotic instruments might be

understated by loadings on conventional market risk factors. Hence, an arbitrage-pricing framework is probably not well suited to study of cross-sectional variation in risks of stable-NAV MMFs.

4.2 Investor risk19

As figure 2 illustrates, institutional and retail shareholders subjected MMFs to starkly different

degrees of investor risk during the run in 2008. But why? Compared with their retail counterparts, many institutional MMF shareholders may be particularly risk-averse and, at the same

time, possess greater sophistication and resources to monitor MMFs carefully and redeem shares

preemptively at the first sign of any heightened risk. Institutional investors may face very strong

19Broadly speaking, investor risk is a form of liquidity risk, which also includes portfolio risks arising from the illiquidity of (some) portfolio assets. However, I use the term “investor risk” to highlight the liquidity risks attributable

specifically to a fund’s investors, and I employ “portfolio risk” to encompass the liquidity risks arising from the illiquidity of assets. This terminology not only eases exposition but also reflects the difficulty of distinguishing portfolio

liquidity risks from other portfolio risks, particularly during crises.

15
incentives to avoid losses on MMF shares; capital losses in client accounts may expose institutional investors with fiduciary responsibilities to legal liability, and losses may jeopardize careers

of corporate treasurers. Some institutional investors are precluded by law, regulation, or policy

from investing in funds without stable NAVs (Investment Company Institute, 2009), so any risk

of NAV fluctuations may prompt these investors to redeem MMF shares.

Sophisticated, risk-averse investors also may be particularly attuned to differences between

MMF share prices, which are rounded to the nearest penny, and their mark-to-market values (Standard & Poor’s, 2007, pp. 27). Rounded NAVs help MMFs maintain stable NAVs, but they also

create destabilizing arbitrage opportunities for shareholders when a fund suffers a small capital

loss (less than 0.5 percent of assets) that is rounded away. When redeeming investors receive $1

for shares that are worth less, the mark-to-market value of remaining shares declines as losses are

concentrated among fewer shares, and the fund moves closer to breaking the buck. So, any loss

(or expected loss) in a rounded-NAV fund triggers an incentive for shareholders to redeem before

others do.

Net flows to MMFs following changes in short-term interest rates—which affect the markto-market value of MMF shares but not their $1 share prices—indicate that some investors, particularly institutional investors, exploit even small discrepancies between prices and values. For

example, Lyon (1984) estimated that arbitrage flows reduced the net yields earned by passive investors in institutional MMFs by about 10 basis points annually. Monetary policy easing cycles

present numerous opportunities for investors to take advantage of MMFs’ rounded prices, and

the resulting arbitrage flows have occurred mostly in institutional funds.20 Sophisticated investors

who are more prone to exploit share-price inefficiencies probably subject funds to heightened investor risk during crises.

The relatively higher degree of investor risk in institutional funds may also reflect greater

flow volatility in these funds and a greater responsiveness of cash flows to net yield (see section

A.3). That is, institutional funds may have relatively larger shares of “hot money” than retail

MMFs. As noted in section 3.4, in the aftermath of the run in 2008, regulators and ratings organizations have identified “hot money” as a source of investor risks for money funds.

Other than the distinction between institutional and retail funds, publicly available data

provide little direct information about individual funds’ investors and the risks they represent.

Below, I suggest four proxies for the degree of investor risk in MMFs.

20Reductions in the federal funds rate (FFR) target that raise the value (but not the price) of MMF shares may be

especially easy to exploit because the profitable transaction is a purchase of MMF shares (even after the policy action),

so investors need not hold shares in advance to exploit the opportunity. Of the thirteen reductions in the FFR target

between 2001 and 2003, the second-to-last, a 50-basis-point cut in the target on November 6, 2002, prompted the largest

inflows, perhaps because institutional investors had learned from others’ behavior as the easing cycle progressed.

Assets under management in money funds, which had been trending down for almost a year before this policy action

amid very low short-term interest rates, jumped $168 billion (8 percent of assets) in the three weeks following the

reduction in the policy rate. Institutional funds attracted virtually all ($167 billion) of the inflow.

16
4.2.1 MMF expense ratios as a proxy for investor risk

One indicator of the sophistication of the investors in a money fund—and the risks of large redemptions should the fund encounter difficulties—is the fund’s expense ratio. Domian and Reichenstein (1998) found that the net yields of prime funds with more than $300 million in assets

“are driven exclusively or almost exclusively by expenses,” and they warned that “expenses are

a dead-weight loss to investors.” Still, someone must hold shares in high-expense MMFs, and

Christoffersen and Musto (2002) argued that such investors “have distinguished themselves from

the population in general . . . as relatively more willing to pay high prices for bad prospects.”

Hortac ̧su and Syverson (2004) argued that lower expense ratios (in equity funds) attract more sophisticated investors with lower search costs and less need for costly services. In addition, lower

expense ratios may reflect lower shareholder servicing costs (per dollar of assets under management) that are associated with larger average account sizes. While holders of larger accounts are

not necessarily more sophisticated than smaller shareholders, investors with more at stake in a

fund may have greater incentives to monitor it carefully.

As shown on line 4 of table 2a, the average expense ratio for institutional funds in the year

prior to the run (column 1) was less than half of that for retail funds (column 5). At the same time,

there was wide dispersion in expense ratios for both types of funds (columns 2 through 4 and 5

through 8). Line 4 of table 2b shows summary statistics for the year before the ABCP crisis.

4.2.2 MMF growth, flow volatility, and yield sensitivity as proxies for investor risk

I use three proxies for the investor risks posed by MMFs’ exposure to “hot money” and other

sources of flow volatility. The first is the growth of the fund over the previous year, which is

probably only a crude indicator of the hot money in a fund, since many factors might contribute

to its growth. (I measure growth and flows throughout this paper in terms of the logarithm of net

flows, that is, the natural log of the sum of one plus the ratio of net flow to lagged assets.21) The

second measure is a fund’s weekly flow volatility (the standard deviation of weekly log net flows

for the previous year), which may reflect a fund’s exposures to hot money and to concentrations of

similar investors with correlated transactions patterns. The third, the sensitivity of a fund’s weekly

net flows in the previous year to its own lagged net yields, is designed to capture the component

of a fund’s flow variability that is due to hot-money flows. For each fund, this measure is the

estimated coefficient from a regression of the fund’s weekly log net flow on its relative net yield

in the previous week.

Lines 5 through 7 of table 2a list summary statistics for the three “hot money” investor risk

proxies for the year ending August 2008. All three measures were considerably larger, on average,

21I multiply by 100 to make this measure similar to net flows as a percentage of lagged assets: Log net flow =

100 × ln(1 + net flow

lagged assets ). Net flow is the change in assets, net of increases due to accrual of yield.

17
for institutional funds (column 1) than for retail funds (column 5). Table 2b shows analogous

summary statistics for the year ending July 2007.

4.3 Sponsor risk

When the mark-to-market value of an MMF’s shares falls below 99.5 cents, the fund’s sponsor

(that is, its adviser and the adviser’s affiliates or parent firm) has two choices: provide capital

support to bring the value back to at least 99.5 cents, or allow the fund to reprice its shares below

$1—that is, break the buck.22 Sponsor support is expressly voluntary, and a sponsor’s ability and

commitment to support its funds is not officially monitored or regulated, so MMF investors can

never be certain that a sponsor will support an ailing fund. Historically, however, sponsors with

the resources to support their funds have done so, and even asset managers that subsequently exited the MMF business have supported their money funds to preserve their broader reputations.23

In both instances in which MMFs did break the buck, the sponsor simply lacked the wherewithal

to absorb the fund’s losses (Eaton, 1994; U.S. Securities and Exchange Commission, 2009b) and its

reputation as an investment adviser was destroyed. Hence, sponsors have gone to such lengths

to prevent passing capital losses along to MMF shareholders, and sponsor support has come to be

seen as a form of “private insurance” (Stigum and Crescenzi, 2007). However, as is the case for

other forms of private insurance, the surety of sponsor support for MMFs depends on the financial strength of the sponsor. Sponsor risk reflects the possibility that a sponsor will not be able to

support its funds.

4.3.1 Bank affiliation as a proxy for sponsor risk

As one proxy for sponsor risk, I use a dummy indicating whether an MMF’s sponsor was affiliated

with a bank. Bank affiliation presumably would have been associated with lower sponsor risk

because banks have access to lender-of-last-resort liquidity through the Federal Reserve’s discount

window and may have greater resources for supporting affiliated funds during a crisis. Indeed,

many MMFs received support from bank affiliates during the ABCP crisis in 2007 and the run in

2008, and some sizable non-bank firms obtained bank charters during the turmoil that followed

22The MMF’s board of directors—not its sponsor—ultimately must determine whether to reprice shares below $1.

That decision, according to rule 2a-7, should be based on the board’s “prompt consideration” of whether “the extent

of any deviation from the money market fund’s amortized cost price per share may result in material dilution or other

unfair results to investors or existing shareholders.” However, the sponsor must decide whether to provide financial

support to prevent such a deviation. (Sponsors generally do not commit in advance to support an MMF that might

later suffer losses, because an explicit commitment would subject the sponsor to potentially large contingent liabilities

and likely force the sponsor to consolidate the fund on its balance sheet.)

23Credit Suisse reported a total of $1.8 billion in losses related to support of its MMFs on its income statements in

2007 and 2008, even though the firm exited the MMF business in late 2008 (Credit Suisse, 2008, pp. 65-66; Credit Suisse,

2009, pp. 27, 45, 434).

18
the Lehman bankruptcy, presumably to obtain access to the discount window.24 Indeed, Baba et

al. (2009) reported that bank-affiliated MMFs experienced smaller-than-average net redemptions

during the run. Line 8 of tables 2a and 2b show that about half of both institutional and retail

prime MMFs were affiliated with banks at the onset of each crisis episode.

4.3.2 Sponsor CDS spreads as a proxy for sponsor risk

As a second proxy for sponsor risk, I use the five-year CDS spread of the sponsor’s senior debt

securities. Because the financial condition of some large institutions that sponsored MMFs deteriorated quickly before the beginning of the run in 2008, I use each sponsor’s CDS spreads averaged

over the first week of September 2008. (Spreads during the week after the Lehman bankruptcy

might be endogenous if MMF outflows worsened strains on sponsors). CDS spreads are only

available for sponsors of 43 percent of prime MMFs (line 11 of table 2a). Mean spreads for sponsors of both institutional and retail funds averaged about 140 basis points (line 9). Both the average

CDS spread and the dispersion of spreads were much larger immediately before the run in 2008

than they had been just prior to the onset of the ABCP crisis (table 2b, line 9).

4.4 Interactions among risk proxies

Tables 3a and 3b provide simple correlations for the MMF risk proxies used in this paper, as measured before the run in 2008 and prior to the onset of the ABCP crisis in 2007, respectively. Panels A

and B of each table show correlations for institutional funds and retail funds, respectively. (Numbering of the variables in the rows and columns of these tables is the same as that for tables 2a and

2b). For clarity, I report only correlations that are significant at the 5 percent level.

The correlations point to some distinctions among the proxies for portfolio risk. For example, among institutional funds in the year before the run in 2008 (table 3a, panel A), none of the

pairwise correlations among the portfolio-risk proxies—gross yield, triple-A ratings, and Lehman

exposure (the first three lines and columns)—was significant. Intriguingly, table 3b shows that

triple-A ratings for both institutional and retail funds were uncorrelated with distressed ABCP

exposure during the ABCP crisis (line 3, column 2). However, MMFs with such exposures did

exhibit higher gross yields in the year before the ABCP crisis (line 3, column 1).

The evidence for links among the proxies for investor risk was also mixed. Lower expense

ratios in institutional funds were correlated with other measures of investor risk both before the

run in 2008 (table 3a, lines 6 and 7, column 4) and before the ABCP crisis (table 3b, lines 5 and

6, column 4), but other pairwise correlations were generally not significant. The two proxies for

24In analyzing the run in September and October 2008, I treat MMF sponsors that obtained bank charters after

Lehman’s bankruptcy as non-bank entities, except in the weekly regressions discussed in section 5.3. (In those regressions, a sponsor’s bank-affiliation is recorded as of the beginning of each week.) Relabeling sponsors that obtained

bank charters during the run as bank-affiliated does not materially alter my results.

19
sponsor risk, CDS spread and bank affiliation, were significantly negatively correlated only for

institutional MMFs in the year before the ABCP crisis.

Some of the correlations among proxies for different types of risk are relevant to interpretation of my results. For example, in the year prior to the run in 2008, MMF gross yields were not

significantly correlated with three of the measures of investor risk and were negatively correlated

with the standard deviation of weekly flows (see both panels of table 3a, lines 4 through 7, column

1). Although higher gross yields may attract riskier investors, the correlations support the view

that gross yield during this episode was a proxy for portfolio risk, rather than investor risk.

