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.
Wei Jiang can be reached at wj2006@columbia.edu.
3
Corresponding author. Zhongyan Zhu can be reached at zhongyan@cuhk.edu.hk.
2
1
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.
2
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
3
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.
4
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 fixedincome 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.
6
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
7
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
8
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
9
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
10
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.
11
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
13
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.
14
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 onestandard 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.
15
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-andsell” 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 fundperiod 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.
16
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.
17
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 nearzero 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
18
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
19
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 1128 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
20
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.
21
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
22
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).
23
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.
24
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.
Fund Family
PIMCO
PIMCO
Funds
CIK (Series trust)
Fund (Portfolio)
CDS holding
Total Return
Fund
...
Low Duration
Fund
...
25
PIMCO Variable
Insurance Trust
...
All Asset
Portfolio
...
...
...
Display: Details of CDS holdings (Filer: PIMCO Funds, CIK = 810893)
Counterparty
Bear Stearns Cos I
Merrill Lynch & Co
Bk of America Corp
Deutsche Bk AG
JPMorgan Chase Bk
Citigroup Inc
Deutsche Bk AG
Reference Entity
General Motors Corp. 7.125% due
07/15/2013
General Motors Corp. 7.125% due
07/15/2013
General Motors Corp. 7.125% due
07/15/2013
General Motors Corp. 7.125% due
07/15/2013
General Motors Corp. 7.125% due
07/15/2013
General Motors Corp. 7.125% due
07/15/2013
General Motors Corp. 7.125% due
07/15/2013
Buy/Sell
Notional
Amount
($1,000)
Mark-to-market
($1,000)
Period
Ending
Fund name
Sell
11,700
-249
6/30/2008
TOTAL RETURN FUND
Sell
25,000
-249
6/30/2008
TOTAL RETURN FUND
Sell
30,300
-9,841
6/30/2008
TOTAL RETURN FUND
Sell
6,400
-2,079
6/30/2008
TOTAL RETURN FUND
Sell
7,000
-2,265
6/30/2008
TOTAL RETURN FUND
Sell
6,400
-2,063
6/30/2008
TOTAL RETURN FUND
Sell
14,600
-4,706
6/30/2008
TOTAL RETURN FUND
26
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
× 100
𝑇𝑁𝐴
Similarly, Panels B and C plot gross selling and gross buying, respectively, in percentage points of the
funds’ TNA.
Panel A: Net selling
20.0%
18.0%
16.0%
12.0%
10.0%
8.0%
6.0%
4.0%
2.0%
Sell Intensity
27
11, 12
> 12
9, 10
7, 8
5, 6
3, 4
1, 2
-1, 0
-3, -2
-5, -4
-7, -6
-9, -8
-11, -10
0.0%
< -12
Frequency
14.0%
Panel B: Gross selling
35.0%
30.0%
Frequency
25.0%
20.0%
15.0%
10.0%
5.0%
8, 9
9, 10
10, 11
11, 12
> 12
8, 9
9, 10
10, 11
11, 12
> 12
7, 8
6, 7
5, 6
4, 5
3, 4
2, 3
1, 2
0, 1
Buy only
0.0%
Gross Sell Intensity
Panel C: Gross buying
35.0%
30.0%
20.0%
15.0%
10.0%
5.0%
7, 8
6, 7
5, 6
4, 5
3, 4
2, 3
1, 2
0, 1
0.0%
Sell only
Frequency
25.0%
Gross Buy Intensity
28
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
3098
4468
5886
6509
7281
9329
7667
6305
4922
3732
3868
3430
Amount
($1,000s)
13,154,975
15,898,276
20,034,953
22,064,073
24,317,488
