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Mutual Fund Holdings of Credit Default Swaps:

Liquidity Management and Risk Taking1

Wei Jiang2

Columbia Business School

Zhongyan Zhu3

CUHK Business School, The Chinese University of Hong Kong

First draft: October, 2014

This draft: April, 2015

1 We thank Martin Oehmke for valuable comments and suggestions.

2 Wei Jiang can be reached at wj2006@columbia.edu.

3 Corresponding author. Zhongyan Zhu can be reached at zhongyan@cuhk.edu.hk.

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Mutual Fund Holdings of Credit Default Swaps:

Liquidity Management and Risk Taking

ABSTRACT

Using a comprehensive dataset of mutual funds’ quarterly holdings of credit default swap

(CDS) contracts in 2007-2009, we analyze the motivation for and consequences of mutual

funds’ participation in the CDS market, especially during a financial crisis. Consistent with

Oehmke and Zawadowski (2014), funds resort to CDS (especially selling) when they face

unpredictable liquidity needs and when the CDS securities are themselves are liquid relative

to the underlying bonds. Smaller funds followed leading funds in initiating the selling or

speculative buying of CDS contracts on new reference entities, consistent with the “tail risk

taking” in Rajan (2006). Moreover, the reference entities that attracted the most selling

interest from the large mutual funds are disproportionately firms that were perceived to be

“too large to fail” or “too systemic to fail,” thus indicating that the use of CDS by mutual

funds heightened the correlation in financial health among the large and systemically

important financial institutions.

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By 2007, the CDS market had grown to over $60 trillion in total notional value, and more than 60 percent

of all fixed income mutual funds in the U.S. had some CDS positions (see Jarrow (2011) for a review of

the market). Despite the popularity of credit default swaps as a synthetic security to gain or hedge credit

exposure and the numerous research studies examining the effect of CDS contracts on the issuers’

borrowing costs, there has been little empirical research on how and why individual funds initiate and

hold CDS contracts on individual reference entities. Due to data availability, most empirical research

based on mutual fund holdings has focused on their long positions in equity and bonds. A comprehensive

study on their use of CDS contracts provides us a more complete understanding of mutual funds’

investment strategies, and more importantly, their impact on the functioning and stability of the financial

markets.

Since the financial crisis in the late 2000s, there have been renewed concerns over whether the

asset management industry could be a destabilizing force in markets. In January 2014, the Financial

Stability Board (FSB)—an international organization aimed at preventing financial crises—proposed that

some large fund managers might need to be designated “systematically important financial institutions”

(SIFIs), which would require them to be subject to heavier regulation. In the meanwhile, the SEC is also

preparing rules to request more portfolio data from large asset managers and to conduct stress tests. The

rules that have been discussed thus far are similar to the post-crisis requirements put in place for big

banks and other large financial institutions that regulators believe could pose a risk to the financial system

and broader economy if they were to collapse.

In the context of the asset management industry, the worry is mainly two-fold. First, if there is a

herding pattern in risk-taking among fund managers, this behavior might lead to a general sell-off, such as

that which occurred with mortgage securities in 2008. This concern is amplified by the fact that the fund

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management industry is becoming more and more concentrated.4 Second, although mostly unlevered,

market participants like mutual funds may become the locus of potential financial instability because they

are largely motivated by relative performance (Feroli, Kashyap, Schoenholtz, and Shin, 2014). The

potential consequences of mutual funds’ increasing use of derivatives, especially CDS contracts, are at the

center of this debate because of their unique features that allow fund managers to take “hidden tail risk”

and to mimic the behavior of leading funds, both of which were succinctly and presciently summarized in

Rajan (2006).

Credit default swaps, like many other derivative contracts, allow funds to gain leveraged returns

compared to transactions that occur directly in the underlying securities. Funds may utilize CDSs to seek

incremental returns by buying or selling credit protection on a the bond of a reference entity, which will

often require less capital to create the same return profile than buying or selling a comparable bond of the

same issuer. The incremental returns from selling CDS come at the cost of a “hidden tail risk” that is

similar to selling disaster insurance and that is usually not captured in the benchmark. CDS selling allows

fixed income funds to have the appearance of producing high alphas (returns adjusted by the conventional

notions of risk). Since the true performance can only be assessed over a period that is much longer than

the typical horizon set for the average fund manager or implied by the latter’s expected tenure and

incentive schemes, managers will have an incentive to take such risk. Every once in a while, such tail risk

manifests itself in disastrous returns, which often coincide with a systemic meltdown.

Such hidden tail risk could “spread” given the relative performance-based incentives in the

mutual fund industry, which are explicit in most incentive contracts for portfolio managers and implicit in

the strong and convex flow-to-performance responses (Chevalier and Ellison, 1997). After some

entrepreneurial mutual funds venture into the relatively new CDS world and produced superior returns

during the “good time,” the relative performance evaluation created the incentive for other funds to herd

4 According to Morningstar (a leading investment research firm), at the end of 2012, the top five mutual fund

complexes managed 48 percent of total assets of equity funds and 53 percent of fixed income funds. The same

numbers for the top 25 mutual fund complexes are both around 75 percent.

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into the same market or even to the same reference entities. Such herding into the same tail risk space

could create amplified and wide-spread losses for many mutual funds at the same time, destabilizing the

financial system due to both “runs” (or run-like behavior) on the funds and price pressure on the securities

market. We observe episodes of such nature in the “breaking the buck” period in 2008 (when a lot of the

money-market mutual funds in the U.S. came close to having their net asset value break the $1 par, while

investors had considered money market funds to be virtually riskless). We note that Chen, Goldstein, and

Jiang (2010) documented a milder version of “runs” on the funds.

Analyzing a comprehensive dataset of CDS quarterly holdings by all U.S. fixed income mutual

funds from 2007 through 2009, we find evidence supporting both hypotheses. First, mutual funds as a

whole were net sellers of CDS, where the total selling notional amount during the sample period ($141

billion) exceeded the buying notional amount by 41 percent. Nevertheless, the sell-buy skew was

concentrated at the top— Pacific Investment Management Company, LLC (PIMCO), the largest fixed-

income mutual fund complex, sold 76 percent more CDS contracts than it purchased, while the smallest

70% of mutual fund families were net buyers of CDS. This contrast indicates that tail risks tended to be

concentrated among the largest funds which are also the most important to the stability of the financial

markets. Second, we document a strong lead-follow pattern in CDS trading between PIMCO and the

smaller funds. In particular, the probability that a smaller mutual fund initiated selling CDS on a new

reference entity doubles after PIMCO disclosed a large selling position in the same reference entity in the

previous period. A similar effect also exists in CDS buying, but the lead-follow connection is noticeably

weaker. The tendency to herd seems to be stronger in taking on more risk rather than in buying

protection.

What potentially further aggravates the tail risk and herding effects is our finding that mutual

funds systematically bet on institutions that were perceived as “too big to tail” or “too systematic to fail.”

That is, mutual funds, and especially the large funds, demonstrated a strong preference for the sale, but

not the purchase, of CDS positions in large reference entities. Among the large reference entities, the

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effect was most notable in the “systematically important financial institutions.” Our finding thus suggests

that mutual funds contributed to the fragility of the financial system by their heightened correlation with

the financial health of the SIFIs due to their CDS positions. Stultz (2010) attributed the bail-out of

financial institutions forced upon the taxpayers to a “web of linkage across financial institutions” with

derivatives, especially credit default swaps. Our study also provides a concrete picture of such a web

involving mutual funds.

Indeed, thanks to the government bailout, the mutual funds in our sample ironically came out of

the financial crisis mostly unscathed despite a period during which their CDS selling positions incurred

colossal losses on paper. A safe landing in a time of rare opportunity when the tail risk was supposed to

take its toll and to exert discipline no doubt makes it even more difficult to prevent asset managers from

adopting the same strategy that generates benefits to the funds/managers but exerts negative externalities

to the financial system. Our study thus offers a justification for the largest asset managers to be

designated as SIFIs despite that they do not take risk directly with their own capital, the most commonly

used defense by parties who oppose such a proposal.5

Our study also provides strong empirical support for the theoretical research on the difference

between trading in CDS and in the underlying bonds (Oehmke and Zawadowski (2013a)). We find that

mutual funds with more volatile fund flows, and hence more frequent trading needs in the fixed income

market, are more likely to substitute long positions in underlying bonds with short positions in CDS to

take advantage of the relatively higher trading liquidity. The economic magnitude of this relation is

sizable, a one-standard deviation increase in the volatility of funding flows raises the propensity in CDS

selling by about 40%. Moreover, higher CDS trading liquidity predicts more CDS usage, and

significantly more so for CDS selling than for buying. a one-standard deviation increase in the proxy for

CDS liquidity doubles the likelihood of CDS selling at the fund-issuer-period level, but only increase that

of CDS buying by 10%. On the other hand, higher bond trading liquidity predicts more CDS buying but

5 See, for example, “Fund managers: Assets or liabilities,” The Economist, August 2, 2014.

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not selling. The asymmetry reflects the distinct motives for CDS selling vs. buying. While CDS selling

substitutes bond buying (hence CDS, rather than bond, liquidity is important), CDS buying complements

bond buying due to the predominant hedging motive (hence both CDS and bond liquidity explains the

long positions).