The correlations on table 3a also preview the links between MMF risks and outflows from

institutional and retail funds during the run in 2008. For example, as shown on line 12 of panel A,

several risk factors were associated with larger outflows from institutional funds during the run

in 2008: higher gross yield (column 1), a lower expense ratio (column 4), greater flow sensitivity

(column 7), a higher CDS spread (column 9), and larger size (column 10). Correlations between

net flows and risks for retail funds (panel B) were quite different from those for institutional funds.

This point is explored in more detail in section 5.1.

The size of an MMF was an important predictor of outflows during the run in 2008 and,

particularly for institutional funds, was correlated with multiple risk proxies in both episodes (see

line 10 and column 10 in the top panels of tables 3a and 3b). For example, in both episodes, larger

institutional funds had lower expense ratios and thus probably faced greater investor risk from

relatively sophisticated shareholders. Even controlling for expense ratios, larger, sophisticated

investors may have been more likely to invest in bigger funds. Larger institutional funds were

more likely to have had triple-A ratings, an indicator of lower portfolio risk, but larger funds also

were more likely to have held Lehman debt in 2008 and distressed ABCP in 2007. In addition,

fund size may have intensified concerns about sponsor risk because greater amounts of capital

might have been needed to absorb losses in larger funds. Thus, links between size and multiple

forms of MMF risk complicate the interpretation of the role of fund size in explaining outcomes

during crises.

5 MMF risks and the cross section of outflows during the 2008 run

To analyze prime MMF flows during the run that followed the Lehman bankruptcy, I run a series

of cross-sectional regressions in which the dependent variable is net flows from September 9 to

October 7, 2008 (the Lehman bankruptcy occurred on September 15, and ICI aggregate data indicate that prime funds had large outflows through the first week of October). As discussed in

section 2, I use data from iMoneyNet for prime money funds that had been in existence for at least

a year, had assets exceeding $100 million, and had reliable flows data during the run. The sample

includes 116 institutional funds and 135 retail funds. Explanatory variables include the proxies

20
for fund risks (and fund size), as listed on tables 2a and 4:

f lowi

run = β1gyieldi

t∈P + β2AAAi

t=P ̄ + β3Lehmanit=P ̄

+ β4exprati

t∈P + β5growthit∈P + β6 f lowSDit∈P + β7 f lowSensit∈P

+ β8banki

t=P ̄ + β9CDSit=P ̄ + β10assetsit=P ̄ + constant + ε

i

. (1)

Here, f lowi

run is fund i’s log net flow during the run, gyieldi

t∈P

is the gross yield of fund i over the

year ending August 2008 (period P), and AAAi

t=P ̄

is a dummy variable equal to one if and only

if fund i had a triple-A rating from at least one ratings organization as of the end of August 2008

(at time P ̄). Other explanatory variables are listed on tables 2a and 4 in the same order that they

appear in equation (1).

Table 4 reports results from six regressions. These include separate estimations for institutional and retail MMFs, because their outflow dynamics differed so starkly during the run, as well

as selected significance results from a pooled regression that includes both institutional and retail

funds. Because CDS spreads are available for sponsors of less than half of the money funds in the

sample, I run an extra set of regressions to include these spreads.

5.1 Empirical results

The first three columns of table 4 summarize the results of regressions that exclude CDS spreads.

Column 1 shows results for institutional funds.

Line 1 shows that greater portfolio risk, as measured by higher gross yields in the year prior

to September 2008, was associated with significantly larger outflows during the run. Other indicators of portfolio risk—whether the fund had a triple-A rating (line 2) or had Lehman exposure

(line 3)—had no significant effect on outflows. The insignificance of the Lehman dummy is surprising, given the role of Lehman’s bankruptcy in triggering the run. However, I show in section

5.3 below that Lehman exposures did have a significant negative effect on fund flows early in the

run, but that the impact was reversed in later weeks.

Investor risk also was an important predictor of institutional MMF outflows during the run

in 2008: Three of the four investor risk proxies had significant effects (with the expected signs)

on net flows. Line 4 indicates that funds with higher expense ratios had larger net flows (smaller

outflows) during the run—a result that stands in contrast with the usual negative effect of expense

ratios on flows (see section A.3 of the appendix). Although a fund’s growth over the previous year

(line 5) had no significant effect, lines 6 and 7 show that institutional funds with greater weekly

flow volatility and higher sensitivity to yields had significantly greater outflows during the run.

In fact, three indicators of investor risk (the expense ratio, flow volatility, and flow sensitivity) can explain much of the enormous difference in the outflows from institutional and retail

funds during the run in 2008. The regression results for institutional funds imply that an institu21
tional fund with investor risks similar to those of a retail fund would have had net flows that were

20.2 “percentage points” larger than an institutional fund with average investor risk.25 This total

is a very substantial portion of the 20.8 percentage point difference in mean flow for institutional

and retail funds.26 The calculation is merely illustrative, as it only takes into account differences

in investor risk proxies and ignores the striking differences in institutional and retail investors’

responses to risks. Still, the explanatory power of these risk proxies shows that a simple distinction

between institutional and retail investors is not the only important measure of investor risk.

Bank affiliation (line 8) did not have a significant effect on net flow. However, line 10 shows

that larger institutional MMFs suffered proportionally more severe outflows during the run; indeed, twelve large MMFs accounted for more than half of the aggregate outflow from institutional

funds. As discussed in section 4.4, fund size may have been linked to multiple forms of MMF risk.

Column 3 shows results for retail prime funds. A couple of risk factors with significant

explanatory power for institutional MMF outflows during the run were less helpful in explaining

retail fund flows, and R-squares for retail MMFs are lower. The estimated coefficient on gross

yield, line 1, is insignificant. Higher expense ratios were associated with smaller outflows (line 4),

but the magnitude of the effect was much smaller than it was for institutional MMFs. Indeed, as

indicated in column 2, estimated coefficients on gross yield and the expense ratio are significantly

different for institutional and retail funds. This column reports significance levels for tests that

institutional and retail fund coefficients are different, based on a pooled regression of both types

of funds in which an institutional fund dummy is interacted with each explanatory variable.27

Nonetheless, investor risk does appear to have been important in explaining the variation

in retail MMF flows during the run. In addition to the expense ratio, flow volatility (line 6) enters

the regression significantly with the expected sign. Finally, fund size (line 10) was also a significant

predictor of outflows from retail funds.

Sponsor risk, too, had significant consequences for institutional prime funds during the

run. Columns 4 and 6 report results from regressions that include sponsor CDS spreads, at a cost of

considerable reductions in sample size.28 As shown in column 4, higher sponsor CDS spreads (line

9) were associated with significantly greater outflows. (In this sample, the coefficient on the bankaffiliation dummy, an indicator of low sponsor risk, is positive but marginally significant.) For

25This result is based on regression coefficients from the full-sample institutional regression (lines 4, 6, and 7 in the

first column of table 4). I multiply coefficients for each of these three investor-risk proxies by the difference between the

proxy’s sample mean among retail funds and its sample mean among institutional funds (columns 5 and 1, respectively,

of table 2a) and sum these products: 20.2 = 39.30(0.63 − 0.29) − 2.06(2.76 − 5.13) − 0.38(1.51 − 6.91). For ease of

exposition, I refer to the units of log flows in terms of percentage points. The actual units are 100 times the natural

logarithm of the sum of one plus the ratio of net flow to lagged assets. See note 21.

26Compare columns 1 and 5 on line 12 of table 2a.

27An F-test rejects (with a p-value of essentially zero) the null hypothesis that the coefficients for institutional and

retail funds in this pooled regression are all equal.

28I also ran regressions similar to those reported in columns 1 and 3 of table 4, but with dummy variables indicating

whether CDS spreads were available for each fund’s sponsor. The dummies were generally negative but insignificant.

22
retail funds, however, sponsor risk was not important in explaining outflows (column 6). Other

results for the CDS spreads sample are similar to those for the full sample, although coefficients

on the investor risk proxies are generally estimated with less precision (for example, weekly flow

sensitivity is no longer significant in the institutional-fund regression).29

The significance of proxies for portfolio, investor, and sponsor risk in these regressions

show that the run in 2008 was not simply an indiscriminate panic. MMFs with greater risks bore

the brunt of the outflows during the run. R-squares from the regressions indicate that risk proxies explain a sizable portion of the cross-sectional variance in flows, particularly for institutional

funds. Moreover, the particularly strong links between risk proxies and outflows from institutional funds point to interactions in MMF risks: Funds with greater investor risks (due, in part, to

their more sophisticated investors) were also more sensitive to portfolio and sponsor risks.

Table 5 provides some additional insight into the economic significance of the regression

results by reporting estimated impacts on net flow of 1-standard-deviation increases in each righthand-side variable (for dummy variables, which are indicated with an asterisk (*), the entry represents the estimated effect of a 0-to-1 change in that variable on net flow, which typically represents

about a 2-standard-deviation change).30 For example, line 1 of column 1 shows that, for institutional prime funds, a 1-standard-deviation increase in gross yield paid over the year ending in

August 2008 was, all else equal, associated with 9 percentage points greater outflow during the

run. Normalized changes in other risk proxies also had substantial predicted effects on net flows

for institutional funds (columns 1 and 3). For retail funds (columns 2 and 4), normalized changes

in most risk proxies had much smaller predicted effects on net flows.

5.2 Does the link between outflows and gross yields reflect portfolio risk?

The role of gross yields in explaining outflows from institutional MMFs during the run in 2008

underlines the importance of interpreting the information contained in these yields. Section 4.1.1

and section A.2 of the appendix offer evidence that an MMF’s gross yield is a useful summary

statistic for its portfolio risk. As noted in section 4.4, one alternative interpretation—that higher

gross yields indicated greater investor risk—is not supported by any significant pairwise correlation between gross yield and the proxies for investor risk. Moreover, the multiple controls for

investor risk in equation (1) should pick up effects of investor risk that are related to gross yield.

Another possibility, discussed in section 4.1.4, is that higher gross yields also reflected

greater portfolio manager talent. One way to control for any confounding effect of manager ability

is to use portfolio characteristics to instrument for gross yields in regressions of the form of equation (1). As noted in section A.2 of the appendix, gross yields of individual prime MMFs varied

29Again, an F-test rejects (with a near-zero p-value) the null hypothesis that the coefficients for institutional and

retail funds are all equal in a pooled regression includes CDS spreads.

30See columns 2 and 6 in table 2a for standard deviations of the explanatory variables.

23
significantly with the shares of their assets held in different types of instruments and with their

WAMs. Manager ability should have been orthogonal to these broad portfolio measures. Thus,

I run a two-stage least-squares (2SLS) regression in which the portfolio measures from table A1

serve as instruments for gross yields.31

Results are shown in table 6. For comparison, column 1 repeats the output of the baseline

regression for institutional funds from the first column of table 4. The 2SLS regression results,

listed in column 2, are quite similar; the estimated coefficient on gross yield is highly significant

but a little smaller than that from the regression in which gross yield appears directly. The instrumental variables results corroborate the view that institutional MMFs with higher gross yields had

larger outflows because of investors’ concerns about greater portfolio risks.

5.3 MMF risks and outflows by week during the 2008 run

The run on MMFs in September and October 2008 was punctuated by very significant financial

and policy events. As indicated by panel B of figure 2, these included the Lehman bankruptcy

early on Monday, September 15; Reserve’s announcement at 4 p.m. on Tuesday, September 16

that the Primary Fund had repriced its shares at 97 cents; the announcements of the Treasury’s

Temporary Guarantee Program for MMFs and the Federal Reserve’s AMLF on the morning of

Friday, September 19; and the announcement of the Federal Reserve’s Commercial Paper Funding

Facility (CPFF) on October 7. Hence, a closer look at the timing of the links between MMF risks

and outflows may be informative in understanding the role of risks during the run.

To do so, I run weekly regressions similar to equation (1) but in which the dependent variable is net flow measured over a particular week.32 The more precise timing of the flows in these

regressions allows some modifications to the sample, which are described more fully in section

A.1. For example, several MMFs that are excluded from the full-sample regressions because of

measurement problems can be included in a subset of the weekly regressions, and each weekly

regression includes CDS spreads measured over the previous week, to capture the effects of any

deterioration in sponsors’ financial condition. For each specification (including and excluding

CDS), I run a separate regression for each of the seven weeks ending on Tuesdays from September 2 to October 14, reflecting the timing of iMoneyNet’s weekly taxable MMF data. Results are

31An alternative approach is to include portfolio measures instead of (rather than instruments for) gross yield. For

example, including only funds’ ABCP portfolio shares corroborates Duygan-Bump et al.’s (2010) finding—based on an

analysis of all prime funds and a different sample period than I use—that funds with higher ABCP exposures had larger

outflows during the run. (If gross yield and ABCP share are both included in my baseline specification for institutional

funds, the coefficient on gross yield remains significantly negative and ABCP share is negative and significant at the

12 percent level.) A temptation here is to run every permutation of the portfolio characteristics in a “horse race” to

pinpoint the key characteristics that spooked investors. My aim is simpler—to show that portfolio risk was important

in prompting outflows during the run.

