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Do liquidity or credit effects explain
the behavior of the LIBOR-OIS spread?
Russell Poskitta
a Department of Accounting and Finance, University of Auckland, New Zealand
Abstract:
This paper decomposes the LIBOR-OIS spread into credit risk and liquidity components
using premia of credit default swaps written on LIBOR panel banks as a proxy for the credit
risk premia. The decomposition analysis reveals that changes in liquidity rather than changes
in credit risk lie behind the major swings in the LIBOR-OIS spread over the course of the
global financial crisis. Regression analysis shows that the behavior of the credit risk
component is well-explained by a structural model of default. The behavior of the residual
liquidity component is also well-explained by two liquidity variables derived from the
offshore market for three-month US dollar funding. I find evidence of liquidity variables
affecting the credit risk premia but no evidence of credit variables affecting the liquidity
premia.
(198 words)
Key words: LIBOR-OIS spread, credit risk, liquidity premia, financial crisis
JEL classification: G01, G10, G15
* Corresponding author. Address: University of Auckland Business School, Private Bag 92019, Auckland 1142,
New Zealand. Tel: +64 9 373 7599; Fax: +64 9 373 7406; Email: [email protected].
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1. Introduction
The financial crisis which began in August 2007 impaired the functioning of US dollar
funding markets and disrupted a number of traditional money market spread relationships, of
which the LIBOR-OIS spread is one of the more significant.1 Prior to August 2007, three-
month US dollar LIBOR was typically 5 to 10 basis points above three-month OIS but at the
height of the financial crisis this spread reached over 350 basis points before declining to
more normal levels during 2009.2 In normal circumstances, arbitrage should ensure that the
LIBOR-OIS spread does not reach these extreme levels. For example, when three-month US
dollar LIBOR is well above the three-month OIS rate, arbitrageurs would make a three-month
term loan in the interbank market, funding the loan by rolling over overnight borrowing in the
federal funds market and hedging the interest rate risk by purchasing a three-month OIS
contract (Gorton and Metrick, 2009). An excessive LIBOR-OIS spread is a sign that stress in
1 LIBOR (London Interbank Offer Rate) is an indicator of the average interbank borrowing rate in the offshore
or eurocurrency market. The US dollar LIBOR fixing takes place daily in London for 15 different maturities
ranging from overnight to 12 months. Fixings are a trimmed mean of the estimated borrowing rates submitted
by a panel of sixteen banks comprising the largest and most active banks in the US dollar market. The sixteen
banks on the US dollar LIBOR panel are listed in Table A1 in the Appendix. The OIS (overnight indexed swap)
rate is the fixed rate on a fixed/floating interest rate swap where the floating rate is an overnight interest rate
such as the federal funds rate. The OIS rate has emerged as the benchmark risk free rate in the money market.
Although Treasury bills are acknowledged as risk free, they entail significant liquidity risk due to the
“convenience yield” they offer investors (Feldhutter and Lando, 2008) OIS are not free of credit risk, but the
credit risk is generally regarded as minor because the contracts do not involve the exchange of principal and the
residual risk is mitigated by collateral and netting arrangements (Michaud and Upper, 2008). They are also
considered to have minimal liquidity risk because the contracts do not involve any initial cash flow.
2 This paper focuses on the three-month spread since three-month LIBOR is the benchmark for many floating
rate loans and is used in the settlement of derivative contracts such as short-term interest rates futures, forward
rate agreements and interest rate and currency swaps.
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interbank money markets is preventing the arbitrage process from working, either because the
lender is unsure of the borrower’s creditworthiness or is uncertain whether it will continue to
enjoy access to funds in the overnight interbank market for the full term of the loan.
[insert Fig. 1 about here]
From a theoretical perspective the LIBOR-OIS spread can be represented as the sum of credit
risk and liquidity premia (Bank of England, 2007). Accordingly, the fluctuations in the
LIBOR-OIS spread can be attributed to variations in credit risk and liquidity premia. This
paper decomposes the LIBOR-OIS spread into credit risk and liquidity components using
credit default swap (CDS) premia on LIBOR panel banks as a proxy for the credit risk
component.
The focus of this paper is on the behavior of the three-month US dollar LIBOR-OIS spread
between July 2005 and June 2010. I am interested in two questions. First, what is the relative
importance of credit risk versus liquidity premia and how have these components evolved
over the course of the financial crisis. Second, and perhaps more importantly, is the behavior
of these two components of the LIBOR-OIS spread consistent with the behavior of proxies
for credit risk and liquidity. The decomposition of the spread must be meaningful if there is
to be any merit to the subsequent analysis and discussion of the relative importance of credit
risk and liquidity premia. Furthermore, it is also important that any divergence between
LIBOR and the OIS rate can be explained by fundamental economic forces since a poorly
functioning money market will impinge on the cost and availability of credit, with the
potential to affect the real economy, and will likely jeopardize the effectiveness of monetary
policy (Taylor and Williams, 2008).
The unprecedented fluctuations in the LIBOR-OIS spread is a challenge for the existing
theoretical literature on the functioning of interbank markets. This literature acknowledges
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that the existence of an interbank market allows banks to access large volumes of short-term
funds to manage liquidity shocks, thus avoiding the need to liquidate long-term assets
(Bhattacharya and Gale, 1987). However one of the notable features of the global financial
crisis has been the extreme difficulty banks have faced rolling over short-term funding in
wholesale markets (Brunnermeier, 2009; Shin, 2009). It has become evident that one of the
risks that banks face in tapping the interbank market is that suppliers of funds may withdraw
funding based on noisy signals of bank solvency (Huang and Ratnovski, 2008).
Several recent papers highlight the critical role played by asymmetric information in the
functioning of the interbank market. In these models the credit losses suffered by a bank are
private information and the lack of transparency over where credit losses reside gives rise to
an adverse selection problem. Heider et al. (2008) develop a model that explains the
phenomena of very high unsecured rates in interbank markets, liquidity hoarding by banks
and the ineffectiveness of central banks liquidity injections in restoring interbank activity. In
their model the type of interbank regime that arises depends on the level and distribution of
credit risk. When the level and dispersion of credit risk is low, the adverse selection problem
is minor, the interest rate penalty safe borrowers pay for the presence of riskier borrowers is
low and the interbank market functions smoothly with full participation. If the level and
dispersion of credit is significant, however, the penalty built into the interbank rate rises and
safe borrowers drop out of the market due to higher adverse selection costs. When the level
and dispersion of credit risk is high, the prohibitive adverse selection costs cause the
complete breakdown of the interbank market: interest rates are not high enough to
compensate lenders for lending to even riskier borrowers and banks with surplus funds
abandon the market, leading to hoarding of liquidity. In addition, some riskier borrowers find
interest rates too high and prefer to borrow elsewhere.
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Baglioni (2009) presents a model that allows a more central role for liquidity shocks.