Empirical research on institutional investors’ CDS investments is relatively scant (see a recent

survey by Augustin, Subrahmanyam, Tang, and Wang, 2014). The only other paper in finance that

analyzes the use of credit default swaps is Adam and Guettler (2012). While the data and sample have

some overlap, the two papers ask quite different research questions. Adam and Guettler (2012) focus on

how mutual funds use CDS to increase fund risk in order to game the convex incentive implied by the

tournament, and their analysis is mostly based on CDS holdings aggregated at the fund level. Our study

instead explores how mutual funds choose to buy and sell CDS contracts on different reference entities,

how their investment strategy fits the theoretical predictions regarding both liquidity management and

risk taking, and how the funds’ individual risk taking interacts with system-wide risk.

I. Institutional Background and Sample Overview

A. Institutional background

In our study, a “mutual fund” is an investment company registered under the 1940 Investment

Company Act and could be either an open-end or a closed-end fund. CDS are now commonly held by

fixed-income mutual funds despite the fact that derivatives traditionally did not make up a significant

portion of fund holdings (Koski and Pontiff, 1999). A mutual fund may buy CDS, in which case it seeks

protection by paying a yearly premium until a pre-defined credit event occurs or until the contract expires;

or it may sell CDS, in which case it receives the premium but assumes the loss in case of insolvency.

When a mutual fund sells CDS, it receives credit exposure to the reference entity without holding the

underlying bonds—that is, it creates a synthetic bond that enhances the yields on the bond that the CDS

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protects. When a mutual fund buys CDS, it may do so to offset some of the credit risk it takes in its long

bond positions, or to bet on its pessimistic view about the financial health of the reference entity.

There is no legal restraint specifically targeting CDS holdings by mutual funds. Under the

Investment Company Act of 1940, the general restrictions that could potentially apply on CDS positions

come from four sources. First, CDS positions usually count toward the limit on total illiquid investments

made by a fund (no more than 15% of all investments). Second, the embedded leverage in CDS contracts

subject them to the aggregate limit on a fund’s actual and implied leverage (up to 300% of asset

coverage). Third, the diversification requirement prohibits concentrated single counterparty exposure

(below 5% of total assets). And finally, the full commitment requirement states that the notional amount

of total derivatives may not exceed 100% of the total value of the fund. Given that the market value of

CDS contracts are at or close to zero at initiation and are a small percentage of the notional amount in all

but the most extreme cases, these restrictions are quite modest and were never binding in our sample

period (and are very unlikely to be binding in general).

B. Data collection

Figure 1 shows the structure of the data collected from the applicable security filings. Mutual

funds are first organized in “families” containing a group of funds that are sponsored by the same

investment management firm, such as PIMCO. Mutual funds are required to file Form N-Q semiannually

with the SEC to disclose their complete portfolio holdings. In addition, the mandatory filings of annual

and semi-annual reports, Form N-CSR, to shareholders also contain the funds’ securities holdings. The

two types of filings span all four quarters in a year. Both forms are filed at one level below, or at the

“series trust” or “shared trust” level, such as “PIMCO Funds.” A series trust is a legal entity consisting of

a complex of independently managed funds that have the same sponsor, that share distribution and

branding efforts, and that often have unitary (or overlapped) boards. An N-Q or N-CSR filing contains

detailed portfolio information (usually recorded at the quarter end) for each fund which represents a

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distinct portfolio. CDS positions are disclosed in these original forms but are not included in most

processed commercial databases such the Thomson Reuters Ownership database, and are thus available

only via manual collection.

Our sample construction starts with a search of all N-Q and N-CSR filings on the SEC EDGAR

servers for portfolio with period-end dates in 2007 and 2009. The three-year period was chosen to

capture the effect of the financial crisis. For each filing, we identify CDS positions using the following

searching keywords: “Credit Default”, “Default Swap”, “CDS”, “Default Contract” and “Default

Protection” following Adam and Guettler (2012). Given the purpose of our research, it is reasonable to

exclude “accidental” CDS users. Hence we apply a filter that requires a fund (portfolio) to have at least

200 CDS positions or have a total notional amount of $400 million in order to be included in our sample.

Such a filtered search results in 66,495 CDS holding positions on single name entities in 284 funds in 60

trust series (filers) affiliated with 33 fund families. From the portfolio disclosure we are able to record, for

each CDS position, the reference entity, the counterparty, the notional amount, and whether the position

was a buy or a sell. We also retrieve fund-level information, such as the total net assets (TNA) from the

same source. Using the CUSIP as well as the names of the funds and their affiliated families, we are able

to obtain (or construct) more fund-level variables such as the open-/closed-end status, returns, and fund

flows from CRSP and Morningstar.

[Insert Figure 1 here.]

While our key data source is similar to that used in Guettler and Adam (2012), the samples in the

two studies are constructed in quite different ways. Instead of aggregating CDS holdings for each fund in

each period as in Guettler and Adam (2012), we focus on individual CDS positions and collect more

detailed information about the individual holdings (notional amount, buy/sell, etc.) as well as the

reference entities (size of the firm and its CDS spreads, etc.) Moreover, our sample contains all mutual

funds that are regularly hold CDS position rather than just focus on the top fixed income funds. The

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wider spectrum of funds allows us to explore different incentives and behavior among large, medium, and

small mutual funds in the CDS market.

C. Sample overview

Table 1 presents an overview of our sample. Panel A shows the quarterly time-series patterns of

CDS holdings at the fund family, series trust, and fund level. During the three year period, the notional

amount of mutual fund single-entity CDS holdings increased from $13.1 billion at the beginning of 2007

to a peak of $29.2 billion in the second quarter of 2008 before descending smoothly to $16.0 billion by

the end of 2009. About 58% of the positions involve a sale of CDS, indicating that, on the whole, mutual

funds use CDS to seek additional risk taking. This pattern confirms a similar finding in Adam and

Guettler (2012).

[Insert Table 1 here.]

The aggregate statistics, however, mask the huge cross-sectional differences. The distribution of

CDS positions are highly skewed among funds in multiple dimensions. For ease of discussion, we divide

all 33 mutual fund families in our sample into three tiers sorted by total CDS notional amount —leader

(the PIMCO fund complex), large (the next nine mutual funds families after PIMCO; henceforth, the

“Next 9”), and small fund families (the remaining 23 fund families in our sample; henceforth, the “Rest

23”). Panel B shows the breakdown by mutual fund families. PIMCO is the unambiguous leader among

fixed-income investment managers. Its funds account for 62% of the total notional amount of CDS

contracts held by all mutual funds. The “Next 9” group account for 24% of the total CDS holdings. The

remaining 14% goes to the “Rest 23” families.

Concentration of CDS usage among the top/large players aside, the sell-buy imbalance also

exhibits an opposite pattern between the large and small players: while 63.8% of PIMCO’s CDS

positions are on the short side (i.e., they sell protection), the same figures for the “Next 9” and the “Rest

23” are 57.7% and 35.2%, respectively. Though it has been known that mutual funds as a group were net

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sellers of CDS during the sample period, it is worth pointing out that the net selling of single-entity CDS

contracts by mutual funds is driven by the largest players and is actually not the typical behavior among

individual funds. In fact, the “median” fund (among the “Rest 23”) bought more credit protection than it

sold.

Panel C of Table 1 demonstrates concentrated CDS activities along a different dimension: the

underlying reference entities. Out of the 443 reference entities, the top 50 (100) account for 64% (78%)

of the total notional amount. Moreover, large financial institutions constitute a disproportionately large

share among the top reference entities: There are 27 large financial institutions among the top 50

reference entities, and among these 27, eight were designated as “Global Systemically Important

Financial Institutions” (G-SIBs) by the FSB in 2009 and 2010. If fact, six of the eight G-SIBs

headquartered in the U.S. are among the top 50 reference entities.6 Thus, mutual funds, especially the

largest of them, could potentially incur large losses if those financial institutions fail. To the extent that

mutual funds are among the primary forms of savings and investments for households, the pattern

suggests that the mutual fund industry tends to make the “systemically important” financial institutions

even more so.

Panel D of Table 1 classifies mutual funds’ investment in CDS contracts into different categories

based on the underlying purpose of the CDS contract. There is not a norm in the literature in inferring the

purpose of CDS trading based on periodic holdings data. We classify the disclosures such that an

“offsetting” buy is a CDS long position that can be matched to a sell position on the same reference entity

by the same fund in the same quarter (on the same N-Q filing). A pair of offsetting positions is usually

used to bet on the slopes of the term structure. A “hedging” buy represents CDS long positions where the

same fund has a long position in the same underlying bonds during the same period. We only match

positions rather than the exact amount in classifying offsetting and hedging purchases. Finally, a

“speculative” buy represents the remaining long positions on which the funds would profit from the

6 The eight U.S. headquartered G-SIBs are: Bank of America Corp, Citigroup Inc, Goldman Sachs Group Inc, JP

Morgan Chase & Co, Morgan Stanley, and Wells Fargo & Co.

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financial failures of the reference entities. Panel D indicates that while large players (PIMCO in

particular) sell more CDS than they buy; they are less speculative within their long CDS positions. For

example, while 17% of PIMCO’s buy positions are classified as speculative, the same proportion for the

“Rest” group is 56%.

