Exposing The Exposed: Intermediation
Capacity in the Credit Default Swap Market
Job Market Paper
Or Shachar
∗
Stern School of Business, New York University†
January 2012
Abstract
I examine the role of liquidity provision by dealers in the credit default swap (CDS)
market, using traded volume of a sample of CDS contracts on North American financial
firms during 2007 – mid 2009, a period that notably includes the financial crisis. Consistent
with the standard microstructure literature, I find that order imbalances of end-users cause
significant price impact, which depends on their direction relative to the direction of dealers’
inventory. In addition, I establish that counterparty risk, measured by the level of exposure
in the interdealer market, limits the willingness of dealers to provide liquidity. The more
“congested” the interdealer market becomes from a build-up of bilateral credit exposure, the
more averse dealers become towards inventory risk. Dealers’ desire to hedge counterparty
risk leads to limited intermediation in the CDS market.
∗
I am grateful to my advisers Viral V. Acharya, Robert Engle, Joel Hasbrouck, and Itamar Drechsler for helpful
discussions and generous guidance. I also thank Peter Axilrod, Marisol Collazo, Michael Kopcak and Gary Kotlyar
from The Depository Trust & Clearing Corporation for providing the data and for explaining its features. All
errors are the responsibility of the author.
†
Send correspondence to Stern School of Business, New York University, 44 West Fourth Street, Suite 9-197,
New York, NY 10012. Email: oshachar@stern.nyu.edu.
1
Introduction
A central question concerning dealer markets is understanding the role which dealers play in
liquidity provision. The existing microstructure literature addresses this question in equity,
bond, option and FX markets using information-based (e.g., Glosten and Milgrom, 1985,
Easley and O’Hara, 1987), inventory control (e.g., Garman, 1976, Stoll, 1978, Amihud and
Mendelson, 1980, Ho and Stoll, 1983), and search-and-bargaining (e.g., Duffie, Gârleanu, and
Pedersen, 2005, Lagos and Rocheteau, 2009, Lagos, Rocheteau, and Weill, 2011) models.
In this paper, I attempt to answer this key question using a comprehensive proprietary
dataset of Credit Default Swap (CDS) transactions. My empirical study shows that, to an
extent, standard microstructure models can be used to understand the functioning of the
CDS market in normal times, but that counterparty risk is also empirically relevant in the
determination of the intermediation capacity of dealers and in the resilience of the market
during times of stress.
In order to understand the effect of the counterparty risk friction on intermediation, it is
necessary to understand how CDS contracts trade. Unlike equities or bonds, CDS contracts
do not trade by means of transfer of ownership. Instead, they involve a bilateral agreement
that the protection buyer will pay the protection seller regular premium payments until
either the contract matures or the default of the associated credit instrument (for example,
a bond issued by a firm), whichever occurs first. In return, if the associated credit instrument
defaults, the seller of protection pays the buyer for the loss, and the buyer ceases paying
premia to the seller. As such, either party of a CDS contract is exposed to loss both
through the performance of the underlying asset and through the potential default of the
counterparty. The possibility that the other party of the contract will default before meeting
all of his obligations is often referred to as counterparty risk.1
As a liquidity provider, a dealer firm enters into a transaction with a client even if
it does not wish to retain the exposure. To balance its exposures, the dealer firm enters
into offsetting hedge transactions.2 Over time, the dealer may engage in a large number
of interdealer and other hedge transactions to adjust its positions and limit exposure to
market movements. Unlike cash markets, where the passing of inventory does not leave
any residual obligations to the original seller or buyer, these hedge transactions in the CDS
market might eliminate or at least reduce credit risk, but they do not cancel any previous
contract. As such, as long as the contract is in place, the counterparty risk remains, and
builds up as these transactions involve many counterparties. If a counterparty defaults, the
dealer’s hedged exposure will change and the dealer will consequently find itself exposed to
1
See Deutsche Bank, 2009, “Credit Default Swaps: Heading Towards a More Stable System” for further details
on how CDS contracts work.
2
There are many ways in which a dealer might choose to offset its exposure. It might choose the most basic
hedging strategy of entering into offsetting transactions in the same reference entity within a short time period
surrounding end-users trades; alternatively, the exposure can be hedged in the bond market. In this paper I
concentrate only on the CDS market.
1
unanticipated market movements. To neutralize the exposures, the dealer will need to adjust
the portfolio to bring it back into balance, by replacing the defaulted transactions, unwinding
the offsetting hedge transactions, or both. However, if the default of the counterparty is a
harbinger of additional credit problems, non-defaulting parties would have incentives either
to cut back or terminate transactions with troubled counterparties, and the dealer will lose
its ability to engage in offsetting trades. At the aggregate level, this in turn will affect the
ability of other dealers to supply liquidity.
This ongoing hedging and re-balancing process by dealers, coupled with the empirical
fact that the daily market turnover of interdealer trading accounts for more than 60% of total
contract turnover, and that each end-user trade is passed between three dealers on average,
imply that the concentration of exposures in the interdealer market is a good proxy for the
potential consequences of the exit of a major market participant if all the transactions along
the chain of offsetting positions are to be untangled. I refer to this accumulation of credit
risk within the dealing community as “congestion”. Therefore, to determine what affects
the dealers’ capacity to supply liquidity, I explore the interaction between the exposure
level among dealers and the ability of dealers to provide liquidity. I expect that when the
interdealer market is congested, dealers find it harder to mitigate their inventory risk and
become more averse to supplying liquidity to their clients.
It is important to note that this issue has been identified by market participants and
regulators as problematic to the functioning of the market. Since 2008, procedures to reduce
the redundant positions have been developed. The procedure that appears to be most
efficient for reducing the value of outstanding positions is referred to as “compression”.
Compression nets the positions across dealers, taking into account the limits established by
dealers on counterparty risk of other dealers and identifying contracts that can be eliminated
or replaced with new, lower notional value contracts, with the aim of keeping the same risk
profile. Compression, therefore, has resulted in a great reduction of the gross notional value
of CDS positions, but has not resolved the issue completely.
The economic importance of the mechanism of congestion in the interdealer market that
lies at the heart of this paper is reflected in the size of the CDS market. At the end of
2010, the gross notional value of CDS positions registered at the Depository Trust Clearing
Corporation (DTCC) amounted to $26.3 trillion. Of this total, $15 trillion was single name
CDSs and $11.3 trillion was index or tranche CDSs. But these gross notional amounts include
a large number of offsetting trades that can be netted down across investors. For singlename CDS, the $15 trillion gross notional amount nets down to a $1.2 trillion net notional
amount (8% of the gross notional value), while for index and tranche CDS the $11.3 trillion
gross notional amount nets down to a $1.1 trillion net notional amount (approximately 10%
of the gross notional value).
Despite the size of the CDS market and its importance, as the financial crisis has illustrated, there are virtually no empirical studies using transaction flows and dealer inventories.
2
Lack of data on trading volume figures has been the primary impediment to such research.
This paper is, to the best of my knowledge, the first to study the role of dealers in the CDS
market. The empirical study of CDS dealers is particularly interesting since low regulation
has allowed the patterns of liquidity provision to evolve endogenously. I analyze a unique
dataset of transactions between dealers and their customers spanning from February 2007 to
June 2009 for CDS contracts on 35 North American financial firms. This sample is obtained
from the DTCC, the only central trade registry. DTCC claims to cover about 90% of the
notional traded in the CDS market, hence, my sample reflects the vast majority of market
activity in the 35 reference entities.
The data contain an anonymized identification of the parties of each transaction. Each
individual market player has a consistent identifier throughout the dataset and a classification of its type (dealer vs end-user). Hence, I am able to construct the daily order imbalances
of dealers and end-users and study the impact of order imbalances. I refer to a non-dealer
customer as an end-user. End-users in the sample include hedge-funds, pension funds, asset
managers, banks, and non-financial companies. The dealers in the sample include the G14
dealers, who are the major 14 over-the-counter (OTC) derivatives dealers, as well as some
smaller dealer firms.
Using this sample, I first establish benchmark results that are drawn from the standard
microstructure models. For each reference entity I examine (i) the association between
the change in the CDS price and the imbalance between buyer- and seller-initiated orders
arriving at the market; and, (ii) how dealers’ inventory levels affect this price impact. Since
order flow might be correlated with fundamental information, I control for contemporaneous
and lagged equity returns (following Acharya and Johnson, 2007). Both Collin-Dufresne
et al. (2001) and Schaefer and Strebulaev (2008) show that equity price changes seem to be
adequate to capture all fundamental information related to default risk, so the unexplained
component of CDS price changes beyond that can be attributed to microstructure frictions.
I find that there are different price reactions to buying versus selling by end-users. The
market imputes an information content to the trade, both for buying and selling by endusers. Yet, the contemporaneous price impact of buying is much larger than the immediate
price impact of selling. Specifically, I find that a one standard-deviation increase in net
notional amount bought by end-users is associated with a 0.3% contemporaneous increase
in CDS spreads, whereas a one standard-deviation increase in net notional amount sold by
end-users predicts a contemporaneous decrease of 0.06%, which is statistically insignificant,
and about one-fifth the impact of buying. The result that the market reacts differently to
buy and sell orders resembles the results that emerge from the empirical research on block
transactions and institutional trades in equity markets (for a survey of these results see
Saar, 2001). The empirical results also suggest that the price impact is permanent, as the
effect persists for at least five days. The asymmetric impact of buys and sells suggests that
end-users face a more illiquid market when they are buying default protection compared to
3
when they are selling protection to the dealers. The results are consistent with dealers being
naturally net long credit risk, and, therefore, being less willing to sell protection, as it will
further increase their exposure to the financial sector.
As for the effect of dealers’ inventory on price impact, I find evidence consistent with the
microstructure models, such as, Madhavan and Smidt (1991), Madhavan and Smidt (1993),
and Hasbrouck and Sofianos (1993) which predict a lesser price response for order flow that
moves dealer inventory to a preferred position, and that order flow which increases dealer
inventory exposure is associated with a larger price impact. The intuition behind this result
follows from the following example. If dealers hold a long position of CDS protection (i.e.,
they are short credit risk) at the end of day t−1, net-selling by end-users during day t should
increase dealers’ long position further. Therefore, to deter additional potential sellers, or
conversely to attract potential buyers, dealers will lower their quoted prices, intensifying
the price impact of the selling pressure. If, however, during day t, end-users are net-buyers,
this should help dealers revert to their target inventory level (assuming that the quantity
demanded by end-users is smaller than the level of dealers’ inventory).
The benchmark specification, however, is an incomplete description of the CDS trading
architecture as it excludes a key feature – counterparty risk. To this end, I construct a
measure of exposure in the interdealer market based on pairwise exposures between dealers.
Using this measure, I demonstrate that when dealers are more exposed to each other their
willingness to intermediate markets is reduced and they become less predisposed to absorb
supply and demand shocks. Furthermore, the congestion in the interdealer market has a
secondary impact by discouraging dealers from holding buffer inventories. I find that these
two effects increase the price impact of order imbalances, and that dealers’ inventories matter
even more when significant counterparty risk is built up amongst the dealers. Though the
key component of the price impact in “normal” times is a result of the quantity the end-users
demand, the interdealer exposure effect kicks in when the inventory-absorption capacity of
the dealers in the CDS market is strained.
To gauge the economic magnitude of each of the effects, I decompose the total price
impact of trading into the key components found in this paper for: (a) a benchmark case that
is based on mean values of equity and CDS returns, dealers’ inventory level and end-users’
net buying imbalance, and for (b) an extreme case when the realizations of all variables are
at their maximum. Using the mean level of the variables, the “basic” price impact accounts
for 28% and 7% of the total price impact for buys and sells, respectively, and an incoming
order-flow that will increase dealers’ inventory results in 16% and 23% of the price impact
for buys and sells, respectively. While the congestion effect plays a minor role in normal
times, it accounts for as much as 23% and 37% of the total price impact, for buys and sells,
respectively, if all independent variables are taken at their maximum values. In either normal
times or crisis times, the combined microstructure effect of end-users’ order imbalance,
dealers’ inventory and interdealer exposure congestion (including the interactions between
4
the three) is about the same magnitude as the impact of information flow from the stock
market and lagged CDS spreads. These microstructure effects are not “informationless”
and they actually generate new fundamental information over and above the information
generated in the equity markets. I also find that both end-users’ order imbalances in the
CDS market and the information innovations from the CDS market predict stock returns
1-day ahead.
The results for the single-name contracts are contrasted with the trading in the CDX
index, a basket product. Apart from the information advantage one might have when trading
a single-name contract, the trading mechanism of the index is inherently different. Rather
than using offsetting trades to unwind positions, the party that wishes to unwind his position
“assigns” the trade, i.e. transfers his side of the trade, to a new party. Therefore, I repeat
the analysis using the transactions of the CDX index and I find results that are consistent
with the use of the index for hedging purposes by end-users. In the CDX index, dealers do
not seem to play a significant role in liquidity provision.