Although there is no aggregate shortage of liquidity in this model, individual banks do not
know whether their liquidity shock is transitory or permanent. A bank with excess liquidity
can lend in the interbank market on either a short-term or a long-term basis. If the bank
makes a long-term loan and the liquidity shock is permanent then this bank will be forced to
borrow in the interbank market or sell illiquid assets (and possibly incur liquidation losses).
The liquidity risk a bank faces will be more severe during a period of financial turmoil when
liquidity shocks have greater volatility. The model also allows participants in the interbank
market to be hit by a negative credit shock in the future. If this shock is large enough some
banks may be pushed into insolvency. However the distribution of credit losses is private
information giving rise to an adverse selection problem.
In the Bagliono (2009) model the interplay of liquidity and credit risks can lead to gridlock in
the interbank market during a period of financial turmoil when the volatility of liquidity
shocks is high and adverse selection problems are severe. Banks short of liquidity will want
to avoid being forced to sell illiquid assets at a heavy discount to their face value while banks
long on liquidity will demand a premium on interbank term loans. This situation is likely to
lead to the emergence of a spread between term and overnight interbank rates and to the
cessation of lending in the term segment of the interbank market despite their being no
aggregate shortage of liquidity.
The unprecedented fluctuations in the LIBOR-OIS spread since August 2007 has prompted a
number of researchers to examine the respective roles of credit risk and liquidity premia in
the determination of the LIBOR-OIS spread. In one of the earliest studies, Michaud and
Upper (2008) examined the LIBOR-OIS spread in a number of currencies including the US
dollar. They find that the LIBOR-OIS spreads tracked measures of credit risk such as the
premia of CDS written on LIBOR panel banks although they acknowledge that the looseness
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in this relationship reflects the impact of liquidity factors. The authors also noted that of the
ten extraordinary liquidity management operations conducted by central banks during their
sample period, LIBOR-OIS spreads declined in seven cases while CDS premia on LIBOR
panel banks declined in only five cases, offering this as evidence of the important role played
by liquidity factors.3
More relevant to this paper are the studies that have focused on the impact of the Federal
Reserve’s Term Auction Facility (TAF) on the US dollar LIBOR-OIS spread. McAndrews et
al. (2008) regress the daily change in the three-month LIBOR-OIS spread on the lagged level
of the spread, the daily change in the JP Morgan Banking Sector CDS Index and separate
TAF announcement date and operations date dummy variables. The authors report negative
and significant estimates for both types of TAF dummies but the level of significance is
stronger for the announcement date dummy variable. The authors find similar results when
they use the CDS data to decompose the LIBOR-OIS spread into credit risk and non-credit
risk components using CDS premia as a proxy for the credit risk component and regress the
daily change in the non-credit risk component of the LIBOR-OIS spread against the TAF
dummies.
Frank and Hesse (2009) also decompose the US dollar LIBOR-OIS spread into credit risk and
non-credit risk components using CDS premia on LIBOR panel banks. The authors show that
the daily changes in the non-credit risk component were overwhelmingly positive prior to the
December 2007 monetary policy initiatives of the Federal Reserve in mid-December 2007
3 Using e-MID data from the overnight money market, Michaud and Upper (2008) find less evidence that the
behavior of the euro LIBOR-OIS spread can be explained by changes in market liquidity, proxied by the number
of trades, trading volume, bid/ask spreads and the price impacts of trades. However the authors warn that their
results should be treated with caution because the overnight market appears to have been much less affected by
the financial turmoil than the market for term deposits.
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and overwhelmingly negative in the days that followed these initiatives. The authors also
employ a bivariate VAR model – the dependent variables are the daily changes in the
LIBOR-OIS spreads for the euro and US dollar – to capture the impact of the monetary
policy initiatives of both the Federal Reserve and European Central Bank (ECB). The authors
report that the US dollar LIBOR-OIS spread shows evidence of significant narrowing
following the announcement of the Federal Reserves’s TAF and the ECB’s long-term
refinancing operations and following cuts in the federal funds rate and ECB’s discount rate.
However the authors acknowledge that the economic magnitudes are not large and not
particularly effective in restoring LIBOR-OIS spreads to their pre-crisis levels.
Taylor and Williams (2009) estimate an OLS regression model of the US dollar LIBOR-OIS
spread in levels form.4 They regress the three-month LIBOR-OIS spread on three proxies for
credit risk – the median premia on five-year CDS contracts written on LIBOR panel banks,
the LIBOR-TIBOR spread and the LIBOR-REPO spread – and alternative TAF auction date
dummy variables.5 The estimates of the credit risk proxies are found to have the expected
sign and are usually significantly different from zero. However the sums of the estimates of
the TAF auction date dummies are not negative and statistically significant. These broad
results hold when separate tests are conducted using a single TAF dummy variable and/or
when regressions are corrected for first-order serial correlation. The authors also test other
4 McAndrews et al. (2008) observe that estimating the OLS regression model in levels form will only yield
negative estimates on the TAF dummy if the TAF has a permanent effect on the LIBOR-OIS spread; the more
likely temporary effect of the TAF can only be captured by a OLS regression model estimated in first
differences. McAndrews et al. (2008) also note that the presence of a unit root in the LIBOR-OIS spread
suggests that the OLS regression model should be estimated in first differences.
5 The authors use five dummy variables: one dummy variable is set to one on the day of the TAF auction and the
other four are set to one on each of the four days following the auction. The authors claim this specification is
appropriate when the effects of the TAF auction on spreads diminish over time.
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specifications of the TAF variable. When they set the TAF dummy to zero before the TAF
was first announced on 12 December 2007 and one thereafter, they find a negative and
significant effect of the TAF on the LIBOR-OIS, but the economic magnitude, at 8 basis
points, is small. Not surprisingly, when the authors also follow the procedure employed by
McAndrews et al. (2008) and include separate TAF announcement date and TAF operation
date dummy variables in their regression model, they obtain similar results to those reported
in the earlier study. Their regression results using other money market spreads as the
dependent variable find less evidence of significant negative estimates on the TAF dummies.
The authors conclude that, overall, they find more evidence that counterparty credit risk
rather than liquidity premia drives money market spreads during the global financial crisis.
The studies cited above suffer from a number of limitations. First, the sample periods
employed have not extended beyond April 2008 and thus have arguably missed the most
interesting phases of the global financial crisis, the period of intense turmoil that followed the
collapse of Lehman Brothers in September 2008 and the return to more benign conditions in
interbank markets from the middle of 2009 to early 2010. The sample period in the current
paper extends to the end of June 2010 and provides a much richer data set.
Second, not all prior research decomposes the LIBOR-OIS spread into credit risk and non-
credit risk (i.e. liquidity) components and seeks to identify the driving forces behind the
behavior of each component. The current study starts with this decomposition which is
fundamental to understanding the interplay between credit risk and liquidity factors.
Third, the studies cited above have employed dummy variables to capture the liquidity
impacts of the emergency monetary policy measures. This is not necessarily the most
appropriate way to understand the evolution of liquidity premia in the interbank market. The
current paper introduces three new measures of liquidity conditions. Two of these measures
are developed from a time series of intraday quote data from the offshore market for three-
9
month US dollar funding and the third comes from the domestic US commercial paper
market.