32iMoneyNet provides daily assets data for a subset of the funds it tracks, although for many MMF share classes,

daily asset observations appear to be flatlined for each week. I also ran regressions at a daily frequency, but the daily

results added little to the insights that the weekly regressions provide.

24
depicted graphically in figures 3a (for regressions that omit CDS spreads) and 3b (regressions that

include CDS spreads). For brevity, I only report results for institutional MMFs.33

The figures illustrate the timing of both the economic and statistical significance of each

of the risk proxies. The solid line in each panel plots, for each week, the predicted effect of a

one-standard-deviation increase in an explanatory variable on net flow as a percentage of lagged

assets.34 (The plots also show predicted effects of one-standard-deviation changes in dummy

variables, rather than 0-to-1 changes.) The dashed lines are the upper and lower bounds of a quasi

95 percent confidence interval for the predicted effects derived by using a two-standard-error

confidence interval for each estimated coefficient.35

Figure 3a shows that, in the full-sample regressions that exclude CDS, the risk proxies had

virtually no significant effect on fund flows in the two weeks prior to the Lehman bankruptcy

(the weeks ending September 2 and 9). However, by the third week (September 10-16), which

included the Lehman bankruptcy and ended just as the Reserve Primary Fund announced that

it had broken the buck, risk proxies began to have significant effects. Two investor risk proxies

(the expense ratio, panel 4, and flow sensitivity to yield, panel 7), size (panel 10), and—notably—

the Lehman-exposure dummy (panel 3) are all significant with the predicted signs. Institutional

MMFs suffered their worst outflows of the entire episode during the fourth week (September

17-23), and, on the 19th, the government announced both the Treasury Guarantee Program for

MMFs and the Federal Reserve’s AMLF. In that week, higher gross yields (panel 1) began to enter

significantly and two investor risk proxies remained significant (panels 4 and 7). However, the

coefficient on the Lehman-exposure dummy (panel 3) was positive! By the fifth week, with the

government programs in place, only a couple of risk proxies were significant, and in the final two

weeks (ending in October), almost none were.

When CDS spreads are added to the regression, the patterns of significance among the

proxies change somewhat (in part because the sample size declines), but the overall picture (figure

3b) is similar. Gross yield (panel 1) was a significant predictor of flows one week earlier (during

the week ending September 16) than it was in the full-sample regression. Two investor risk proxies

(panels 6 and 7) and the Lehman-exposure dummies (panel 3) are no longer significant. The CDS

spread itself was significant at least at the 10 percent level for four weeks spanning September 3-

30—starting even before the Lehman bankruptcy—although the magnitude of the predicted effect

increased substantially in the week that included the Lehman collapse and Reserve’s break-thebuck announcement.36

33Results for retail funds, as well as results for all MMFs in tabular form, are available upon request.

34Predicted effects reported here have been converted from log net flows to net flows as a percent of lagged assets.

35For example, upper bounds for the predicted effects of higher gross yield (panel 1) are computed by adding two

standard errors to each weekly estimated coefficient on gross yield and multiplying the result by the standard deviation

of gross yield for institutional prime funds.

36 p-values for the null hypothesis that the CDS spread was zero for the weeks ending September 9, 16, 23, and 30

were 0.044, 0.093, 0.057, and 0.002, respectively.

25
In summary, the week-by-week regressions indicate that proxies for risk were generally

most significant—and had the largest predicted effects—just as the run was peaking. Three additional points are worth noting. First, the Lehman-exposure dummy predicted significantly greater

outflows (at least in the full sample) in the week from September 9-16 but smaller outflows in the

following week. One possible explanation is that institutional investors redeemed shares preemptively in the days just before the Reserve Primary Fund broke the buck but subsequent announcements of support actions by sponsors of MMFs (other than Reserve) with Lehman exposure allayed investor concerns and slowed the outflows. For example, on September 17, BNY Mellon

announced support for its Dreyfus MMFs that held Lehman debt (Condon, 2008). This may explain why the Lehman-exposure dummy was not significant in the regressions that cover the full

period of the run.

Second, the weekly graphs highlight that fund size had a strikingly large and significant

effect on outflows at the peak of the run. As noted in section 4.4, size probably reflected multiple

forms of MMF risk.

Third, a weekly analysis of retail funds (not shown) indicates that MMF risk proxies did

have significant effects on fund flows, but only rather late in the run. For the week ending September 30, coefficients for Lehman exposure, weekly flow volatility, weekly flow sensitivity, and fund

size are negative and significant. The delayed response of retail investors is also apparent in panel

B of figure 2; outflows from retail funds peaked later than those from institutional funds.

6 The ABCP crisis and MMF risks

The crisis in the market for ABCP that erupted in August 2007 caused severe strains for some

MMFs and for short-term funding markets. However, the broader consequences were far less

momentous than those of the run in 2008, in part because capital losses of MMFs in 2007 were

absorbed by their sponsors, no fund broke the buck, and MMFs suffered no large-scale run. As

such, MMF risks played a different role in this crisis than they did during the run a year later.

Below, I examine links between MMF risks and three types of ABCP crisis outcomes for individual

MMFs: prime funds’ net flows in the three weeks from August 7 to 28, 2007; sponsor interventions

to support MMFs that held distressed ABCP; and fund exposures to distressed ABCP.

6.1 MMF risks and the cross-section of outflows during the ABCP crisis

Given the links between MMF risks and outflows during the run in 2008, a natural question is

whether the small aggregate outflow from prime funds during the ABCP crisis masked significant

cross-sectional correlation between fund risks and flows. Anecdotal evidence suggests such a

story; as noted in section 3.2, at least one MMF suffered very large outflows because of holdings

of distressed ABCP. Still, the standard deviation of prime institutional MMF flows was much

26
smaller during the ABCP crisis (table 2b, line 12, column 2) than it was during the 2008 run (table

2a). The dispersion of retail fund flows as the ABCP crisis unfolded (table 2b, line 12, column 6)

nearly matched that of institutional funds.

To test the relationship between MMF risks and net flows during the ABCP crisis, I estimate

slightly revised versions of equation (1), with net flows to institutional and retail prime MMFs in

the three weeks ending August 28, 2007 as dependent variables. Explanatory variables are the risk

proxies described in section 4 and are listed in table 2b. The only important differences between

these risk factors and those used to analyze the run in 2008 are the dates over which the measures

are computed and the specific distressed-asset exposures of concern (line 3). For analysis of the

ABCP crisis, the relevant exposures were to distressed ABCP issues that caused problems for other

MMFs. Correlations among the risk proxies appear on table 3b.

Regression results are shown in table 7. Whereas multiple risk proxies were useful in predicting the net flows of institutional prime MMFs during the 2008 run, net flows to these funds

exhibited little significant responsiveness to risks during the ABCP crisis. A couple of explanatory

variables are marginally significant in the full-sample regression (column 1), but one of these (flow

sensitivity, line 7) has the “wrong” sign. In the regression that includes CDS spreads (column 4),

these spreads are only marginally significant (at the 11 percent level).37

Oddly, retail fund flows appear to have been somewhat more responsive to MMF risks, but

only in the smaller sample that includes CDS spreads (column 6). In that specification, holdings of

distressed ABCP (line 3) had a statistically significant negative effect on net flow, and the investor

risk proxies were all significant at least at the 10 percent level—but two of them (the expense ratio,

line 4, and growth over the previous year, line 5) had the “wrong” signs. None of the risk proxies

is significant at the 5 percent level in the full-sample regression for retail funds (column 3).38

Taken together, the risk proxies had far less explanatory power for the cross-sectional variation in net flows during the ABCP crisis than they did for variation in flows during the run in 2008:

Three of the four R-squares on table 7 are less than half those for the corresponding regressions

on table 4. For MMF investors, money fund risks apparently remained mostly latent through the

ABCP crisis, amid the widespread interventions by sponsors to absorb losses. Again, the evidence

points to important interactions among MMF risks: When sponsor risk was perceived to be low,

portfolio and investor risks had little impact on net flows.

37Although the estimated coefficient on CDS spreads for institutional prime MMFs in this regression is only

marginally significant, it is nearly as large in magnitude as that on CDS spreads for such funds during the run in

2008. However, even if we take these magnitudes as given, higher sponsor CDS spreads during the ABCP crisis were

much less consequential for MMFs than such spreads were during the run in 2008. A one-standard-deviation increase

in CDS spread in the first week of August 2007 was only 25 basis points and hence would predict only 3.5 percentage

points more outflow from these funds. As shown on line 9, column 3 of table 5, a one-standard-deviation increase in

sponsor CDS spread in early September 2008 was associated with 12 percentage points more outflow during the run.

38An F-test based on a pooled institutional-retail regression that excludes CDS spreads does not reject the null hypothesis that the coefficients for institutional and retail funds (reported in columns 1 and 3 of table 7) are jointly equal.

However, such a test does reject the null (at the 5 percent level) when CDS spreads are included.

27
6.2 MMF risks and sponsor financial support following the ABCP crisis

For some MMF sponsors, however, the consequences of money fund risks were quite costly during the ABCP crisis (Crane, 2009; Credit Suisse, 2008, 2009). Indeed, sponsors’ record of financial

support for their funds provides another perspective on MMF outcomes during this episode. Because sponsors’ actions reveal their concerns about actual or expected losses in funds’ portfolios,

as well as concerns about investors’ reactions to fund risks, the support actions indicate where

NAV fluctuations or large outflows likely would have occurred in the absence of interventions.39

Some of the predicted relationships between MMF risks and sponsor support actions should

be similar to the links between risks and outflows during the run in 2008. Greater portfolio risk,

all else equal, should be associated with a higher probability of a sponsor bailout. Support actions

might also be linked to investor risk, since heavy redemptions can exacerbate losses for MMFs.

However, a key distinction between MMF outflows and sponsor support as outcomes of

interest is in their links to sponsor risk. Section 5 shows that MMF investors responded to sponsor

risk by disproportionately redeeming shares from MMFs with riskier sponsors during the run in

2008. As such, sponsor risk is an important dimension of MMF risk. But when the outcome of

concern is a sponsor’s intervention to support an ailing fund, sponsor risk may have multiple

roles. A sponsor with deep financial resources may be more likely to bail out a troubled MMF, so

lower sponsor risk may predict the “bad” outcome (support). But a link between sponsor financial

strength and MMF support also might reflect moral hazard, if portfolio managers made riskier

investments believing that deep-pocketed affiliates could absorb losses. In addition, anticipation

of a sponsor’s need to provide support for its MMFs might drive up its CDS spreads, so sponsor

risk itself may be endogenous.

As described in section 2, I obtained information about sponsor-support interventions from

public sources, including the SEC’s website and the financial statements of asset managers, as well

as from confidential SEC records. These data are thus a more complete record of support than is

available from public records, although, as noted earlier, some sponsor interventions still may

be unrecorded. Table 8 provides some summary statistics, measured just before the ABCP crisis,

for all prime funds in my sample (column 1) and for the funds with records of sponsor support

(column 2).40 The table combines data for institutional and retail funds because I find no statistical

evidence that the relationship between MMF risks and support actions was different for the two

types of funds.

39Ideally, sponsor support records would quantify the market value of each sponsor’s contribution to a fund’s assets

to allow estimation of the magnitude of NAV decline that would have occurred without intervention. However, most

of the records provide, at best, information about the total amounts of distressed assets purchased from funds or the

notional size of wraps provided for distressed assets. Thus, I was not able to construct a continuous measure of support

applicable to all the funds that received it.

40As noted in section 2, the sample includes funds with at least $100 million in assets and 12 months of history in

the iMoneyNet database as of July 31, 2007. The unit of analysis is the money market fund; multiple share classes for a

single fund are combined into one observation (see note 4).

28
Of the 249 prime money funds in the sample as of July 31, 2007 (line 1), 39 funds (16 percent)

had records of sponsor support due to holdings of distressed ABCP.41 Line 2 shows that 20 of the

116 institutional prime MMFs received support, as did 19 of the 133 retail prime funds. Supported

funds were somewhat larger than average (line 3) and had earned gross yields that were just a

touch higher than average in the year before the ABCP crisis began (line 4). Supported funds also

were more likely than other prime funds to have had a triple-A rating: One-third of all MMFs had

such a rating, but triple-A funds accounted for almost half the funds that received support (line

5). The next four lines indicate that sponsor-supported funds exhibited greater investor risk than

the rest of the prime fund industry by several measures: They had lower expense ratios (line 6),

more rapid growth in the previous year (line 7), and greater flow volatility and sensitivity to yield

(lines 8 and 9). Supported funds were more likely than average to be bank-affiliated (line 10) and

to have sponsors with CDS spreads in the Markit database (line 11). The CDS spreads of sponsors

of MMFs that received support were somewhat higher than average, both in the week before the

onset of the crisis (line 12) and in the year beforehand (line 13). In addition (not shown), CDS

spreads for bank sponsors averaged 16 basis points less than those of other sponsors in the first

week of August 2007, and 10 basis points less in the year before the onset of the ABCP crisis.