All panels in Table 1 combined indicate that the aggregate statistics of mutual funds’ holdings of

CDS contracts generally do not reflect the behavior of typical funds because of the different strategies

utilized by the large (especially funds from the leading fund family, PIMCO) and small funds. Overall,

PIMCO funds use CDS contracts to seek levered credit exposure in large companies, and in

disproportionately large and systematically important financial situations. Funds from small mutual fund

families, other the other hand, are net buyers of CDS protection and therefore reduce their credit exposure

using the derivatives. The behavior of the middle group is somewhere in between the two extremes, but

has more similarity to the strategy used by PIMCO.

Figure 2 displays the conditional empirical distribution of selling and buying intensity at the fund

level, defined as the notional amount of net selling aggregated over all single reference entity contracts,

scaled by the funds’ total net assets. When we equal-weight all funds, the distribution appears to be quite

symmetric, with both the average and median close to zero at –0.03 percent and –0.02 percent,

respectively. To reconcile our summary statistics with those in Adam and Guettler (2012), we compute

the average total CDS notional amount and net selling intensity among the top 100 fixed income funds to

be 3.84 percent and –0.84 percent, both of which are smaller than the equivalent numbers in Adam and

Guettler (2011) (6.16 percent and –1.67 percent). The difference is mainly due to the inclusion of CDS

positions on index products and sovereign debts in their sample.

[Insert Figure 2 here.]

The summary statistics of the main variables used in the analyses, both at the fund-quarter level,

and fund-reference entity-quarter level, are reported in Table 2.

[Insert Table 2 here.]

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II. Mutual Fund CDS Holdings: Liquidity Management

This section analyzes the first motivation for mutual funds to take CDS positions: liquidity

management and risk raking. The CDS market allows mutual funds from large and median sized fund

families to gain additional exposure to corporate credit risk. The same exposure could be accomplished

by simply investing in the underlying bonds. Oehmke and Zawadowski (2014b) propose that funds have

an important liquidity incentive to choose CDS over the bonds issued by the reference entities in order to

obtain roughly equivalent credit exposure. In particular, if bonds are relatively illiquid relative to CDS

contracts, then investors with more need for liquidity-driven trades should have a preference for short

positions in CDS over long positions in the underlying bonds given the prevailing bond-CDS basis in the

market. In all analyses, the subscripts i, j, t serve as indices for fund, CDS reference entity, and time

period, respectively.

A. Fund Level Analysis

First, we assess the relation between CDS selling intensity and two proxies for mutual funds’

liquidity needs at the fund level. Results are reported in Table 3. In Panel A, the dependent variable in

the analyses is a dummy variable for a fund to hold any CDS position during a period. The relevant

sample is thus all fund-quarter observations where CDS usage is a possibility. To construct such a sampe

of “potential” users of CDS, we resort to the Lipper fund style categories and include all funds from 37

out of the 182 categories in which at least one fund was a CDS user in our sample. Such a procedure

results in about 20,000 fund-quarter observations, out of which 994 fund-quarter pairs hold CDS sell

positions, 935 hold CDS buy positions, and 673 hold both.

[Insert Table 3 here.]

The first proxy for funds’ liquidity needs, is Flow volatility, defined as the standard deviation of

estimated monthly fund flows (which is the return adjusted change in fund asset value, as is commonly

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used in the literature) during the 24-month window ending in the same month as the portfolio period end

date corresponding to the filing. The flow measure reflects the basic open-ended nature of mutual funds.

Most open-end mutual funds offer daily liquidity to investors who can buy new shares or redeem shares

from the fund at the funds’ NAV until just before the market closes. Providing investors with such a

service imposes on the funds’ liquidity management, requiring these funds to keep adequate cash reserves

and invest some of the fund assets in a set of securities that boast adequate liquidity to trade in and out

upon short notice. Edelen (1999) shows that providing this liquidity service is quite costly for even

mutual funds that primarily invest in the U.S. public equity market, and Chen, Goldstein, and Jiang

(2010) showed how the complementarities among investors due to the open-end structure can impose

challenges on the funds’ liquidity management. The challenge of accommodating fund flows increases

with their unpredictability, which the flow volatility measure captures. Given the general lack of liquidity

among corporate bonds (Edwards, Harris, and Piwowar, 2007; Bao, Pan, and Wang, 2011) and the

liquidity advantage of the CDS market (Oehmke and Zawadowski, 2013a), the CDS market should be a

more desirable venue for credit risk exposure for mutual funds with higher flow-driven, or external,

liquidity needs.

The second proxy for funds’ liquidity needs is Portfolio turnover, the annualized fund portfolio

turnover rate, calculated as the lesser of the total amount of new securities purchased or the amount of

securities sold, divided by the total net asset value (NAV) of the fund. The variable was reported in the

CRSP Mutual Fund database. Turnover could be forced by fund flows, or by internal motives due to

discretionary trading. The portfolio turnover rate is commonly considered a proxy for the active

management of mutual funds (in either stock selection or market timing), and more active portfolio

management gives rise higher portfolio-driven, or internal, liquidity needs. Finally, Flow volatility and

Portfolio turnover are modestly correlated (with a correlation coefficient of 9.6%), suggesting that they

capture quite distinctive aspect of funds’ liquidity needs.

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The first two columns in Panel A of Table 3 adopts the standard logistic regression. Apart from

the key variables proxying for funds’ liquidity needs, we include common control variables such as fund

size (logarithm of total net assets), fund age (logarithm of years since inception), and funds’ performance

rank (from 0, or worst, to 100, or best) within their respective Lipper fund style categories during the

previous year. The regressions further include dummy variables for the four periods, as well as for the 37

Lipper fund style categories. The logit coefficients are “log ratio of odds ratios” (henceforth, simply “log

odds ratios” as commonly used). In our context, the exponentiated coefficients indicate the multiple of

the ratio Prob(CDS Usage)/[Prob(No CDS Usage)] relative to the base level due to a one-unit change in

the regressors.

Consistent with theoretical predictions, coefficients on both Flow volatility and Portfolio turnover

turn out to be significantly (at the 1% level) positive. The economic magnitude is sizable, too. A one-

standard deviation increase in Flow volatility (3.8 percentage points) is associated with an odds ratio for

any CDS selling of 1.60. Relative to the unconditional probability of CDS selling (4.6%), this implies an

incremental probability of 2.5 percentage points.7 Due to the small unconditional probability of CDS

usage among all mutual funds, the odds ratios are roughly the same as the multiples of probability.

Similarly, a one-standard deviation increase in Portfolio turnover is associated with roughly a 2.3

percentage point increase in the probability of CDS selling.

The first two columns of Panel A show very similar coefficients for CDS selling and buying,

which could be due to the large overlap of the two outcome variables. That is, the majority (54%) of

fund-quarter observations where it holds CDS positions the fund also engages in both buying and selling.

We use a multinomial logit model as an attempt to differential the determinants for CDS selling from

those for buying. The last three columns in Panel A report results from one multinomial logit regression

7 The detailed procedure of calculation, using column (1) of Table 2 Panel A (CDS selling) as an example, is as

follows: The base line odds ratio is Prob(CDS Selling)/[Prob(No CDS Selling)]= 994/(21840-994) = 0.048. A one

standard deviation increase in Flow volatility increases the odds ratio to 0.076 (=0.048* exp(12.39*0.038)), which

implies that Prob(CDS Selling)=0.071 (=0.076/(1+0.076), or an incremental probability of 0.025 (=0.071-0.046).

The same calculation applies to other discussions of odds ratios.

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where the baseline outcome is no-buy-and-no-sell. The coefficients in the “buy-no-sell” column (column

(4)) are the log odds ratio for a fund to hold some buy position but no sell position during a period,

relative to the baseline outcome, for a one unit change in a regressor. The coefficients for “both-buy-and-

sell” and “sell-no-buy” follow analogously.

Overall, the coefficients suggest that the relation between CDS usage and fund liquidity needs is

more driven by selling rather than buying. For example, the key coefficient of Flow volatility, is not

statistically different between the “both-buy-and-sell” and “sell-no-buy” outcomes, but is significantly

different (at the 10% level) between the “both-buy-and-sell”/ “sell-no-buy” and “buy-no-sell” outcomes.

Not surprisingly, larger and older funds are more likely to engage in CDS investments. Prior performance

does not predict CDS usage.8

Panel B of Table 3 proceeds with analyzing the determinants for the intensity of, rather than

propensity to, CDS usage by mutual funds. Here the dependent variable is gross selling (or buying)

intensity, defined as the total notional amount of CDS selling (or buying), scaled by the fund TNA during

the period. The estimation method is the tobit regression to accommodate the large number of fund-

period observations with zero CDS holdings. Results again support the liquidity management hypotheses

as both proxies for fund liquidity needs are significantly (at the 1% level) positive. A one-standard

deviation increase in Flow volatility is associated with a 1.61 (1.12) percentage points increase in CDS

selling (buying) intensity, both sizable relative to the unconditional average intensity of 0.13 percentage

points for selling and 0.14 for buying.