Fourth, and lastly, the results of prior research appear to be influenced by whether the
regression models are estimated in levels or first differences. I seek to circumvent this
problem by using both specifications.
I decompose the LIBOR-OIS spread into credit risk and liquidity components using CDS
premia on LIBOR panel banks as a proxy for the credit risk components, treating the residual
as the liquidity component. The decomposition analysis shows that the liquidity component
invariably exceeded the credit risk component and that variations in the liquidity component
were largely responsible for the dramatic swings in the LIBOR-OIS spread. For example, on
10th October 2008, at the height of the turmoil following the collapse of Lehman brothers, the
LIBOR-OIS spread reached 364.4 basis points, of which 23.0 basis points was attributed to
credit risk and the remaining 341.4 basis points was attributed to liquidity factors. Over the
following months, the LIBOR-OIS spread contract while the credit risk component increased!
These events demonstrated clearly that changes in the LIBOR-OIS spread were primarily a
liquidity phenomenon.
Following the literature on credit spreads and CDS premia, I then estimate a structural model
of the credit risk components using data in both levels and first differences form. When using
daily data in levels form, I find strong evidence that variations in the credit risk component
can be explained by variations in standard proxies for credit risk, namely bank stock prices,
stock price volatility and the risk free rate. Results are weaker but still supportive of the
structural model when data in weekly first differences is employed.
Turning to the liquidity component of the LIBOR-OIS spread, I introduce two new measures
of liquidity in the offshore market for three-month US dollar funding. I find strong evidence
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from daily levels data that the liquidity premia can be explained by tightness in interbank
markets proxied by the average bid/ask spread on three-month US dollars and by competition
for interbank US dollar deposits proxied by the number of active dealers. Results are similar
when data in weekly first differences is employed. I also find that the liquidity component of
the LIBOR-OIS spread is inversely related to the liquidity of the domestic US commercial
paper market for financial institutions.
Lastly I investigate the relationship between credit risk and liquidity factors. I find some
evidence that credit risk premia reflect liquidity factors, consistent with the argument that
banks facing funding difficulties are in greater risk of default. However I find no evidence
that liquidity premia reflect credit risk factors.
I make several contributions to the literature. First, prior research has not settled the issue as
to the respective roles played by credit risk and liquidity factors in the dislocation of the
LIBOR-OIS spread following the onset of the global financial crisis. Our decomposition of
the LIBOR-OIS spread into credit risk and liquidity components shows that the liquidity
component underwent the more dramatic changes over the sample period, suggesting that the
importance attached to fluctuations in liquidity as a fundamental driving force of the LIBOR-
OIS spread cannot be overstated.
Second, the principal results presented in this paper are consistent with evidence from other
markets disrupted by the financial crisis. For example, the finding that measures of liquidity
in the offshore US dollar market can explain the behavior of the liquidity component of the
LIBOR-OIS spread is consistent with recent research which highlights the role played by the
shortage of US dollar funding in the dislocation of the FX swap market (Baba and Packer,
2009). Thus this paper adds to our understanding of the critical role played by market
illiquidity in the disruption of traditional money market relationships during the global
financial crisis.
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Third, the evidence I present of the linkage between various liquidity measures and the
liquidity component of the LIBOR-OIS spread is an improvement on prior research which
has been limited to using monetary policy dummy variables and other money market spreads
to help identify liquidity effects in US dollar funding markets (e.g., McAndrews et al., 2008;
Frank and Hesse, 2009; Taylor and Williams, 2009).
The remainder of the paper is organized as follows. Section 2 describes the methodology
employed in this paper, namely the decomposition of the LIBOR-OIS spread into credit risk
and liquidity risk components. Section 3 presents the results of the decomposition analysis
and subsequent regression analysis of the credit risk and liquidity risk components of the
LIBOR-OIS spread. Section 3 also addresses the issue of the interrelationship between credit
risk and liquidity components. Section 4 concludes.
2. Methodology
2.1 Decomposition of LIBOR-OIS spread
In the absence of any frictions, the spread between an interbank rate and a risk free rate
reflects two risks: the risk of the borrower defaulting on repayment (default or credit risk) and
the ease with which funding can be raised (liquidity risk).6 Thus the LIBOR-OIS spread on
day t is written as the sum of credit risk and liquidity premia in the offshore market:
ttt LIQCRDOISLIBOR (1)
6 In reality the assumption that the spread can be decomposed into separate credit risk and liquidity risk premia
is problematic since the two risks can be related. For example, a bank facing difficulty in raising funding is also
in greater risk of default and the inability to raise funds will likely be factored into CDS premia. Similarly, the
uncertainty over the creditworthiness of banks could lead some banks to withdraw from the interbank market,
raising liquidity premia.
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where LIBOR-OISt is the LIBOR-OIS spread on day t, CRDt represents the credit risk
premium associated with the LIBOR panel on day t and LIQt represents the liquidity
premium faced by the LIBOR panel in the offshore market on day t.
I use CDS premia to decompose the LIBOR-OIS spread over the risk free rate into credit risk
and liquidity risk components (Bank of England, 2007). I require the premia of three-month
CDS contracts written on LIBOR panel banks to accomplish this decomposition. In the
absence of market data on premia on three-month CDS contracts, I take one-quarter of the
premia of a one-year CDS contract as a crude approximation.7 I use the median of these
estimated premia for three-month CDS contracts written on LIBOR panel banks as a proxy
for CRD, thus enabling the identification of the liquidity component of the spread, LIQ.8
2.2 Modeling the credit risk component
I start with the framework of a structural model of default to help identify variables that can
explain changes in CRD.9 This approach has been used to examine the theoretical
7 DataStream does not provide premia on CDS contracts with a maturity of less than one year. Although CDS
premia are unlikely to be linear in their term – for example, premia on five-year CDS are less than five times the
premia on one-year CDS – I use this linear approximation since any bias is likely to be small for shorter-term
CDS contracts and alternative “rules of thumb” are just as likely to lead to bias.
8 This approach implicitly assumes that changes in CDS premia reflect changes in default expectations and/or
recovery rates and not liquidity premia in CDS markets. Blanco et al. (2005) note that the CDS market is
relatively young and demand/supply imbalances can often cause sharp movements in CDS premia unrelated to
changes in default probabilities or recovery rates.
9 A structural model of default describes the relationship between the credit spread on a firm’s debt and firm
value (Merton, 1974; Longstaff and Schwartz, 1995). Changes in credit spreads can be likened to changes in the
premium of the put option held by shareholders. Thus factors that raise the value of the put option will widen
credit spreads. Examples include a fall in the value of the firm, an increase in volatility and a reduction in the
risk free rate.
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determinants of corporate bond spreads (Collin–Dufresne et al., 2001) and CDS premia
(Ericcson et al., 2009). The results in the latter study indicate that the minimal set of
determinants include financial leverage, firm-specific volatility and the risk-free rate. The
authors find that the explanatory power of these determinants is approximately 55% – 60%
for levels data and 20% – 25% for data in first difference form.