Finally, to support the main results in the paper, I also provide evidence that dealers’
inventories mean-revert in a non-linear fashion. Underlying the inventory result lies an implicit assumption that dealers have a desired level of inventory. As they buy and sell, their
inventory deviates from the target and, therefore, they require compensation. Rather than
just assume this, I test a prediction of Ho and Stoll (1983) about competitive dealer markets
pertaining to mean reversion in dealers’ relative inventories, using the methodology in Hansch et al. (1998). Similar to the results found by Hansch et al. (1998) for the London Stock
Exchange dealers, I also find evidence that dealers with extreme inventory positions trade
larger quantities that move their inventories toward desired levels, and that the intensity of
mean reversion increases with the deviation of the inventory from its desired level. Nevertheless, the time-scale of mean reversion is not days, but weeks or months. The half-life of a
dealer’s inventory that is one standard-deviation away from the median level of all dealers is
about 160 days, compared to 11 days for an inventory level that is five standard-deviations
from the median. Dealers’ persistent positions are also consistent with the result that the
price impact it permanent.
The novel contribution of this paper – the “congested interdealer market” hypothesis
– can be extended to other markets trading bilateral agreements, such as, FX derivatives,
interest rate swaps, and OTC equity derivatives, where counterparty risk is present, and
to markets that are linked to them through an arbitrage relationship. For example, in
related work, Choi and Shachar (2011) use this hypothesis to shed light on the persistent
“violation” of the close relation between CDS and corporate bond spreads – the CDS-Bond
Basis – during the crisis.3
3
Bai and Collin-Dufresne (2010) and Fontana (2011) also study the deviation of the CDS-bond basis from
parity during the financial crisis. These papers document association with the following risk factors: funding cost
risk, counterparty risk, and collateral quality. Choi and Shachar (2011), on the other hand, focus on counterparty
risk and the transmission mechanism between dealers’ liquidity provision and hedge funds’ trading.
5
My results have a number of important implications. From a modeling perspective, my
findings suggest that counterparty exposure in the interdealer market is a critical determinant of liquidity provision by dealers. It follows that existing OTC trading models can
benefit from a better understanding of the structure of the interdealer network and how
exposures are spread in this network. The microstructure frictions found in this paper also
help to shed some light on the possible use of CDS prices for regulation purposes, e.g. Hart
and Zingales (2009). The economic magnitude of these frictions should be estimated and
accounted for before extrapolating the credit risk component from CDS prices. Finally, my
results give insight regarding the potential effects of a central clearing counterparty (CCP)
in the CDS market that is currently being discussed by policy makers (Duffie and Zhu,
2009, Acharya, Shachar, and Subrahmanyam, 2010). Given that heightened levels of credit
exposure in the interdealer market inhibit liquidity provision in times of stress, it seems
that CCPs will primarily create value by reducing counterparty risk during crisis periods,
conditional on them not succumbing to aggregate crisis.
Related Literature
This paper is closely related to the inventory control models in dealer markets. In particular,
the key finding of this paper is anchored in a trading mechanism that was identified in the
Foreign Exchange (FX) market. Lyons (1997) studies the repeated passing of inventory
imbalances between dealers, i.e. “hot-potato trading,” in the FX market. He shows that
hot-potato trading reduces the information content of interdealer trades. Since the FX
market is also an OTC derivative market like the CDS market, it is not surprising that I
find evidence that such hot-potato trading also happens in the CDS market. In the context
of the CDS market, I find that this trading mechanism imposes a negative externality as
interdealer exposure builds up.
My paper is related to several themes within the CDS literature. Acharya and Johnson
(2007) study the flow of information from the CDS market to the equity market during 20012004. Berndt and Ostrovnaya (2007) conduct a similar analysis of the flow of information
across the CDS, option and equity markets. My work shows that there is also an additional
information in the microstructure effects that flows from the CDS market to the equity
market. Arora et al. (2010) use a proprietary dataset of CDS transaction prices and quotes
to identify how dealers’ credit risk affects the prices of CDS contracts. They find that
counterparty risk is barely priced in the CDS market, which is consistent with a market
structure in which participants require collateralization of swap liabilities by counterparties.
Whether counterparty risk is priced directly in the CDS price or not, I show that it imposes a
significant cost on trading in stress times through the liquidity channel. My analysis suggests
that the quantities traded between dealers demonstrate the importance of counterparty risk.
My paper is also tangential to the literature on OTC markets, and limited risk-bearing capacity in market-making. The theoretical OTC market literature that was recently reignited
6
by Duffie, Gârleanu, and Pedersen (2005, 2007) identifies counterparty search and bargaining over the terms of trade as important frictions to trading in this type of market. In
these search models there is no role of liquidity provision by dealers in the face of temporary
demand pressures due to the assumptions that investors hold 0 or 1 unit of the asset and
dealers are restricted from holding asset inventories. Yet, as my empirical results show in
the context of the CDS market, dealers’ inventories appear to be an important determinant
of their ability to provide liquidity and the price of that liquidity to end-users. Lagos, Rocheteau, and Weill (2011) relax the restriction on dealers’ and investors’ inventory, to allow
them to hold any positive amount. They find that when frictions are severe, even wellcapitalized, profit-maximizing dealers may not find it optimal to “lean against the wind,”
that is, to accumulate inventories during the crisis and to unload these assets when the
economy recovers. Though the theoretical prediction of reduced liquidity provision during
the crisis seems to be consistent with my empirical findings in the CDS market, this prediction is driven by a shock to investors’ risk-aversion, not dealers’. Dealers, however, are able
to trade continuously with each other. My main insight suggests that the lack of liquidity
provision by dealers comes from the search of dealers for other dealers with sufficiently low
counterparty risk. A future theoretical model that desires to explain liquidity provision in
the CDS market should incorporate such friction.
In the broader context of intermediated markets, trading capacity constraints result in
limits to arbitrage. Liquidity suppliers profit from providing immediacy to investors. They
have, however, limited risk-bearing, and thus inventory-carrying, capacity. The limits to
arbitrage arguments rely on the idea that these liquidity suppliers will take the role of
arbitrageurs and accommodate buying or selling pressure, only if they are compensated by
favorable subsequent price movements. Different papers identify different sources for what
limits arbitrage. Shleifer and Vishny (1997) show that capital constraints due to agency
problems hinder dealer firms exploiting mispricing. Deviation from fundamental value might
be also a result of the time it takes for capital to come to the market. Grossman and Miller
(1988), Mitchell et al. (2007), and Duffie (2010), to name a few, document alternative reasons
for capital dislocations. Gromb and Vayanos (2002) and Brunnermeier and Pedersen (2009)
study how leverage constraints affect the ability of dealers to supply liquidity to outside
clients. These frictions result in some part of the risk in derivatives becoming unhedgeable,
leading to an upward sloping supply curve (Bollen and Whaley, 2004, Çetin et al., 2006,
and Gârleanu et al., 2009).
2
Hypotheses Development
Trading in the CDS market integrates essentially two parallel trading environments: a trading environment where end-users trade directly with dealers, and an interdealer trading
environment where dealers are able to share their inventory risks across the dealers com-
7
munity rather than waiting for the arrival of offsetting customer orders. Hence, the effect
of each trading arena on the other should be carefully assessed. Although I draw from the
existing microstructure literature, the inherent differences of the CDS market architecture
pose some questions regrading the validity of the use of the existing theoretical models that
are applied to cash markets. I try to bridge this gap by supplying empirical evidence on
what should be added to any theoretical model that would describe the CDS market.
Theoretical motivation for the empirical investigation of the order imbalances I undertake comes from the standard market microstructure models which suggest that trades are
associated with price movements resulting from the presence of trading frictions – such as
inventory costs, information asymmetry, funding constraints, and search costs. In the inventory control paradigm (e.g., Garman, 1976, Stoll, 1978, Amihud and Mendelson, 1980,
Ho and Stoll, 1983, among others) dealers accommodate buying and selling by outside investors and adjust their quoted prices to restore their inventories to some desired level. The
implication of these models is that price may depart from expectations of value if the dealer
is long or short relative to its desired inventory, giving rise to transitory price movements
during the day and possibly over longer periods. In the asymmetric information models
(e.g., Glosten and Milgrom, 1985, Easley and O’Hara, 1987, among many others), a subset of the market participants have private information about the asset’s (expected) value,
so order imbalances sometimes signal private information, which should reduce liquidity at
least temporarily, and could also move the market price permanently, as also suggested by
the Kyle (1985) model of price formation.
Hypothesis 1. End-users’ net buying (selling) positively (negatively) affects price changes.
The microstructure literature has extensively explored the relation between trading activity and returns. Both theory and empirical papers conclude that order imbalances should
have an important effect on asset returns and liquidity. Order imbalance can sometimes
signal private information, which should reduce liquidity at least temporarily and could also
move the market price permanently, as also suggested by the well-known Kyle (1985) model
of price formation. His model predicts that the coefficient on order flow will be positive.
Order imbalance can also be caused by a random large order. Then, it might exacerbate
the dealer’s inventory problem only temporarily until it is reversed by other traders.
Hypothesis 2. The impact of end-users’ demand depends on whether their demand will
improve the aggregated level of inventory held by dealers or move it away from an optimal
level.
Stoll (1978) and O’Hara and Oldfield (1986) use inventory-control models to show how
risk-averse dealers charge premia to accommodate order imbalances. Since dealers use quotes
to elicit trades to balance their inventory positions, dealers’ inventory is expected to affect the
price response to order flow (Madhavan and Smidt, 1991, and Madhavan and Smidt, 1993).
These models predict that market depth is greater (lower price response) for order flow
8
which moves dealers’ inventory to a preferred position, and that order flow which increases
dealers’ inventory exposure, in either direction, is associated with larger price impacts.
Hypothesis 3. Dealers’ inventories matter more when significant counterparty risk has
built up within the interdealer market.
Since there are no natural sellers of protection, dealers stand ready to absorb temporary
imbalances between demand and supply. The interdealer market then serves as a vital venue
for dealers to offload their inventory risk and share it with other dealers. In the process,
new trades are created until the credit risk finds a willing home. The data shows that an
end-user trade is passed between three dealers, on average. Each link in the chain involves a
different exposure to counterparty risk. As long as the contract is in effect, the counterparty
risk remains. The interdealer activity is concentrated among five major dealers, so such
circular trading might increase the overall counterparty exposure among them.
If the level of counterparty risk exposure amongst dealers increases, they become less
able or predisposed to absorb supply and demand shocks, and to hold buffer inventories of
assets. Under extreme conditions, dealers might even exit the market, which in turn might
leave fewer dealers to make most of the market. The inventory levels of these active dealers
is expected to build up. Then, the price impact of a trade must be greater for them to
absorb the demand of end-users.
Hypothesis 4.
• The inventory level of an individual dealer relative to its peers determines the direction
and size of their trades.
• The force of mean reversion in a dealer’s relative inventory position should be increasing
in the relative inventory level.
In the theoretical inventory models of dealer markets, the likelihood of execution depends
on the degree of competitiveness of the dealer’s quotes, which in turn depends on his relative
inventory position. When a dealer has an extreme inventory position, he is able to post
competitive quotes on one side and stands a better chance of executing the end-users’ order
flow in the desired direction. This results in a relatively quick reduction of his inventory
imbalance. On the other hand, when a dealer’s inventory is close to the median, he is not
able to post competitive prices and therefore stands a poor chance of executing the end-users
order flow. As a result, his inventory takes a longer time to revert to the desired level. This
implies that in competitive dealer markets, the relative inventories of the dealers should
be mean reverting and the strength of mean reversion should be increasing in the relative
inventory level.
9
3
Data Description & Sample Construction
I was fortunate to be given access to an extensive CDS transactions data by DTCC, who
provides clearing, settlement and information services for OTC derivatives. In November
2006, DTCC established its automated Trade Information Warehouse as the electronic central registry for CDS contracts. Since that time, the vast majority of CDS contracts traded
have been registered in the Warehouse. In addition, all of the major global CDS dealers
have registered in the Warehouse many of the contracts that were executed among each
other before that date.
The data covers all transacted CDS contracts of 35 financial firms between February
2007 and June 2009. Each transaction contains the following information: the name of
the reference entity, the trade date and the effective date, the (expected) maturity of the
contract, anonymized identities of the participating counterparties including the type (dealer
or end-user), and the executed notional amount. The dealers, who generally provide liquidity
and trade on both sides, include the “G14 dealers”: Bank of America-Merrill Lynch, Barclays
Capital, BNP Paribas, Citibank, Credit Suisse, Deutsche Bank, Goldman Sachs, HSBC,
J.P. Morgan, Morgan Stanley, The Royal Bank of Scotland, Société Générale, UBS, and
Wachovia Bank, as well as smaller dealers. The data also contain a finer classification of
the end-users to: asset managers, banks, financial services, hedge-funds, insurance firms,
and “other” (the last group accounts for less than 3% of the notional amount traded by
end-users). Information about the identities of parties trading through DTCC is privileged,
and their agreements with the participating institutions do not allow them to reveal it. Yet,
each counterparty is identified by a unique number over time, and over underlyings, which
allows me to follow counterparties’ positions over-time and across names.
Most transactions datasets, even of equity markets, do not identify buyers and sellers,
forcing researchers to classify buyer-initiated or seller-initiated categories using some algorithm. The Lee and Ready (1991) algorithm is often used and it classifies a trade as
buyer-initiated if it is closer to the ask of the prevailing quote and as seller-initiated if it is
closer to the bid. Since transaction prices are unavailable for the entire time-series, using
this algorithm is infeasible. Nevertheless, the DTCC dataset does include a classification of
the buyer and the seller. Hence, for the purpose of analyzing the price formation process, I
assume that end-users are the ones who initiate trades in this market, rather than dealers.