29,161,554
25,989,193
22,565,593
21,158,307
15,392,066
15,694,105
15,976,014
Sell Amount
($1,000s)
8,535,859
9,315,313
13,654,085
14,306,067
15,024,279
17,443,943
15,986,209
11,880,053
9,560,698
8,080,759
8,446,495
9,145,908
66495
241,406,597
141,379,668
Period
#Positions
2007Q1
2007Q2
2007Q3
2007Q4
2008Q1
2008Q2
2008Q3
2008Q4
2009Q1
2009Q2
2009Q3
2009Q4
2007-2009
29
#Families
#Trusts
#Funds
#Entities
28
30
32
33
33
33
33
33
31
32
29
31
51
56
57
58
58
58
55
58
54
48
49
49
152
203
208
214
219
228
210
197
189
170
173
168
290
335
345
350
377
396
384
354
327
291
272
257
33
60
284
445
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
Next 9
Rest
26943
23312
16240
149,074,496
57,445,008
34,887,093
62%
24%
14%
95,106,951
33,149,319
13,123,398
53,967,545
24,295,689
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
Next 50
Rest 345
32326
10906
23263
154,945,903
32,928,095
53,532,599
64%
14%
22%
114,762,631
7,885,987
18,731,050
40,183,271
25,042,108
34,801,549
0.0294
0.0144
0.0268
0.1072
0.0339
0.0767
0.1199
0.0205
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
30
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, 50th (median), and 75th 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
19,454
19,454
19,454
19,454
19,454
19,454
19,454
19,454
19,454
19,454
19,454
0.052
1.09
1438.9
5.563
14.518
2.416
0.491
0.051
0.049
0.144
0.159
0.037
1.572
5990.3
1.838
12.212
0.722
0.278
0.221
0.215
1.098
1.149
0.025
0.31
77.9
4.355
7.145
1.966
0.254
0
0
0
0
0.043
0.62
251.4
5.527
12.089
2.492
0.488
0
0
0
0
0.068
1.21
885.6
6.786
17.022
2.835
0.727
0
0
0
0
Fund-period level
Flow volatility
Portfolio turnover
Fund size ($ million)
Log Fund size
Fund age (years)
Log Fund age
Performance rank
Any CDS sell
Any CDS buy
Gross sell intensity
Gross buy intensity
31
Variable name
# Obs
Mean
Std Dev
25th
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
4,147,385
0.05
1.075
1464.1
5.571
14.624
2.422
0.49
0.058
10.178
9.87
0.001
0.001
0.003
0.004
0.035
1.446
6060.5
1.83
12.426
0.72
0.278
0.071
1.296
1.472
0.027
0.037
0.166
0.166
0.023
0.32
78.3
4.361
7.252
1.981
0.253
0.013
10
8.858
0
0
0
0
Median
75th
Fund-issuer-period level
Flow volatility
Portfolio turnover
Fund size ($ million)
Log Fund size
Fund age (years)
Log Fund age
Performance rank
Bond turnover
# CDS contracts
Assets reference entity
Any CDS sell
Any CDS buy
Gross sell intensity
Gross buy intensity
32
0.04
0.62
248.2
5.514
12.071
2.491
0.487
0.036
11
9.666
0
0
0
0
0.064
1.21
889.5
6.791
17.011
2.834
0.725
0.072
11
10.509
0
0
0
0
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.
33
Panel A. Propensity to CDS usage by mutual funds
Dependent variable:
Flow volatility
Portfolio Turnover
Log Fund size
Log Fund age
Performance rank
# observations
% (Dep var =1)
Pseudo R squared
Logit
Any sell
Any buy
(1)
(2)
12.51***
(2.442)
0.294***
(0.0552)
0.555***
(0.0595)
0.192
(0.151)
-0.206
(0.232)
8.919***
(2.494)
0.279***
(0.0538)
0.430***
(0.0553)
0.103
(0.155)
-0.403*
(0.214)
19,137
5.22%
0.2390
19,454
4.87%
0.1968
Logit, vs. no buy no sell
Buy no sell
Both buy and sell
Sell no buy
(4)
(5)
(6)
3.055
(3.757)
0.123***
(0.0456)
0.261***
(0.0628)
0.0492
(0.200)
0.106
(0.295)
16,000
1.64%
0.1133
12.44***
(2.786)
0.316***
(0.0615)
0.535***
(0.0665)
0.151
(0.173)
-0.522**
(0.259)
13.57***
(3.984)
0.168***
(0.0611)
0.623***
(0.0875)
0.340
(0.239)
0.554
(0.394)
18,565
3.68%
0.2418
15,291
2.06%
0.2380
Panel B: Intensity of CDS usage by mutual funds
Dependent variable:
Gross sell intensity
(1)
Gross buy intensity
(2)
38.65***
(9.285)
0.913***
(0.171)
1.606***
(0.219)
0.418
(0.435)
-1.121
(0.701)
26.83***
(8.831)
1.093***
(0.207)
1.369***
(0.188)
0.0386
(0.517)
-1.471**
(0.724)
19,454
0.1427
19,454
0.1241
Flow volatility
Portfolio Turnover
Log Fund size
Log Fund age
Performance rank
# observations
Pseudo R squared
34
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.