The first structural variable is STK, the value of an equally-weighted portfolio of stocks of the
LIBOR panel. The value of this portfolio should capture changes in the credit quality and the
perceived financial stability of its panel members. As stronger relative stock price
performance should be reflected in lower perceived credit risk, I expect to observe a negative
relationship between STK and CRD.10
The next structural model variable is stock price volatility, IVOL, measured by the implied
volatility of put options written on the stocks of LIBOR panel banks. I use implied volatility
rather than historical volatility because prior research finds that implied volatility is more
successful than historical volatility in explaining variations in CDS premia (Cao et al., 2010).
I construct IVOL from implied volatility data on put options written on the stocks of LIBOR
panel banks where the individual options satisfy two criteria: the term to expiration is
between 61 and 120 days and the ratio of the exercise price-to-stock price is between 0.85
and 1.15. Increases in implied volatility reflect a higher volatility of firm value and if the
volatility of the firm’s assets increases, there is a greater probability of default (Merton,
1974). Thus I expect to observe a positive relationship between IVOL and CRD.
The third structural model variable is RATE, the yield on five-year Treasury bonds. Longstaff
and Schwartz (1995, p 808) argue that “an increase in the interest rate increases the drift rate
10 Although leverage is a key variable in the structural model it is difficult to measure this variable accurately
over short sampling intervals such as a day or a week. Instead I proxy changes in a bank’s financial health with
the bank’s stock price (Blanco et al., 2005).
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of the risk-neutral process for firm value, which in turn makes the risk-neutral probability of
default lower”. Thus I expect to observe a negative relationship between RATE and CRD.
An additional variable employed in the empirical literature is SLOPE, the slope of the yield
curve (Collin–Dufresne et al., 2001). SLOPE is designed to capture the term structure of
interest rates and is constructed by subtracting the yield on one-year Treasury bonds from the
yield on ten-year Treasury bonds. An upward-sloping yield curve implies higher short-term
interest rates in the future. Following the argument of Longstaff and Schwartz (1995), an
increase in interest rates should lower the probability of default. Hence I expect to observe a
negative relationship between SLOPE and CRD.11
To summarize, the full structural regression model is as follows:
tttttt uSLOPERATEIVOLSTKCRD 43210 (2)
where CRDt is the default component of the LIBOR-OIS spread on day t, STKt is the value of
an equally-weighted portfolio of stocks of the LIBOR panel on day t, IVOLt is the median
implied volatility of near-the-money put options on the stocks of LIBOR panel banks on day
t, RATEt is the risk free rate on day t proxied by the yield on five-year Treasury bonds,
SLOPEt is the slope of the yield curve measured by subtracting the yield on ten-year Treasury
bonds less the yield on one-year Treasury bonds on day t and ut is a random error term.
11 I choose not to include credit ratings of panel banks in the regression model because changes in credit ratings
tend to lag market information. More immediate market measures of creditworthiness such as share price and
CDS premia are likely to be more relevant measures of changes in the credit quality of banks. The often poor
timeliness of credit ratings changes can be attributed to two features of the ‘through-the-cycle’ methodology
employed by ratings agencies (Altman and Rijken, 2006). First, ratings agencies disregard short-term
fluctuations in default risk in an attempt to avoid excessive rating reversals. Second, ratings agencies typically
employ a conservative migration policy whereby ratings are only partially adjusted towards the level suggested
by the permanent risk component.
15
Daily data on the stock prices of the LIBOR panel banks, Treasury bond yields and the
premia of one-year CDS contracts written on LIBOR panel banks are all obtained from
DataStream. Stock price data is only available for 13 banks as Norinchukin Bank, Rabobank
and West Landesbank are not publicly-listed. CDS data is not available for Royal Bank of
Canada. Daily data on the implied volatility of put options written on the stocks of LIBOR
panel banks are obtained from Thomson Reuters Tick History database of Securities Industry
Research Centre Asia-Pacific (SIRCA).12
2.3 Modeling the liquidity component
It was suggested earlier that liquidity was the principal part of the non-default component of
the LIBOR-OIS spread. Testing this proposition requires suitable measures for liquidity,
which in the context of the interbank money market, is broadly defined as the ability of a
bank to raise uncollateralized funding promptly and without incurring much in the way of
transaction costs.
Liquidity measures can be divided in two broad categories: trade-based measures such as
trading volume, the number of trades and the turnover ratio; and order-based measures such
as the bid-ask spread and price impact costs (Aitken and Comerton-Forde, 2003). While this
dichotomy is useful when considering liquidity measures in exchange-traded markets, it is
less useful in over-the-counter (OTC) interbank markets which are typically less transparent
than exchange-traded markets. For example, it is usually very difficult to obtain data on
trading volumes and transaction prices; often the only data that is available are the indicative
12 Options data is not available for the three non-listed panel banks, Norinchukin Bank, Rabobank and West
Landesbank. In addition, only a small number of implied volatility observations are available on options on
Mitsubishi UFJ, the parent company of Bank of Tokyo-Mitsubishi UFJ. I calculate the mean implied volatility
each day for each of the remaining 12 bank from data on the implied volatility of individual option series. I then
calculate the median of these 12 bank-specific daily averages.
16
bid and ask quotes of dealers. In light of these constraints, I develop two liquidity measures
based on indicative quote data from the offshore market for three-month US dollar funding:
the daily average bid-ask spread and the number of dealers active in the market.
A time series of indicative quotes from the OTC market for three-month US dollars is
obtained from the Thomson Reuters Tick History database of SIRCA. After filtering this data
to remove one-sided quotes (i.e. either the bid or ask quote is missing) and quotes posted
outside the hours 6.00AM to 4.00PM GMT on weekdays, I am left with a final sample of
694,305 quotes posted by 147 dealers.
This sample of quotes is used to construct daily time series of two variables, BAS and
DLRS.13 BAS is defined as the equally-weighted bid/ask spread across all dealers with a
market share of at least 0.5% and quoting on at least 25% of the days across the sample
period. A total of 19 dealers meet these two criteria.14 DLRS is defined as the number of
dealers posting at least two quotes per day on the Reuters network.
I expect that BAS will widen when liquidity in the interbank market worsens; thus I expect to
observe a positive relationship between LIQ and BAS. However the relationship between LIQ
and DLRS is not so straightforward. Normally, one would expect that an illiquid market is
associated with the presence of fewer dealers and thus for there to be an inverse relationship
between LIQ and DLRS (Huang and Masulis, 1999). However if banks are finding it difficult 13 The microstructure literature decomposes dealer bid/ask spreads into three components: order processing
costs, inventory holding costs and adverse selection costs (Stoll, 1989). To the extent that the adverse selection
cost incorporate the costs of transacting with poor credit risks the bid/ask spread will incorporate an element of
credit risk.
14 The alternative of using all posted quotes to measure the daily bid/ask spread suffers from the problem that
different dealers adopt different spread conventions and thus changes in the bid/ask spread over time will be
influenced by changes in the composition of the quoting dealers rather than by fundamental changes in market
liquidity.