To test the relationship between sponsor support and MMF characteristics, including risk

proxies, I define:

S

i ≡

(

1 if fund i received sponsor support due to exposure to distressed ABCP

0 otherwise.

I use a probit regression to estimate:

Pr(S

i = 1) = Φ



β1gyieldi

t∈P + β2AAAi

t=P ̄ + β3exprati

t∈P + β4growthit∈P

+ β5 f lowSDi

t∈P + β6 f lowSensit∈P + β7banki

t=P ̄ + β8CDSi

t=P ̄ + β9CDSi

t∈P

+ β10insti

t=P ̄ + β11assetsi

t=P ̄ + constant

. (2)

Explanatory variables are listed, in the order in which they appear in equation (2), in table 9. I

estimate one pooled regression for institutional and retail funds because the relationship between

support and MMF characteristics was not statistically different for institutional and retail funds.42

The explanatory variables are the same as those used to estimate equation (1), with three changes.

41The SEC indicated that 44 funds had received such support (U.S. Securities and Exchange Commission, 2009a,

note 38). I found records of support for 43 unique MMFs, but two were for government-only funds, one was for a fund

with no iMoneyNet data, and one for a fund that was less than one year old.

42In a pooled regression of institutional and retail funds (not shown), in which an institutional-fund dummy was interacted with each explanatory variable, none of the individual estimated coefficients was significantly different (at the

5 percent level) for the two types of funds, and the interactive institutional dummy variables were jointly insignificant.

29
First, I do not include the indicator variable for exposures to distressed securities, which I constructed using securities identified in sponsor-support records. Second, equation (2) includes a

dummy for institutional funds.

A third change is motivated by concerns about the endogeneity of CDS spreads. Although

sponsors’ early-August CDS spreads (CDSi

t=P ̄

) are useful for examining the sponsor-risk effect on

subsequent net flows, spreads measured on the eve of the ABCP crisis may have already reflected

concerns about the costs of MMF bailouts for sponsors. If so, early-August spreads should enter

the regression positively, while CDS spreads measured earlier should be less predictive—or enter

negatively, if sponsor financial strength caused moral hazard for managers. Hence, I include in

the regression average CDS spreads measured over the preceding year (CDSi

t∈P

).

The first two columns of table 9 list the results of estimating equation (2). Column 1 shows

results for the full sample of MMFs when CDS spreads are excluded, and column 2 provides

results for the subsample for which sponsor CDS spreads are available.

As shown on line 1, portfolio risk—as indicated by funds’ gross yields earned over the

previous year—was a statistically significant predictor of sponsor support during the ABCP crisis.

It was also economically important: Based on the full-sample result (column 1), a one-standarddeviation increase in a fund’s gross yield (only 3 basis points in the pooled sample of all prime

funds) is associated with a 13 percentage point increase in the probability that the fund was the

recipient of sponsor support. (Based on the regression that includes CDS spreads, the predicted

effect is 39 percentage points.) The importance of gross yield in predicting sponsor support makes

sense: Riskier portfolios were more likely to experience losses that sponsors ultimately absorbed.

In contrast, a triple-A rating (line 2) had no significant predictive power in the full sample and had

the “wrong” sign in the CDS sample: Controlling for CDS spreads, funds with triple-A ratings

were more likely to have been the recipients of sponsor support.43

The evidence for a link between investor risk and sponsor bailouts is less compelling.

Among the indicators of investor risk, only the volatility of net flows over the previous year

(line 5) was significant (marginally so in the CDS regression). Moreover, as noted above, tests for

institutional-retail differences in the relationship between sponsor support and MMF risks found

no evidence for any distinction. Given the overwhelming evidence during the run in 2008 that

institutional funds represented greater investor risk, this suggests that such risk was not a factor

in sponsor interventions. One interpretation of these results is that sponsor support was provided

when portfolio assets soured, regardless of the additional risks that investors may have posed.

Sponsor risk had a significant but somewhat ambiguous role in predicting sponsor support. Bank-affiliated MMFs were more likely to receive support (line 7). Indeed, Baba et al. (2009)

43One possible explanation is that a triple-A rating reflected a sponsor’s motivation (conditional on financial strength)

to support an ailing fund, although the ratings organizations’ narrow focus on portfolio risks, particularly prior to 2008,

makes such an explanation seem dubious. As shown in section 6.3 below, triple-A ratings were not helpful in predicting

whether MMFs held distressed ABCP.

30
noted that bank-affiliated fund managers “were over-represented among support providers.” As

noted above, the link between banks and MMF support may reflect a greater propensity of deeppocketed sponsors to bail out troubled funds, conditional on similar exposures to distressed securities, or it may reveal a moral hazard problem for bank-affiliated portfolio managers. An additional consideration is that the banks may have been more likely to disclose financial support for

affiliated MMFs because banks face more rigorous regulatory oversight and disclosure requirements than some other financial services firms.

The link between sponsor CDS spreads and support is more puzzling: The estimated coefficient on early-August spreads (line 8) is negative and insignificant, while that on spreads registered over the preceding year is positive and highly significant. This pattern is exactly opposite

what we would expect if spreads were endogenously reflecting the likelihood of sponsor support.

But the role of sponsor risk is odd: Apparently, aside from a sponsor’s bank affiliation, riskier

sponsors were more likely to intervene later to support their funds.44

6.3 MMF risks and holdings of distressed ABCP

Analysis of MMFs’ holdings of distressed ABCP can help interpret links between risks and poor

outcomes for the funds. For example, a finding that bank-affiliated advisers were more likely to

have invested in problematic ABCP would be evidence in favor of a moral-hazard explanation for

the link between bank affiliation and sponsor support. The potential incompleteness of sponsorsupport records also makes portfolio exposures information valuable for the study of MMFs during this episode.

Using funds’ most recent SEC filings (see section 2) prior to the onset of the ABCP crisis, I

identified 124 MMFs that held ABCP that ultimately triggered sponsor support actions for at least

one MMF. The third column of table 8 provides some summary statistics on these funds, which

accounted for nearly half of the money funds in the sample—more than triple the number that

reportedly received sponsor support. Compared with the full sample (column 1), funds that held

distressed paper were a bit more likely to be institutional funds, were larger, had flows that were

more sensitive to yield, and were more likely to be affiliated with banks. Otherwise, funds that

held distressed paper were similar to other MMFs.

Finally, as shown in column 4 of the table, I combine the indicators of sponsor support and

exposure to distressed paper to capture MMFs that apparently held problematic securities that

were not identified using the portfolio records. This combination adds just four funds to the list

of those that held distressed securities.

To examine the relationship between exposures to distressed ABCP and the riskiness and

44If bank affiliation is dropped from the regression, coefficients on sponsor CDS are similar to but smaller in magnitude than those reported in column 2 (the coefficient on CDS spreads over the previous year remains significant).

31
other characteristics of MMFs, I redefine:

S

i ≡

(

1 if fund i held distressed ABCP

0 otherwise.

Using this definition of S

i

, I reestimate equation (2) separately for the two criteria for identifying funds that held distressed ABCP: portfolio disclosures alone and portfolio disclosures plus

sponsor support records. Results appear in columns 3 through 6 of table 9.

Again, as shown on line 1, portfolio risk—as measured by gross yield—was an important

predictor of MMF problems. Gross yield is statistically and economically significant in every

regression reported here; a one-standard-deviation increase in gross yield is predicted to raise the

likelihood that a fund held distressed paper by 15 to 18 basis points, depending on specification.

In contrast, fund ratings (line 2) were not helpful in predicting exposures to problematic ABCP.

The link between investor risk and holdings of distressed paper is less clear. One indicator

of lower investor risk (the expense ratio, line 3) and one of greater investor risk (flow sensitivity

to yield, line 6) are positively related to holdings of distressed ABCP in most of the specifications

listed in columns 3 through 6. Taken literally, this might imply that funds with less sophisticated,

hot-money investors were more likely to hold distressed securities. An institutional-fund dummy

(line 9) is positive and significant in a couple of specifications (only marginally so in one), but tests

of the null hypothesis that all estimated coefficients for institutional and retail funds are jointly

equal do not reject the null for any of the regressions shown on table 9.

As line 7 shows, MMFs with bank-affiliated sponsors were significantly more likely to

hold distressed ABCP than other funds. Depending on specification and sample, bank affiliation increased the probability that a fund held distressed paper by between 26 and 41 percentage

points. The strength of this result aids in interpreting the link between bank affiliation and sponsor

support—bank-affiliated funds evidently were more likely to receive support because they were

more likely to hold problematic ABCP—and points to a potential moral hazard problem for bankaffiliated MMF managers. Moral hazard is not the only possible explanation, but some others are

no more charitable. For example, it is possible that bank-affiliated managers were more likely to

purchase risky ABCP for their funds because they had more institutional familiarity than other

managers with complex instruments like paper issued by structured investment vehicles (SIVs).

The role of sponsor CDS spreads in predicting holdings of distressed paper again seems

puzzling. In the regression defining S

i based solely on portfolio disclosures (column 4), CDS

spreads are insignificant. When S

i ≡ 1 also for funds that received support (column 6), coefficients

on sponsors’ CDS spreads in the year before the crisis are positive and significant (line 9) but

spreads immediately beforehand (line 8) are insignificant. Controlling for bank affiliation, funds

with riskier sponsors were more likely to purchase problematic assets.45

45When bank affiliation is dropped from the regression, sponsor CDS is insignificant. As noted above, bank-affiliated

32
Finally, larger MMFs (line 10) were significantly more likely to hold distressed paper than

their smaller counterparts. A one standard deviation increase in fund assets boosted the predicted likelihood of such holdings by about 15 percentage points in the regressions summarized

in columns 3 through 6.

7 Conclusions and policy implications

This paper argues that MMFs are subject to three types of risk: (1) portfolio risks arising from

the credit, liquidity, and interest-rate risks posed by a fund’s assets; (2) investor risk due to the

composition of an MMF’s investors and the likelihood that they will suddenly and disruptively

redeem shares; and (3) sponsor risk that reflects the possibility that an MMF sponsor will not

provide financial support for an ailing fund. I describe proxies for each type of risk and provide

evidence that these measures were useful in explaining the cross-sectional variation in outcomes

for funds during crises, particularly the catastrophic run on MMFs in September and October 2008.

Indeed, one important finding of this paper is that the run on MMFs was not indiscriminate; ex

ante risk proxies explain a large portion of the substantial variance in outflows during the crisis.

Portfolio risk is the focus of much of the risk-limiting regulation that governs MMFs as

well as the ratings criteria applied to MMFs by ratings organizations. I show that portfolio risk,

as measured by gross yield, was a significant and economically important predictor of outflows

during the run in 2008. Moreover, the portfolio risks that motivated redemptions during the run

were broader than direct exposure to Lehman debt, which prompted larger outflows only early

in the run. Gross yield was also useful in predicting sponsor support for ailing funds during the

ABCP crisis in 2007 and for predicting holdings of distressed ABCP during that episode. In retrospect, these results are intuitive: Risky securities should pay higher rates that boost MMF portfolios’ gross yields and should—if yields reflect systematic risk—perform poorly during crises.

The ABCP crisis and the run on MMFs a year later provided a laboratory to test whether higher

yields in MMFs were, in fact, conveying information about risks that was observable well before

the crises. I find that higher yields did offer such a signal.

However, I find that another possible indicator of portfolio risk—whether a fund had a

triple-A rating—was of little use in predicting crisis outcomes, including outflows during the run

in 2008 or exposure to distressed paper during the ABCP crisis. This is perhaps surprising, as

ratings organizations’ publications suggest that a top rating should be useful as an indicator of an

MMF’s (low) risk, particularly as reflected in its portfolio quality.

The link between other forms of MMF risk and outcomes is more complex. Investors generally have fled to, not from, MMFs during episodes of financial turmoil, and sponsors have been

sponsors had lower CDS spreads than those of other sponsors just prior to the ABCP crisis. Because the bank-affiliated

funds were more likely to hold distressed ABCP, failing to control for bank affiliation misses the broader positive

correlation between sponsor CDS spreads in the year before the crisis and holdings of distressed assets.

33
seen as a source of stability rather than risk. Hence, investor and sponsor risks of MMFs have remained largely latent, even during crises. Not surprisingly, these risks historically have received

less attention from regulators, ratings organizations, and academics than have portfolio risks.

Investors’ muted response to the ABCP crisis illustrates the usual latency of investor and

sponsor risks. Souring paper put considerable strains on MMFs, and many would have broken the

buck without sponsor support. Nonetheless, investors’ net flows to MMFs as the crisis erupted

in August 2007 were not significantly related to sponsor and portfolio risks, and there was no

consistent pattern of association between outflows and investor risk.

