The interbank market risk premium, central bank interventions, and measures of market liquidity Annika Alexius , Helene Birenstam and Johanna Eklund y September 17, 2012 Abstract The risk premium on the interbank market soared during the nancial crisis, thereby creating a wedge that prevented the interest rates ac- tually paid by consumers and investors to follow policy interest down- ward. Central banks were attempting to ameliorate the situation by supplying liquidity to the interbank market. This paper studies the Swedish interbank market risk premium using a unique data set on traded volume between banks and between banks and the Riksbank. We nd that more trade both between Swedish banks and between the banks and the Riksbank results in a lower interbank market risk pre- mium. The main determinants of the Swedish interbank premium are international variables such as US and EURO area risk premia. Inter- national exchange rate volatility and the EURO/USD deviations from CIP also matters, while standard mesures of domestic market liquidity and domestic credit risk are typically insignicant. Keywords: JEL classications: F31, F41 Annika Alexius, Department of Economics, Stockholm University, 106 91 Stockholm, Sweden. E-mail: [email protected]. y Johanna Eklund, Financial Stability Department, Sveriges Riksbank. E-mail: jo- [email protected]. 1
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The interbank market risk premium, central bankinterventions, and measures of market liquidity
Annika Alexius�, Helene Birenstam and Johanna Eklund y
September 17, 2012
Abstract
The risk premium on the interbank market soared during the �nancialcrisis, thereby creating a wedge that prevented the interest rates ac-tually paid by consumers and investors to follow policy interest down-ward. Central banks were attempting to ameliorate the situation bysupplying liquidity to the interbank market. This paper studies theSwedish interbank market risk premium using a unique data set ontraded volume between banks and between banks and the Riksbank.We �nd that more trade both between Swedish banks and between thebanks and the Riksbank results in a lower interbank market risk pre-mium. The main determinants of the Swedish interbank premium areinternational variables such as US and EURO area risk premia. Inter-national exchange rate volatility and the EURO/USD deviations fromCIP also matters, while standard mesures of domestic market liquidityand domestic credit risk are typically insigni�cant.
Keywords:JEL classi�cations: F31, F41
�Annika Alexius, Department of Economics, Stockholm University, 106 91 Stockholm,Sweden. E-mail: [email protected].
yJohanna Eklund, Financial Stability Department, Sveriges Riksbank. E-mail: [email protected].
1
1 Introduction
The interbank market received relatively little academic attention until the
�nancial crisis 2007-09, when risk premia in this particular market sky rock-
eted. The resulting wedge between central bank policy interest rates and
the interest rates paid by consumers and investors turn into a major con-
cern during the recession because it hampered the expansionary e¤ects of
monetary policy. For instance, the Federal Funds Rate was lowered by 5.25
percent between September 2007 and December 2008, but bank lending rates
only fell by 1.1 percent during the same period. Instead, the interbank mar-
ket risk premium increased from a stable level around x basis points to a
modern time record of 3.60 percent. Policy makers and researchers started
to devote much more attention to the interbank market risk premium, how
it is determined and what central banks can do to a¤ect it. The relative
importance of illiquidity versus credit risk (or other risks) is important not
only from an academic point of view but also because di¤erent policy mea-
sures are called for depending on which factors that cause x in the interbank
market. Above all, central banks have a variety of tools at their disposal
for improving market liquidity, but these interventions are less e¤ective if
the interbank market risk premium is mainly determined by other variables
than illiquidity.
Taylor and Williams (2008) and others conclude that movements in the
interbank market risk premium are mostly due to credit risk, while their
measure of liquidity does not have signi�cant e¤ects.1 Using di¤erent mea-
sures of both liquidity and credit risk, Schwartz (2010) �nds that liquidity
risk is the more important factor. Fukuda (2011) studies interbank rate rates
1REFs �nding that credit risk is important
2
across currency denominations to identify the e¤ects. For instance, both the
Tokyo Interbank O¤ered Rate (TIBOR) and the London Interbank O¤ered
Rate (LIBOR) are available in both Yen and US dollars. He �nds that
counter party credit risk in a speci�c country a¤ects the TIBOR-LIBOR
interest rate di¤erential across currency denominations, while liquidity con-
ditions appear to be currency speci�c and a¤ect dollar denominated interest
rates (TIBOR and LIBOR) di¤erently from Yen denominated interest rates.