17
to raise funds in other US dollar markets I could expect more dealers to turn to the interbank
market to raise funds and there to be a positive relationship between LIQ and DLRS.
An additional liquidity variable is FINCPO, the amount of outstanding commercial paper
issued by financial institutions in the US commercial paper market. This weekly time series is
available from the Federal Reserve Board. I use this variable to measure the availability of
funding from alternative markets for US dollar funding. It is expected that increased
availability of funds from these alternative markets will reduce liquidity pressures in the
interbank market and hence narrow the liquidity premium in the LIBOR-OIS spread. Thus I
expect to observe a negative relationship between LIQ and FINCPO.
The full liquidity regression model is as follows:
ttttt eFINCPODLRSBASLIQ 3210 (3)
where LIQt is the non-default or liquidity component of the LIBOR-OIS spread on day t,
BASt is the equally-weighted spread across all active dealers on day t, DLRSt is the number of
dealers active on day t, and FINCPOt is the amount of outstanding commercial paper issued
by financial institutions in the US commercial paper market on day t and et is a random error
term.
3. Empirical results
3.1 Spread decomposition
The decomposition of the LIBOR-OIS spread into credit risk and liquidity risk components is
illustrated in Fig. 2. Descriptive statistics on the spread and its decomposition are presented in
Table 1. Panel A of Table 1 reports a mean (median) LIBOR-OIS spread of 41.9 (11.5) basis
points over the full sample period. The mean LIQ (30.6 basis points) was significantly higher
than the mean CRD (11.3 basis points), although the difference between the medians was
smaller. However both the t- and Wilcoxon test statistics show that the null hypothesis of
18
equal CRD and LIQ is strongly rejected. The descriptive statistics also show that LIQ varied
enormously over the sample period, ranging between a maximum of 342.2 and -12.6 basis
points. On the other hand, CRD moved within a much narrower range, with a maximum of
60.2 basis points and a minimum of 0.4 basis points.
[insert Fig. 2 about here]
I find it useful to partition the five-year sample period into four sub-periods. Period I starts on
1 July 2005 and ends on 8 August 2007 and is designated the pre-crisis period. Period II
commences on 9 August 2007 and ends on 15 September 2008, the day Lehman Brothers
filed for bankruptcy. The conventional wisdom is that a structual shift occurred in money
markets after 9 August 2007 as the first signs of the turmoil to follow became apparent (e.g.,
Gyntelberg and Wooldridge, 2008; Brunnermeier, 2009). On this day, BNP Paribas
suspended three of its investment funds that held mortgage backed securities and AIG warned
that defaults were spreading beyond the sub-prime sector.
Period III begins on 16 September 2008 and ends on 17 October 2008. This period of
approximately one-month was a period of intense turmoil in interbank markets sparked by the
collapse of Lehman Brothers. The failure of Lehman Brothers is regarded as a seminal event
because it shattered the belief that large banks were “too big to fail”. The resulting turmoil
prompted co-ordinated intervention by the governments and central banks of the leading
industrial nations to restore investor confidence in the banking system and forestall the
complete implosion of wholesale financial markets.15 The end of this period of intense
15 For example, the UK government introduced plans to recapitalize the banking system and guarantee bank debt
funding while the Bank of England injected liquidity into the financial system (Treasury, 2008). The US
introduced similar measures to recapitalize banks and guarantee bank debt funding. The Federal Reserve also
19
turmoil is dated to the week ending 17 October 2008. The final sub-period, Period IV, begins
on 20 October 2008 and extends to the end of the sample period and is referred to as the post-
crisis period.
[insert Table 1 about here]
The data in Panel B of Table 1 show a mean (median) LIBOR-OIS spread of 7.9 (8.0) basis
points during the pre-crisis period. CRD was virtually insignificant during this period, with a
mean (median) of 0.8 (0.7) basis points. The low level of CRD is consistent with the general
underpricing of credit risks during this period. The mean (median) level of LIQ was 7.2 (7.2)
basis points. The test statistics reported in the last column confirm that the null hypothesis of
equal CRD and LIQ is strongly rejected.
The data in Panel C of Table 1 show that the LIBOR-OIS spread was significantly higher
during period II, with a mean (median) level of 69.2 (70.8) basis points. The mean (median)
level of CRD rose to 10.1 (11.0) basis points while the mean (median) level of LIQ rose to
59.1 (59.1) basis points. The test statistics reported in the last column confirm that the null
hypothesis of equal CRD and LIQ is strongly rejected. The data in Panel C suggest that
although both CRD and LIQ rose during this period the rise in the LIBOR-OIS spread was
principally a liquidity phenomenon.
The turmoil associated with period III led to a sharp rise in the LIBOR-OIS spread, with the
mean (median) spread rising sharply to 247.3 (258.6) basis points during this period. The data
in Panel D of Table 1 show that although the mean (median) CRD approximately doubled to
22.0 (21.6) basis points, LIQ rose nearly four-fold, with a mean (median) of 225.2 (233.3)
basis points. Again, the test statistics reported in the last column confirm that the null
began to purchase commercial paper from high quality issuers and established swap lines with foreign central
banks to ease pressure in global short-term dollar markets (Paulson, 2008).
20
hypothesis of equal CRD and LIQ is strongly rejected. The data in panel D confirm that the
rise in the LIBOR-OIS spread during this period was principally a liquidity phenomenon.
The data in Panel E of Table 1 show that period IV was accompanied by a dramatic decline in
the LIBOR-OIS spread, with the mean (median) spread during this period of 55.6 (30.9) basis
points. CRD increased slightly during this period, recording a mean (median) of 24.7 (24.4)
basis points.16 LIQ abated sharply, with a mean (median) of 30.3 (3.5) basis points. The test
statistics reported in the last column confirm that the null hypothesis of equal CRD and LIQ is
strongly rejected. Although the mean and median for CRD and LIQ data give conflicting
signals, the sharp decline in the LIBOR-OIS spread at a time when CRD was rising suggests
that the contraction in the spread was driven by the sharp decline in LIQ.
The discussion above, supported by casual inspection of Fig. 2, suggests that the variations in
liquidity premia are the key to the dramatic swings in the LIBOR-OIS over the course of the
global financial crisis. This conclusion is, of course, conditional on an accurate
decomposition of the LIBOR-OIS spread into credit risk and liquidity premia components.
One potential problem is the decision to use the median CDS premia, rather than the mean
CDS premia, of the LIBOR panel as the proxy for the credit risk component of the LIBOR-
OIS spread. This decision was motivated by the need to minimize the potential impact of a
disproportionately large CDS premia of a single panel bank on the decomposition analysis.17
16 The rise in both the LIBOR-OIS spread and CRD in the second quarter of 2010 has been attributed to
concerns over counterparty credit risk, in particular, the heightened concerns of market participants over the
exposure of European banks to the sovereign debt of a number of fiscally-challenged European countries
(Gyntelberg et al., 2010).