In contrast, the severity of the financial crisis in September 2008 and Reserve’s inability to

absorb losses in its Primary Fund undermined confidence in sponsors’ ability to support MMFs

and brought sponsor and investor risks to the fore. Institutional investors reacted to sponsor risk

by redeeming shares more aggressively from MMFs with sponsors that had higher CDS spreads

on the eve of the run. Meanwhile, the consequences of investor risk were apparent in the aggregate

pattern of outflows during the run, as institutional investors redeemed shares at ten times the

pace of retail redemptions. Even among institutional funds, I find that proxies for investor risk,

including funds’ expense ratios, flow volatility, and flow sensitivity to yield, helped predict the

cross section of outflows.

The two crises also underline important interactions among the three types of MMF risks.

For example, the particularly strong links between risk proxies and outflows from institutional

funds during the run in 2008 show that funds with greater investor risks were also more sensitive

to portfolio and sponsor risks. In addition, the contrast between the responsiveness of institutional fund flows to all three types of MMF risks during the run in 2008 and the relative inertness

of funds’ flows during the ABCP crisis in 2007 indicates that heightened sponsor risk in 2008

intensified the consequences of other risks.

Although sponsor risk was not a significant factor in the cross-section of net flows during

the ABCP crisis, one proxy for sponsor risk—whether an MMF was affiliated with a bank—was

a significant predictor of poor outcomes during this episode. Bank-affiliated money funds were

more likely to receive sponsor support and to hold distressed ABCP in their portfolios. Somewhat

more puzzling is that, controlling for bank affiliation, riskier sponsors—those with higher pre-crisis

CDS spreads—were more likely to have experienced problems in their MMFs.

This paper provides some useful lessons both for policymakers and investors. The significance of MMF risks in explaining poor outcomes in past crises highlights the importance of monitoring these risks, and I offer some useful proxies for doing so. For example, shareholders and

regulators might track funds’ gross yields for early signs of problematic portfolio risks, particularly given asset managers’ incentives to boost yields. My indicators of investor risk may be useful

in the SEC’s ongoing efforts to reflect such risk in setting liquidity requirements for MMFs.46

46In adopting new liquidity requirements for MMFs, the SEC noted that institutional and retail MMFs have substan34
This paper’s findings also raise concerns about the systemic risks associated with sponsor

support actions for MMFs and the expectations of safety that these actions have fostered among

investors. Clearly, sponsor support of MMFs was critical in helping funds weather the ABCP crisis

in 2007 and the run in 2008. But the extensive record of sponsor support has probably attracted

many highly risk-averse investors who would not hold MMFs without the conviction that the

funds are effectively (privately) insured. Hence, sponsor support has likely increased investor

risk for MMFs. The fact that funds with bank sponsors were more likely to have held distressed

ABCP and to have received sponsor bailouts in the wake of the ABCP crisis also suggests that the

possibility of sponsor support may undermine incentives for prudent asset management.

Furthermore, during the run in 2008, concerns about the ability of sponsors to support

their MMFs evidently prompted heavier redemptions from money funds with weaker sponsors,

and thus transmitted the sponsors’ strains to off-balance-sheet MMFs and into short-term funding

markets. Thus, by fostering expectations of implicit recourse to sponsors, past support actions

had created a channel for the transmission during crises of strains between entities that should not

have been related. Whether or not such support was actually delivered, it may have contributed to

financial strains. Bailouts of MMFs during the run required scarce capital from sponsors at a time

when liquidity was in short supply and worsened some sponsors’ financial condition (Standard

& Poor’s, 2008a). But Reserve’s failure to provide support that investors had come to expect was

catastrophic for the Reserve franchise and destabilizing for the financial system. Moreover, despite

the apparent importance of sponsor support for MMFs, the practice is discretionary, unregulated,

and opaque, and it is probably most unreliable when systemic risks are most salient. Indeed,

other forms of discretionary financial support did not fare well during the financial turmoil, such

as dealers’ support for auction-rate securities (Han and Li, 2009).

Thus, my findings argue for additional attention to the systemic risks posed by the MMF

industry’s reliance on sponsor support. However, a full discussion of the strengths and weaknesses of the current “system” of discretionary sponsor support for MMFs is well beyond the

scope of this paper. Simplistic responses to the risks posed by sponsor support—such as a ban on

support actions—may do more harm than good. The SEC’s action in 2010 to require more extensive disclosure of sponsor interventions should at least improve researchers’ and policymakers’

understanding of the practice.

tially different investor risks and liquidity needs, but set a single requirement for both types of funds, in part because

of the ”difficulty in drawing bright lines” between them (see note 11). Still, the SEC indicated that it would continue

to look for a “workable objective definition that would accurately identify funds with lower liquidity needs” (U.S. Securities and Exchange Commission, 2010, pp. 58-61). Of course, proxies that are useful for measuring risk may have

drawbacks if used to determine regulatory distinctions: Although MMFs with lower expense ratios exhibited greater

investor risks during the run, this author would not suggest imposing more stringent liquidity standards for MMFs

because they charge lower fees!

35
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39
(1) (2)

MMF characteristic Mean1

Cross‐sectional

standard deviation2

1. Gross yield 3.57 0.12

2. Net yield 3.08 0.29

3. Expense ratio 0.49 0.27

Table 1. Gross yields, net yields, and expense ratios of prime MMFs, 2004‐2008

(percent)

Notes.  

1. Sample averages of unadjusted fund‐level annual data for all prime MMFs (1348 observations

over 5 years).

2. For each variable, the standard deviation is calculated after subtracting from each observation

the annual asset‐weighted average for that variable for all prime MMFs in the same year.
Table 2a. MMF characteristics and risk proxies before and during the run in 2008

(1) (2) (3) (4) (5) (6

Mean S.D. 10th pctl. 90th pctl. Mean S.D

Portfolio risk proxies

1. Gross yield1 3.88 0.14 3.69 4.03 3.88 0.

2. Triple‐A rating?2,3 0.53 0.50 0.00 1.00 0.20 0.3. Lehman exposure?3 0.12 0.33 0.00 1.00 0.12 0.

Investor risk proxies

4. Expense ratio1 0.29 0.18 0.13 0.51 0.63 0.5. Growth (log net flow), previous year1,4 13.31 37.18 ‐34.36 56.69 4.11 29.

6. Standard deviation of weekly flow1 5.13 3.18 1.78 8.98 2.76 3.

7. Weekly flow sensitivity to relative yield1 6.91 14.24 ‐5.54 25.68 1.51 5.

Sponsor risk proxies

8. Bank‐affiliated fund?3,5 0.53 0.50 0.00 1.00 0.48 0.9. Average CDS spread, Sept. 2‐9, 20086 1.40 0.81 0.84 2.18 1.39 0.

Other

10. Log of assets (in $ billions) as of Sept. 9, 2008 1.25 1.68 ‐1.07 3.38 0.41 1.

11. Has CDS spread data, Sept. 2‐9, 2008?3 0.43 0.50 0.00 1.00 0.43 0.

Memo: Log net flow, Sept. 9 ‐ Oct. 7, 20084 ‐25.81 31.13 ‐62.31 5.30 ‐4.98 14.

Institutional funds (N=116) Retai

Notes.1. Year from September 1, 2007 to August 31, 2008.

2. As of August 31, 2008.

3. Indicator variable: 1=yes and 0=no.

4. Log net flow is 100 times the natural logarithm of the sum of 1 and the ratio of net flow over this episode to assets at beginn

100*ln(1+flow/lagged assets).5. Bank‐affiliation data are as of September 14, 2008 (when Bank of America purchased Merrill Lynch).

6. CDS spreads are available only for 50 observations for institutional funds and for 58 observations for retail funds.
Table 2b. MMF characteristics and risk proxies before and during the ABCP crisis in 2007

(1) (2) (3) (4) (5) (6

Mean S.D. 10th pctl. 90th pctl. Mean S.D

Portfolio risk proxies

1. Gross yield1 5.37 0.02 5.34 5.39 5.37 0.

2. Triple‐A rating?2,3 0.53 0.50 0.00 1.00 0.18 0.3. Distressed ABCP exposure?3 0.57 0.50 0.00 1.00 0.44 0.

Investor risk proxies

4. Expense ratio1 0.29 0.17 0.13 0.51 0.65 0.5. Growth (log net flow), previous year1,4 18.80 36.98 ‐14.39 62.73 2.41 32.

6. Standard deviation of weekly flow1 5.16 3.18 2.04 9.37 2.54 2.

7. Weekly flow sensitivity to relative yield1 2.85 65.48 ‐56.38 72.36 ‐1.59 18.

Sponsor risk proxies

8. Bank‐affiliated fund?2,3 0.52 0.50 0.00 1.00 0.47 0.9. Average CDS spread, August 1‐7, 20075 0.41 0.25 0.20 0.83 0.35 0.

Other

10. Log of assets (in $ billions) as of August 7, 2007 1.22 1.57 ‐0.97 3.15 0.35 1.

11. Has CDS spread data, August 1‐7, 2007?3 0.53 0.50 0.00 1.00 0.47 0.

Memo: Log net flow, August 7‐28, 20074 ‐0.80 11.50 ‐14.41 10.32 0.46 10.

Institutional funds (N=116) Retai

Notes.1. Year from July 1, 2006 to June 30, 2007.2. As of June 30, 2007.

3. Indicator variable: 1=yes and 0=no.

4. Log net flow is 100 times the natural logarithm of the sum of 1 and the ratio of net flow over this episode to assets at beginn

100*ln(1+flow/lagged assets).

5. CDS spreads are available only for 61 observations for institutional funds and for 63 observations for retail funds.
A. Institutional

MMFs (N=116)

1. Gross yield 12. Triple‐A rating? 1

3. Lehman exposure? 14. Expense ratio ‐0.345 * 1

5. Growth, prev. year 16. SD weekly flow ‐0.338 * 0.223 ‐0.319 * 17. Flow sensitivity 0.199 ‐0.254 * 1

8. Bank‐affiliated? 1

9. CDS spread1 1

10. Log of assets 0.285 * 0.261 * ‐0.338 * 0.257 * 111. Has CDS spreads? 0.186 

12. Log flow (event) ‐0.311 * 0.424 * ‐0.368 * ‐0.310 ‐0.537 *

B. Retail

MMFs (N=135)

1. Gross yield 12. Triple‐A rating? ‐0.237 * 13. Lehman exposure? 0.188 14. Expense ratio 15. Growth, prev. year 16. SD weekly flow ‐0.247 * 0.377 * 1

7. Flow sensitivity 0.188 1

8. Bank‐affiliated? 0.185 0.204 19. CDS spread2 ‐0.272 1

10. Log of assets 0.196 0.188 111. Has CDS spreads? 0.272 * 

12. Log flow (event) ‐0.297 * 0.234 * ‐0.490 * ‐0.183 ‐0.237 * ‐0.172

Held

Lehman?

Expense

ratio

Log

assets

Table 3a. Correlations of MMF characteristics and risk proxies before and during the run in 2008

Gross

yield

Aaa

rated?

Held

Lehman?

Expense

ratio

Growth

prev. yr.

SD of

flow

Bank‐

affil.?

CDS

spread1

(1) (2) (3)

Flow

sens.

Gross

yield

Aaa

rated?

(7)

(9)

(8)

Growth

prev. yr.

SD of

flow

Notes. See table 2a for variable descriptions. Correlations shown are significant at the 5 percent level (* indicates signficance at 

percent level).

1. Based on 50 observations with CDS spreads.

2. Based on 58 observations with CDS spreads.

Flow

sens.

Bank‐

affil.?

CDS

spread2

Log

assets

(1) (2) (3) (4) (5) (6) (7) (8)

(10) (10)

(4) (5) (6) (9)
A. Institutional

MMFs (N=116)

1. Gross yield 12. Triple‐A rating? 1

3. ABCP exposure? 0.295 * 14. Expense ratio ‐0.323 * 1

5. Growth, prev. year ‐0.319 * 16. SD weekly flow ‐0.307 * 1

7. Flow sensitivity 0.255 * 0.290 * ‐0.291 * 18. Bank‐affiliated? ‐0.272 * 19. CDS spread1 ‐0.554 * 1

10. Log of assets 0.285 * 0.260 * 0.259 * ‐0.326 * 0.275 * ‐0.199 1

11. Has CDS spreads? 0.326 * 

12. Log flow (event) 0.213 ‐0.252 * ‐0.263 ‐0.238 *

B. Retail

MMFs (N=133)

1. Gross yield 12. Triple‐A rating? 1

3. ABCP exposure? 0.266 * 14. Expense ratio ‐0.188 15. Growth, prev. year ‐0.170 16. SD weekly flow ‐0.179 0.426 * ‐0.172 1

7. Flow sensitivity ‐0.227 * 18. Bank‐affiliated? 0.189 0.272 * 0.178 ‐0.369 * 19. CDS spread2 ‐0.302 1

10. Log of assets 0.292 * ‐0.179 0.257 * ‐0.236 * 1

11. Has CDS spreads? 0.289 * 0.184 0.381 * 12. Log flow (event) ‐0.172 ‐0.174

Table 3b. Correlations of MMF characteristics and risk proxies before and during the ABCP crisis in 2007

Gross

yield

Aaa

rated?