Given that assumption, I am able to infer the trade direction and to classify transactions
as buyer-initiated or seller-initiated. The signing of transactions allows me to borrow from
the common techniques used in the microstructure literature of the equity market. I believe
that this assumption is not so far from what happens in practice, as incoming trades are
generally initiated by customers for which the dealer will always be the supplier of liquidity. Note that transactions are submitted post-execution and are not time-stamped for the
actual “execution” time, so the price-impact analysis can be performed only at the daily
level.
10
Another important feature of the dataset is the specification of the type of the transaction,
which determines the traded notional amounts and the exposures. Specifically, a transaction
can be a new trade, an assignment of an already existing trade, or a termination of an existing
trade. When an investor wants to enter into a CDS contract he can either enter into a new
contract or find a counterparty that wishes to assign his position to him. When an investor
wants to unwind an existing contract, he faces three alternatives: enter into a new offsetting
transaction, assign the contract to a new counterparty, or terminate the transaction with
the original CDS counterparty. Each alternative carries different implications for the level
of counterparty risk exposure in the market. For further details on each of these three
mechanisms see Appendix A.
The transaction-level dataset is augmented with quotes of CDS prices from Markit Group
Limited, a financial data provider, specialized in security and derivatives pricing. One of
its services is to gather, validate and distribute end-of-day composite CDS spreads provided
by major dealers. These reported prices are averaged for each CDS contract after eliminating outliers. The maturities of Markit CDS contracts range between 6 months and 30
years. In the benchmark analysis, I use the prices of the most popular and liquid 5-year
CDS contracts on senior unsecured obligations of North American reference entities with
modified restructuring clauses. I choose to use the modified restructuring since the default
trading style in North America moved from trading with restructuring to trading without
restructuring only towards the end of the sample in April 2009 along with the introduction
of the Big Bang Protocol and “Standard” trading. CDS spreads of other contract maturities
ranging between 1- and 10-year are relatively liquid and are used for robustness checks. I
observe that the vast majority of quotes lie between 0 and 300 basis points. However, quotes
occasionally exceed 3,000 bps.4
After the initial filtration of the DTCC data and the merge with Markit and CRSP
datasets, some reference entities are rarely traded and would not provide reliable observations. Therefore, to be included in the sample, I require that a CDS contract on a specific
reference entity is traded at least 10% of the trading days in its sample. I also exclude CDS
contracts that their underlyings are Real Estate Investment Trusts (REITs) as their trading
characteristics might differ from ordinary single-name contracts. The final sample consists
of 35 individual, financial-related reference entities financial firms. Table 1 lists the different
firms. Of the reference entities, 5 are classified as security and commodity brokers, dealers,
and services, 7 are depository institutions, 5 are non-depository credit institutions, 23 are
insurance related, 1 is real-estate related, and 1 is an asset management firm.
To investigate the role of supply and demand in the CDS market I assess the relation
4
Such high spreads were observed in the sample especially during September 2008 and May 2009 in the following
reference entities: Ambac Financial Group, American International Group Inc, Avis Budget Group, Genworth
Financial Inc, Radian Group Inc. These levels are associated with a very high probability of default in the shortterm horizon. For example, if it were known with certainty that an entity would default in 1 year and that there
would be no recovery, then the loss would be 100% 1 year from now and to cover this cost it would be necessary
to charge a CDS spread of about 10,000 bps per year.
11
between net buying pressure of end-users and dealers and the change in CDS spreads. I
define the change in inventory in a specific reference entity of each agent as the difference
between the notional amount bought and sold by him during the trading day, regardless of
the contract maturity,5 where the direction of the transaction is determined based on the
assumption that the end-user initiates the trade. Specifically, I construct the time series
Qu,d,t , the net notional amount bought by (Qu,d,t > 0), or sold to (Qu,d,t < 0), dealer d in
P
reference entity u on day t, and Iu,d,t = Iu,d,0 + ts=1 Qu,d,s , the inventory of dealer d in the
CDS contract on reference entity u accumulated by day t, where Iu,d,0 is the inventory at
the start of the sample period. In my sample, u = 1, 2, . . . , 35 and d = 1, 2, . . . , Du , where
Du is the number of dealers in CDS u, which varies between 4 and 17 dealers depending
P u
on the reference entity. I further construct Qu,d2d,t = D
d=1 Qu,d,t , the daily aggregated net
Pt
P u
notional amount bought by, or sold to, dealers, and Iu,t = D
s=1 Qu,d,s , the
d=1 Iu,d,0 +
daily aggregated inventory of the dealers as a whole.
Different contracts have different spreads, trading intensities, volume levels and volatility,
and, therefore, cannot be compared directly. To facilitate comparison across securities and
across dealers, I standardize the inventories series using Hansch et al. (1998) methodology
for equities. I scale the inventory changes both at the reference entity level, and at the
dealer level. More precisely, for each of the inventory series, Iu , I compute the sample timeseries mean (I¯u ) and the sample standard-deviation (σu ), and define Iˆu,t , the standardized
aggregated inventory of dealers in CDS u at day t, as:
Iˆu,t :=
=
Iu,t − I¯u
σu
P
Iu,0 + tτ =1 Qu,τ −
Pt
τ =1 Qu,τ
=
−
1
T
PT
1
T
PT
τ =1
τ =1 (Iu,0
+
Pτ
r=1 Qu,r )
σu
Pτ
(1)
r=1 Qu,r
σu
This equation implies that, although the initial inventory level is unobserved in the data,
the changes in inventories and the standardized inventories that I use in the empirical tests
are independent of that initial level, Iu,0 , although notice that Iˆu,0 is usually non-zero.
Similarly, I standardized the inventory series of individual dealers as follows:
Iˆu,d,t :=
=
=
Iu,d,t − I¯u,d
σu,d
P
Iu,d,0 + tτ =1 Qu,d,τ −
1
T
Pt
σu,d
Pτ
τ =1 Qu,d,τ
−
1
T
PT
τ =1
PT
τ =1 (Iu,d,0
+
Pτ
r=1 Qu,d,r )
(2)
r=1 Qu,d,r
σu,d
5
This definition treats all maturities as essentially the same security, as price movements on different maturities
are strongly correlated.
12
where I¯u,d is the sample time-series mean of the inventory that dealer d holds in CDS u
and σu,d is the sample standard-deviation of that inventory time-series. This standardization captures the notion that different dealers perceive the inventory risk in a similar way
when their inventory is measured in terms of the distance (in standard-deviations) of their
standardized inventory from the respective sample mean.
In order to be consistent with the standardization process, I measure the daily net
positions in units of standard-deviation of dealers’ inventory in that reference entity (σu ).
I define Q̂u,eu,t as the net change in the inventory of end-users as a result of engaging in
Nu,eu,t trades on day t:
Q̂u,eu,t :=
Nu,eu,t
1 X
qu,eu,t (n)
σu
(3)
n=1
where qu,eu,t (n) is the signed quantity of the nth trade, where qu,eu,t (n) < 0 if an end-user
sold to a dealer and qu,eu,t (n) > 0 if an end-user bought from a dealer.
Table 2 details the summary statistics for the key variables that are used in the empirical
analysis. Tables 3–5 provide summary statistics of different aspects of the trading activity
of the reference entities. They present the trading activity in each of the reference entities
in the sample in terms of days when there was a transaction and in terms of traded contracts, respectively. These summary statistics compare the activity of end-user/dealer and
dealer/dealer activity.
Table 3 details the number of transactions each group executed over the sample period.
The table also breaks down the type of transaction. The table suggests that asset managers,
banks and hedge funds are the most active traders among end-users. Dealers engage in more
assignments than end-users. While end-users do use assignments to unwind positions, they
are particularly active as transferors. That is, they seldom take a new position through
an assignment. Investors, and especially hedge funds, tend to prefer to unwind a position
through an assignment rather than through an offset because they are reluctant to incur
additional counterparty exposure and potentially additional margin requirements on the
offsetting swap. They generally prefer assignment to termination because termination forces
them to accept the price proposed by the original counterparty, and can provide insights into
trading strategies to the counterparty. The table indicates that the share of assignments is
roughly 25%, which is consistent with the results of the March 2007 Report of the Committee
on Payment and Settlement Systems, a survey of dealers in the OTC derivatives market.
The last row in Table 3 breaks down the market share of each of the aforementioned
class. It might seem from this table that end-users trade on both sides of the market, but
this is not the case. The reported traded notional takes into account the round-trip of the
investors, that is, it counts the initial buy and then the sell. When the traded notional
amounts are calculated only for new trades, most end-user groups are more active as buyers
of protection rather than as sellers.
13
Table 4 suggests that there is some relation between the activity in the interdealer market
and the activity in the client-dealer market. On most trading days there is activity in the
client-dealer front as well as in the interdealer market. There is no trading activity only in a
small fraction of the business days, and the least traded contract trades on more than 70%
of the business days. Table 5 further supports the links between interdealer trading and
client-dealer trading. It breaks down the activity in terms of number of traded contracts.
Additional support for the importance of the interdealer market can be found in the concentration of the trading activity among the G14 dealers. Using the Herfindahl-Hirschman
Index, I find that the activity is moderately concentrated among these dealers. The five
most active participants made up almost 50% of the buys and about 50% of the sells for all
trades in terms of notional amount traded.
In contrast to the single-name contracts, the summary statistics for the CDX index
indicate that the trading mechanism for the CDX index, a basket product, is quite different.
Equivalent to Table 3, Table 6 details the summary statistics for the CDX index. It is
interesting to note that the majority of the trading in the CDX index is done through
assignments rather than through trades. Among the end-users, the most active participants
in assignments are hedge funds and asset managers. This observation is key in interpreting
some of the empirical results. Mainly, the common use of unwinding a position in the CDX
index by assigning it to another party relaxes some of the counterparty risks that raise from
creating another offsetting new trade, as common in the trading of the single name contracts.
4
The Ability of Dealers to Absorb End-Users’ De-
mand
As a first step to assess the role of liquidity provision by dealers in the CDS market, I explore
how price changes relate to net (pooled) order flow in the CDS market. Methodologically,
my analysis of CDS transactions data draws from the microstructure literature on equity and
fixed-income markets that considers the price impact in the presence of trading frictions –
such as, information asymmetry, funding constraints, search and inventory costs – and their
interplay leads to “price impact”, in which purchases (sales) of assets typically increase
(depress) the price. Given the price impact of end-users’ order imbalances, I then examine
the ability of dealers to absorb that demand, conditioning on their inventory level.
To estimate end-users’ impact on market depth (Hypothesis 1), I regress CDS returns on
the contemporaneous imbalance of end-users’ order-flow and lags of order imbalance. I also
account for the flow of information originating from the equity market by considering the
contemporaneous effect of stock returns on CDS returns (following Acharya and Johnson,
2007), and lagged log-returns of the CDS contracts. I use these controls because order flow
might be correlated with fundamental information. Collin-Dufresne et al. (2001) and Schaefer and Strebulaev (2008) show that the simple Merton (1974) model seems to be adequate
14
to capture all fundamental information, as far as default risk goes. Therefore, after controlling for equity returns, the unexplained component of the CDS returns might be specific to
the microstructure of the CDS market. Specifically, I use the following specification:
∆ log Su,t = α +
K
X
K
X
γk Q̂u,eu,t−k 1>0 +
δk Q̂u,eu,t−k 1<0
k=0
+
5
X
k=0
ζn ∆ log Pu,t−n +
n=0
5
X
ηn ∆ log Su,t−n + εu,t
(4)
n=1
To account for the asymmetries of buying and selling pressure, I use Q̂u,eu,t−k 1>0 :=
n
o
n
o
max Q̂u,eu,t−k , 0 and Q̂u,eu,t−k 1<0 := max −Q̂u,eu,t−k , 0 . Su,t denotes the 5-year CDS
quoted spread for reference entity u at the end of day t; and, Pu,t denotes the equity
price of the same firm at the end of day t. If no price changes are recorded during the
trading day, price change is designated as zero. Also, since outliers might coincide with
high illiquidity, I run the regression for log-difference: ∆ log Su,t := log Su,t − log Su,t−1 ,
∆ log Pu,t := log Pu,t − log Pu,t−1 , ∆Su,t := Su,t − Su,t−1 , and ∆Pu,t := Pu,t − Pu,t−1 , as it is
better in terms of not having undue influence of outliers. This is the baseline structure of the
regression specifications. Since I perform a pooled regressions (all reference entities and all
trading days), I must account for correlation across underlyings and across time. To address
this, I compare the White standard errors and clustered standard errors (when I cluster by
time, by firm, and by both firm and time), and I find weak firm- and time-effects in the
panel data. Hence, I conduct the empirical analysis by adjusting for heteroscedasticity and
controlling for the time effect by adding daily time dummies. Because of the presence of time
dummies, I do not include any other macroeconomic variables in the regression analysis.
Table 7 reports the results of the pooled price-impact regressions that are based on the
standardized traded notionals. I find that when end-users buy more contracts, CDS returns
tend to increase, and when they sell more contracts the returns diminish. The price reaction
to buying versus selling by end-users seems to be asymmetric. The contemporaneous effect
of net buying by end-users is significant, while the net selling effect is insignificant in the
majority of the specifications. This is not surprising, considering the composition of endusers in the CDS market, who mostly buy CDS contracts to hedge their core businesses. To
address the issue that a termination of a CDS contract might not be as informative as a new
CDS contract in which an end-user is the seller, I re-run this regression with a restricted
sample with only new trades. I get the same qualitative asymmetry between buys and sells.