17 For example, on 10th March 2009, the estimated three-month CDS premia for Citigroup was 233.9 basis
points. This contributed to a situation where the mean three-month CDS premia of 70.3 basis points exceeded
the median three-month CDS premia of 58.5 basis points.
21
For a given LIBOR-OIS spread, an artificially high CRD will reduce LIQ, and in extreme
cases, lead to a negative LIQ. In fact, using the median CDS premia still gives rise to 145
cases (out of a total of 1,262 sample observations) where LIQ is negative. All but one of these
negative LIQ observations occur in period IV. The maximum negative LIQ is -12.6 basis
points and the mean negative LIQ is -3.3 basis points. In contrast, when CRD is proxied by
the mean CDS premia of the LIBOR panel, the incidence of negative LIQ increases to 189
observations, again all but one of these occurring in period IV. The maximum negative LIQ is
-11.7 basis points and the mean negative LIQ is -4.4 basis points.
The lower incidence and size of negative LIQ when using the median CDS premia as the
proxy for CRD suggests that the median CDS premia is a better proxy for CRD than the mean
CDS premia. While the incidence of negative LIQ is still troubling, the relatively small size
of the negative LIQ suggests that the decomposition analysis has not been adversely
impacted.
The following sections present a more rigorous check on the accuracy of our decomposition.
This involves testing if the variations in the CRD and LIQ components can be explained by
variations in fundamental proxies for credit risk and liquidity.
3.2 The credit risk component
3.2.1 Descriptive statistics and correlations
Table 2 reports summary data on the structural model variables over the sample period. CRD,
proxied by the median premia on CDS written on panel banks, has a mean (median) of 11 (7)
basis points and ranges between 0 and 60 basis points. STK has a mean (median) of 699.43
(760.60) versus a level of 1,000 on 8 August 2007. The large differences between the
maximum and minimum levels of 1,072.18 and 175.08 respectively illustrate the dramatic
changes that occurred in the fortunes of many LIBOR panel banks over the sample period.
22
IVOL has a mean (median) of 42.89% (37.29%) and ranges between a maximum of 189.97%
and a minimum of 14.66%. The large difference between the maximum and minimum IVOL
also illustrate the large swings that occurred in investor sentiment towards LIBOR panel
banks over the sample period. Large changes also occurred in the level and slope of the yield
curve during the sample period. RATE, proxied by the five-year Treasury bond yield, ranges
between 5.23% and 1.26% while SLOPE ranges between 3.53% and -0.48%.
[insert Table 2 about here]
Table 3 reports the correlations between the structural model variables over the sample
period. CRD is highly positively correlated with IVOL and SLOPE and highly negatively
correlated with STK and RATE. These correlations are also consistent with those reported by
Ericcson et al. (2009) in their study of the determinants of CDS premia. The sign of the
correlations between CRD, STK and IVOL are as predicted by the structural model. The high
correlations between the explanatory variables of the structural model suggest that
multicollinearity could be an issue if most or all of these variables are included as
explanatory variables in the same regression model.
[insert Table 3 about here]
3.2.2 Regression model estimates
I begin the regression analysis by estimating simple versions of the structural model
represented by equation (1) using daily data in levels form. Column (1) of Table 4 reports
estimation results for the basic regression model. The estimation results show that STK has
the expected negative sign and the estimates are significant at the 0.001 level. IVOL has the
23
expected positive sign and is also significant at the 0.001 level. RATE has the expected
negative sign and is significant at the 0.001 level.18
The estimation results for STK, IVOL and RATE are consistent with the intuition underlying
the structural model: the default components of the spread over the risk free rate is increasing
in equity volatility and decreasing in the stock price and interest rate. These estimation results
are also broadly consistent with the results of prior research into the determinants of CDS
premia (Blanco et al., 2005; Ericcson et al., 2009; Cao et al., 2010). The explanatory power
of the structural model is higher than reported by Ericcson et al. (2009) at 85% versus 55% –
60%.
Column (2) of Table 4 reports the results when SLOPE is added to the regression. I include
SLOPE in the model to control for the potential effect of the slope of the yield curve on the
default component of the LIBOR-OIS spread. The signs and significance levels of the
estimates of the existing explanatory variables are similar to those reported in column (1).
More importantly, SLOPE has a negative sign that is significant at the 0.05 level. The
estimation results for SLOPE are consistent with those reported in prior research (Cao et al.,
2010).19
[insert Table 4 about here]
18 I also estimate equation (1) using three alternative measures of implied volatility: (i) implied volatility
calculated using data from the five UK-based banks on the LIBOR panel (which account for 73.2% of the daily
implied volatility estimates on individual options); (ii) implied volatility calculated from all put option on
LIBOR panel banks regardless of their term to expiration and moneyness; and (iii) the VIX index which
represents a weighted average of the implied volatility of near-the-money options on the S&P500 index.
Estimation results are broadly the same.
19 However Ericcson et al. (2009) do not find that SLOPE is significant when included in the structural model.
24
One econometric issue that has been ignored to date is that of potential multicollinearity
between the explanatory variables of the structural model. This is likely to be more of a
concern when the data is measured in levels form. In view of the high correlations between
the structural model explanatory variables evident in Table 3 I conduct variance inflation
factor (VIF) analysis. In turn each explanatory variable is regressed on all other explanatory
variables and the variance inflation factor computed. A common rule of thumb is that
multicollinearity could be influencing OLS estimates if the VIF exceeds 10 (Neter et al.,
1989). STK, SLOPE and RATE have VIFs of 15.93, 10.64 and 10.56 respectively, whilst
IVOL has a VIF of 4.56. This suggests that STK, SLOPE and RATE should be excluded from
the regression model but this is not feasible for STK and RATE since both variables are key
elements of the structural model and are not easily replaced.
The second econometric issue is that of the stationarity of the data. I conduct an Augmented
Dickey-Fuller test for a unit root in CRD. The test statistics show that the null hypothesis of a
unit root cannot be rejected. When I test for a unit root in the first difference of CRD the null
hypothesis of a unit root is rejected. These results suggest that the regression model should be
estimated in first differences.
The structural model is re-estimated using first differences of weekly average data. The
results reported in columns (3) and (4) of Table 4 are weaker than reported for levels data.
The estimates of IVOL and RATE have the same signs as reported in columns (1) and (2) and
are still significantly different from zero. The estimates of STK are still negative but are no
longer significantly different from zero. The estimate of SLOPE is negative and significant.
The explanatory power of the regression model is also much reduced when measured in first
difference form. This is not surprising given the amount of noise in the data. However it
should also be borne in mind that the explanatory power of the structural model estimated in
first differences is marginally above the level of 20% – 25% reported by Ericcson et al.
25
(2009). Overall, the estimation results using first differences are broadly supportive of the
predictions of the structural model. The estimation results also lend credibility to the results
of the decomposition analysis.
3.3 The liquidity component
3.3.1 Descriptive statistics and correlations
Table 5 reports summary data on LIQ, BAS and DLRS over the sample period. LIQ has a
mean (median) of 31 (8) basis points and ranges between a maximum of 342 and a minimum
of -13 basis points. BAS has a mean (median) of 0.149% (0.104%) and ranges between
0.717% and 0.0%. DLRS has a mean (median) of 7.8 (7.0) and ranges between 23 and 0. The
wide swings in LIQ, BAS and DLRS show that large changes in liquidity occurred over the
course of the global financial crisis.