ABCP

problem?

Expense

ratio

Growth

prev. yr.

(1) (2) (3) (4) (5) (8) (9)

CDS

spread1

(7)

(2) (3) (4)

(6) (10) 

Bank‐

affil.?

SD of

flow

Flow

sens.

(1) (5) (9) (10) 

Flow

sens.

Bank‐

affil.?

CDS

spread2

Log

assets

Gross

yield

Aaa

rated?

ABCP

problem?

Expense

ratio

Growth

prev. yr.

Notes. See table 2b for variable descriptions. Correlations shown are significant at the 5 percent level (* indicates signficance at 

percent level).

1. Based on 61 observations with CDS spreads.

2. Based on 63 observations with CDS spreads.

Log

assets

(6) (7) (8)

SD of

flow
Model:       

(1) (2) (3) (4) (5) (6)

Investor type: Institutional * Retail Institutional * Retail

Portfolio risk proxies

    1. Gross yield (percentage points) ‐67.60 *** ‐4.05 ‐161.23 *** ‐5.25

        September 2007 ‐ August 2008 (‐4.40) (‐0.76) (‐4.69) (‐0.49)

    2. Triple‐A rating? 3.43 ‐3.26 6.73 ‐0.46

        (1=yes, 0=no) (0.77) (‐0.76) (0.93) (‐0.07)

    3. Lehman exposure? 3.50 ‐0.30 1.85 ‐0.92

        (1=yes, 0=no) (0.48) (‐0.17) (0.20) (‐0.20)

Investor risk proxies

    4. Expense ratio (percentage points) 39.30 ** 11.11 57.46 ** 15.08

        September 2007 ‐ August 2008 (3.48) (2.44) (3.58) (1.55)

    5. Growth (log net flow) 0.06 0.04 0.04 0.01

        September 2007 ‐ August 2008 (0.83) (0.86) (0.38) (0.08)

    6. Standard deviation of weekly flow ‐2.06 ‐1.95 ‐3.00 ‐2.14

        (percentage points) (‐2.84) (‐2.77) (‐1.67) (‐2.78)

    7. Weekly flow sensitivity to  ‐0.38 ‐0.28 ‐0.30 0.06

        relative net yield (‐2.34) (‐1.10) (‐1.00) (0.23)

Sponsor‐risk proxies

    8. Bank affiliated? 4.62 ‐3.58 12.91 ** ‐6.61

        (1=yes, 0=no) (1.00) (‐1.73) (1.92) (‐1.58)

    9. Average CDS spread (pctg. points) ‐15.85 ** ‐1.05

        September 2‐9, 2008 (‐2.84) (‐0.69)

Other

    10. Log of assets ‐8.12 *** ‐1.95 ‐8.29 ** ‐3.78

        September 9, 2008 (‐5.74) (‐2.99) (‐4.51) (‐5.13)

    11. Constant 242.97 *** 12.56 619.83 *** 19.52

(4.06) (0.62) (4.44) (0.45)

Number of observations 116 135 50 58

R‐squared 0.501 0.369 0.649 0.475

No CDS Spreads Includes CDS spread

Notes.  Dependent variable is log flow from September 9 to October 7, 2008.  Log flow is defined as 100*ln(1+flow/lagged

assets), where flow is equal to the change in assets less accrued yield.  t‐statistics in parentheses are based on robust

standard errors.

* t‐tests of the hypothesis that coefficients for institutional and retail funds are the same, based on a pooled institutional‐

retail fund regression with interactive coefficients (an indicator variable for institutional funds multiplied by each

coefficient).  */**/*** denotes significance at the 10/5/1 percent level.

Table 4.  Regression results:  MMF risk proxies and net flows in September and October 2008
Model:

(1) (2) (3) (4)

Investor type: Institutional Retail Institutional Retail

Portfolio risk proxies

    1. Gross yield (percentage points) ‐8.9 ‐0.6 ‐16.4 ‐0.8

        September 2007 ‐ August 2008

    2. Triple‐A rating?* 3.5 ‐3.2 7.0 ‐0.5

    3. Lehman exposure?* 3.6 ‐0.3 1.9 ‐0.9

Investor risk proxies

    4. Expense ratio (percentage points) 7.2 2.2 10.3 2.8

        September 2007 ‐ August 2008

    5. Growth (log net flow) 2.3 1.2 1.9 0.2

        September 2007 ‐ August 2008

    6. Standard deviation of weekly flow ‐6.3 ‐6.0 ‐8.1 ‐8.9

    7. Weekly flow sensitivity to  ‐5.2 ‐1.5 ‐4.2 0.3

        relative net yield

Sponsor‐risk proxies

    8. Bank affiliated?* 4.7 ‐3.5 13.8 ‐6.4

    9. Average CDS spread (pctg. points) ‐12.0 ‐1.0

        September 2‐9, 2008

Other

    10. Log of assets ‐12.8 ‐2.9 ‐12.9 ‐5.4

        September 9, 2008

Table 5.  Regression results:  Estimated effects of 1‐standard‐deviation changes in explanatory

                 variables on net flows during the run in 2008 (percentage points of flow)

No CDS Spreads Includes CDS spread

Notes.  Estimates based on regressions summarized in table 4.  For each continuous variable, estimated effect is computed by

multiplying the estimated coefficient on the variable by the standard deviation of the variable.  

*Estimated effect for each dummy variable is the estimated coefficient on the variable converted to percentage points of

assets.  
(1) (2)

Specification OLS 2SLS1

Portfolio risk proxies

    1. Gross yield (percentage points) ‐67.60 ‐54.74

        September 2007 ‐ August 2008 (‐4.40) (‐3.10)

    2. Triple‐A rating? 3.43 2.92

        (1=yes, 0=no) (0.77) (0.63)

    3. Lehman exposure? 3.50 0.52

        (1=yes, 0=no) (0.48) (0.07)

Investor risk proxies

    4. Expense ratio (percentage points) 39.30 38.80

        September 2007 ‐ August 2008 (3.48) (3.31)

    5. Growth (log net flow) 0.06 0.06

        September 2007 ‐ August 2008 (0.83) (0.84)

    6. Standard deviation of weekly flow ‐2.06 ‐1.63

        (percentage points) (‐2.84) (‐1.91)

    7. Weekly flow sensitivity to  ‐0.38 ‐0.39

        relative net yield (‐2.34) (‐2.11)

Sponsor‐risk proxies

    8. Bank affiliated? 4.62 4.20

        (1=yes, 0=no) (1.00) (0.91)

Other

    9. Log of assets ‐8.12 ‐8.51

        September 9, 2008 (‐5.74) (‐5.82)

    10. Constant 242.97 191.92

(4.06) (2.74)

Number of observations 116 112

R‐squared 0.501 0.500

Table 6.  MMF risk proxies, instruments for gross yield, and net flows to institutional

                 prime MMFs in September and October 2008

Notes.  Dependent variable is log flow from September 9 to October 7, 2008.  Log flow is defined as

100*ln(1+flow/lagged assets), where flow is equal to the change in assets less accrued yield.  t‐statistics in

parentheses are based on robust standard errors.

1. Instruments for gross yield are portfolio shares of various instruments (Treasury and agency securities,

repurchase agreements, time deposits, ABCP, floating rate notes, other domestic‐bank obligations, other foreign‐

bank obligations), WAM, and the share of assets maturing in seven days or less.  The instruments are listed on

lines 1‐7 and lines 9‐10 of table A1 in the appendix.
Model:

(1) (2) (3) (4) (5) (6)

Investor type: Institutional * Retail Institutional * Retail

Portfolio risk proxies

    1. Gross yield (percentage points) 7.42 44.19 69.76 ‐1.57

        August 2006 ‐ July 2007 (0.17) (1.13) (1.26) (‐0.07)

    2. Triple‐A rating? 3.79 1.44 2.21 0.80

        (1=yes, 0=no) (1.56) (0.78) (0.68) (0.31)

    3. Distressed ABCP exposure? ‐1.91 ‐3.62 ‐2.59 ‐3.59

        (1=yes, 0=no) (‐0.66) (‐1.11) (‐0.53) (‐2.23)

Investor risk proxies

    4. Expense ratio (percentage points) 10.46 ** ‐7.42 18.11 ** ‐8.00

        August 2006 ‐ July 2007 (1.82) (‐1.36) (1.50) (‐1.83)

    5. Growth (log net flow) ‐0.06 0.04 ‐0.06 0.05

        August 2006 ‐ July 2007 (‐1.39) (0.47) (‐0.90) (2.04)

    6. Standard deviation of weekly flow ‐0.03 ‐0.72 0.27 ‐0.86

        (percentage points) (‐0.09) (‐1.70) (0.42) (‐2.43)

    7. Weekly flow sensitivity to 0.03 ** ‐0.07 0.02 ** ‐0.10

        relative net yield (1.64) (‐1.66) (0.68) (‐2.47)

Sponsor‐risk proxies

    8. Bank affiliated? 0.88 ‐0.37 ‐1.77 ‐2.21

        (1=yes, 0=no) (0.44) (‐0.22) (‐0.53) (‐1.51)

    9. Average CDS spread (pctg. points) ‐14.46 0.87

        August 1‐7, 2007 (‐1.64) (0.22)

Other

    10. Log of assets ‐1.46 ‐0.35 0.08 ‐0.08

        August 7, 2007 (‐1.78) (‐0.67) (0.07) (‐0.17)

    11. Constant ‐41.95 ‐228.67 ‐374.47 18.84

(‐0.18) (‐1.11) (‐1.27) (0.16)

Number of observations 116 133 61 63

R‐squared 0.159 0.135 0.221 0.414

No CDS Spreads Includes CDS spread

Notes.  Dependent variable is log flow from August 7 to August 28, 2007.  Log flow is defined as 100*ln(1+flow/lagged

assets), where flow is equal to the change in assets less accrued yield.  t‐statistics in parentheses are based on robust

standard errors.

* t‐tests of the hypothesis that coefficients for institutional and retail funds are the same, based on a pooled institutional‐

retail fund regression with interactive coefficients (an indicator variable for institutional funds multiplied by each

coefficient).  */**/*** denotes significance at the 10/5/1 percent level.

Table 7.  Regression results:  MMF risk proxies and net flows in August 2007
(1) (2) (3) (4)

All prime

money

market

funds

Funds that

received sponsor

support for

distressed ABCP

Funds that

held

distressed

ABCP

Funds that either

received sponsor

support for or held

distressed ABCP

1. Number of funds 249 39 124 128

    (share of all MMFs, percent) (100) (16) (50) (51)

2. Number of institutional funds 116 20 66 68

    (share of line 1, percent) (47) (51) (53) (53)

3. Average assets under management 6.66 7.77 8.81 8.81

    (billions of dollars)

4. Average gross yield, year ending 5.37 5.38 5.38 5.38

    July 31, 2007 (percent)

5. Number of triple‐A rated funds 85 19 45 46

    (share of line 1, percent) (34) (49) (36) (36)

6. Average expense ratio, year ending 0.48 0.43 0.47 0.47

    July 31, 2007 (percent)

7. Average growth, year ending 10.56 24.09 13.67 14.92

    July 31, 2007 (percent)

8. Average standard deviation of log flow, 3.76 4.78 3.77 3.76

    year ending July 31, 2007 (percent)

9. Average flow sensitivity to net yield, 0.48 8.56 9.30 10.24

    year ending July 31, 2007

10. Number of bank‐affiliated funds 122 26 72 74

      (share of line 1, percent) (49) (67) (58) (58)

11. Number of funds with CDS spread data 124 33 75 79

      (share of line 1, percent) (50) (85) (60) (62)

12. Average CDS spread 0.38 0.40 0.37 0.38

      week of August 1‐7, 2007 (pctg. pts.)

13. Average CDS spread 0.15 0.18 0.15 0.16

      year ending July 31, 2007 (pctg. pts.)