The regression results also suggest that the trend in the price impact is not temporary,
not reversing even five trading days later. This might lend support to an information-based
story, where end-users as a whole are more informed than dealers, while the last take the
other side of the trade as part of their role to provide liquidity in the market.
I have checked for robustness using a number of different variations of Equation (4). The
15
results are not significantly affected by: (i) the inclusion or exclusion of lagged CDS returns;
(ii) the use of unscaled imbalance measures; (iii) the use of the number of transactions
rather than the dollar volume; and, (iv) use of new trades only. The asymmetry response
to net-buying and net selling could have been a by-product of the type of the transaction.
In other words, it is possible that taking the selling side in termination carries different
implication in terms of information than taking the selling side in a new trade. Therefore, I
re-estimated the results for new trades alone and the results are substantively similar to the
results presented in Table 7. These robustness checks are discussed in detail in Section 7.
Hypothesis 2 suggests that the effect of end-users’ order-imbalances depends on the
dealers’ inventory level. To test this hypothesis, I estimate the following regression:
∆ log Su,t
= α + Q̂u,eu,t 1>0 β1 + δ1 1|Iˆu,t−1 +Q̂u,eu,t |% + Q̂u,eu,t 1<0 β2 + δ2 1|Iˆu,t−1 +Q̂u,eu,t |%
+ γ1|Iˆu,t−1 +Q̂u,eu,t |% +
5
X
ζn ∆ log Pu,t−n +
n=0
5
X
ηn ∆ log Su,t−n + εu,t
(5)
n=1
where the dummy variable captures whether the demand of end-users during day t increases
dealers’ t − 1 absolute aggregate inventory of underlying u. Specifically,
1|Iˆu,t−1 +Q̂u,eu,t |%
 1 Iˆu,t−1 > Iˆu,t :=
0 otherwise
(6)
All microstructure models predict that the coefficient on order flow will be positive. The
inventory management models suggest that the coefficient on the dummied order flow will
also be positive. Intuition for the prediction can be developed by considering the case of
a dealer with long inventory. End-user sales (negative order flow) to the dealer lead to
inference that “true” price is lower, as in Kyle (1985), and this is captured in the term
β Q̂u,eu,t . However, selling to a dealer who is long also increases the dealer’s inventory
exposure, which according to the theory, further lowers the price at which the market is
willing to make a purchase.
The results of this regression are presented in Table 8. I find that aggregated inventory
level of dealers indeed amplifies the price impact of end-users if the incoming order flow
further increases the absolute value of the inventory. The inventory effect seems to be
symmetric for net-buying and net-selling, although the definition of the dummy variable
might conceal any asymmetry. Furthermore, a more dynamic definition of the indicator
variable for the increase of dealers’ aggregated inventory takes into account the speed of the
mean reversion of dealers’ inventories (as I later show in Section 6). Using the mean reversion
coefficients, one can calculate the expected inventory level, and then determine whether there
was a greater than expected increase in the aggregated level of dealers’ inventory.
Column (4) of Table 8 details the regression results for the CDX index. I present these
results to contrast the importance of end-users’ information and dealers’ inventory in single-
16
name contracts versus a basket product such as the CDS index. The results suggest that
end-users’ net-selling is associated with an increase in the CDX index price, whereas endusers’ net-buying is associated with a decrease in price. The lags of the order-imbalance is
statistically insignificant, so the two results combined imply that the microstructure of the
trading in the CDX index is very different from the single-name contracts. Information does
not seem to play a role, and the results are consistent with the intuition that end-users use
the CDX index for hedging purposes.
Columns (5) and (6) show the results of the regression if we condition on the period
before and after the crisis (taking the break point as Lehman’s failure in September 15,
2008). It emerges from the table that the inventory effect is statistically significant when
end-users were net-buyers before the crisis and when end-users were net-sellers during the
crisis.
The results in this section are consistent with the standard inventory models of dealer
markets. Continuing to build upon this line of research, one would expect interdealer trading to occur when the inventories of the dealers diverge a lot and when the dealers choose
between the uncertain probability of an arrival of an end-user trade and the certainty of
interdealer trading (Ho and Stoll, 1983). Thus dealers have a greater willingness to trade
in the interdealer market when they have divergent inventories. In other words, the interdealer market facilitates the management of inventory risks and allows dealers to take large
inventory position that they would be unwilling to take in market-types where they can only
unwind their inventory positions against the end-users order flow. Yet, due to counterparty
risk, “redundant” interdealer trading, in terms of transferring credit risk, might restrict the
level of liquidity provision by dealers. This implication of counterparty risk on trading in
the CDS market is explored in the next section.
5
The Effect of Interdealer Exposure on Dealers’
Liquidity Provision
The objective of a CDS dealer, along with making a two-way market, is to earn profits
by managing the risk of its inventory. When a dealer takes on risk from a client, the
dealer typically hedges the risk but might choose to leave some of the risk uncovered. The
willingness of dealers to leave some risks uncovered – that is, to speculate – adds liquidity
to the market but requires close management. Typically, however, dealers remain “flat” by
hedging their risks in some manner. Most simply, a dealer might offset the risk of a new
deal against that of other clients. Further, the dealer might hedge the risk of a deal in the
underlying market, that is, the cash bond market. Finally, a dealer might choose to offset
risks by means of offsetting transactions with other dealers; this is a primary function of the
interdealer market.
17
In this section I focus on the interdealer market and explore whether it facilitates or
impedes liquidity provision. Reiss and Werner (1998) and Bjønnes and Rime (2005) show
that in the equity and FX market, respectively, interdealer markets are mainly used by
dealers to share inventory risks. Yet, in the CDS market along with the market risks of their
positions and the accompanying basis risks, dealers also have to manage the counterparty risk
associated with their positions. With each trade in the interdealer market, a new exposure
between a pair of dealers is being created in addition to the credit exposure to the underlying.
Since I observe that an end-user’s trade may pass between three dealers on average, an
interesting mechanism arises. As dealers pass the end-user’s trade, the credit exposure
to the underlying is unchanged, but the counterparty exposure between dealers is tripled.
Although there are various ways to manage counterparty risk, such as collateralization of
net exposures, the passing-on of inventory among dealers creates a cascade of new positions
that entail new risks. That is, dealers’ desire to hedge counterparty risk effectively creates
increased exposure among dealers.
The figure overleaf demonstrates the potential implications of repeated passes among
dealers. First, consider Case 1 where a company B buys credit protection from dealer C,
which hedges its exposures with dealer D, while D passes on the risk to monoline insurer
E. In this case three individual contracts have been written, yet, in economic terms, only
one party at the very end of the risk transfer chain bears the risk that the reference entity
defaults. At the same time, aggregate gross notional value has been inflated to three times
the aggregate net exposure. By contrast, consider Case 2, where there are two contracts,
but neither of the protection selling parties passes on its risk to another company. Under
these circumstances, net notional amount equals gross notional amount. Both dealer C and
end-user E bear the economic risk that the reference entity defaults. Case 1 illustrates the
importance of the exposure measure. Even though dealers might not bear any credit risk
associated with the “open” positions, they still have to hedge the counterparty risk that is
associated with the existing contractual ties, until the unwinding of the trades.
If the exposure level to counterparty risk amongst dealers increases, they become less
able or predisposed to absorb supply and demand shocks, and to hold buffer inventories of
assets. Under extreme conditions, dealers might even exit the market, which in turn might
leave fewer dealers to make most of the market. The inventory levels of these active dealers
is expected to build up. Then, the price impact of a trade must be greater for them to
absorb the demand of end-users.
To test this hypothesis, I construct a measure of exposure in the interdealer market.
First, I calculate the daily bilateral exposure of each pair of dealers at the reference entity
level, and I denote it by Ei,j,u,t . Then, these exposure quantities can be netted within
and across underlyings, and can also be netted across dealers. In reality, however, other
than compressions that net across dealers, day-to-day operations include only netting of
bilateral positions (multilateral positions cannot be observed in the OTC market). To keep
18
(a) Case 1
(b) Case 2
Source: Deutsche Bank (2009)
the interpretation of the results simple, I chose to net the exposures of dealers only at the
pair level, without netting across other reference entities in the sample. Note that for each
dealer that buys a CDS contract there is a dealer that sells a CDS contract. Hence, to
calculate the overall exposure in the interdealer market, I sum the (long) exposures over all
dealers.
As reported in Section 3, the trading activity is moderately concentrated among the
G14 dealers. The measure of exposure, however, captures another angle of concentration,
namely, the concentration of counterparty risk that remains in the system. Figure 3(a)
depicts the network of exposures among the top five dealers over the entire sample period,
whereas Figure 3(b) represents the changes that occurred after Lehman Brothers’ collapse.
The two figures suggest that the total average exposure among the top five dealers increased
in the period after Lehman, although the trading activity remained at the same level.
Using this measure for the exposure levels in the interdealer market, I test Hypothesis 3.
Specifically, I run the following regression:
∆Su,t = α + Q̂EU
u,t β1 + β2 1|Iˆu,t−1 +Q̂u,eu,t |% + β3 D2D Exposure + β4 1|Iˆu,t−1 +Q̂u,eu,t |% × D2D Exposure
+β5 1|Iˆu,t−1 +Q̂u,eu,t |% + β6 D2D Exposure + β7 1|Iˆu,t−1 +Q̂u,eu,t |% × D2D Exposure + εu,t
where D2D Exposure is the dealers’ long exposure to other dealers.
The regression results are detailed in Table 9. The results in Panel A of Table 9 show
that dealer-to-dealer exposure intensifies the effect that dealers’ inventory has on the price
impact of end-users. Panel B of Table 9 refines the picture by separating the price impacts
19
(7)
of net-buying and net-selling of end-users. It is evident that the “triple” interaction term
Q̂EU
× 1|Iˆu,t−1 +Q̂u,eu,t |% × Dealer-to-Dealer Exposure is positive for net buying demand and
t
negative for net selling pressure, while the magnitude for net selling is almost twice the
impact when clients want to buy. Also, the results suggest that the exposure effect is
prominent in extreme market scenarios.
Figures 4–7 summarize the economic magnitude of each of the effects. I decompose
the total price impact of trading into the key components found in this paper for: (a) a
benchmark case that is based on mean values of equity and CDS returns, dealers’ inventory
level and end-users’ net buying imbalance, and for (b) an extreme case when the realization
of all variables is at their maximum. Using the mean level of the variables, the “basic” price
impact accounts for 28% and 7% of the total price impact for buys and sells, respectively,
and an incoming order-flow that will increase dealers’ inventory results in 16% and 23% of
the price impact for buys and sells, respectively. While the congestion effect plays a minor
role in normal times, it does account for as much as 23% and 37% of the total price impact,
for buys and sells, respectively, if all independent variables are taken at their maximum
values. In either normal times or crisis times, the combined microstructure effect of endusers’ order imbalance, dealers’ inventory and interdealer exposure congestion (including the
interactions between the three) is about the same magnitude as the impact of information
flow from the stock market and lagged CDS spreads. These microstructure effects are
not “informationless” and they actually generate new fundamental information over and
above the information generated in the equity markets. I also find that both end-users’
order imbalances in the CDS market and the information innovations from the CDS market
predict stock returns 1-day ahead.
6
How Do Dealers Manage Their Inventory?
The analysis in previous sections assumes the existence of a preferred inventory position for
dealers. In this section, I test Ho and Stoll (1983) model pertaining to mean reversion in
dealers’ relative inventories, using the methodology in Hansch et al. (1998). Similar to the
results found by Hansch et al. (1998) for the London Stock Exchange dealers, I also find
evidence that dealers with extreme inventory positions trade larger quantities that move
their inventories toward desired levels, and that the intensity of mean reversion increases
with the deviation of the inventory from its desired level.
To test the relation between a dealer’s relative inventory position and the force of mean
median to be the median inventory level of reference entity u at the end
reversion I define Iu,t
of day t across dealers, and RIu,d,t = Iˆu,d,t − I median to be dealer d’s inventory position in
u,t
reference entity u relative to the median inventory at time t. Then, I estimate the following
20
piecewise linear regression:
∆RIu,d,t = α +
5
X
βn Dn RIu,d,t−1 + εu,d,t
(8)
n=1
where



1 if (n − 1) ≤ |RIu,d,t−1 | < n and n ∈ {1, 2, 3, 4}


Dn = 1 if (n − 1) ≤ |RIu,d,t−1 | and n = 5



0 otherwise
The Dn are indicator variables that allow for differences in the degree of mean reversion
as a function of different bands of relative inventory levels. For example, β1 captures the
intensity of mean reversion when the relative inventory level lies between zero and one
standard-deviation, β2 captures the intensity of mean reversion when the relative inventory
level is greater than or equal to one but less than two standard-deviations, and so on. I
compute the inventory level dependent mean reversion coefficients (βn ’s) from this piecewise
linear regression for all dealers in all underlyings.
The findings in Table 10 show that the variation in the degree of mean reversion is
strongly related to the level of inventory. In particular, the speed of mean reversion increases as the inventory divergence increases, evidence consistent with the predictions of the
inventory models of dealer markets. If I constrain all β’s to be equal to each other, I find a
mean reversion coefficient of −0.0064, which implies an inventory half-life of 108.66 days.6
This result is obviously considerably different from the inventory-level dependent half-lives
that range from 11.15 days starting from 5σ to 159.85 days starting from 1σ (see Table
10). I see that the speed of mean reversion reduces as the inventory divergence increases.