35
Panel A: Propensity to CDS usage by mutual funds
Dependent variable:
Flow volatility
Portfolio turnover
Log Fund size
Log Fund age
Performance rank
Bond turnover
# CDS contracts
Assets reference entity
# observations
% (Dep var =1)
Pseudo R squared
Any sell
Any buy
Any sell
Any buy
(1)
(2)
(3)
(4)
Buy no
sell
(4)
14.00*** 7.520**
7.645**
(3.389)
(3.091)
(3.156)
0.233*** 0.328*** 0.329***
(0.0559) (0.0530)
(0.0541)
0.653*** 0.751*** 0.750***
(0.0868)
(0.142)
(0.145)
-0.00736
-0.169
-0.166
(0.181)
(0.165)
(0.168)
0.220
-0.218
-0.219
(0.332)
(0.419)
(0.428)
0.221
0.263
0.220
0.268
0.0174
(0.221)
(0.252)
(0.224)
(0.256)
(0.275)
0.724*** 0.0770*** 0.732*** 0.0788*** 0.0631***
(0.0556) (0.0178) (0.0555) (0.0182)
(0.0163)
0.589*** 0.137*** 0.596*** 0.141*** 0.0943***
(0.0417) (0.0266) (0.0431) (0.0275)
(0.0318)
4,094,042 4,117,080 4,094,042 4,117,080
0.075%
0.1455
0.140%
0.1085
0.075%
0.2285
36
0.140%
0.2455
Both buy
and sell
(5)
Sell no
buy
(6)
6.852
(4.429)
0.328***
(0.0657)
0.857***
(0.186)
-0.224
(0.236)
-0.0572
(0.579)
2.005***
(0.510)
0.568***
(0.140)
0.706***
(0.0881)
15.01***
(3.455)
0.222***
(0.0594)
0.644***
(0.0867)
0.0173
(0.189)
0.295
(0.346)
-0.132
(0.231)
0.770***
(0.0644)
0.581***
(0.0422)
4,073,070 2,756,756 4,047,326
0.131%
0.2425
0.015%
0.2596
0.065%
0.2176
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
1.457
(0.962)
2.895***
(0.342)
2.596***
(0.281)
0.861
(0.729)
0.229***
(0.0668)
0.434***
(0.102)
57.98***
(16.00)
1.016***
(0.250)
2.593***
(0.340)
0.0797
(0.749)
-0.00815
(1.369)
1.656*
(0.957)
2.899***
(0.348)
2.603***
(0.284)
4,147,385
0.1143
4,147,385
0.0831
4,147,385
0.1714
Portfolio turnover
Log Fund size
Log Fund age
Performance rank
Bond turnover
# CDS contracts
Assets reference entity
# observations
Pseudo R squared
37
22.24**
(9.404)
1.066***
(0.202)
1.988***
(0.257)
-0.601
(0.476)
-1.334
(1.330)
1.028
(0.732)
0.238***
(0.0735)
0.448***
(0.0996)
4,147,385
0.1735
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 t1 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
(1)
PIMCO Lead (t-1)
2009
(3)
(4)
(5)
(6)
1.512***
(0.0760)
104,587
0.934***
(0.0951)
0.272***
(0.0221)
6.415***
(0.333)
-0.0886***
(0.0281)
87,999
2.137***
(0.187)
77,546
0.619***
(0.0996)
0.352***
(0.0261)
38.14***
(1.471)
-0.00151
(0.0321)
65,150
79,172
1.698***
(0.216)
0.445***
(0.0523)
0.628*
(0.339)
0.425***
(0.0830)
64,451
0.950%
0.953%
0.823%
0.819%
0.148%
0.163%
0.0428
0.1228
0.0502
0.0864
0.0636
0.1403
1.405***
(0.0840)
Assets Reference Entity
(log)
Spread-5year (t-1)
Fund TNA (log)
# observations
% (Dep var =1)
Pseudo R squared
2008
(2)
38
Panel B: The remaining 23 mutual fund families
2007
(1)
(2)
PIMCO Lead (t-1)
2009
(4)
(5)
(6)
2.003***
(0.0990)
165,411
1.109***
(0.123)
0.433***
(0.0293)
2.979***
(0.605)
0.133***
(0.0390)
139,143
2.852***
(0.188)
117,795
1.356***