To summarize, results in Table 3 are highly consistent with predictions from Oehmke and

Zawadowski (2014a) that CDS serves as an effective tool for institutional investors to management

liquidity needs because the CDS market tends to be more liquid than the underlying reference bonds.

B. Reference Entity Level Analysis

8 This no-result does not contract Adam and Guettler (2012) because Adam and Guettler (2012) analyze the motives

for mutual funds to resort to CDS investment based on interim relative performance within a year.

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To sharpen the test, we relate CDS usage directly to security-level liquidity needs by analyzing

the determinants of CDS usage by funds at the issuer (reference entity) level, incorporating the effects of

bond as well as CDS contracts characteristics, mostly importantly, their respective liquidity. Results are

reported in Table 4. As in Table 3, Panel A performs logit regressions to analyze the propensity to

holding CDS positions (buy or sell), and Panel B adopts tobit regressions to further assess the intensity of

CDS holdings. Moreover, the regressions in Table 4 also incorporate dummy variables for the four

periods, as well as for the 37 Lipper fund style categories.

[Insert Table 4 here.]

Panel A of Table 4 affirm the relation between CDS holdings and fund liquidity needs at the

issuer level. More important from this analysis is the relation between CDS holdings and the liquidity

measure for CDS securities and that for the underlying bonds. For the former, we follow the literature to

settle on #CDS Contracts, defined as the number of quoted CDS contracts by the issuer as covered by

Markit during the period. There are usually multiple CDS contracts traded on the reference entity,

varying in both term structure (from six months to ten years) and contractual terms related to the

definition of trigger events and deliverable obligations.9 #CDS Contracts proxies for the liquidity of an

issuer’s CDS securities by capturing the density of contingency coverage for the credit risk of a reference

entity.10

For bond liquidity, we follow the literature to adopt the Bond turnover variable, defined as the

ratio of monthly dollar trading volume of bonds of the issuers over the issuance amount, using data from

TRACE. When there are multiple bond issues for a given issuer-period, we pick the one with the largest

issuance dollar amount. Somewhat surprisingly, the correlation between #CDS contract and Bond

turnover is close to zero (−0.006), suggesting that the CDS and the underlying bond market could have

9 The common categories include “full restructuring” (FR), “modified restructuring” (MR), “modified-modified

restructuring” (MM), and “no restructuring” (NR). The five-year MR contracts are usually the most liquid. 10

Another commonly used CDS liquidity measure is the number of dealers providing quotes on a reference entity,

as covered by Markit. In our sample we find that #CDS Contracts entails more within sample variation and hence

sharper results than # Dealers.

18

their relative advantages in trading liquidity. Finally, we add as control variable the asset size (in

logarithm) of the issuer.

As expected, #CDS contracts significantly (at the 1% level) predicts higher propensity of CDS

usage by mutual funds. A one-standard deviation change in #CDS contracts (1.25 contracts) is associated

with an odds ratio of 2.40 (1.07) for CDS selling (buying). Given the tiny unconditional probability of

CDS selling/buying at a fund-issuer-period level (about 0.07% for selling and 0.13% for buying), the

odds ratios imply that a one-standard deviation increase in the CDS liquidity proxy more than double the

likelihood of a CDS selling while increases the likelihood of CDS buying only by 7%. Though both

effects are statistically significant, one cannot fail to notice that CDS liquidity is far more effective

predicting CDS selling than buying. The difference between the coefficients on #CDS contracts is

significant (at the 1% level) between CDS buying and selling. Such evidence offers direct support to

Oehmke and Zawadowski’s (2014a) predication that mutual funds may sell the liquid CDS contracts in

lieu of bond long positions to achieve the same credit risk exposure. The same theoretical prediction for

CDS buying is much weaker, and so is the empirical relation.

Coefficients on Bond turnover are overall insignificant, expect in the specification explaining

“Both buy and sell.” There, a one-standard deviation increase in Bond turnover (0.071) is associated with

an odds ratio of 1.11 of (roughly an 11% increase in) the likelihood for a fund to be in both buying and

selling of CDS contracts. It thus indicates that bond liquidity also make the derivative contract more

desirable in general, but is not specifically related to the buying or selling of CDS.

Results from tobit regressions for CDS selling/buying intensity reported in Panel B affirm the

same economic relations suggested by those in Panel A. A one-standard deviation increase in #CDS

contract is associated with a 3.51 (0.23) percentage points increase in gross selling (buying) intensity,

both of which are statistically significant (at the 1% level) and economically substantial given the near-

zero unconditional average CDS holdings at the fund-issuer-period level. The asymmetry between the

effects of CDS liquidity on CDS selling and buying is also salient, and the difference between the two

19

coefficients is also statistically significant at the 1% level. The proxy for bond liquidity, on the other

hand, does not significantly predict CDS selling or buying intensity.

III. Mutual Fund CDS Holdings: Lead-follow and risk taking

Given PIMCO’s absolute leader status among fixed income mutual funds and the relatively new

CDS market, we hypothesize a lead-follow pattern between PIMCO and other funds in both CDS

investments and in the directional betting. That is, a non-PIMCO fund is more likely to initiate a buying

or selling CDS on a new reference entity if PIMCO disclosed a large position in the same reference entity

and in the same direction during the previous period. We analyze the possibilities of both following

PIMCO’s selling and following PIMCO’s buying.

A. Following PIMCO’s CDS Selling

We conduct both unconditional and conditional analyses to explore a lead-follow pattern in CDS

selling. First, we ask whether a non-PIMCO mutual fund is more likely to initiate a new net selling

position on a reference entity if PIMCO had disclosed a large net selling position in the same reference

entity in the previous quarter. More specifically, the sample for the unconditional analysis is the

“universe” of all reference entities, which consists of all reference entities that ever appear in our sample,

excluding the net selling positions that a fund already held in the previous period.

We run a probit regression at the fund-reference entity-period level where the dependent variable,

New Sellingi,j,t is a dummy variable equal to one if fund i discloses CDS net selling in the reference entity

j in period t and the fund did not disclose any net selling position j in period t-1. If a reference entity j is

not among the disclosed net selling positions of fund i in period t, then New Sellingi,j,t is coded zero. The

key independent variable, PIMCO Leadj,t-1, is a dummy variable equal to one if both of the following two

conditions are met: (1) The reference entity is among the top 50 net selling positions during period t-1 by

PIMCO funds, and (2) PIMCO’s selling position in the entity is larger than that by any other mutual fund

20

families in notional dollar amount in period t-1. Control variables include the following: The assets of

the reference entity (in log); the five-year CDS spread on the reference entity using data from Markit (a

leading data provider for the CDS market); and the total net assets of the fund (in log) as disclosed in the

N-Q filing. All control variables are recorded at the end of the previous quarter. The results are

presented in Table 5, where Panels A and B analyze the “Next 9” and “Rest 23” mutual fund families

separately.

[Insert Table 5 here.]

Results in both panels of Table 5 show that the independent variable of key interest, PIMCO

Leadj,t-1 is significant at less than the 5% level in all but one specification for the funds from the “Next 9”

fund families, and is uniformly highly significant in all specifications for funds from the “Rest 23” fund

families. When PIMCO displayed a large selling position in a previous period on a particular reference

entity, the probability that a “Next 9” fund will initiate a selling position in the same entity, other things

equal, increases by 0.8, 1.2, and 0.7 percentage points during 2007 (the latter three quarters), 2008, and

2009, respectively, if the fund did not already hold a position in the entity. Such incremental probabilities

are sizable, given the unconditional probabilities for the funds to initiate a net selling position in a new

reference entity in the given years (95, 82, and 16 basis points respectively). The corresponding

incremental probabilities are lower for the “Rest 23” funds, at 0.5 – 1.0 percentage points. However,

these numbers are even more significant relative to the much lower unconditional probability (about 11-

28 basis points) for this group of funds to initiate selling in a new reference entity.

The evidence so far suggests that non-PIMCO mutual funds often follow the direction that their

industry leader took in that they are more likely to take credit exposure on a new reference entity after

observing a major bet made by PIMCO in a previous disclosure period. This unconditional relation is

further affirmed by a conditional one: conditional on a new position being initiated by a fund in a period,

it is far more likely to be a selling rather than buying position if PIMCO had a large selling position in the

previous period. Such an analysis is more applicable if a portfolio manager conducts research into a

21

particular reference entity for possible CDS transactions, and takes into account PIMCO’s position in

deciding her own trading directions. Table 6 reports the results from this analysis.

[Insert Table 6 here.]

The framework for the analysis reported in Table 6 is very similar to that in Table 5 except that the

sample of fund-reference entity-periods is restricted to reference entities the fund hold in period t. The

dependent variable is an indicator variable set to 1 if the position reflects a net selling of CDS, and is

coded zero if the position is a net-buy or involves an equal notional amount in buying and selling.

Though the majority of the new positions by non-PIMCO funds during the five semi-years starting in the

second half of 2007 are buying (60 percent), the probability of the position being selling increases by 20

to 42 percentage points among the “Next 9” funds and 16 to 57 percentage points among the “Rest 23” if

PIMCO had a large net selling position in the reference entity during the previous period. The

coefficients are significant at 1% level across all specifications, supporting the hypothesis that PIMCO’s

lead has a strong impact on other funds’ directional betting in CDS selling.