I repeat the same analysis using standardized inventories and find that they also exhibit
considerable level-dependent variation.
A more refined picture of the mechanism of dealers for controlling their inventory is
obtained from the analysis of whether dealers actively manage their inventories by increasing
trading among themselves as their inventories diverge, or do they passively wait for a random
incoming end-users’ flow. For this purpose I decompose the change in the inventory of a
dealer into two components, one arising out of his trading with other dealers and the other
EU
arising out of his trading with end-users. Similarly to the definition of Q̂ID
u,t and Q̂u,t in
EU
Section 3, I define Q̂ID
u,d,t and Q̂u,d,t as the net change in the inventory of dealer d as a result
6
ln 2
The implied half-life of inventories is calculated as − ln(1+β)
, which follows from the solution of It ≡ It−1 +∆It =
t
(1 + β) It−1 = (1 + β) × I0 .
21
ID and N EU interdealer and end-users trades on day t, respectively:
of engaging in Nu,d,t
u,d,t
Q̂ID
u,d,t =
1
σu,d
ID
Nu,d,t
X
ID
qu,d,t
(n)
Q̂EU
u,d,t =
and
n=1
1
σu,d
EU
Nu,d,t
X
EU
qu,d,t
(n)
(9)
n=1
where qu,d,t (n) is the size of the nth trade and σu,d is the standard-deviation of dealer d’s
inventory in that CDS contract.
I express the change in the inventory of dealer d in day t, Q̂u,d,t , as the sum of the change
due to his trading with end-users and the change due to his trading with other dealers; that
EU
is, Q̂u,d,t = Q̂ID
u,d,t + Q̂u,d,t . In order to investigate whether the relative proportion of end-user
and interdealer trading depends on the level of inventory, I run the following two regressions:
Q̂ID
u,d,t
= α
ID
+
5
X
γnID Dn Iˆu,d,t−1 + εID
u,d,t
(10)
n=1
EU
+
Q̂EU
u,d,t = α
5
X
γnEU Dn Iˆu,d,t−1 + εEU
u,d,t
(11)
n=1
where

ˆ

1
if
(n
−
1)
≤
I
< n and n ∈ {1, 2, 3, 4}

u,d,t−1


Dn = 1 if (n − 1) ≤ Iˆu,d,t−1 and n = 5




0 otherwise
and where d denotes the dealer, and Iˆu,d,t represents the standardized inventory of dealer d
at the end of day t.
Equations (10) and (11) divide βn ’s, the mean reversion coefficients for inventories obtained in Table 10, into two parts: γnID captures the mean reversion in inventories due to
interdealer trading, and γnEU captures the mean reversion in inventories due to trading with
end-users. Thus, γnID + γnEU equals βn . This decomposition enables me to evaluate the extent to which interdealer trading contributes to mean reversion in inventories across different
inventory levels.
Table 11 summarizes the findings. I observe that a large proportion in the mean reversion
in inventories occurs due to interdealer trading. The more the inventory level diverges from
the mean, the greater the importance of the interdealer trading.
7
Robustness of Results
The reporting problems of the DTCC dataset coupled with the trading features of the
CDS market pose several problems to the empirical methodology used. For example, trade
22
frequency in the CDS market is relatively low, so there are many days with zero order-flow
for some reference entities. In the price impact tests I use quoted prices from Markit rather
than transaction prices. This combination results in a change of price that is not associated
with end-users’ order flow. Such a scenario can indeed happen due to information percolation
from other asset markets, such as, the equity, bond and/or option markets. Therefore, I not
only control for information flow from the equity market, which is considered the most liquid,
but I also check the relation between the quoted and transaction prices. In a robustness
check, I look at new trades first for which I can estimate the transaction price.7 I find
that end-of-day quoted prices are closely related to the intra-day average of transaction
prices, and I choose to use the quotes as they cover a longer time-series. Also, I repeat
the regressions where I consider days with zero order-flow as missing observations. The
empirical results are also qualitatively robust to the use of arithmetic differences rather
than log-differences and to the differentiation of contracts by maturity rather than pooling
them together and considering them as a 5-year maturity contract.
The rest of this section details the different specifications and their results.
7.1
Different Sources for CDS Prices
In my main empirical analysis I use the composite prices provided by Markit Group. Markit
collects CDS prices from about 23 dealers to produce its end-of-day prices. The quotes are
subject to filtering that removes outliers and stale observations. Markit then computes a
daily composite spread only if it has two or more contributors. I opt to use Markit as the
data source for the main analysis since it has a longer time series and is one of the most
widely employed in the literature related to CDS.
Other possible sources for CDS prices include the Credit Market Analytics DataVision
(CMA) and Fitch. CMA gets prices by parsing e-mails from dealers to 36 investment firms,
aggregating data where possible to produce continuous prices. It relies on its relationships
with buyside investors, including major global investment banks, hedge funds, and asset
managers, and any prices it gets from dealers come only from second-tier market makers
for comparison purposes. Fitch uses prices from 20 dealers, who are tier-one sellside market
makers.
Additional robustness check that I undertake uses the transaction prices from the DTCC
database (see Figure 9). I am able to use only the prices that are associated with new trades
without upfront payment. Due to reporting standards I am unable to interpret the prices
that are associated with assignments, while new trades with an upfront payment require
7
Many of the trades were traded for a fixed spread and an upfront payment even before the Big Bang Protocol
in April 2009. To be able to compare prices across different transactions, I need to convert the upfront payment,
which is usually in USD terms, to basis-points. The conversion involves some assumptions about the recovery
rate and the risky term-structure that could have been different from trade to trade. Thus, using the same set
of assumptions for all trades will not necessarily deliver the true transaction price. Section 7.1 fleshes out the
conversion details.
23
some conversion to basis points that its accuracy depends on the assumptions made. As
part of the Big Bang Protocol, commenced on April 8, 2009, North American corporate
names are traded with a fixed coupon. The coupon is either 100 bps or 500 bps and upfront
payments are exchanged. Therefore, to be able to compare all transaction prices to the
quoted prices, I need to calculate the equivalent (full) spread to the sum of the fixed coupon
and the upfront payment for these observations.
To convert the fixed coupon and the upfront payment to a CDS price, I replicate the
ISDA CDS pricing model that is being used as the standard conversion model. For the
conversion, I use the following formula:
K
X
1
(1 − R) × [Z(0, tk−1 ) + Z(0, tk )] × [Q(0, tk−1 ) − Q(0, tk )]
2
k=1
K
1X
= Upfront Payment + S0
∆(tk−1 , tk )Z(0, tk ) [Q(0, tk−1 ) + Q(0, tk )]
2
k=1
{z
}
|
(12)
Risky PV01
where S0 is a fixed payment (%, annualized) that the buyer of the protection pays for the
duration of the protection period, or up to a default event; Q(t, T ) is the time t survival
probability of the reference entity to time T ; Z(t, T ) is the Libor discount curve which is
anchored to the CDS effective date; R is the expected recovery rate as a percentage of par;
and, ∆(tk−1 , tk ) is the day count fraction between dates tk−1 and tk in the appropriate day
count conversion, typically Actual 360.
I assume a constant recovery as a fraction of par (40%), a piecewise constant risk neutral
hazard rates, and independent default events of changes in the default free yield curve. The
raw transaction prices and the converted prices are presented in Figures 9–10, where the
conversion was applied for AIG contracts as an example.
7.2
The Distribution of CDS Contracts’ Maturity
In the construction of the time-series for volume, inventory and exposure variables, I aggregate the position regardless of the maturity of the contract. This leads to a maturity
mismatch problem. This aggregation at the maturity level might therefore mask the true
ramifications on dealers’ portfolios. The alternatives I face include:
1. Constrain the sample to a specific maturity, say CDS contracts with 5 year maturity,
as it is the most liquid
2. Run separate regressions for different maturities
3. Pool together different contracts with different maturities
On the one hand, there is a difference in counterparty risk if one holds a 5-year contract
vs 1-year contract. On the other hand, a dealer might buy a 5-year contract with the
24
expectation to hold it only for one year because the 5-year contract is more liquid. Using
only contracts with 5-year maturity (most liquid) leaves some extra information in other
maturities unexploited. Since the majority of the traded transactions are for 5-year maturity
(see Figure 8), I choose to use contracts with all maturities in my benchmark analysis and
to run robustness check in the form of different regressions for different maturities, using
the CDS return for this specific maturity.
7.3
Using Arithmetic Differences
In all the results presented thus far, I measure CDS returns by logarithmic differences
CDSt
), i.e., the percentage changes in CDS spreads. An alternain CDS spreads (log CDS
t−1
tive way of measuring CDS return is to calculate the arithmetic difference in CDS spreads
(CDSt − CDSt−1 ). While both these methods are legitimate, the arithmetic difference in
CDS spreads has a certain intuitive appeal for measuring CDS return because it approximately equals the return generated from buying an underlying bond of the CDS entity at
t − 1 and selling it at t. Table 12 reports the re-estimation of Equation (4) using CDS return
from arithmetic differences. The empirical results remain unchanged. The re-estimation of
the other regressions using arithmetic difference delivers qualitatively similar results, not
reported here.
8
Concluding Remarks
In this paper I examine the intermediation capacity of dealers in the CDS market by exploiting a proprietary dataset of transactions in 35 single-name reference entities. I find three
important results. First, the traditional inventory-based market microstructure models can
be used in the setting of the seemingly more complex and opaque market, such as the CDS
market. Order imbalances of end-users appear to impact CDS price changes, and dealers’
aggregated inventory position amplifies this effect. Second, the force of mean reversion of
dealers’ inventories to the target level is stronger as the deviation from the target increases,
and in turn, the price impact of end-users’ order flow is greater at such points in time.
Finally, counterparty risk among dealers affects liquidity in the CDS market, especially in
stress conditions. It would be interesting in future research to examine how this effect is
being translated in other derivative markets, such as FX derivatives, interest rate swaps,
and OTC equity derivatives, where counterparty risk is also present.
Further, my empirical analysis provides a factual basis on the trading activity and the
provision of liquidity in the CDS market about which little was known until this point. These
results might lend some guidance to the effect of the anticipated migration of trading from
an OTC setting to one intermediated by central clearing counterparty (CCP). The CCP
would stand between the two original counterparties, acting as the seller to the original
buyer, and as the buyer to the original seller. This will allow a CCP to net the positions
25
across investors, and should mitigate the key impediment of counterparty risk to liquidity
provision by dealers raised in this paper. It seems that the establishment of a CCP can
lead to a reduction in risk and a substantial improvement in allocational efficiency, as long
as it will be able to avoid becoming systemically risky itself (see Acharya, Shachar, and
Subrahmanyam, 2010 and Duffie and Zhu, 2009 for further details).
Especially in the midst of the transition of the CDS market from an OTC structure
to central clearing, CDS transactions data, and order imbalance data in particular, open
avenues of research beyond those that are explored in this paper. Ever since Lehman’s
collapse, the DTCC has published weekly data on individual reference entities, gross and
net volumes of CDSs outstanding. This data is cruder, but it allows forming a broader
picture of the CDS market activity and to address issues at the cross-section. Nevertheless,
the dataset that I exploit in this paper is much more detailed and includes a unique identifier
for the identity of investors, which is consistent across firms and over time. This information
will allow me to examine the role of bargaining power and repeated interactions in the terms
of trade in future papers.
26
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29
Table 1
The Reference Entities in The Sample
This table details the 35 reference entities that compose the sample. All 35 reference entities are North American
and are associated with the financial industry. The specific sub-industry of each firm is also detailed in the table
below.