Table 8.  Summary statistics for sponsor support and exposures to distressed ABCP

                 (as of July 31, 2007, unless noted)
Table 9.  Probit analysis of sponsor support and holdings of distressed securities

Criterion:

(1) (2) (3) (4) (5) (6)

Explanatory variables

No CDS

Spreads

Includes

CDS

No CDS

Spreads

Includes

CDS

No CDS

Spreads

Includes

CDS

Portfolio risk proxies

    1. Gross yield (percentage points) 17.44 40.65 14.68 17.04 16.24 24.47

        August 2006 ‐ July 2007 (4.34) (4.00) (4.46) (3.33) (4.70) (3.76)

    2. Triple‐A rating? 0.25 0.80 ‐0.02 0.19 ‐0.04 0.24

        (1=yes, 0=no) (1.05) (2.11) (‐0.08) (0.65) (‐0.20) (0.78)

Investor risk proxies

    3. Expense ratio (percentage points) ‐0.09 0.33 1.27 1.41 1.21 1.34

        August 2006 ‐ July 2007 (‐0.17) (0.39) (2.35) (1.70) (2.18) (1.54)

    4. Growth (log net flow) 0.00 0.00 0.00 0.00 0.00 0.01

        August 2006 ‐ July 2007 (1.29) (0.50) (‐0.65) (0.41) (‐0.06) (1.41)

    5. Standard deviation of weekly flow 0.08 0.07 0.04 0.05 0.05 0.04

        (percentage points) (2.29) (1.84) (1.26) (1.24) (1.35) (0.96)

    6. Weekly flow sensitivity to 0.00 0.00 0.01 0.00 0.01 0.01

        relative net yield (0.76) (‐0.21) (2.53) (1.26) (3.18) (2.16)

Sponsor‐risk proxies

    7. Bank affiliated? 0.57 1.33 0.67 0.85 0.71 1.14

        (1=yes, 0=no) (2.71) (3.61) (3.61) (2.67) (3.80) (3.15)

    8. Average CDS spread (pctg. points) ‐0.80 ‐0.34 ‐0.24

        August 1‐7, 2007 (‐0.90) (‐0.46) (‐0.31)

    9. Average CDS spread (pctg. points) 10.36 3.18 5.21

        August 2006 ‐ July 2007 (3.22) (1.48) (1.99)

Other

    10. Institutional fund? ‐0.38 ‐0.17 0.52 0.15 0.49 0.12

          (1=yes, 0=no) (‐1.23) (‐0.43) (2.01) (0.42) (1.86) (0.33)

    11. Log of assets 0.10 ‐0.02 0.25 0.28 0.25 0.28

          August 7, 2007 (1.24) (‐0.15) (3.96) (3.12) (3.91) (2.81)

    12. Constant ‐95.35 ‐221.94 ‐80.29 ‐93.31 ‐88.60 ‐133.64

(‐4.40) (‐4.04) (‐4.53) (‐3.37) (‐4.77) (‐3.80)

Number of observations 249 124 249 124 249 124

Pseudo R‐squared 0.161 0.325 0.192 0.216 0.215 0.301

Funds that

received

sponsor support for

distressed ABCP

Funds that either

received sponsor

support for or held

distressed ABCP

Funds that held

distressed ABCP

Notes.  Dependent variable for each regression is equal to 1 for money market funds that met the criterion indicated in the

column header and 0 for all others.  z‐statistics based on robust standard errors are in parentheses.
Figure 1

 0

 200

 400

 600

 800

1000

1200

1400

1600

1800

2000

2200

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

 0

 200

 400

 600

 800

1000

1200

1400

1600

1800

2000

2200

8/25

Billions of dollars

Prime

Government-only

Tax-exempt

A. Assets under Management in Money Market Funds by Investment Objective

 Weekly

Source: Investment Company Institute.

Lehman

bankruptcy

Start of

ABCP Crisis

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

-0.4

-0.3

-0.2

-0.1

 0.0

 0.1

 0.2

 0.3

 0.4

 0.5

 0.6

8/31

Billions of dollars Percent, annualized

Assets (left axis)

Relative net yield (right axis)

Relative gross yield (right axis)

0

10

20

30

40

50

60

B. Reserve Primary Fund: Assets and Relative Yields*

 Monthly

*Institutional share classes only.

Note: Relative net (gross) yield is net (gross) yield less asset-weighted average net (gross) yield for all institutional prime money market funds.

Source: iMoneyNet and author’s calculations.

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008

-0.4

-0.3

-0.2

-0.1

 0.0

 0.1

 0.2

 0.3

 0.4

 0.5

8/31

Percent Percent, annualized

Market share (left axis)

Relative net yield (right axis)

Relative gross yield (right axis)

0

1

2

3

4

5

C. Reserve Primary Fund: Market Share and Relative Yields*

 Monthly

*Institutional share classes only.

Note: Relative net (gross) yield is net (gross) yield less asset-weighted average net (gross) yield for all institutional prime money market funds.

Source: iMoneyNet and author’s calculations.
Figure 2

 200

 400

 600

 800

1000

1200

1400

1600

1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

 200

 400

 600

 800

1000

1200

1400

1600

8/4

A. Assets under Management in Prime Money Market Funds by Investor Type Billions of dollars

 Source: Investment Company Institute.

Weekly

Institutional

Retail

Lehman

bankruptcy

Start of

ABCP Crisis

-120

-100

 -80

 -60

 -40

 -20

 0

 20

 40

Billions of dollars

8/26 9/2 9/8 9/12 9/18 9/24 9/30 10/6 10/10

 Institutional

 Retail

 Total

B. Aggregate Daily Net Flows to Prime Money Market Funds

 Daily

Source: iMoneyNet and author’s adjustments (see section A.1 of the appendix).

Lehman

bankruptcy

Reserve breaks

the buck

Treasury Guarantee,

AMLF announced

CPFF

 0

10

20

30

40

50

60

70

Number of funds

 Institutional

 Retail

-50 -40 -30 -20 -10 0 10 20

C. Distribution of Net Flows to Prime Money Market Funds

 Net flow from September 9 to October 7, 2008

Net flow (percent of assets as of September 9)

Source: iMoneyNet. Excludes funds with less than $100 million in assets, funds less than one year old as of August 2008, and funds with

asset-reporting problems (see section A.1 of the appendix).
Figure 3a

Predicted Effects of Risk Proxies by Week

Institutional funds (no CDS spreads)

Note.This figure plots predicted effects based on seven weekly regressions described in section 5.3 of the text. The solid line in each panel plots---for each of the seven weeks

ending on the specified dates---the predicted effects on net flows of one-standard-deviation increases in the explanatory variable in the panel title. Predicted effects on net

flows are expressed as percentages of lagged assets. The dashed lines in each panel are the upper and lower bounds of quasi 95 percent confidence intervals for the predicted

effects. Confidence intervals are computed by using a two-standard-error confidence interval for each estimated coefficient. 

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent

Week ending

Upper bound

Predicted effect Weekly Lower bound

1. Gross yield

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent

Week ending

Upper bound

Predicted effect

Lower bound

Weekly

2. Triple-A rating?

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent 3. Lehman exposure

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent 4. Expense ratio

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent 5. Growth, previous year

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent 6. Standard deviation of weekly flow

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent

7. Weekly flow sensitivity

to relative net yield

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent 8. Bank affiliated?

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-12

 -9

 -6

 -3

 0

 3

 6

Percent 10. Log of assets
Figure 3b

Predicted Effects of Risk Proxies by Week

Institutional funds (including CDS spreads)

Note.This figure plots predicted effects based on seven weekly regressions described in section 5.3 of the text. The solid line in each panel plots---for each of the seven weeks

ending on the specified dates---the predicted effects on net flows of one-standard-deviation increases in the explanatory variable in the panel title. Predicted effects on net

flows are expressed as percentages of lagged assets. The dashed lines in each panel are the upper and lower bounds of quasi 95 percent confidence intervals for the predicted

effects. Confidence intervals are computed by using a two-standard-error confidence interval for each estimated coefficient. 

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent

Week ending

Upper bound

Predicted effect Weekly Lower bound

1. Gross yield

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent

Week ending

Lower bound

Predicted effect

Upper bound

Weekly

2. Triple-A rating?

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent 3. Lehman exposure

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent 4. Expense ratio

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent 5. Growth, previous year

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent 6. Standard deviation of weekly flow

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent

7. Weekly flow sensitivity

to relative net yield

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent 8. Bank affiliated?

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent 9. Average CDS spread, previous week

9/2 9/9 9/16 9/23 9/30 10/7 10/14

-15

-10

 -5

 0

 5

 10

Percent 10. Log of assets
Appendix

A.1 Adjustments to iMoneyNet data

I make several types of adjustments to the iMoneyNet data. First, some variables in the iMoneyNet

data set—notably MMF ratings and the bank-affiliation of MMF sponsors—represent only current

(not historical) information. iMoneyNet provided historical snapshots of these data to assist in my

compilation of real-time information.

Second, I drop several funds from the sample. For the analysis of each crisis episode, I

include only MMFs with at least 12 months of iMoneyNet data prior to the onset of the crisis and

at least $100 million in assets (the average prime MMF had $7 billion in assets on the eve of the

Lehman bankruptcy). The very small funds typically had flows that were highly volatile relative

to their assets and, given the economies of scale in the MMF industry (Domian and Reichenstein,

1998), probably served different roles for asset managers than funds of more typical size. For analysis of the run in 2008, I drop two extreme outliers with inflows exceeding 100 percent of assets

(no other fund in the sample grew more than 28 percent during the run), although dropping these

funds has no material effect on my results. I also drop five MMFs with flow measurement problems during the run (including funds that disappeared), but I include them where appropriate—in

some cases, after correcting assets data (see below)—in the analysis of weekly flows discussed in

section 5.3. All told, the 54 funds I exclude from the analysis of the ABCP crisis managed 1 percent

of prime MMF assets at the time, and the 50 funds dropped from the baseline analysis of the run

in 2008 accounted for 7 percent of assets on the eve of that crisis.

Third, I make corrections to assets (and net flows) reported for three MMFs in September

and October 2008: the Reserve Primary Fund, the Federated Prime Obligations Fund, and the Putnam Prime Money Market Fund. I use these corrected assets data only in the weekly regressions

reported in section 5.3 (the funds are excluded from the full-sample regressions). Weekly results

are similar whether data for these funds are included or not.

According to iMoneyNet data, the Reserve Primary Fund’s assets dropped from $62.6 billion on Friday, September 12, 2008 to $39.8 billion on Monday the 15th, $35.3 billion on the 16th,

and $7.1 billion on the 17th. However, Reserve announced in October 2008 that “[t]he Fund’s total

assets have been approximately $51 billion since the close of business on September 15” (The Reserve, 2008), apparently because the fund failed to honor redemption requests (U.S. Securities and

Exchange Commission, 2009b). To estimate the assets of individual Primary Fund share classes

(which included both institutional and retail classes) on September 15 and the following days, I

distribute the $11.6 billion decline in assets that Reserve reported for September 15 to different

share classes in proportion to their declines in assets that day as reported by iMoneyNet.

The Putnam Prime Money Market Fund was closed on September 17, and its $12.3 billion

in assets were absorbed by the Federated Prime Obligations Fund on September 24 (Federated

A-1
Investors and Putnam Investments, 2008). iMoneyNet reports only $8.4 billion in assets for the

Putnam fund as of September 17 (down from $12.3 billion the previous day) and a jump in the

Federated MMF’s assets from $21.8 billion on September 23 to $34.1 billion on September 24.

Hence, to track aggregate daily and weekly assets and net flows, I hold the Putnam fund’s assets at

$12.3 billion from September 16 to 23. The disappearance of the Putnam fund’s assets on September 24 offsets that day’s jump in assets for the Federated fund. However, I do not use net flows

data for the Federated Prime Obligations Fund after September 23 in my cross-sectional analyses

because the merger appears to have affected net flows to that fund even after the merger. Although the Federated MMF’s assets had declined only 3.1 percent from September 19 (when the

Treasury Guarantee and the AMLF were announced) to September 23, assets fell 23.5 percent in

the two days following the merger—perhaps as former Putnam investors redeemed shares after

losing liquidity for a week.

Fourth, the narrow sample periods that I use in the weekly regressions discussed in section

5.3 allow some modifications to the sample employed for the full-period regressions. Several

MMFs, including the three discussed above, that are dropped from the full-period regressions

because of measurement problems can be included for some of the weekly regressions. This adds

four funds to the institutional sample for the first three weeks that I study (they begin to drop

out after that) and one additional fund to the retail sample. The institutional and retail samples

that include CDS spreads are unchanged. Inclusion or exclusion of the additional funds makes no

material difference in the weekly regression results.

In addition, each weekly regression includes CDS spreads measured over the previous

week, to capture the effects of any deterioration in sponsors’ financial condition. Finally, two

large MMF sponsors (Morgan Stanley and Goldman Sachs) became bank holding companies on

September 21; in the weekly regressions, the bank-affiliation dummy is set as of the beginning of

each week.

A.2 Gross yield and portfolio risk

To explore the link between gross yield and portfolio risk in greater detail, I run a panel regression

of each prime MMF’s annual gross yield in each year from 2004 to 2008 on a number of its portfolio

characteristics measured in the same year. The first two columns of table A1 provide summary

statistics for the explanatory variables in the regression. Lines 1 through 8 list portfolio shares held

in different types of assets, including relatively safe assets, such as Treasury and agency securities

(line 1) and repurchase agreements (line 2), and some relatively riskier ones, such as ABCP (line

4), floating-rate notes (line 5), and other domestic and foreign bank obligations (lines 6 and 7).47

Portfolio weighted average maturity (line 9), which averaged 41 days in this period, and the share

47Other domestic and foreign bank obligations are bank liabilities other than time deposits and CP. These obligations

are mostly jumbo CDs.