The sign and magnitude of the coefficients on the control variables are of interest on their own.

First, mutual funds are far more likely to initiate new CDS short positions on large entities on during the

sample period. The top reference entities in our sample include Ford Motor, General Motors, Procter &

Gamble, General Electric Capital Corp, American International Group, Morgan Stanley, Lehman

Brothers, Goldman Sachs, and Citigroup Inc. That is, the largest players in the mutual fund sector were

making concentrated bets on the good financial health of business behemoths in other sectors. A

perception (which was ex pose justified) that certain large firms are “too big to fail” or “too systemic to

fail” might have influenced these players. The second notable relation is that mutual funds initiated and

sold new CDS contracts on reference entities that already appeared to be risky, as measured by the

spreads on the most liquid five-year contracts with modified restructuring terms as reported in Markit.

The conditional relation is much stronger than the unconditional one.

22

Interestingly, the tendency for mutual funds to initiate new bets on relatively risky firms (as

captured by the lagged five-year CDS spreads) peaked in 2007 before the financial crisis unfolded. The

“Next 9” funds continued to favor selling new reference entities with relatively high CDS spreads in

2008, but the preference was weaker than in 2007 and more or less disappeared by 2009. The “Rest 23”

funds, on the other hand, started with a much lower sensitivity to risk in their new directional betting in

2007 and 2008, and ended in about the same level as the “Next 9” by 2009.

B. Following PIMCO’s CDS Buying

We conduct analogous analyses on the lead-follow pattern in CDS buying. We follow our earlier

classification scheme (see Table 1 Panel D) to analyze speculative buys and hedging buys separately.

The small “offsetting” category (4.0 percent of the sample total notional amount) is ignored because such

positions do not represent the type of risk taking we are analyzing.

We start with speculative buys where the long positions in CDS are not offset by any long

positions in the bonds of the same issuer. Following the same set up, the dependent variable now

becomes New Speculative Buyingi,j,t ,a dummy variable equal to one if fund i discloses CDS buying in the

reference entity j in period t and the fund did not disclose any buying position j in period t-1. The key

independent variable of interest, PIMCO Leadj,t-1, is revised to be a dummy variable equal to one if both

of the following two conditions are met: (1) The reference entity is among the top 50 speculative buying

positions during period t-1 by PIMCO funds, and (2) PIMCO’s speculative buying position in the entity

is larger than that by any other mutual fund family in notional dollar amount in period t-1. The same set

of control variables is included as in Table 5. Results are reported in Table 7.

[Insert Table 7 here.]

It turns out that a large speculative buy by PIMCO in a previous period increases the probability

that other funds initiate new speculative buy positions in the same reference entities by 0.4 – 0.9

percentage points for the “Next 9” funds and 0.3 – 0.5 percentage points for the “Rest 23” funds. The

23

coefficients on PIMCO Leadj,t-1 are significant at the 1% level across all specifications. The economic

magnitude is also sizable given that the unconditional probability that the other funds will initiate a new

speculative CDS buy position in a given period ranges from 0.2-0.3 percent.

Both the sell and buy sides convey the same herding pattern for risk taking: First, prior to the

crisis, funds were more likely to exhibit herding behavior in which they took the same risk rather than to

exhibit herding in which they took the same protection. Additionally, large mutual funds were more

aggressive in following PIMCO’s lead than small ones, presumably because the former were more closely

benchmarked, explicitly or implicitly, to the industry leader. Second, the financial crisis (and the looming

losses) reduced mutual funds’ tendency to follow their leader’s risk behavior, but increased their tendency

to take similar precautionary positions. Such pro-cyclical herding in risk taking constitutes an amplifying

force in the systemic risk of all financial institutions.

IV. Conclusion

Exploring a comprehensive dataset of mutual funds’ quarterly holdings of credit default swap

(CDS) contracts in 2007-2009, we test the motivations for institutional investors to hold CDS positions as

modeled by Oehmke and Zawadowski (2013) and document evidence supporting predictions of Rajan

(2006) that asset managers facing relative performance evaluations are incentivized to take “hidden tail

risk” by selling CDS contracts. Additionally, we find that asset managers are prone to herding behavior

which concentrates risk in the financial system. Further, the CDS market increases the correlation of

financial health between the largest mutual funds and the largest and systemically important firms

(especially financial institutions).

24

References:

Adam, Tim and Andre Guettler, 2012. The use of Credit Default Swaps in Fund Tournaments, working

paper, Humboldt University and EBS Business School.

Atkeson, Andrew G., Andrea L. Eisfeldt, and Pierre-Olivier Weill, 2014. Entry and exit in OTC

derivatives markets, working paper, University of California Los Angeles.

Augustin, Patrick, Marti Subrahmanyam, and Sarah Qian Wang, 2014. Credit default swap (CDS): A

survey. Foundations and Trends in Finance, forthcoming.

Bao, Jack, Jun Pan, and Jiang Wang, 2011. The illiquidity of corporate bonds, Journal of Finance 65,

911-946.

Chen, Qi, Itay Goldstein, and Wei Jiang, 2010. Payoff complementarities and financial fragility:

Evidence from mutual fund outflows, Journal of Financial Economics 97, 239-262.

Chevalier, Judith A., and Glenn Ellison, 1997. Risk taking by mutual funds as a response to incentives,

Journal of Political Economy 105, 1167-1200.

Edelen, Roger, 1999. Investor flows and the assessed performance of open-end fund managers, Journal

of Financial Economics 53, 439-466.

Edwards, Amy K., Lawrence E. Harris, and Michael S. Piwowar, 2007. Corporate bond market

transaction costs and transparency, Journal of Finance 62, 1421-1451.

Feroli, Michael, Anil K Kashyap, Kermit Schoenholtz, and Hyun Song Shin, 2014. Market tantrums and

monetary policy, working paper, University of Chicago, New York University, and Princeton University.

Jarrow, Robert A., 2011. The economics of credit default swaps, Annual Review of Financial Economics

3, 235-257.

Koski, Jennifer Lynch and Jeffrey Pontiff, 1999. How are derivatives used? Evidence from the mutual

fund industry, Journal of Finance 54, 791-816.

Oehmke, Martin and Adam Zawadowski, 2014a. The anatomy of the CDS market, working paper,

Columbia University and Boston University.

Oehmke, Martin and Adam Zawadowski, 2014b. Synthetic or real? The equilibrium effects of credit

default swaps on bond markets, working paper, Columbia University and Boston University.

Rajan, Raghuram G., 2006. Has finance made the world riskier? European Financial Management 12,

499-533.

Stultz, René M., 2010. Credit Default Swaps and the credit crisis. Journal of Economic Perspectives 24,

74-92.

25

Figure 1: Structure of Mutual Funds’ CDS Holdings Data

This chart illustrates the structure of mutual funds’ CDS holdings data disclosed in N-Q, N-CSR and N-CSRS forms. A form is filed at the

CIK (Series Trust) level where multiple filers could be affiliated with the same mutual fund family (or complex). The disclosure reveals holdings

at the fund (portfolio) level where each series trust often encompasses several funds with similar or related investment strategies.

CDS holding

Fund (Portfolio)

CIK (Series trust)

Fund Family PIMCO

PIMCO Funds

Total Return Fund

...

Low Duration Fund

...

...

PIMCO Variable Insurance Trust

All Asset Portfolio

...

...

...

26

Display: Details of CDS holdings (Filer: PIMCO Funds, CIK = 810893)

Counterparty Reference Entity Buy/Sell

Notional

Amount

($1,000)

Mark-to-market

($1,000)

Period

Ending Fund name

Bear Stearns Cos I General Motors Corp. 7.125% due

07/15/2013 Sell 11,700 -249 6/30/2008 TOTAL RETURN FUND

Merrill Lynch & Co General Motors Corp. 7.125% due

07/15/2013 Sell 25,000 -249 6/30/2008 TOTAL RETURN FUND

Bk of America Corp General Motors Corp. 7.125% due

07/15/2013 Sell 30,300 -9,841 6/30/2008 TOTAL RETURN FUND

Deutsche Bk AG General Motors Corp. 7.125% due

07/15/2013 Sell 6,400 -2,079 6/30/2008 TOTAL RETURN FUND

JPMorgan Chase Bk General Motors Corp. 7.125% due

07/15/2013 Sell 7,000 -2,265 6/30/2008 TOTAL RETURN FUND

Citigroup Inc General Motors Corp. 7.125% due

07/15/2013 Sell 6,400 -2,063 6/30/2008 TOTAL RETURN FUND

Deutsche Bk AG General Motors Corp. 7.125% due

07/15/2013 Sell 14,600 -4,706 6/30/2008 TOTAL RETURN FUND

27

Figure 2. Distribution of Mutual Funds’ CDS Net Selling

The three charts plot the distributions of CDS net selling, gross selling, and gross buying intensity

at the fund (portfolio) level. For each fund in each period, we aggregate the notional amount of each CDS

contract (buy or sell) for each reference entity. There are a total of 284 funds represented in the charts

Panel A plots the distribution of net selling (where a buy is considered a negative sell), scaled by the

funds’ total net assets (TNA), and expressed in percentage points:

𝑁𝑒𝑡 𝑠𝑒𝑙𝑙𝑖𝑛𝑔 =𝑡𝑜𝑡𝑎𝑙 𝑠𝑒𝑙𝑙𝑖𝑛𝑔 𝑛𝑜𝑡𝑖𝑜𝑛𝑎𝑙 𝑎𝑚𝑜𝑢𝑛𝑡 − 𝑡𝑜𝑡𝑎𝑙 𝑏𝑢𝑦𝑖𝑛𝑔 𝑛𝑜𝑡𝑖𝑜𝑛𝑎𝑙 𝑎𝑚𝑜𝑢𝑛𝑡

𝑇𝑁𝐴× 100

Similarly, Panels B and C plot gross selling and gross buying, respectively, in percentage points of the

funds’ TNA.