Michaud and Upper () document a long run relationship between credit risk
and the interbank market risk premium but no signi�cant e¤ects on day to
day movements in interbank spreads. They also investigate a cross section
of LIBOR interbank rates for di¤erent banks and �nd that credit default
swap prices for the borrowing banks were statistically unrelated to the risk
premia of these banks. According to Angelini et al. (2011), two thirds of
the soaring interbank spread during the crisis was instead due to increased
risk aversion rather than credit risk or liquidity.
The empirical results concerning the relative importance of market liq-
uidity for the interbank market risk premium appear to be systematically
related to the proxies used to capture the di¤erent variables. Credit risk is
typically measured using data on bank credit default spreads (Taylor and
Williams (2008), Fukuda (2011), Michaud and Upper (xx)). The main ex-
ception is Schwartz (2010), who calculates the spread between interbank
rates paid by banks with high versus low credit ratings. Since the probabil-
ity of default can be calculculated using CDS prices, several studies calculate
rather than estimate the e¤ect of counterparty credit risk on the interbank
market risk premium (REFs). Capturing liquidity in an OTC (over the
counter) market turns out to be more di¢ cult. Data on the measures of liq-
uidity frequently used in studies of stock markets (bid-ask spreads, traded
3
volume) are typically unavailable in case of the interbank market since it
is dominated by bilateral transaction, detailed information about which are
rarely collected. Taylor and Williams (2008), Fukuda (2011) and others
estimate the impact of liquidity provisions by central banks and conclude
that liquidity does not a¤ect the interbank market risk premium if liquidity
provisions do not have signi�cant e¤ects. However, liquidity provided by
central banks is not necessarily a perfect substitute for market liqudity. Fol-
lowing xx, Schwartz (2010) uses the spread between bonds with (ideally) the
same expected cash �ows but di¤erent liquidity charachteristics to capture
the liquidity premium. Similarily, Valenzuela (2010) measures liquidity risk
as the spread between US sovereign bonds and AAA bonds of international
organizations. The bond market liquidity premium is presumably corre-
lated with the interbank market liquidity premium but does not necessarily
capture liquidity problems that are speci�c to the interbank market.
This paper utilizes a unique data set on the transaction volume in the
Swedish interbank market to investigate how liquidity, credit risk, Riksbank
interventions, and other factors a¤ect the interbank market risk premium
in Sweden. Transaction volume data from the Riksbank are compared to
the measure of interbank market liquidity risk used by Schwartz (2010)
and Valenzuela (2010), the interest rate spread between bonds with iden-
tical pay o¤ structures but di¤erent liquidity charachteristics. These two
measures of liquidity are barely correlated with eachother, but the bond
spread displays high correlations with measures of credit risk and measures
of �nancial market risk (such as stock market volatility). The single most
important variable behind movements in the Swedish interbank market risk
premium turns out to be the US interbank market risk premium, followed
by international exchange rate.volatiliy and the deviations from covered in-
4
terest rate parity between the EURO and the USD. Measures of domestic
liquidity have signi�cant but quantitatively small e¤ects on the interbank
market risk premium. Somewhat surprisingly, we �nd that domestic credit
risk measured as average credit default spreads for major Swedish banks
does not a¤ect the interbank market risk premium signi�cantly once we
control for international variables. The Riksbank interventions result in a
signi�cant reduction of the risk premium in most speci�cations, but this
�nding is not fully robust.
Data on traded volume are weakly correlated to the interest spread be-
tween similar bonds with di¤erent liquidity charachteristics. The latter mea-
sure displays much higher correlations to credit risk, stock market volatility
and other measures of market risk than the volume measure. More trade
between the banks and the Riksbank results in a lower internbank market
risk premium, although the e¤ect is signi�cant only at the 10 percent level
and the magnitude is much smaller than for trade between banks. Domestic
credit risk as measured by CDS prices does not appear to have signi�cant
e¤ects on the interbank market risk premium. This implies that methods
based on �rst deducting the theoretical credit risk premium as calculated
using CDS data and estimate the e¤ects of liquidity and other variables on
the remaining part of the risk premium (as in the papers by x, y) may be
less relevant.
2 Measuring interbank market liquidity
As a general notion, liquidity is de�ned as the ease with which assets are
traded. An illiquid asset is more di¢ cult to sell than a liquid asset. Simi-
larily, it is more di¢ cult to buy and sell assets on an illiquid market than
5
on a liquid market. This is often modeled as an exogenous transaction cost
that may vary over time and/or between di¤erent assets and markets. The
most common empirical counterpart is bid-ask spreads, data on which is
typically unavailable in the case of over the counter (OTC) markets such as
the interbank market.