[insert Table 5 about here]
Table 6 reports the correlations between LIQ, BAS and DLRS over the sample period. LIQ is
positively correlated with BAS and DLRS. BAS and DLRS are negatively correlated,
consistent with prior research in OTC markets (Huang and Masulis, 1999). All correlations
are significant at the 0.001 level. The high positive correlation between LIQ and BAS is
expected. The positive correlation between LIQ and DLRS suggests that funding pressures in
wholesale financial markets are associated with more dealers posting quotes in the interbank
market. The relatively low (but significant) correlation between BAS and DLRS suggests that
both these proxies for liquidity can be included as explanatory variables in the same
regression model without any concerns about potential multicollinearity.
[insert Table 6 about here]
26
3.3.2 Regression model estimates
I begin the regression analysis by estimating simple versions of the model represented by
equation (2) using daily data in levels form. The estimation results reported in column (1) of
Table 7 shows that BAS has the expected positive sign and is significant at the 0.001 level.
The estimation results reported in column (2) of Table 7 shows that DLRS also has the
expected positive sign and is significant at the 0.01 level. However the explanatory power of
the DLRS regression is much lower than the BAS regression model. The estimation results
reported in column (3) of Table 7 shows that both BAS and DLRS retain their positive signs,
and that both estimates are significant at the 0.001 level, when both variables are included in
the same regression model.
The estimation results for equation (2) are broadly consistent with the intuition that funding
pressures in wholesale markets, signaled by wider spreads and greater completion for funds,
are associated with higher liquidity premia and a higher liquidity component of the LIBOR-
OIS spread. The sign and significance of the BAS estimates is also consistent with the
findings of Longstaff et al. (2005) who report that the non-default component of corporate
bond spreads is positively related to the bid/ask spread on corporate bonds.
[insert Table 7 about here]
Equation (2) is re-estimated using first differences of weekly average data. Not surprisingly
the results reported in column (4) of Table 7 are weaker than reported for levels data in terms
of levels of significance and the explanatory power of the regression model. BAS and DLRS
continue to have positive signs that are significant at either the 0.05 or 0.01 level. FINCPO
has the expected negative sign and is significant at the 0.05 level. This suggests that
reductions in the supply of funds obtained via the US commercial paper market are associated
with a higher liquidity premium in the offshore US dollar market.
27
Overall, the estimation results reported in Table 7 show that the changes in the non-default
component of the LIBOR-OIS spread are consistent with changes in the proxies for liquidity.
This also suggests that the decomposition of the LIBOR-OIS spread into default and non-
default components has some validity.
3.4 Interaction between credit and liquidity effects
It was noted earlier that the decomposition of the LIBOR-OIS spreads into default and non-
default components ignores the possibility that default and non-default factors are likely to be
positively correlated. That is liquidity premia are likely to include credit risk premia and vice
versa. Indeed it is possible that this explains why liquidity premia were so high during the
fourth quarter of 2008 – because it included an element of credit risk premia not captured
directly by CDS premia.
I test for these joint effects by including liquidity risk variables in the CRD regression model
and by including credit risk variables in the LIQ regression model. I estimate these extended
regression models using first differences of weekly data. OLS estimation results are reported
in Table 8.
Columns (1) to (3) report the OLS estimation results for the extended CRD model. The
estimation results reported in column (1) show that BAS has a positive and significant
coefficient, showing that illiquidity in US dollar funding markets exerts significant upward
pressure on CDS premia, suggesting that CDS premia incorporate the risk posed to banks by
tightness in funding markets. The estimation results reported in columns (2) and (3) show that
DLRS and FINCPO have no significant impact on CDS premia. Columns (4) to (6) report the
OLS estimation results for the extended LIQ model. The estimation results show that credit
risk proxied by STK, IVOL and RATE do not have any significant impact on liquidity risk
premia.
28
Overall, these results do not lend any support to the argument that liquidity risk premia
incorporate an element of credit risk. However there is some evidence, albeit limited, that
illiquidity in the US dollar funding markets is incorporated into CDS premia, presumably
reflecting that fact that the risk of bank failure is higher when banks face greater difficulty in
raising funds in the interbank market.
4. Conclusion
This paper examines the behavior of the LIBOR-OIS spread for three-month US dollars over
the course of the global financial crisis by decomposing the LIBOR-OIS spread into credit
risk and liquidity risk premia using CDS premia on LIBOR panel banks as a proxy for the
credit risk premia. The results of our decomposition analysis show that LIQ is the major
component of the LIBOR-OIS spread over much of the course of the global financial crisis
and that changes in market liquidity rather than changes in credit risk lie behind the major
swings in the LIBOR-OIS spread. Using data in both levels and first difference form, the
subsequent analysis shows that the behavior of each component is consistent with theoretical
predictions. For example, I find evidence that variations in the credit risk component of the
spread can be explained by variations in standard proxies for credit risk, namely bank stock
prices, stock price volatility and the risk free rate. I also find evidence that the liquidity risk
component can be explained by tightness in interbank markets proxied by average bid/ask
spread, by the liquidity of the domestic US commercial paper market for financial institutions
and by competition for interbank deposits proxied by the number of active dealers. This is an
improvement on prior research which has been limited to using dummy variables to
investigate the effectiveness of emergency monetary policy measures in narrowing the
LIBOR-OIS spread. Lastly I investigate the relationship between credit risk and liquidity risk
factors. I find some evidence that credit risk premia reflect liquidity risk factors, consistent
29
with the argument that banks facing funding difficulties are in greater risk of default.
However I find no evidence that liquidity risk premia reflect credit risk factors.
The results of this paper have implications for our understanding of the relative importance of
credit and liquidity factors in the evolution of the LIBOR-OIS spread over the course of the
global financial crisis. The decomposition of the LIBOR-OIS spread into credit risk and
liquidity risk components shows that the liquidity risk component underwent the more
dramatic changes over the sample period. The results of the subsequent regression analysis
for the liquidity component of the LIBOR-OIS spread suggests that the importance attached
to fluctuations in the liquidity premia as a fundamental driving force of the LIBOR-OIS
spread cannot be overstated.