Panel A: Net selling

0.0%

2.0%

4.0%

6.0%

8.0%

10.0%

12.0%

14.0%

16.0%

18.0%

20.0%

< -

12

-11

, -1

0

-9, -

8

-7, -

6

-5, -

4

-3, -

2

-1, 0

1, 2

3, 4

5, 6

7, 8

9, 1

0

11

, 12

> 1

2

Fre

qu

en

cy

Sell Intensity

28

Panel B: Gross selling

Panel C: Gross buying

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Bu

y o

nly

0, 1

1, 2

2, 3

3, 4

4, 5

5, 6

6, 7

7, 8

8, 9

9, 1

0

10

, 11

11

, 12

> 1

2

Fre

qu

en

cy

Gross Sell Intensity

0.0%

5.0%

10.0%

15.0%

20.0%

25.0%

30.0%

35.0%

Sell

on

ly

0, 1

1, 2

2, 3

3, 4

4, 5

5, 6

6, 7

7, 8

8, 9

9, 1

0

10

, 11

11

, 12

> 1

2

Fre

qu

en

cy

Gross Buy Intensity

29

Table 1.

Table 1 provides an overview of CDS positions disclosed by mutual funds at the quarterly frequency for

2007-2009. Panel A shows CDS trading amounts in 2007, 2008 and 2009. #Positions is the number of

CDS holdings on record. Amount is the notional amount of CDS contracts, and Sell Amount is the

notional amount of contracts for which the mutual funds took the short side. #Families is number of

mutual fund families. #Trusts is the number of series trusts (filers with unique CIK). #Funds is the

number of mutual funds (portfolios). #Entities is the number of different CDS reference entity names.

Each year is divided into the first and second half year. Panel B reports CDS notional amounts from 3

groups of mutual fund families: PIMCO (the top fund family by total CDS notional amount), the next 9,

and the remaining fund families by CDS holdings. Panel C reports the CDS positions of the three groups

of reference entities, sorted by their total CDS notional amount in the sample. Panel D further classifies

CDS buying into three categories: An “offsetting” buy is a CDS long position that can be matched to a

sell position on the same reference entity by the same fund during the same period. A “hedging” buy

represents CDS long positions where the same fund has holdings in the same underlying bonds during the

same period. We only match positions rather than the exact amount in classifying offsetting and hedging

buys. Finally, a “speculative” buy represents the remainder of the long positions.

Panel A. Mutual fund CDS holding by period

Period #Positions Amount

($1,000s)

Sell Amount

($1,000s) #Families #Trusts #Funds #Entities

2007Q1 3098 13,154,975 8,535,859 28 51 152 290

2007Q2 4468 15,898,276 9,315,313 30 56 203 335

2007Q3 5886 20,034,953 13,654,085 32 57 208 345

2007Q4 6509 22,064,073 14,306,067 33 58 214 350

2008Q1 7281 24,317,488 15,024,279 33 58 219 377

2008Q2 9329 29,161,554 17,443,943 33 58 228 396

2008Q3 7667 25,989,193 15,986,209 33 55 210 384

2008Q4 6305 22,565,593 11,880,053 33 58 197 354

2009Q1 4922 21,158,307 9,560,698 31 54 189 327

2009Q2 3732 15,392,066 8,080,759 32 48 170 291

2009Q3 3868 15,694,105 8,446,495 29 49 173 272

2009Q4 3430 15,976,014 9,145,908 31 49 168 257

2007-2009 66495 241,406,597 141,379,668 33 60 284 445

30

Panel B. Mutual Fund CDS holding by mutual fund family

#Positions

Amount

($1,000s)

% of

total

Sell Amount

($1,000s)

Buy Amount

($1,000s)

All 66495 241,406,597 100% 141,379,668 100,026,929

PIMCO 26943 149,074,496 62% 95,106,951 53,967,545

Next 9 23312 57,445,008 24% 33,149,319 24,295,689

Rest 16240 34,887,093 14% 13,123,398 21,763,695

Panel C. Mutual Fund CDS holding by CDS reference entities

#Positions Amount

($1,000s) % of total

Sell

Amount

($1,000s)

Buy

amount

($1,000s)

Avg

spread

in 2007

Avg

spread

in 2008

Avg

spread

in 2009

All 66495 241,406,597 100% 141,379,668 100,026,929 0.0259 0.0847 0.0849

Top 50 32326 154,945,903 64% 114,762,631 40,183,271 0.0294 0.1072 0.1199

Next 50 10906 32,928,095 14% 7,885,987 25,042,108 0.0144 0.0339 0.0205

Rest 345 23263 53,532,599 22% 18,731,050 34,801,549 0.0268 0.0767 0.0522

Panel D. Mutual fund CDS holdings by classified purposes

Sell Amount

($1,000s)

Buy: Offsetting

($1,000s)

Buy: Hedging

($1,000s)

Buy: Speculation

($1,000s)

PIMCO 95,106,951 3,524,628 36,914,185 13,528,732

Next 9 33,149,319 1,509,001 7,497,575 15,289,112

Rest 13,123,398 319,601 9,862,411 11,581,684

Total 141,379,668 5,353,230 54,274,171 40,399,528

31

Table 2. Summary Statistics of Main Variables

The sample in this Table includes all funds that are actual or potential CDS investors. For each mutual

fund category by the Lipper class, if there is one fund holds any CDS position during any quarter, all

fund-quarter observations from the Lipper class are included in our sample. This table reports the number

of observations, mean, standard deviation, and values at the 25th, 50

th (median), and 75

th percentiles, of the

main variables both at the fund-period and fund-issuer-period level. Flow volatility is the monthly

standard deviation of estimated fund flows (measured as the change in return-adjusted fund asset value, as

commonly used in the literature) during the 24-month window ending in same month as the portfolio

period end date corresponding to the N-Q, N-CSR and N-CSRS filing. Portfolio turnover is a fund’s

annualized portfolio turnover rate during the last year. Fund size is the logarithm of the total net assets of

the fund that holds the position. Fund age is the logarithm of number of years since the fund first offered.

Performance rank is the past 12-month performance ranking for mutual fund, within its Lipper

classification. Any CDS sell (buy) is a dummy variable if a fund holds any CDS selling (buying) position

in a given reference entity during a period. Gross sell (buy) intensity is the ratio of total notional amount

of CDS selling (buying) by a fund in a given reference entity during a period, scaled by a fund’s total net

assets during the same period.

Variable name # Obs Mean Std Dev 25th Median 75th

Fund-period level

Flow volatility 19,454 0.052 0.037 0.025 0.043 0.068

Portfolio turnover 19,454 1.09 1.572 0.31 0.62 1.21

Fund size ($ million) 19,454 1438.9 5990.3 77.9 251.4 885.6

Log Fund size 19,454 5.563 1.838 4.355 5.527 6.786

Fund age (years) 19,454 14.518 12.212 7.145 12.089 17.022

Log Fund age 19,454 2.416 0.722 1.966 2.492 2.835

Performance rank 19,454 0.491 0.278 0.254 0.488 0.727

Any CDS sell 19,454 0.051 0.221 0 0 0

Any CDS buy 19,454 0.049 0.215 0 0 0

Gross sell intensity 19,454 0.144 1.098 0 0 0

Gross buy intensity 19,454 0.159 1.149 0 0 0

32

Variable name # Obs Mean Std Dev 25th Median 75th

Fund-issuer-period level

Flow volatility 4,147,385 0.05 0.035 0.023 0.04 0.064

Portfolio turnover 4,147,385 1.075 1.446 0.32 0.62 1.21

Fund size ($ million) 4,147,385 1464.1 6060.5 78.3 248.2 889.5

Log Fund size 4,147,385 5.571 1.83 4.361 5.514 6.791

Fund age (years) 4,147,385 14.624 12.426 7.252 12.071 17.011

Log Fund age 4,147,385 2.422 0.72 1.981 2.491 2.834

Performance rank 4,147,385 0.49 0.278 0.253 0.487 0.725

Bond turnover 4,147,385 0.058 0.071 0.013 0.036 0.072

# CDS contracts 4,147,385 10.178 1.296 10 11 11

Assets reference entity 4,147,385 9.87 1.472 8.858 9.666 10.509

Any CDS sell 4,147,385 0.001 0.027 0 0 0

Any CDS buy 4,147,385 0.001 0.037 0 0 0

Gross sell intensity 4,147,385 0.003 0.166 0 0 0

Gross buy intensity 4,147,385 0.004 0.166 0 0 0

33

Table 3. CDS Selling and Funding Liquidity: Fund Level Analysis

This table explores the fund-level relation between CDS usage and mutual funds’ funding liquidity needs

and other fund-level characteristics using logit regressions. The sample includes all mutual funds