Rather than just assuming an exogenous transaction cost to model illiq-
uidity, this cost can be derived endogenously through several di¤erent mech-
anisms. In models with asymmetric information and adverse selection, the
transaction cost depends on the probability of dealing with an informed
trader and on how much additional information she has. The quantitative
importance of this mechanism can be captured by measuring the impact
of an additional net order �ow on market prices. The more information
revealed by an additional unit of trade, the more it a¤ects the equilibrium
price and the higher is the transaction cost due to asymmetric information.
A common empirical proxy is the average change in market price per unit
of trade (Korajdsyk and Sadka, 2008). There are also more complex mea-
sures such as the di¤erence in bid-ask spread between small orders and large
orders divided by the corresponding di¤erence in order size (Goyenko et al.).
A second strand of literature on liquidity as an endogenously derived
transaction cost focuses the inventory risk of market makers. It is more
risky and hence more costly to hold an asset before being able to re-sell it
if returns are volatile, traded volume or turnover is low and/or volatile, the
more of this asset the market maker already holds and the less diversi�able
these risks are. The theoretical concept of liquidity here centers around
the value of "immediacy", i.e. the willingness to pay for immediate trade
with a market maker rather than delaying the transaction while �nding a
buyer. As above, empirical measures capture how much the market price
6
is temporarily depressed by an unexpected sell order. A standard proxy is
again the average change in market price per unit of trade.
The third mechanism for creating endogenous transation costs is par-
ticularily relevant to OTC markets, such as the interbank market. Search
frictions and bilateral bargaining can cause di¤erences in return also be-
tween assets with identical expected cash �ows if they have di¤erent liquid-
ity charachteristics. The theoretical measure of illiquidity in these model is
the time it takes before the asset can be sold. Assets that are traded more
frequently have lower search costs and hence a lower liquidity premium.
Vayanos and Wang (2007) develop a search-based model of asset pricing
with risk neutral investors. RISK AVERSION? Du¢ e, Garleanu and Ped-
ersen (2005) assume that there is a market maker, while Du¢ e, Garleanu
and Pedersen (2007) assume that investors meet randomly and bargain bile-
tarally. Vayanos and Weill () show how bonds with identical cash �ows
but di¤erent liquidity characteristics can trade at di¤erent prices. Securities
with larger �oat (supply) or higher trading volume have less severe search
problems and correspondingly lower liquidity premia. Observable related
proxies are asset supply, traded volume, or the return di¤erential between
assets with the same cash �ow but di¤erent liqudity charachteristics (such
as the yield spread between soverreign and covered bonds).
Given that liquidity is modelled as a transaction cost (exogenous or
endogenous), the next question is how it is priced in equilibrium. The the-
oretical literature contains several points about the pricing of liquidity that
are relevant to the choice of empirical proxies for liquidity in the interbank
market. For instance, asset speci�c liquidity and market liqudity are two dif-
ferent concepts. In standard models, deterministic di¤erences in transaction
costs (liquidity) between assets only commands a negligable (second order)
7
premium in equilibrium because optimizing investors allocate their portfolios
to trade their liquid assets while holding their illiquid assets (Constantinides
1986, Heaton and Lucas, 1996). In contrast, market wide liquidity shocks
can result in a substantial liquidity premium, especially if the shocks are
permanent rather than transitory.
There is also a conceptual di¤erence between the amount of liquidity (as
measured by e.g. the volume of trade on a speci�c market) and the equil-
brium price of liquidity (as measured by yield di¤erences between two oth-
erwise identical assets with di¤erent liquidity charachteristics). In Acharya
and Pedersen (2005), the asset speci�c liquidity premium is higher for as-
sets whose illiquidity covaries positively with market illiquidity and market
return.2 Investors pay a premium for assets that are liquid in times of low
market return and vice versa; the liquidity premium is higher for assets that
are more di¢ cult to sell during market downturns. The largest component
of the estimated asset speci�c liquidity premium is due to the covariance
between asset speci�c liquidity and market returns (REFERENCE!). This
may be relevant when measuring market liquidity as the di¤erence between
two asset speci�c premia.
The most common empirical measure of liquidity is bid-ask spreads. For
instance, when a recent study by Goyenko et al. () investigate how well 24
di¤erent proxies of liquidity actually capture the desired phenomenon, bid-
ask spreads are used as the "true" benchmark measure of liquidity against
which other proxies are evaluated. A disadvantage of using bid-ask spreads
2 In contrast to the standard result, asset speci�c liquidity risk is priced in Acharya and
Pedersen (2005) because they assume that all assets have to be sold each period. Also,
because liquidity is stochastic, the liquid asset can be hit by a shock to the transaction
cost.