A-2
of assets maturing in seven days or less (line 10) are indicators of portfolio maturity and liquidity.

I also include in the regression the natural log of assets (line 11), in part because other researchers

have found that MMF yields vary with size (see, for example, Domian and Reichenstein, 1998,

and Jank and Wedow, 2008). Finally, the regression includes an indicator of whether a fund was

institutional (line 12). I estimate a simple panel regression of the form:

r

i

t = ΓX

i

t + constant + ε

i

t

. (A-1)

Each variable in the regression is expressed net of its asset-weighted average among all

prime MMFs in the same year. For example, r

i

t

is fund i’s “relative” gross yield—that is, its gross

yield in year t less the asset-weighted average gross yield of all other prime funds in year t.

48 Xi

t

is a vector of the fund characteristics listed on lines 1 through 12 of table A1 (other CP, line 8, is

omitted from the regression).

The third column of the table shows estimation results. As indicated on lines 1 and 2, larger

shares of Treasury and agency securities and repurchase agreements depressed gross yields, on

average.49 In contrast, larger shares of fund assets held in riskier asset classes, including ABCP,

floating-rate notes, and other bank obligations (lines 4 through 7), were associated with greater

relative gross yields. For example, a 1-standard-deviation (16.86 percentage point) increase in the

share of an MMF’s portfolio held in floating-rate notes would be associated with a 3.3 basis point

(16.86 percentage points ×

0.0020 percent

percentage point) increase in gross yield—a substantial predicted effect,

given the small variation in gross yields among MMFs during this period (see table 1). Longer

WAM also increased gross yields (line 9), but other fund characteristics had no significant effect on

gross yields over this period. (The significance of the portfolio characteristics in explaining gross

yields motivates my use of the attributes listed on lines 1-7 and 9-10 of table A1 as instruments for

gross yield. See section 5.2 of the text.)

One potential shortcoming of gross yield as a measure of portfolio risk is that it may not

capture concentration risks among a fund’s assets, although the nature of such risks in MMFs

suggests that funds with greater concentration risks typically also would have higher gross yields.

SEC rules generally prohibit an MMF from holding more than five percent of its assets in the

securities of any single non-government issuer, but the private debt instruments held by prime

MMFs generally have been heavily concentrated in financial-sector issues. Thus, a fund’s holdings

of private debt may be a crude measure of its concentration risk.50 Since private debt holdings

48A panel regression with time (year) fixed effects yields similar results but amounts to computing relative measures

using unweighted means.

49The regression coefficient on the share of a particular asset category represents the estimated effect on gross yield

of raising the share of assets in that category by 1 percentage point while reducing by the same amount the share held

in the category excluded from the regression (other CP).

50Consider, for example, the asset categories listed in table A1. Private debt instruments (lines 3 through 8) made

up an average of 81 percent of MMF portfolios over the five-year span covered by this table. Of that, at least one-third

(time deposits, ABCP, and other domestic and foreign bank obligations) was financial-sector exposures. In addition,

A-3
boost MMFs’ gross yields, these yields likely would vary positively with concentration risks.

A.3 Gross yield and net flow to money market funds

To examine the relationship between gross yield and net flows to MMFs, I use monthly data

from iMoneyNet for a sample period extending from January 1997 to August 2008 (just before the

Lehman bankruptcy, Reserve’s capital loss, and the run on MMFs). For each MMF in each month,

I decompose gross yield into three components: (1) The average effective federal funds rate (FFR)

prevailing in that month; (2) the MMF’s category-average gross yield, that is, the asset-weighted

mean gross yield for all MMFs of the same category in that month, less the average effective FFR;

and (3) the MMF’s relative gross yield, which is defined as its gross yield less its category-average

gross yield. (The analysis covers just two categories of money funds: institutional and retail prime

MMFs.) I estimate an empirical model of net new cash flow to each fund as a function of these

three components of gross yield and several other controls:

f

i

t = πr

i

t−1 + γc

i

t−1 + φFFRt−1 + βExpRatiot−1 +

5

∑

s=1

θsf

i

t−s + ΓX

i

t + ε

i

t

. (A-2)

The unit of observation for this regression is the MMF-month, and each fund-level observation

combines data for all of the share classes of the fund.51 The dependent variable in the regression,

f

i

t

, is fund i’s log net flow in month t.

52

The model includes the three components of gross yield, lagged one month: relative gross

yield, r

i

t−1

, category-average gross yield, c

i

t−1

, and the effective federal funds rate, FFRt−1. Also

included are the expense ratio and four lags of net flow. The vector X

i

t

is a set of additional

controls, including the logarithm of the MMF’s assets (lagged 5 months); the fund’s 12b-1 fee, if

any; the fee waiver, if any; a dummy for whether the fund was waiving fees; fund fixed effects;

calendar-year time fixed effects; and calendar-month seasonal fixed effects.53

I estimated equation (A-2) separately for institutional and retail MMFs. Results for institutional funds are shown in the first column of table A2. Lines 1 through 3 show estimated

coefficients for the three components of gross yields: relative gross yields, category-average gross

yields, and the effective FFR. Most relevant to a cross-sectional analysis of MMF risks is the highly

significant estimated effect of relative gross yield, line 1, on net flow. This effect is also economically important. Based on the estimated coefficient on relative gross yield of 7.29, a one-standarddeviation increase in gross yield (8.3 basis points over this sample period) was associated with an

most “other” (unsecured) CP, which accounted for one-third of MMF assets on average, was likely financial-sector

issuance, since the share of financial CP in total unsecured CP averaged 82 percent over this period.

51See in note 4 in the text.

52That is, fi

t = 100 × ln(1 + net flow

lagged assets ). See note 21 in the text.

53Christoffersen (2001) found that fee waivers played an important role in attracting assets to retail money funds, in

particular. Farinella and Koch (2000) documented seasonal patterns in MMF net flows.

A-4
0.6 percent increase in net flow per month.54 An institutional fund that maintained a gross yield

that was one standard deviation above its category average for a year would have expected to attract net flows that were 6 percentage points (of assets) larger than the flows received by a typical

competitor.55

The regression results shown in table A2 underscore the incentives that MMF advisers had

to boost gross yields, whether they passed the increase on to shareholders (by keeping expense

ratios fixed and allowing net yield to rise) or not. Indeed, the results provide some marginally

significant evidence that a fund that raised gross yield and expenses by equal amounts—and thus

boosted adviser revenue but left net yield unchanged—attracted larger subsequent inflows. Point

estimates of coefficients on relative gross yield (line 1) are larger in magnitude than those for expense ratios (line 4), and line 13 of the table reports significance levels for F tests of the hypothesis

that π + β = 0, which is rejected at the 10 percent level.

Results for retail funds are shown in column 3. For such funds, the advantages of a relatively high gross yield are statistically significant, but less compelling than those for institutional

funds. The difference between retail and institutional funds’ sensitivity to relative gross yields is

not only statistically significant, as indicated by column 2,56 but also economically important. A

retail fund that maintained a one-standard-deviation gross-yield spread over its category average

for a year would, all else equal, have expected to attract only 2 percentage points more flow than

its average competitor. This result suggests that managers of retail funds faced weaker incentives

to take portfolio risks than managers of institutional funds.

Retail and institutional investors responded differently to category-average gross yields,

too. Larger category-average gross yields tended to draw assets to all retail funds; indeed, retail

investors appear to have been about as sensitive to average MMF yield as they were to the relative yields earned by individual funds. In contrast, institutional investors responded to relative

gross yield but not to category-average gross yield, suggesting that these investors tended to purchase shares in the highest yielding funds, regardless of the average yield spread (relative to the

FFR) achieved by the MMF industry. One interpretation of these results is that for institutional

investors, MMFs served as substitutes for direct holdings of money market instruments, which

paid yields that moved closely with average MMF yields. Thus, only relative yields differentiated

MMFs from these substitutes. For retail investors, MMFs competed with bank deposits (Investment Company Institute, 2010, pp. 34-35), which paid rates that did not move closely with funds’

average yields, so category-average yields attracted inflows to retail funds.

54That is, 0.6 percent = 100(e(0.00083×7.29/100) − 1).

55The boost to annual flow from a higher gross yield is somewhat less than the annualized one-month impact because of the negative estimated coefficients on lagged net flow.

56Significance levels reported in this column are those for interactive coefficients from a combined regression of

institutional and retail MMFs (not shown) in which an institutional fund dummy is interacted with each explanatory

variable listed on table A2.

A-5
Table A1. Gross yields of prime MMFs and selected fund characteristics, 2004‐2008 (annual data)

(1) (2) (3)

MMF characteristic

(units are percent, unless indicated) Mean1

Cross‐sectional

standard deviation2

Gross‐yield regression:

Est. coeff. and t‐statistic3

Portfolio share held in:

       1. Treasury and agency securities 8.06 12.49 ‐0.0019

(‐7.37)

       2. Repurchase agreements 10.64 11.73 ‐0.0008

(‐2.66)

       3. Time deposits 2.16 4.03 0.0000

(‐0.01)

       4. ABCP 12.23 13.51 0.0011

(4.76)

       5. Floating‐rate notes 20.05 16.86 0.0020

(10.03)

       6. Other domestic‐bank obligations 7.10 8.79 0.0027

(7.85)

       7. Other foreign‐bank obligations 6.17 9.82 0.0012

(3.76)

       8. Other CP (omitted from regression) 33.60 21.83

9. Portfolio weighted average maturity (days) 41.29 9.83 0.0015

(4.52)

10. Share of assets maturing in seven days 29.97 14.53 ‐0.0002

(‐0.72)

11. Log of assets (in billions of dollars) 0.40 1.59 ‐0.0035

(‐1.75)

12. Instititional‐fund dummy 0.47 0.50 0.0032

(0.54)

13. Constant ‐0.0005

(‐0.10)

Number of observations 1348

Adjusted R‐squared 0.252

Notes.  

1. Sample averages of unadjusted fund‐level annual data for all prime MMFs.

2. For each variable, the standard deviation is calculated after subtracting from each observation the annual asset‐weighted

average for that variable for all prime MMFs in the same year.

3. Dependent variable is each MMFʹs annual gross yield (in percent) in a given year, less the asset‐weighted mean gross yield

for all prime MMFs in the same year.  Unit of observation is the fund‐year.  All explanatory variables are expressed in terms

of deviation from annual asset‐weighted averages for all prime MMFs in the same year.  t‐statistics in parentheses are based

on robust standard errors.
Table A2. Net Flows to Prime Money Market Funds and Gross Yields in Previous Month

(1) (2) (3)

Institutional

funds

Difference significant?

(p‐value)*

Retail

funds

1. Relative gross yield (π ) 7.29 0.000 1.85

    in previous month (5.73) (3.23)

2. Category‐average gross yield ‐0.93 0.015 1.72

    in previous month (‐0.95) (3.68)

3. Effective FFR 0.59 0.884 0.62

    in previous month (3.34) (6.37)

4. Expense ratio (β ) ‐3.27 0.970 ‐3.34

    in previous month (‐1.68) (‐4.58)

5. 1st lag of log flow ‐0.09 0.546 ‐0.08

(‐8.23) (‐8.58)

6. 2nd lag of log flow ‐0.04 0.044 ‐0.01

(‐5.15) (‐1.38)

7. 3rd lag of log flow 0.01 0.497 0.00

(1.09) (0.00)

8. 4th lag of log flow ‐0.02 0.133 ‐0.01

(‐4.08) (‐1.48)

9. 5th lag of log assets ‐2.33 0.002 ‐1.56

(‐11.35) (‐10.98)

10. 12b‐1 fee in previous month ‐1.32 0.134 2.63

(‐0.54) (2.67)

11. Fee waiver 1.29 0.628 0.38

      in previous month (0.73) (0.59)

12. Any fee waiver in previous month? ‐0.38 0.791 ‐0.28

      (1=yes, 0=no) (‐1.11) (‐1.53)

13. p‐ value for test that π +β =0 0.071 0.100

Number of observations 15704 22840

Adj. R‐squared 0.066 0.073

Notes.  Dependent variable is monthly log flow to prime MMFs from January 1997 to August 2008.  Monthly

log flow is defined as 100*ln(1+flow/lagged assets).  Flow is equal to the change in assets less accrued yield.  

t‐statistics in parentheses are based on robust standard errors.  Model also includes fund fixed effects, calendar‐

year time fixed effects, and calendar‐month seasonal fixed effects.

*p‐values based on a pooled institutional‐retail fund regression with interactive coefficients (an indicator

variable for institutional funds multiplied by each coefficient).