(whether they hold CDS positions or not) that could potentially invest in CDS. Operationally we include

all fund-quarter observations from 37 out of the Lipper fund style categories in which at least one fund

holds any CDS position in any quarter. Panel A relates the propensity to hold CDS buy and/or sell

positions to fund characteristics. The first two columns adopt logit regressions where the dependent

variable is a dummy variable equal to one if a fund holds any CDS sell (column (1)) or CDS buy (column

(2)) position during a period. The last three columns report results from a multinomial logit regression

which estimates the risk ratio of each of the three parallel outcomes at the fund-period level: Buy no sell,

both buy and sell, and sell no buy, relative to the base outcome of no buy or sell. In all columns, the

reported coefficients represent the logarithm of the odds ratio of a particular outcome (relative to the base

outcome) for a one-unit change in the corresponding covariate. Panel B relates the intensity of CDS

usage to the same set of independent variable using tobit regressions. The dependent variables are Gross

sell intensity and Gross buy intensity, defined at the total notional amount of all CDS sell (or buy)

positions during a fund-period, scaled by a fund’s total net assets. The two key independent variables are

proxies for mutual fund liquidity needs. The first is Flow volatility, the monthly standard deviation of

estimated fund flows (measured as the change in return-adjusted fund asset value, as commonly used in

the literature) during the 24-month window ending in same month as the portfolio period end date

corresponding to the N-Q, N-CSR and N-CSRS filing. The second is Portfolio turnover, a fund’s

annualized portfolio turnover rate during the last year. Control variables include Fund size, the logarithm

of the total net assets of the fund that holds the position; Fund age, the logarithm of number of years since

the fund first offered; Performance rank, the past 12-month performance ranking for mutual fund, within

its Lipper classification. All regressions include dummy variables for time periods and Lipper fund

categories for which the coefficients are suppressed. Standard errors adjust for heteroskedasticity and

clustering at the fund level. *,

**, and

*** indicate statistical significance at the 10%, 5%, and 1% level

respectively.

34

Panel A. Propensity to CDS usage by mutual funds

Logit Logit, vs. no buy no sell

Dependent variable: Any sell Any buy Buy no sell Both buy and sell Sell no buy

(1) (2) (4) (5) (6)

Flow volatility 12.51*** 8.919*** 3.055 12.44*** 13.57***

(2.442) (2.494) (3.757) (2.786) (3.984)

Portfolio Turnover 0.294*** 0.279*** 0.123*** 0.316*** 0.168***

(0.0552) (0.0538) (0.0456) (0.0615) (0.0611)

Log Fund size 0.555*** 0.430*** 0.261*** 0.535*** 0.623***

(0.0595) (0.0553) (0.0628) (0.0665) (0.0875)

Log Fund age 0.192 0.103 0.0492 0.151 0.340

(0.151) (0.155) (0.200) (0.173) (0.239)

Performance rank -0.206 -0.403* 0.106 -0.522** 0.554

(0.232) (0.214) (0.295) (0.259) (0.394)

# observations 19,137 19,454 16,000 18,565 15,291

% (Dep var =1) 5.22% 4.87% 1.64% 3.68% 2.06%

Pseudo R squared 0.2390 0.1968 0.1133 0.2418 0.2380

Panel B: Intensity of CDS usage by mutual funds

Dependent variable: Gross sell intensity Gross buy intensity

(1) (2)

Flow volatility 38.65*** 26.83***

(9.285) (8.831)

Portfolio Turnover 0.913*** 1.093***

(0.171) (0.207)

Log Fund size 1.606*** 1.369***

(0.219) (0.188)

Log Fund age 0.418 0.0386

(0.435) (0.517)

Performance rank -1.121 -1.471**

(0.701) (0.724)

# observations 19,454 19,454

Pseudo R squared 0.1427 0.1241

35

Table 4. CDS Selling and Funding Liquidity: Reference Entity Level Analysis

This table explores the reference entity-level relation between CDS usage and both fund-level and

reference entity level characteristics using logit regressions. The sample includes all potential CDS

positions by all mutual funds which could potentially invest in CDS positions. The “potential” CDS

positions include all reference entities that appear in our sample. The set of mutual funds are the same as

in Table 2. Panel A relates the propensity to hold CDS buy and/or sell positions to fund and reference

entity characteristics using logit regressions. Panel B relates the intensity of CDS usage to the same set of

independent variable using tobit regressions. In both panels the specification are the same as in the

corresponding panels of Table 2, except that all variables are recorded at the fund-reference entity-period

level. The fund-level variables are defined the same way as in Table 2. The key reference entity-level

independent variables are the proxies for bond and CDS liquidity. The first is Bond turnover defined as

the ratio of monthly dollar trading volume of bonds of the issuers over the issuance amount. When there

are multiple bond issues for a given issuer-period, we pick the one with the largest issuance dollar

amount. The second is #CDS contracts defined as the number of quoted CDS by the issuer as covered

by Markit during the period. The additional reference-entity level control variables is Assets Reference

Entity, the logarithm of the reference entity’s assets. All regressions include dummy variables for time

periods and Lipper fund categories for which the coefficients are suppressed. Standard errors adjust for

heteroskedasticity and clustering at the fund level. *,

**, and

*** indicate statistical significance at the 10%,

5%, and 1% level respectively.

36

Panel A: Propensity to CDS usage by mutual funds

Dependent variable: Any sell Any buy Any sell Any buy Buy no

sell

Both buy

and sell

Sell no

buy

(1) (2) (3) (4) (4) (5) (6)

Flow volatility 14.00*** 7.520** 7.645** 6.852 15.01***

(3.389) (3.091) (3.156) (4.429) (3.455)

Portfolio turnover 0.233*** 0.328*** 0.329*** 0.328*** 0.222***

(0.0559) (0.0530) (0.0541) (0.0657) (0.0594)

Log Fund size 0.653*** 0.751*** 0.750*** 0.857*** 0.644***

(0.0868) (0.142) (0.145) (0.186) (0.0867)

Log Fund age -0.00736 -0.169 -0.166 -0.224 0.0173

(0.181) (0.165) (0.168) (0.236) (0.189)

Performance rank 0.220 -0.218 -0.219 -0.0572 0.295

(0.332) (0.419) (0.428) (0.579) (0.346)

Bond turnover 0.221 0.263 0.220 0.268 0.0174 2.005*** -0.132

(0.221) (0.252) (0.224) (0.256) (0.275) (0.510) (0.231)

# CDS contracts 0.724*** 0.0770*** 0.732*** 0.0788*** 0.0631*** 0.568*** 0.770***

(0.0556) (0.0178) (0.0555) (0.0182) (0.0163) (0.140) (0.0644)

Assets reference entity 0.589*** 0.137*** 0.596*** 0.141*** 0.0943*** 0.706*** 0.581***

(0.0417) (0.0266) (0.0431) (0.0275) (0.0318) (0.0881) (0.0422)

# observations 4,094,042 4,117,080 4,094,042 4,117,080 4,073,070 2,756,756 4,047,326

% (Dep var =1) 0.075% 0.140% 0.075% 0.140% 0.131% 0.015% 0.065%

Pseudo R squared 0.1455 0.1085 0.2285 0.2455 0.2425 0.2596 0.2176

37

Panel B: Intensity of CDS usage by mutual funds

Dependent variable: Gross sell intensity Gross buy intensity Gross sell intensity Gross buy intensity

(1) (2) (3) (4)

Flow volatility 57.98*** 22.24**

(16.00) (9.404)

Portfolio turnover 1.016*** 1.066***

(0.250) (0.202)

Log Fund size 2.593*** 1.988***

(0.340) (0.257)

Log Fund age 0.0797 -0.601

(0.749) (0.476)

Performance rank -0.00815 -1.334

(1.369) (1.330)

Bond turnover 1.457 0.861 1.656* 1.028

(0.962) (0.729) (0.957) (0.732)

# CDS contracts 2.895*** 0.229*** 2.899*** 0.238***

(0.342) (0.0668) (0.348) (0.0735)

Assets reference entity 2.596*** 0.434*** 2.603*** 0.448***

(0.281) (0.102) (0.284) (0.0996)

# observations 4,147,385 4,147,385 4,147,385 4,147,385

Pseudo R squared 0.1143 0.0831 0.1714 0.1735

38

Table 5. Lead-Follow in CDS New Net Selling: Unconditional Analysis

This table reports results of logit regressions for the probability of non-PIMCO mutual funds’ net selling

of CDS on new reference entities. The unit of observation is at the fund-reference entity-period (quarter)

level, and the universe of reference entities in any period t consists of all potential entities (including any

reference entity that ever appears in our sample) for which a fund did not already have a net short position

on in period t-1. The dependent variable is a dummy variable equal to one if a fund takes a new net

selling CDS position in a reference entity (the new position is defined as a CDS net selling position by a

mutual fund in period t on a reference entity for which the mutual fund did not have a net selling position

in period t-1). The key independent variable, PIMCO Lead is a dummy variable equal to one if the

following two conditions are met: (1) The entity is among the top 50 net selling positions during period t-

1 by PIMCO funds, and (2) PIMCO’s position in the entity is larger than that by any other mutual fund

family in notional dollar amount. Control variables include the following: The assets of the reference

entity (in log) in 2006; the five-year CDS spread on the reference entity in period t-1 using data from

Markit; and the total net assets of the fund (in log) as disclosed in the N-Q, N-CSR and N-CSRS filing.