Reference Entity
Industrical Classfication
AMBAC Financial Group
American Express Company
American International Group
Aon Corp
Avis Budget Group
Bank Of America Corp
Berkshire Hathaway
Capital One Financial Corp
CIT Group
Citigroup
CNA Financial Corp
Credit Suisse
Fairfax Financial Holdings Limited
Genworth Financial
Goldman Sachs Group
Hartford Financial Services Group
HSBC Finance Corp
JPMorgan Chase & Co
Lehman Brothers Holdings
Lincoln National Corp
Loews Corp
Marsh & Mclennan Companies
MBIA
Merrill Lynch & Co
Metlife
Morgan Stanley
PMI Group
Prudential Financial
Radian Group
SLM Corp
The Chubb Corp
The Travelers Companies
UNUM Group
Wells Fargo & Company
Western Union Company
Financial Service
Personal Credit Institutions
Fire, Marine & Casualty Insurance
Accident & Health Insurance
Real Estate Agents & Managers (For Others)
National Commercial Banks
Fire, Marine & Casualty Insurance
Personal Credit Institutions
Miscellaneous Business Credit Institution
National Commercial Banks
Fire, Marine & Casualty Insurance
State Commercial Banks
Fire, Marine & Casualty Insurance
Life Insurance
Security Brokers, Dealers & Flotation Companies
Insurance Agents, Brokers & Service
Commercial Banks, NEC
National Commercial Banks
Investment Advice
Life Insurance
Fire, Marine & Casualty Insurance
Surety Insurance
Surety Insurance
Security Brokers, Dealers & Flotation Companies
Insurance Agents, Brokers & Service
Security Brokers, Dealers & Flotation Companies
Surety Insurance
Life Insurance
Surety Insurance
Federal & Federally Sponsored Credit Agencies
Fire, Marine & Casualty Insurance
Fire, Marine & Casualty Insurance
Accident & Health Insurance
National Commercial Banks
Functions Related To Depository Banking, NEC
30
31
13,396
6,447,799
0
17,118,913
0
582,171,000
Exclude Zero Observations
N
4,778
Mean
18,077,587
Median
10,000,000
Std Dev
24,727,874
Minimum
0
Maximum 582,171,000
N
Mean
Median
Std Dev
Minimum
Maximum
End-Users’
Net Buying
6,877
0.07
0.04
0.10
0.00
1.54
18,102
0.03
0.00
0.07
0.00
1.54
Scaled End-Users’
Net Buying
5,249
16,737,791
10,000,000
23,010,850
11,208
313,077,546
13,396
6,558,425
0
16,559,499
0
313,077,546
End-Users’
Net Selling
13,396
-326,840,603
-335,024,020
857,823,262
-2,866,393,059
3,401,515,206
13,396
-326,840,603
-335,024,020
857,823,262
-2,866,393,059
3,401,515,206
Dealers’ Inventory
4,778
0.08
0.04
0.11
0.00
1.54
13,396
0.03
0.00
0.07
0.00
1.54
Scaled End-Users’
Net Buying
5,249
0.07
0.04
0.09
0.00
1.28
13,396
0.03
0.00
0.07
0.00
1.28
Scaled End-Users’
Net Selling
7,412
0.06
0.04
0.10
0.00
2.34
18,102
0.03
0.00
0.07
0.00
2.34
Scaled End-Users’
Net Selling
Panel B: Excluding Reference Entities that are Dealer Firms
18,102
-740,637,735
-455,095,881
1,256,586,827
-5,089,908,434
3,401,515,206
Exclude Zero Observations
N
6,877
7,412
Mean
24,318,768
23,839,210
Median
12,699,075
11,000,000
Std Dev
35,626,081
54,887,473
Minimum
0
11,208
Maximum 855,450,000 2,254,051,416
Dealers’ Inventory
18,102
-740,637,735
-455,095,881
1,256,586,827
-5,089,908,434
3,401,515,206
18,102
9,238,767
0
24,929,183
0
855,450,000
End-Users’
Net Selling
18,102
9,761,144
0
37,025,306
0
2,254,051,416
N
Mean
Median
Std Dev
Minimum
Maximum
End-Users’
Net Buying
13,396
0.00
-0.02
1.00
-2.90
4.59
13,396
0.00
-0.02
1.00
-2.90
4.59
Scaled Dealers’
Inventory
18,102
0.00
-0.07
1.00
-2.90
4.59
18,102
0.00
-0.07
1.00
-2.90
4.59
Scaled Dealers’
Inventory
This Table details the summary statistics of the key variables in the sample: end-users’ net-buying, end-users’ net-selling, and dealers’ inventory. I also
report the summary statistics for the scaled variables as detailed in Section 3. Panel A includes all 35 reference entities, whereas Panel B excludes 9 reference
entities that are also dealer themselves. Later, I use the two samples to show that the correlation between the underlyings and the suppliers of liquidity does
not affect any of the results.
Panel A: Entire Sample
Table 2
Summary Statistics
32
8,273
6,283
432
326
1,713
2,457
285
258
Banks
13,548
10,970
586
251
4,124
6,903
1,428
87
Asset
Managers
Banks
Financial
Services
72
24
326
2,159
134
143
1,029
1,407
Financial
Services
Hedge
Funds
205
271
5,263
341
1,186
404
22,311
9,777
Hedge
Funds
New Trade
As a buyer
55 %
57 %
42 %
70 %
As a seller
45 %
43 %
58 %
30 %
Assignment Step-In
As a buyer
70 %
57 %
48 %
75 %
As a seller
30 %
43 %
52 %
25 %
Assignment Step-Out
As a buyer
37 %
41 %
13 %
94 %
As a seller
63 %
59 %
87 %
6%
Matured
As a buyer
94 %
52 %
75 %
43 %
As a seller
6%
48 %
25 %
57 %
Total Market Share (out of the total notional bought and sold)
As a buyer
21%
21 %
2%
49 %
As a seller
24 %
19 %
3%
49 %
New Trade
As a buyer
As a seller
Assignment Step-In
As a buyer
As a seller
Assignment Step-Out
As a buyer
As a seller
Matured
As a buyer
As a seller
Asset
Managers
83 %
17 %
11%
1%
37 %
63 %
3%
2%
3%
3%
100 %
0%
84 %
16 %
100 %
46 %
54 %
54 %
46 %
Other
End-Users
29
0
300
56
2
8
873
738
Other
End-Users
20 %
80 %
75 %
25 %
Pension
Funds
15
3
14
0
0
169
57
Pension
Funds
56 %
44 %
69 %
31 %
Insurance
Firms
Panel B
37
63
258
308
5
4
1,036
473
Insurance
Firms
Panel A
19 %
19 %
75 %
25 %
50 %
50 %
67 %
33 %
61 %
39 %
Total
End-Users
2,071
706
11,998
12,224
2,345
1,136
47,239
29,705
Total
End-Users
81 %
81 %
46 %
54 %
50 %
50 %
49 %
51 %
47 %
53 %
Dealers
7,771
9,136
35,296
35,074
52,063
53,272
133,432
150,966
Dealers
100 %
100 %
53 %
47 %
50 %
50 %
51 %
49 %
52 %
48 %
Total
11,913
10,548
59,292
59,522
56,753
55,544
227,909
210,376
Total
This table breaks down the transactions for each group of investors and transaction type. Panel A presents number of transactions and Panel B presents the
fraction.
Table 3
Breakdown of Number of Transaction by Investors Group and Transaction Type: The 35 Single Name Contracts
33
AMBAC Financial Group
American Express Company
American International Group
Aon Corp
Avis Budget Group
Bank Of America Corp
Berkshire Hathaway
Capital One Financial Corp
CIT Group
Citigroup
CNA Financial Corp
Credit Suisse
Fairfax Financial Holdings Limited
Genworth Financial
Goldman Sachs Group
Hartford Financial Services Group
HSBC Finance Corp
JPMorgan Chase & Co
Lehman Brothers Holdings
Lincoln National Corp
Loews Corp
Marsh & Mclennan Companies
MBIA
Merrill Lynch & Co
Metlife
Morgan Stanley
PMI Group
Prudential Financial
Radian Group
SLM Corp
The Chubb Corp
The Travelers Companies
UNUM Group
Wells Fargo & Company
Western Union Company
Firm
574
585
587
446
542
599
553
583
592
594
440
282
394
524
595
564
571
599
404
370
527
560
590
475
558
599
588
499
590
560
519
360
411
587
281
# of
Trading Days
1
1
2
9
2
1
2
3
0
0
11
4
25
2
1
4
5
1
0
6
10
4
1
1
5
0
0
6
0
0
11
14
14
2
1
# of Days with
No Activity
E2D
466
484
522
240
362
553
372
457
568
538
208
96
157
311
585
433
464
557
399
154
343
416
526
468
421
584
544
313
532
529
335
169
216
541
132
QD2D 6= 0 and
Q
+ QD2E 6= 0
89
75
52
154
151
36
156
99
22
43
185
109
181
169
8
91
90
30
4
150
129
111
53
4
104
10
36
134
48
23
131
137
149
36
119
QD2D 6= 0 and
Q
+ QD2E = 0
E2D
18
25
11
43
27
9
23
24
2
13
36
73
31
42
1
36
12
11
1
60
45
29
10
2
28
5
8
46
10
8
42
40
32
8
29
QD2D = 0 and
Q
+ QD2E 6= 0
E2D
This table contains a detailed description of the trading activity in terms of trading days for each of the reference entities in the sample, and presents the
relation between interdealer activity and dealer-client activity. If there is activity among dealers in a specific day then QD2D 6= 0. If end-user sells to a dealer
(QE2D ) or a dealer sells to an end-user (QD2E ), then QE2D + QD2E 6= 0, otherwise QE2D + QD2E = 0. Note that the sum QE2D + QD2E denotes the gross
notional traded, not the net.
Table 4
Non-Zero Trading Days For Each Reference Entity
34
AMBAC Financial Group
American Express Company
American International Group
Aon Corp
Avis Budget Group
Bank Of America Corp
Berkshire Hathaway
Capital One Financial Corp
CIT Group
Citigroup
CNA Financial Corp
Credit Suisse
Fairfax Financial Holdings Limited
Genworth Financial
Goldman Sachs Group
Hartford Financial Services Group
HSBC Finance Corp
JPMorgan Chase & Co
Lehman Brothers Holdings
Lincoln National Corp
Loews Corp
Marsh & Mclennan Companies
MBIA
Merrill Lynch & Co
Metlife
Morgan Stanley
PMI Group
Prudential Financial
Radian Group
SLM Corp
The Chubb Corp
The Travelers Companies
UNUM Group
Wells Fargo & Company
Western Union Company
All Firms
5
4
6
3
3
8
2
4
6
9
2
2
2
3
10
4
4
7
14
3
2
3
7
13
3
11
6
3
5
8
3
3
2
6
2
6
3
2
3
1
2
4
1
3
4
5
1
1
1
2
6
2
3
4
9
1
2
2
4
8
2
5
4
2
4
5
2
2
1
4
1
3
6
4
8
6
4
10
3
6
5
11
3
2
2
4
14
6
5
11
18
3
3
5
8
13
5
21
6
4
7
13
4
4
4
6
2
10
2,169
1,417
2,749
491
805
3,947
616
1,593
2,760
4,283
392
188
245
641
5,807
1,445
1,403
3,366
5,643
312
557
1,180
3,298
5,618
1,004
5,930
2,903
777
2,591
3,685
716
309
370
2,820
197
72,226
6
4
7
2
3
8
3
4
6
9
2
2
2
3
11
4
4
7
17
3
2
3
7
14
4
11
6
3
6
10
3
2
2
7
2
6
3
2
4
1
2
5
2
3
4
5
1
1
1
2
7
2
2
4
9
1
1
2
4
9
2
6
4
2
4
5
2
1
1
4
1
3
6
5
10
3
3
10
4
6
6
11
3
2
2
3
15
5
5
10
25
4
3
4
8
15
5
22
6
4
7
21
5
3
4
8
2
11
2,258
1,669
3,213
441
816
4,045
956
1,686
2,970
4,666
417
204
238
765
6,180
1,506
1,569
3,621
6,647
370
597
1,040
3,286
6,347
1,226
6,405
3,033
932
2,787
4,974
800
355
400
3,256
214
79,887
11
8
14
4
7
17
5
9
18
16
4
2
3
5
21
7
8
14
28
3
4
7
14
28
7
21
14
4
16
18
4
3
4
13
3
11
7
6
9
3
4
10
3
6
14
11
2
1
2
3
15
4
5
9
19
2
3
4
9
20
5
14
10
3
11
11
3
2
2
10
2
6
13
10
18
7
12
21
8
10
17
18
9
2
4
5
22
10
9
17
36
4
5
9
16
49
10
24
16
6
18
28
7
7
6
14
3
18
6,122
4,590
8,139
1,737
3,595
9,719
2,548
4,922
10,428
9,353
1,579
432
1,117
2,222
12,224
3,828
4,170
7,966
11,136
1,032
1,917
3,453
8,301
13,345
3,707
12,287
8,192
1,928
9,244
9,723
1,980
1,027
1,340
7,641
842
191,785
This table contains a detailed description of the trading activity in terms of number of CDS contracts for each of the reference entities in the sample. The
number of contracts sold to end-users represents the number of transactions in which end-users were the buyers of protection; the number of contracts bought
by end-users represents the number of transactions in which end-users were the seller of protection. In calculating the number of transactions, terminations
were considered as sells. Therefore, it might seem that the difference between end-users’ buying and selling is smaller than when only new trades are
considered.
# Contracts Bought by End Users
# Contracts Sold to End Users
# Contracts Traded Among Dealers
Firm
Mean Median S.D.
Total
Mean Median S.D.
Total
Mean Median S.D.
Total
Table 5
Number of Traded Contracts For Each Reference Entity
35
New Trade
As a buyer
As a seller
Assignment Step-In
As a buyer
As a seller
Assignment Step-Out
As a buyer
As a seller
Matured
As a buyer
As a seller
New Trade
As a buyer
As a seller
Assignment Step-In
As a buyer
As a seller
Assignment Step-Out
As a buyer
As a seller
Matured
As a buyer
As a seller
57 %
43 %
100 %
25 %
75 %
100 %
67 %
33 %
49 %
51 %
35 %
65 %
Financial
Services
48 %
52 %
Banks
Asset
Managers
0
1
47 %
53 %
1
3
0
2
338
250
21 %
79 %
1,627
1,683
8,729
9,611
4
2
44 %
56 %
100
115
74
285
9
17
Financial
Services
48 %
52 %
34
43
Banks
176
190
Asset
Managers
50 %
50 %
48 %
52 %
48 %
52 %
59 %
41 %
Hedge
Funds
4
4
8,936
9,705
638
698
161
114
Hedge
Funds
49 %
51 %
9%
91 %
70 %
30 %
Insurance
Firms
Panel B
0
0
1,611
1,702
1
10
62
27
Insurance
Firms
Panel A
100 %
50 %
29 %
71 %
Pension
Funds
0
1
126
128
3
4
4
10
Pension
Funds
58 %
42 %
100 %
50 %
50 %
Other
End-Users
0
0
107
78
0
15
1
1
Other
End-Users
31 %
69 %
48 %
52 %
42 %
58 %
53 %
47 %
Total
End-Users
5
11
21,474
23,157
820
1,129
447
402
Total
End-Users
50 %
50 %
51 %
49 %
50 %
50 %
49 %
51 %
Dealers
555
549
36,776
35,086
64,710
64,407
1,225
1,270
Dealers
50 %
50 %
50 %
50 %
50 %
50 %
50 %
50 %
Total
560
560
58,250
58,243
65,530
65,536
1,672
1,672
Total
This table breaks down the transactions for each group of investors and transaction type. Panel A presents the number of transactions and Panel B presents
the fraction.