(0.136)
0.410***
(0.0350)
24.67***
(2.468)
0.168***
(0.0478)
98,854
114,831
2.273***
(0.217)
0.441***
(0.0520)
0.459
(0.305)
0.338***
(0.0688)
93,472
0.284%
0.281%
0.258%
0.268%
0.106%
0.119%
0.0510
0.1063
0.0628
0.1031
0.1366
0.2017
1.932***
(0.112)
Assets Reference Entity
(log)
Spread-5year (t-1)
Fund TNA (log)
# observations
% (Dep var =1)
Pseudo R squared
2008
(3)
39
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
(1)
PIMCO Lead (t-1)
1.653***
(0.141)
Assets Reference Entity
(log)
Spread-5year (t-1)
Fund TNA (log)
# observations
% (Dep var =1)
Pseudo R squared
2,216
2008
2009
(2)
(3)
(4)
(5)
(6)
0.855***
(0.199)
0.388***
(0.0453)
90.68***
(5.085)
-0.135**
(0.0581)
1,932
1.556***
(0.105)
0.703***
(0.150)
0.228***
(0.0322)
18.44***
(1.183)
-0.353***
(0.0421)
3,075
3.432***
(0.288)
2.249***
(0.354)
0.618***
(0.0858)
3.761***
(1.168)
0.0245
(0.119)
981
3,521
1,112
33.3%
32.1%
24.5%
23.4%
10.5%
10.7%
0.1015
0.3614
0.0689
0.2004
0.2339
0.3417
40
Panel B: The remaining 23 mutual fund families
2007
(1)
(2)
PIMCO Lead (t-1)
2009
(4)
(5)
(6)
2.190***
(0.123)
3,557
1.071***
(0.181)
0.297***
(0.0407)
14.21***
(1.713)
0.134***
(0.0506)
3,229
3.510***
(0.222)
2,123
1.103***
(0.219)
0.147***
(0.0507)
39.77***
(4.709)
0.289***
(0.0648)
1,927
2,207
3.340***
(0.329)
0.0508
(0.0774)
1.605**
(0.658)
0.235**
(0.0934)
1,980
15.7%
14.4%
12.0%
11.6%
5.5%
5.6%
0.1012
0.1789
0.1357
0.1811
0.3115
0.3439
1.964***
(0.141)
Assets Reference Entity
(log)
Spread-5year (t-1)
Fund TNA (log)
# observations
% (Dep var =1)
Pseudo R squared
2008
(3)
41
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 entityperiod (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
(1)
PIMCO Lead (t-1)
0.629***
(0.143)
Assets Reference Entity
(log)
Spread-5year (t-1)
Fund TNA (log)
# observations
% (Dep var =1)
Pseudo R squared
85,480
0.649%
0.0050
2008
2009
(2)
(3)
(4)
(5)
(6)
0.529***
(0.150)
-0.195***
(0.0349)
-40.16***
(5.462)
0.146***
(0.0369)
71,708
0.708%
0.0253
0.500***
(0.156)
0.558***
(0.162)
0.0771**
(0.0326)
-3.180**
(1.449)
0.118***
(0.0397)
99,877
0.370%
0.0229
0.708**
(0.283)
0.728**
(0.289)
0.0779
(0.0630)
-0.964
(1.139)
0.272***
(0.0829)
70,995
0.139%
0.0255
42
118,865
0.364%
0.0171
87,275
0.131%
0.0118
Panel B: The remaining 23 mutual fund families
2007
(1)
(2)
PIMCO Lead (t-1)
1.183***
(0.116)
Assets Reference Entity
(log)
Spread-5year (t-1)
Fund TNA (log)
# observations
% (Dep var =1)
Pseudo R squared
117,330
0.480%
0.0158
2008
1.193***
(0.121)
0.0480*
(0.0290)
-30.85***
(4.998)
-0.0477
(0.0336)
98,359
0.530%
0.0282
43
2009
(3)
(4)
(5)
(6)
0.473***
(0.141)
0.434***
(0.142)
-0.0402
(0.0311)
-11.56***
(2.237)
-0.172***
(0.0318)
145,288
0.339%
0.0410
1.001***
(0.167)
0.853***
(0.178)
-0.0300
(0.0438)
-3.206***
(1.142)
-0.0474
(0.0481)
101,437
0.238%
0.0423
172,937
0.312%
0.0304
124,746
0.212%
0.0373