All regressions include quarterly dummies. Panels A and B analyze the “Next 9” and “Rest 23” mutual

fund families, respectively. Standard errors clustered at the fund level are reported in parentheses, and *,

**, and

*** indicate statistical significance at the 10%, 5%, and 1% level respectively.

Panel A: The next 9 mutual fund families

2007 2008 2009

(1) (2) (3) (4) (5) (6)

PIMCO Lead (t-1) 1.405*** 0.619*** 1.512*** 0.934*** 2.137*** 1.698***

(0.0840) (0.0996) (0.0760) (0.0951) (0.187) (0.216)

Assets Reference Entity

0.352***

0.272***

0.445***

(log)

(0.0261)

(0.0221)

(0.0523)

Spread-5year (t-1)

38.14***

6.415***

0.628*

(1.471)

(0.333)

(0.339)

Fund TNA (log)

-0.00151

-0.0886***

0.425***

(0.0321)

(0.0281)

(0.0830)

# observations 77,546 65,150 104,587 87,999 79,172 64,451

% (Dep var =1) 0.950% 0.953% 0.823% 0.819% 0.148% 0.163%

Pseudo R squared 0.0428 0.1228 0.0502 0.0864 0.0636 0.1403

39

Panel B: The remaining 23 mutual fund families

2007 2008 2009

(1) (2) (3) (4) (5) (6)

PIMCO Lead (t-1) 1.932*** 1.356*** 2.003*** 1.109*** 2.852*** 2.273***

(0.112) (0.136) (0.0990) (0.123) (0.188) (0.217)

Assets Reference Entity

0.410***

0.433***

0.441***

(log)

(0.0350)

(0.0293)

(0.0520)

Spread-5year (t-1)

24.67***

2.979***

0.459

(2.468)

(0.605)

(0.305)

Fund TNA (log)

0.168***

0.133***

0.338***

(0.0478)

(0.0390)

(0.0688)

# observations 117,795 98,854 165,411 139,143 114,831 93,472

% (Dep var =1) 0.284% 0.281% 0.258% 0.268% 0.106% 0.119%

Pseudo R squared 0.0510 0.1063 0.0628 0.1031 0.1366 0.2017

40

Table 6. Lead-Follow in CDS on New Net Selling: Conditional Analysis

This table reports the results of logit regressions for the probability of non-PIMCO mutual funds’ net

selling of CDS on new reference entities. The unit of observation is at the fund-reference entity-period

(quarter) level. The universe of reference entities consists of all reference entities held by a fund in any

period t for which the fund did not already have a net short position in period t-1. The dependent variable

is a dummy variable equal to one if a fund takes a new net selling CDS position in a reference entity (the

new position is defined as a CDS net selling position by a mutual fund in period t on a reference entity for

which the mutual fund did not have a net selling position in period t-1). The key independent variable,

PIMCO Lead is a dummy variable equal to one if the following two conditions are met: (1) the entity is

among the top 50 net selling positions during period t-1 by PIMCO funds, and (2) PIMCO’s position in

the entity is larger than that by any other mutual fund family in notional dollar amount. Control variables

include the following: The assets of the reference entity (in log) in 2006; the five-year CDS spread on the

reference entity in period t-1 using data from Markit; and the total net assets of the fund (in log) as

disclosed in the N-Q, N-CSR and N-CSRS filing. Panels A and B analyze the “Next 9” and “Rest 23”

mutual fund families respectively. Standard errors clustered at the fund level are reported in parentheses,

and *,

**, and

*** indicate statistical significance at the 10%, 5%, and 1% level respectively.

Panel A: The next 9 mutual fund families

2007 2008 2009

(1) (2) (3) (4) (5) (6)

PIMCO Lead (t-1) 1.653*** 0.855*** 1.556*** 0.703*** 3.432*** 2.249***

(0.141) (0.199) (0.105) (0.150) (0.288) (0.354)

Assets Reference Entity

0.388***

0.228***

0.618***

(log)

(0.0453)

(0.0322)

(0.0858)

Spread-5year (t-1)

90.68***

18.44***

3.761***

(5.085)

(1.183)

(1.168)

Fund TNA (log)

-0.135**

-0.353***

0.0245

(0.0581)

(0.0421)

(0.119)

# observations 2,216 1,932 3,521 3,075 1,112 981

% (Dep var =1) 33.3% 32.1% 24.5% 23.4% 10.5% 10.7%

Pseudo R squared 0.1015 0.3614 0.0689 0.2004 0.2339 0.3417

41

Panel B: The remaining 23 mutual fund families

2007 2008 2009

(1) (2) (3) (4) (5) (6)

PIMCO Lead (t-1) 1.964*** 1.103*** 2.190*** 1.071*** 3.510*** 3.340***

(0.141) (0.219) (0.123) (0.181) (0.222) (0.329)

Assets Reference Entity

0.147***

0.297***

0.0508

(log)

(0.0507)

(0.0407)

(0.0774)

Spread-5year (t-1)

39.77***

14.21***

1.605**

(4.709)

(1.713)

(0.658)

Fund TNA (log)

0.289***

0.134***

0.235**

(0.0648)

(0.0506)

(0.0934)

# observations 2,123 1,927 3,557 3,229 2,207 1,980

% (Dep var =1) 15.7% 14.4% 12.0% 11.6% 5.5% 5.6%

Pseudo R squared 0.1012 0.1789 0.1357 0.1811 0.3115 0.3439

42

Table 7. Lead-Follow in CDS Speculative Buying on New Reference Entities:

Unconditional Analysis

This table reports the results from logit regressions for the probability of non-PIMCO mutual funds’

speculative buys of CDS on new reference entities. The unit of observation is at the fund-reference entity-

period (quarter) level, and the universe of reference entities in any period t consists of all potential entities

(including any reference entity that ever appears in our sample) for which a fund did not already have a

speculative buy position in period t-1. The dependent variable is a dummy variable equal to one if a fund

takes a new speculative buying CDS position in a reference entity (a new position is defined as a CDS

speculative buying position by a mutual fund in period t on a reference entity for which the mutual fund

did not have a speculative buy position in period t-1). The key independent variable, PIMCO Lead is a

dummy variable equal to one if the following two conditions are met: (1) The entity is among the top 50

speculative buying positions during period t-1 by PIMCO funds, and (2) PIMCO’s position in the entity is

larger than that by any other mutual fund family in notional dollar amount. Control variables include the

following: The assets of the reference entity (in log) in 2006; the five-year CDS spread on the reference

entity in period t-1 using data from Markit; and the total net assets of the fund (in log) as disclosed in the

N-Q, N-CSR and N-CSRS filing. Panels A and B analyze the “Next 9” and “Rest 23” mutual fund

families respectively. Standard errors clustered at the fund level are reported in parentheses, and *,

**, and

*** indicate statistical significance at the 10%, 5%, and 1% level respectively.

Panel A: The next 9 mutual fund families

2007 2008 2009

(1) (2) (3) (4) (5) (6)

PIMCO Lead (t-1) 0.629*** 0.529*** 0.500*** 0.558*** 0.708** 0.728**

(0.143) (0.150) (0.156) (0.162) (0.283) (0.289)

Assets Reference Entity

-0.195***

0.0771**

0.0779

(log)

(0.0349)

(0.0326)

(0.0630)

Spread-5year (t-1)

-40.16***

-3.180**

-0.964

(5.462)

(1.449)

(1.139)

Fund TNA (log)

0.146***

0.118***

0.272***

(0.0369)

(0.0397)

(0.0829)

# observations 85,480 71,708 118,865 99,877 87,275 70,995

% (Dep var =1) 0.649% 0.708% 0.364% 0.370% 0.131% 0.139%

Pseudo R squared 0.0050 0.0253 0.0171 0.0229 0.0118 0.0255

43

Panel B: The remaining 23 mutual fund families

2007 2008 2009

(1) (2) (3) (4) (5) (6)

PIMCO Lead (t-1) 1.183*** 1.193*** 0.473*** 0.434*** 1.001*** 0.853***

(0.116) (0.121) (0.141) (0.142) (0.167) (0.178)

Assets Reference Entity

0.0480*

-0.0402

-0.0300

(log)

(0.0290)

(0.0311)

(0.0438)

Spread-5year (t-1)

-30.85***

-11.56***

-3.206***

(4.998)

(2.237)

(1.142)

Fund TNA (log)

-0.0477

-0.172***

-0.0474

(0.0336)

(0.0318)

(0.0481)

# observations 117,330 98,359 172,937 145,288 124,746 101,437

% (Dep var =1) 0.480% 0.530% 0.312% 0.339% 0.212% 0.238%

Pseudo R squared 0.0158 0.0282 0.0304 0.0410 0.0373 0.0423