30
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Table 1 Descriptive statistics of LIBOR-OIS spread, CRD and LIQ by sub-period
LIBOR-OIS CRD LIQ H0: CRD = LIQ
Panel A: Full sample period (1 July 2005 to 30 June 2010)
Mean 0.419*** 0.113*** 0.306*** t = -13.75*** Median 0.115*** 0.072*** 0.079*** W = 9.54*** Max 3.644 0.602 3.422 Min -0.001 0.004 -0.126 Std. Dev. 0.538 0.131 0.480
Panel B: Period I (1 July 2005 to 8 August 2007)
Mean 0.079*** 0.008*** 0.072*** t = -87.15*** Median 0.080*** 0.007*** 0.072*** W = 28.15*** Max 0.181 0.035 0.154 Min -0.001 0.004 -0.008 Std. Dev. 0.018 0.003 0.017
Panel C: Period II (9 August 2007 to 15 September 2008)
Mean 0.692*** 0.101*** 0.591*** t = -52.15*** Median 0.708*** 0.110*** 0.591*** W = 20.43*** Max 1.070 0.294 1.009 Min 0.309 0.026 0.215 Std. Dev. 0.144 0.051 0.148
Panel D: Period III (16 September 2008 to 17 October 2008)
Mean 2.473*** 0.220*** 2.252*** t = -11.21*** Median 2.586*** 0.216*** 2.333*** W = 5.93*** Max 3.644 0.288 3.422 Min 0.991 0.139 0.775 Std. Dev. 0.871 0.042 0.887
Panel E: Period IV (20 October 2008 to 30 June 2010)
Mean 0.556*** 0.247*** 0.303*** t = -2.07*** Median 0.309*** 0.244*** 0.035*** W = 8.44*** Max 2.938 0.602 2.770 Min 0.056 0.071 -0.126 Std. Dev. 0.600 0.130 0.540 Tests of significance are conducted using the t-test and Wilcoxon signed rank test. *** Significant at the 0.001 level.
34
Table 2 Descriptive statistics of CRD and explanatory variables
CRD STK IVOL RATE SLOPE Mean 0.11 699.43 42.89 3.49 1.27 Median 0.07 760.60 37.29 3.45 0.85 Max 0.60 1,072.18 189.97 5.23 3.53 Min 0.00 175.08 14.66 1.26 -0.48 Std. Dev. 0.13 260.44 29.56 1.12 1.33
35
Table 3 Correlation matrix of CRD and explanatory variables
CRD STK IVOL RATE STK -0.852*** IVOL 0.887*** -0.800*** RATE -0.839*** 0.935*** -0.767*** SLOPE 0.701*** -0.904*** 0.588*** -0.901*** *** Significant at the 0.001 level.
36
Table 4 Estimation results for CRD regression model using daily levels and weekly first differences
tutSLOPEtRATEtIVOLtSTKtCRD 43210
where CRDt is the default component of the LIBOR-OIS spread on day t, STKt is the value of an equally-weighted portfolio of stocks of the LIBOR panel on day t, IVOLt is the median implied volatility of near-the-money put options on the stocks of LIBOR panel banks on day t, RATEt is the risk free rate on day t proxied by the yield on five-year Treasury bonds, SLOPEt is the slope of the yield curve measured by subtracting the yield on one-year Treasury bonds from the yield on ten-year Treasury bonds and ut is a random error term.
Levels First differences (1) (2) (3) (4)
Constant 0.1743 (4.32)***
0.3273 (4.37)***
0.0003 (0.21)
0.0006 (0.43)
STK -0.0001 (-3.05)***
-0.0002 (-4.17)***
-0.0001 (-1.28)
-0.0002 (-1.46)
IVOL 0.0025 (5.95)***
0.0020 (4.88)***
0.0012 (3.37)***
0.0012 (3.44)***
RATE -0.0238 (-3.29)***
-0.0366 (-3.53)***
-0.0401 (-3.12)**
-0.0319 (-2.41)*
SLOPE -0.0222 (-2.36)*
-0.0227 (-1.54)
Adjusted R2 0.852 0.857 0.313 0.322 *** Significant at the 0.001 level. ** Significant at the 0.01 level. * Significant at the 0.05 level.
37
Table 5 Descriptive statistics of LIQ and explanatory variables
LIQ DLRS BAS Mean 0.31 7.8 0.149 Median 0.08 7.0 0.104 Max 3.42 23.0 0.717 Min -0.13 0.0 0.00 Std. Dev. 0.48 3.6 0.108
38
Table 6 Correlation matrix of LIQ and explanatory variables
LIQ DLRS DLRS 0.216*** BAS 0.626*** -0.134*** *** Significant at the 0.001 level.
39
Table 7 Estimation results for LIQ regression model using daily levels and weekly first differences
tetFINCPOtDEALERStSPREADtLIQ 3210 where LIQt is the non-default component of the LIBOR-OIS spread on day t, BASt is the equally-weighted spread across all active dealers on day t, DLRSt is the number of dealers active on day, and FINCPOt is the amount of outstanding commercial paper issued by financial institutions in the US commercial paper market and et is a random error term.
Levels Differences (1) (2) (3) (4)
Constant -0.1061 (-1.62)
0.0735 (1.02)
-0.4685 (-4.01)***
-0.0008 (-0.11)
BAS 2.7681 (5.30)***
2.9570 (6.58) ***
0.9280 (2.88)**
DLRS 0.0295 (2.62)**
0.0421 (5.06)***
0.0062 (2.06)*
FINCPO -0.0022 (-1.98)*
Adjusted R2 0.391 0.046 0.484 0.160 *** Significant at the 0.001 level. ** Significant at the 0.01 level. * Significant at the 0.05 level.
40
Table 8 Estimation results for CRD and LIQ regression models using weekly first differences
CRD LIQ (1) (2) (3) (4) (5) (6)
Constant 0.0004 (0.33)
0.0006 (0.23)
0.0005 (0.42)
-0.0011 (-0.16)
-0.0018 (-0.23)
-0.0008 (-0.12)
STK -0.0004 (-3.63)***
-0.0002 (-1.43)
-0.0002 (-1.45)
-0.0002 (-0.34)
IVOL 0.0011 (3.18)**
0.0012 (3.46)***
0.0012 (3.46)***
0.0003 (0.20)
RATE -0.0324 (-2.43)*
-0.0312 (-2.38)*
-0.0323 (-2.46)*
0.0037 (0.04)
SLOPE -0.0295 (-1.96)
-0.0232 (-1.57)
-0.0230 (-1.58)
BAS 0.1549 (2.99)**
0.9463 (2.69)**
0.9154 (2.95)**
0.9264 (2.79)**
DLRS 0.0003 (0.59)
0.0059 (2.05)*
0.0060 (1.99)*
0.0063 (1.84)
FINCPO -0.0001 (-0.34)
-0.0022 (-2.14)*
-0.0023 (-2.08)*
-0.0022 (-1.98)*
Adjusted R2 0.365 0.320 0.208 0.158 0.160 0.156 *** Significant at the 0.001 level. ** Significant at the 0.01 level. * Significant at the 0.05 level.
41
Appendix Table A.1 Membership of the US dollar LIBOR panel, July 2005 – June 2010 Bank of America Bank of Tokyo-Mitsubishi UFJ Barclays Citibank Credit Suisse Deutsche Bank HBOS HSBC JP Morgan Chase Lloyds TSB Norinchukin Bank Rabobank Royal Bank of Canada Royal Bank of Scotland UBS West Landesbank Source: British Bankers Asociation
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Fig. 1 LIBOR, OIS and LIBOR-OIS spread
43
Fig. 2 Decomposition of LIBOR-OIS spread