Table 6
Breakdown of Number of Transaction by Investors Group and Transaction Type: The CDX Index
36
= α+
k=0
K
X
k=0
n=0
n=1
K
5
5
X
X
X
γk Q̂u,eu,t−k 1>0 +
δk Q̂u,eu,t−k 1<0 +
ζn ∆ log Pu,t−n +
ηn ∆ log Su,t−n + εu,t
× 103
× 103
Adjusted R-squared
Number of Observation
Coefficient estimates
Standard errors
Fixed-Effects
n=1 ζn
P5
n=1 ζn
P5
ζn × 103
k=1 δk
PK
× 103
γk × 103
δ0 × 103
k=1
0.43
18, 150
OLS
White
Time (Day)
−139.9∗∗∗
(34.7)
−70.2∗
(41.9)
44.9∗
(25.4)
−3.6
(4.6)
0.43
18, 117
OLS
White
Time (Day)
29.5∗∗∗
(5.4)
14.8∗∗∗
(3.3)
−3.5
(4.6)
−10.1∗∗∗
(3.8)
−139.8∗∗∗
(34.7)
−68.1
(41.7)
46.3∗
(25.3)
31.5∗∗∗
(5.2)
γ0 × 103
PK
K=1
K=0
∆ log Su,t
0.43
18, 052
OLS
White
Time (Day)
29.9∗∗∗
(5.4)
20.3∗∗∗
(4.9)
−2.9
(4.7)
−22.9∗∗∗
(5.7)
−139.5∗∗∗
(34.6)
−67.0
(41.7)
45.2∗
(25.5)
K=3
All Reference Entities
Dependent Variable:
0.43
17, 989
OLS
White
Time (Day)
29.7∗∗∗
(5.5)
23.2∗∗∗
(5.6)
−2.3
(4.7)
−30.5∗∗∗
(6.7)
−139.2∗∗∗
(34.5)
−66.7
(41.7)
41.8
(25.7)
K=5
0.72
4, 590
OLS
White
Time (Day)
15.0∗∗
(6.2)
35.6∗∗∗
(11.9)
−10.1
(10.4)
−36.6∗∗
(14.3)
−64.2
(76.3)
44.0
(108.4)
−135.4∗∗
(53.6)
Ref. Ent. that are
Dealers Themselves
K=5
0.42
13, 399
OLS
White
Time (Day)
31.0∗∗∗
(6.3)
16.8∗∗∗
(5.9)
−1.8
(4.3)
−23.9∗∗∗
(7.1)
−161.6∗∗∗
(30.6)
−48.7
(38.6)
108.1∗∗∗
(29.2)
Excluding Ref. Ent. that are
Dealers Themselves
K=5
amount sold to end-users. Business days without any trading activity are included in the sample with Q̂u,eu,t as zero, before the standardization. Standard
errors that appear in parentheses are heteroscedastic-robust standard errors. ∗ ∗ ∗, ∗∗, and ∗ denotes significance at 1%, 5%, and 10% level, respectively. All
regressions also include an intercept.
where Q̂u,eu,t−k × 1>0 is the standardized net daily notional amount bought by end-users. Similarly, Q̂u,eu,t−k × 1<0 is the standardized net daily notional
∆ log Su,t
In this table I study the asymmetric impact of end-users’ order imbalances on log-returns of CDS spreads. The table details the coefficient of the following
pooled-regression:
Table 7
The Effect of End-Users’ Daily Trading Activity on CDS Spreads
37
1|Iˆu,t−1 +Q̂u,eu,t |% :=
(
if Iˆu,t > Iˆu,t−1 otherwise
1
0
× 103
Adjusted R-squared
Number of Observation
Coefficient estimates
Standard errors
Fixed-Effects
n=1 ζn
P5
n=1 ζn
P5
ζn × 103
γ × 103
δ2 × 103
β2 × 103
× 103
0.43
17, 664
OLS
White
Time (Day)
0.72
4, 624
OLS
White
Time (Day)
20.9∗∗∗
(8.0)
−4.0
(11.8)
14.7
(11.0)
−57.3∗∗
(27.7)
0.7
(0.9)
−63.7
(74.9)
44.7
(108.7)
−148.3∗∗∗
(52.8)
25.3∗∗∗
(5.4)
17.4
(10.7)
6.8
(5.7)
−24.4∗∗
(10.4)
−0.5
(0.6)
−138.8∗∗∗
(34.7)
−67.5
(42.1)
38.7
(25.8)
β1 × 103
δ1 × 103
(2)
Ref. Ent. that
Dealers Themselves
(1)
All
Ref. Ent
Dependent Variable:
∆ log Su,t
0.42
13, 040
OLS
White
Time (Day)
24.9∗∗∗
(6.1)
20.5
(12.6)
3.7
(6.1)
−15.9∗
(8.4)
−0.4
(0.6)
−162.0∗∗∗
(30.9)
−46.7
(39.1)
105.1∗∗∗
(29.4)
(3)
Exc. Ref. Ent. that are
Dealers Themselves
0.14
439
OLS
White
None
164.6
(107.6)
−61.6∗∗∗
(10.4)
19.4
(24.0)
21.7∗∗
(10.7)
5.8
(19.2)
−1.1
(1.9)
(4)
CDX
Index
0.40
5, 546
OLS
White
Time (Day)
26.9∗∗
(13.0)
46.2∗
(27.8)
15.0
(12.6)
−33.6
(21.2)
−1.2
(1.1)
−101.1∗∗∗
(36.1)
−54.4
(51.1)
8.4
(42.9)
(5)
Before
Lehman
0.47
12, 118
OLS
White
Time (Day)
23.5∗∗∗
(5.5)
8.0
(7.8)
3.0
(6.2)
−18.6∗∗
(9.1)
−0.2
(0.5)
−270.3∗∗∗
(27.8)
−125.3∗∗
(50.0)
60.2∗
(30.9)
(6)
After
Lehman (“Crisis”)
and where Iˆd,t−1 is the aggregated inventory of all dealers at time t − 1 scaled by the standard-deviation of dealers’ inventory in reference entity u, σu ,
and 1|Iˆu,t−1 +Q̂u,eu,t |% is an indicator variable that equals 1 when end-users’ net buying/selling at time t increases dealers’ t − 1 aggregate inventory, and 0
otherwise. All regressions also include an intercept, five lags of the dependent variable, the contemporaneous return of the stock associated with the CDS
contract’s reference entity, and five lags of the stock return. Standard errors are in parentheses. ∗ ∗ ∗, ∗∗, and ∗ denotes significance at 1%, 5%, and 10%
level, respectively.
where the dummy variable is defined as:
This table shows the results of the following four regressions:
∆ log Su,t = α + Q̂u,eu,t 1>0 (β1 + δ1 × Dummy) + Q̂u,eu,t 1<0 (β2 + δ2 × Dummy) + γ × Dummy + Controls + εu,t
Table 8
Dealers’ Ability to Absorb End-Users’ Demand
Table 9
The Relation between Inventory and Interdealer Exposures
Panel A of this table details the results of the following regression:
∆Su,t = α + Q̂u,eu,t β1 + β2 1|Iˆu,t−1 +Q̂u,eu,t |% + β3 D2D Exposure + β4 1|Iˆu,t−1 +Q̂u,eu,t |% × D2D Exposure
+β5 1|Iˆu,t−1 +Q̂u,eu,t |% + β6 D2D Exposure + β7 1|Iˆu,t−1 +Q̂u,eu,t |% × D2D Exposure + εu,t
where D2D Exposure is the dealers’ long exposure to other dealers, and 1|Iˆu,t−1 +Q̂u,eu,t |% equals 1 if Iˆu,t > Iˆu,t−1 ,
and 0 otherwise. Panel B of this regression refines the regression above and separate the the price impacts of netbuying and the net-selling by end-users.
Panel A
Dependent Variable:
10, 000∆ log Su,t
Q̂u,eu,t
Q̂u,eu,t × 1|Iu,t−1 +Qu,eu,t |%
Q̂u,eu,t × Dealer-to-Dealer Exposure
Q̂u,eu,t × 1|Iu,t−1 +Qu,eu,t |% × Dealer-to-Dealer Exposure
1|Iu,t−1 +Qu,eu,t |% × Dealer-to-Dealer Exposure
1|Iu,t−1 +Qu,eu,t |%
Dealer-to-Dealer Exposure
157.27∗∗
(69.16)
508.18∗∗∗
(99.94)
27.19
(83.96)
353.61∗∗
(165.70)
5.47
(17.73)
−10.75
(10.03)
26.22∗∗
(10.45)
Panel B
Dependent Variable:
10, 000∆ log Si,t
Q̂u,eu,t × 1>0
Q̂u,eu,t × 1>0 × 1|Iu,t−1 +Qu,eu,t |%
Q̂u,eu,t × 1>0 × Dealer-to-Dealer Exposure
Q̂u,eu,t × 1>0 × 1|Iu,t−1 +Qu,eu,t |% × Dealer-to-Dealer Exposure
Q̂u,eu,t × 1<0
Q̂u,eu,t × 1<0 × 1|Iu,t−1 +Qu,eu,t |%
Q̂u,eu,t × 1<0 × Dealer-to-Dealer Exposure
Q̂u,eu,t × 1<0 × 1|Iu,t−1 +Qu,eu,t |% × Dealer-to-Dealer Exposure
38
402.22∗∗∗
(113.62)
459.74∗∗∗
(159.69)
233.09∗
(119.93)
212.52
(215.37)
80.86
(96.00)
−539.57∗∗∗
(146.64)
228.34∗
(138.66)
−497.24∗
(262.85)
Table 10
Test of Mean Reversion in Dealers’ Inventories
The table shows the average mean reversion coefficients of the following piecewise linear regression:
(RIu,d,t − RIu,d,t−1 ) = α +
5
X
βn Dn RIu,d,t−1 + εu,d,t
n=1
where d denotes the dealer, and RIu,d,t is the inventory of dealer d relative to the median dealer inventory at
the end of day t. The Dn are indicators variables that allow for differences in the degree of mean reversion as a
function of different bands of relative inventory levels, and are defined as follows:


1 if (n − 1) ≤ |RIu,d,t−1 | < n and n ∈ {1, 2, 3, 4}
Dn = 1 if (n − 1) ≤ |RIu,d,t−1 | and n = 5


0 otherwise
I run this regression for each dealer in each reference entity. I also calculate implied half-lives of inventories. The
regression is repeated for the standardized inventories.
Dependent Variable:
Dummy:
Q̂u,eu,t × 1>0
10, 000 × ∆ log Su,t
(1)
(2)
1exceedsE(increase)
1|Iˆd,t−1 +Q̂eu,t |%
459.6∗∗∗
(81.9)
572.9∗∗∗
(93.6)
8.6
(90.6)
−4.9
(10.9)
177.3
(1979.3)
−20.5∗∗∗
(7.7)
Q̂u,eu,t × 1>0 × Dummy
376.5∗
(203.2)
3310.5∗∗
(1332.9)
Q̂u,eu,t × 1<0 × Dummy
−357.1∗∗
(156.1)
−254.5
(1980.8)
Coefficient estimates
Standard errors
Fixed-Effects
OLS
White
Time (d)
OLS
White
Time (d)
0.32
25, 406
0.32
25, 367
Q̂u,eu,t × 1<0
Dummy
R-squared
Number of Observation
39
Table 11
Decomposition of the Mean Reversion Coefficients
The table shows the average coefficient of the following piecewise linear regressions:
Q̂ID
u,d,t
=
5
X
αID +
γnID Dn Iˆu,d,t−1 + εID
u,d,t
n=1
Q̂EU
u,d,t
=
5
X
αEU +
γnEU Dn Iˆu,d,t−1 + εEU
u,d,t
n=1
where d denotes the dealer, Q̂u,d,t is the net amount traded and the superscript ID refers to the interdealer trading,
and the superscript EU refers to dealers’ trading directly with end-users, and Iˆu,d,t is the standardized inventory
of dealer d at the end of day t. The Dn are indicator variables that allow for differences in the degree of mean
reversion as a function of different bands of inventory levels, and are defined as follows:

ˆ

I
1
if
(n
−
1)
≤
< n and n ∈ 1, 2, 3, 4
u,d,t−1


Dn = 1 if (n − 1) ≤ Iˆu,d,t−1 and n = 5



0 otherwise
I run this regression for each dealer in each reference entity. The sum of the end-users and interdealer coefficients
as shown in the Total column is exactly equal to the corresponding mean reversion coefficients of standardized
inventories in Table 10.
Interdealer Trading
Trading with End-Users
Total
Interdealer Trading Fraction
γ1
−0.004
−0.001
−0.005
87%
γ2
−0.007
−0.001
−0.008
86%
γ3
−0.013
−0.003
−0.015
83%
γ4
−0.027
−0.003
−0.030
90%
γ5
−0.038
−0.004
−0.042
90%
40
Soldby
End‐Users
19%
Boughtby
End‐Users
18%
Soldby
End‐Users
23%
Interdealer
56%
Interdealer
63%
B
Boughtby
ht b
End‐Users
21%
(c) In Terms of Notional Amount
(d) In Terms of Number of Trades
Figure 1: Market share
This figure depicts the market share of interdealer trading and of end-users. Figure 1(c) shows the market share
of the traders in the CDS market as a fraction of the total notional amount traded, and Figure 1(d) details the
market shares in terms of the total number of transactions.
41
42
Figure 2: Inventory Positions Of Key Market Participants Over Time
This figure tracks the inventory evolution of CDS contracts on all 35 reference entities held by dealers (blue line),
hedge-funds (red line), asset managers (green line) and banks (orange line). The shaded areas represent key stress
events: 1) the money market freeze on August 9, 2007; 2) Bear Stearns collapse on March 14, 2008; 3) Lehman
Brothers debacle on September 15, 2008; 4) the approval of the Troubled Asset Relief Program on October 2008;
and, 5) the initiation of the stress tests on February 24, 2009.
(a) Over All the Sample Period
(b) After Lehman’s Failure on September 15, 2008
Figure 3: Top Five Dealers’ Exposures Network
Figure 3(a) depicts the network of bilateral exposures among the top five dealers over the entire sample period.
The reported numbers on the edges are the daily averages over the entire sample period of the net amount traded
across all reference entities in million of USD. Figure 3(b) represents the changes that occurred after Lehman
Brothers’ collapse on September 15, 2008. The two figures suggest that the total average exposure among the top
five dealers increased in the period after Lehman.
43
50
Lagged CDS Returns, 8.13 (15%)
40
Lagged Equity Returns, 6.69 (12%)
Exposure, 0.30
(1%)
30
Contemporaneous Equity Return, 9.10
(17%)
Net Pos Demand of EU x Exposure x Inventory, 0.27
(0%)
20
Net Pos Demand of EU x Exposure, 0.58
((1%))
Net Pos Demand of EU x Inventory, 8.83
(16%)
Lagged CDS Returns
10
Lagged Equity Returns
Contemporaneous Equity Return
Net Pos Demand of EU, 15.28
(28%)
Exposure
Inventory
Net Pos Demand of EU x Exposure x Inventory
0
Net Pos Demand of EU x Exposure
Inventory, ‐5.27
(10%)
Net Pos Demand of EU x Inventory
Net Pos Demand of EU
‐10
Figure 4: The Mean Impact of Net Buying by End-Users
This figure breaks-down the price impact in response to net buying of end-users. I use the mean values of equity
and CDS returns, end-users’ net buying quantities, and dealers’ inventory level, and calculate the contribution of
each of the variables using its estimated regression coefficient.
44
15,000
Lagged CDS Returns, 1,176
(5%)
Net Pos Demand of EU x Exposure x Inventory, 5,394
(23%)
10,000
Lagged CDS Returns
Net Pos Demand of EU x Exposure, 5,916
(26%)
5,000
Lagged Equity Returns
Contemporaneous Equity Return
Contemporaneous Equity Return
Exposure
Net Pos Demand of EU x Inventory, 1,060
(5%)
0
Inventory
Net Pos Demand of EU x Exposure x Inventory
Net Pos Demand of EU x Exposure
Net Pos Demand of EU x Exposure
Net Pos Demand of EU, 928
(4%)
Net Pos Demand of EU x Inventory
Net Pos Demand of EU
Contemporaneous Equity Contemporaneous
Equity
Return, ‐7,132
(31%)
‐5,000
Lagged Equity Returns, ‐1,325
(6%)
‐10,000
Figure 5: The Maximum Impact of Net Buying by End-Users
This figure breaks-down the price impact in response to net buying of end-users. I use the maximum values of equity
and CDS returns, end-users’ net buying quantities, and dealers’ inventory level, and calculate the contribution of
each of the variables using its estimated regression coefficient.
45
30
25
Lagged CDS Returns, 8.13
(19%)
20
Lagged Equity Returns, 6.69
(15%)
15
Lagged CDS Returns
10
Exposure, 0.30
(1%)
Contemporaneous Equity Return, 9.10
(21%)
5
Lagged Equity Returns
Contemporaneous Equity Return
Exposure
Inventory
Net Neg Demand of EU x Exposure, 0.56
(1%)
0
Net Neg Demand of EU, 3.02
( %)
(7%)
Net Neg Demand of EU x Exposure x Inventory
Net Neg Demand of EU x Exposure
Net Neg Demand of EU x Exposure
Net Neg Demand of EU x Inventory
Net Neg Demand of EU
Net Neg Demand of EU x Inventory, ‐10.18
(23%)
‐5
‐10
‐15
Net Neg Demand of EU x Exposure x Inventory, ‐0.62
(1%)
Inventory, ‐5.27
(12%)
‐20
Figure 6: The Mean Impact of Net Selling by End-Users
This figure breaks-down the price impact in response to net selling of end-users. I use the mean values of equity
and CDS returns, end-users’ net buying quantities, and dealers’ inventory level, and calculate the contribution of
each of the variables using its estimated regression coefficient.
46
6,000
4,000
Exposure, 50
(0%)
2,000
0
‐2,000
Net Neg Demand of EU, 129
(1%)
Lagged CDS Returns, 1,176
(5%)
Net
Neg Demand of EU x
Net Neg Demand of EU x Exposure, 4,014
(17%)
Net Neg Demand of EU x I
Inventory, ‐862
862
(4%)
Lagged CDS Returns
‐4,000
Net Neg Demand of EU x Exposure x Inventory 8741 74
Exposure x Inventory, ‐8741.74
(37%)
‐6,000
Lagged Equity Returns
Contemporaneous Equity Return
Contemporaneous Equity Return
Exposure
Inventory
‐8,000
‐10,000
Net Neg Demand of EU x Exposure x Inventory
Inventory, ‐10.43
Inventory,
10.43
(0%)
Net Neg Demand of EU x Exposure
Net Neg Demand of EU x Exposure
Net Neg Demand of EU x Inventory
Net Neg Demand of EU
‐12,000
‐14,000
Contemporaneous Equity Return, ‐7,132
Return 7 132
(30%)
‐16,000
‐18,000
Lagged Equity Returns, 1,325
Lagged Equity Returns ‐1
325
(6%)
‐20,000
Figure 7: The Maximum Impact of Net Selling by End-Users
This figure breaks-down the price impact in response to net selling of end-users. I use the maximum values of equity
and CDS returns, end-users’ net buying quantities, and dealers’ inventory level, and calculate the contribution of
each of the variables using its estimated regression coefficient.
47
Figure 8: Distribution of the Contract Maturities
This figure plots the number of contracts that were traded in each maturity. The transactions include new trades
and assignments. There were a few transactions with maturity greater than 10 years that were not included in
this figure.
48
49
Figure 9: Example of Transaction Prices vs Quoted Prices: No Conversion
This figure plots the prices for 5-Y maturity contracts on AIG. The figure compares the end-of-day mid-quote
supplied by Markit and the transaction prices as reported by DTCC. Almost all transaction prices that are not
aligned with the quoted prices starting August 2008 are associated with an upfront payment. Due to the ongoing
developments in the CDS market, single-name CDS contracts in North America started trading with a fixed
coupon of either 100 or 500 bps and an upfront payment after the Big Bang was implemented on April 8, 2009.
Some market participants started trading in this form even before this change became official. The transaction
prices include interdealer prices (blue dot) and dealer-client prices (red dots represent trades that the client sold
to the dealer; green dots represent trade the the client bought protection from the dealer).
50
Figure 10: Example of Transaction Prices vs Quoted Prices: Applying Conversion
This figure plots the prices for 5-Y maturity contracts on AIG. The figure compares the end-of-day mid-quote
supplied by Markit and the transaction prices as reported by DTCC. Prices of trades that were reported as an
upfront payment and fixed spread were converted using Equation 12.
Appendices
A
Unwinding Mechanisms
When an investor wants to unwind an existing contract, he faces three alternatives, each of
which carries different implications for the level of counterparty risk exposure in the market.
The investor may:
1. Enter into a new offsetting transaction: The investor takes the opposite protection
position. This offsetting transaction should have virtually the same contract terms as
the original CDS regarding the reference entity, the maturity date, etc., except for (i)
the premium, which is dictated by the prevailing market level; and (ii) the tenor, which
is the remaining time to maturity of the original CDS.
Strictly speaking, this is hedging rather than unwinding, as it leaves the original position open and creates a new one. It also involves more documentation and additional
legal and counterparty risk. An investor must ensure that any legal or other basis risk
between the two transactions is minimized.
2. Assign the contract to a new counterparty: The investor can transfer the contract to
a new counterparty, and settle the mark-to-market gain or loss with this new counterparty. This assignment, or “novation” as it is called in the ISDA Definitions, involves
the original counterparties (the “Transferor” and the “Remaining Party” in the novation) and the new party (the “Transferee”) all agreeing to the transfer of all the
Transferor’s rights and obligations to the Transferee. Part of this is obviously that the
Remaining Party must agree to take on the counterparty risk of the Transferee. The
Transferor thereby ends his involvement in the transaction.
Assignments of trades are authorized by the ISDA master agreement for derivatives.
However, the transferors must obtain the prior written consent of the original counterparty as ISDA 2005 Novation Protocol entails.
At the inception of the market, assignments were relatively infrequent; the usual
method of exiting a transaction was an offsetting transaction. But as hedge funds
have become more active in trading CDS, assignments have become increasingly common.
3. Terminate the transaction with the original CDS counterparty: The investor can settle
the mark-to-market gain or loss with the original counterparty and terminate the
contract, conditioned on the latter’s consent. There will be no residual cash flows or
exposure to the counterparty’s credit risk.
From above it is clear that assignments do not change the aggregate exposure levels in
the market, but they do increase the notional amount traded.
51
B
Robustness Tables
Table 12
Robustness: The Effect of End-Users’ Daily Trading Activity on CDS Spreads Using
Arithmetic Differences
In this table I study the asymmetric impact of end-users’ order imbalances on first-difference of CDS prices. The
table details the coefficient of the following pooled-regression:
∆Su,t
= α+
K
X
K
5
5
X
X
X
ζn ∆Pu,t−n +
ηn ∆Su,t−n + εu,t
γk Q̂u,eu,t−k 1>0 +
δk Q̂u,eu,t−k 1<0 +
k=0
n=0
k=0
n=1
where Q̂u,eu,t−k ×1>0 is the standardized net daily notional amount bought by end-users. Similarly, Q̂u,eu,t−k ×
1<0 is the standardized net daily notional amount sold to end-users. Business days without any trading activity
are included in the sample with Q̂u,eu,t as zero, before the standardization. Standard errors that appear in
parentheses are heteroscedastic-robust standard errors. ∗ ∗ ∗, ∗∗, and ∗ denotes significance at 1%, 5%, and 10%
level, respectively. All regressions also include an intercept.
Dependent Variable:
γ0
PK
k=1
∆Su,t
K=0
K=1
K=3
K=5
18.8∗∗∗
(6.2)
17.3∗∗∗
(6.0)
19.7
(17.9)
1.7∗∗
(0.8)
16.0∗∗∗
(6.1)
17.1∗∗∗
(6.1)
−11.8
(7.8)
5.0
(11.4)
17.2∗∗∗
(6.0)
19.9
(17.9)
1.7∗∗
(0.8)
16.4∗∗∗
(6.2)
22.8∗∗∗
(7.4)
−10.5
(7.8)
−16.4
(15.0)
17.3∗∗∗
(5.9)
19.3
(17.9)
1.7∗∗
(0.8)
15.7∗∗
(6.3)
29.9∗∗∗
(9.5)
−11.2
(7.8)
−11.2
(16.0)
17.3∗∗∗
(5.9)
19.4
(17.9)
1.7∗∗
(0.8)
OLS
White
Time (Day)
OLS
White
Time (Day)
OLS
White
Time (Day)
OLS
White
Time (Day)
0.15
18, 813
0.15
18, 778
0.15
18, 709
0.15
18, 639
γk
−10.0
(7.5)
δ0
PK
k=1 δk
ζn × 104
P5
n=1 ζn
P5
n=1
× 104
ηn × 10
Coefficient estimates
Standard errors
Fixed-Effects
R-squared
Number of Observation
C
The List of Variables
Qu,eu,t is the net change in the inventory of end-users as a result of all trades on day
t. All transactions are bilateral, between an end-user and a dealer. Qu,eu,t < 0 if
end-users are net sellers for the day, and Qu,eu,t > 0 if end-users are net buyers for the
day.
52
Q̂u,eu,t is the net change in the inventory of end-users divided by the standard-deviation
of dealers’ inventories.
Qu,d2d,t is the gross notional traded among dealers on day t, excluding double-counting
of both sides of the contract.
Q̂u,d2d,t is the gross notional traded among dealers divided by the standard-deviation
of dealers’ inventories.
Iu,t the cumulative position of all dealers in reference entity u on day t.
Figure 11: Assignments Over Time
This figure plots the daily notional amount traded through assignments as well as the daily number of assignments
over all 35 reference entities.
53