8
is that they include various technical transactions costs that may vary for
reasons that are unrelated to true liquidity. Other frequently used measures
of liquidity focus on the volume of trade, such as turnover or xx.
Data on bid-ask spreads or traded volume are rarely available in the case
of the interbank market because information about bilateral transactions be-
tween banks is typically not collected by any agency. Instead, two measures
of liquidity frequently encountered in empirical studies of the interbank mar-
ket are the interest rate spread between bonds with similar expected cash
�ows but di¤erent liquidity charachteristics, and the estimated e¤ects of cen-
tral bank liquidity injections. The idea behind the latter proxy is that the
more the interbank risk premium is reduced by central banks provisons of
liquidity, the larger is the liquidity premium. Liquidity provided by central
banks is however not necessarily a perfect substitute for genuine market liq-
uidity and can also have signalling e¤ects of the opposite sign. For instance,
Brunetti et al. (2011) �nd that unexpected liquidity provisions by ECB
during the �nancial crisis crowded out privately supplied liquidity and in-
creased market volatility by revealing that the central bank had new private
information.
x y z measure interbank market liquidity as the interest di¤erential be-
tween twpo bonds with (ideally) the same cash �ows but di¤erent liquidity.
There are several theoretical objections to this proxy. First, it focuses on
the bond market rather than the interbank market. The market liquidity
shocks of these two markets could be highly correlated, but this is an empir-
ical issue that remains to be studied. Second, the spread betwen two bonds
captures asset speci�c liquidity rather than market liquidity. Some part of
the movements in this spread is probably due to market liquidity shocks,
but how we do not know how much. Third, the yield spread between two
9
bonds concerns di¤erences in the equilibrium price of liquidity rather than
market lliquidity. The empirical �ndings of Acharya and Pedersen (2005)
imply that the bulk of an asset speci�c liquidity premium is due to the co-
variance between the asset speci�c liquidity shocks and market returns. The
covariance between market returns and the asset speci�c liquidity shocks on
covered bonds is a rather di¤erent animal than the interbank market liquid-
ity, which is what we want to measure. Finally, the bundle of bonds covered
by a sovereogn government often includes publicly owned companies with a
non-zero probability of being sold within �ve years, such as SBAB in the
case of Sweden.3 Hence, the premium on covered bonds may include various
factors speci�c to such companies.
Turning to the availability of various proxies for market liquidity, a �rst
observation is that the interbank market is dominated by bilateral trade and
details of these transaction are rarely collected by any agency. There is a
European electronic platsform e-mid, covering 15-20 percent of the Euro-
zone interbank market trade, from which various information is recorded.
Because the share of the European interbank trade covered by e-mid �uctu-
ates over time and has fallen considerably since the 2007-8 �nancial crisis,
volume data from this source is less reliable than e.g. price data. A speci�c
bank can be assumed to pay approximately the same price on transactions
using this platform as through other means, but a change in traded volume
on e-mid can be due to a change in traded volume on the EMU interbank
market or to a change in the banks�choice of trading mode. For instance, zz
argues that banks were more reluctant to use e-mid during the �nancial crisis
because they did not want information about their trading activitites to be
3Note on SBAB
10
revealed. E-mid data are used by Angelini et al. (2011) and Brunetti et al
(2011), but neither paper studies liquidity or the volume of trade since these
papers focus on the e¤ects of bank speci�c factors. Vento and Ganga (2009)
have e-mid transaction data but do not use it in their empirical investiga-
tion.Michaud and Upper (2008) calculate four di¤erent proxies for liquidity
using e-mud data: the number of trades, trading volume, bid-ask spreads,
and the average price impact of trades, but only report eyeball econometrics
of the �nding that market liquidity is important to the interbank market
risk premium. Hejmans et al have a similar data set for Netherlands from a
platform called Target2, but also do not use them to study the relationship
between market liquidity and the interbank risk premium.
Finally, Poskitt (2011) constructs two non-standard empirical proxies for
internbank market liquidity: Average bid-ask spreads as quoted by dealers,
and the number of dealers active in the market, using intraday quote data
from the o¤shore market for three month US dollar funding from the Thom-
son Reuters Tick History database of SIRCA. He �nds sign�cant e¤ects of
liquidity on the theoretically derived non-default (or credit) component of
the LIBOR-OIS spread.4 The market transaction volume is a straight for-
ward measure of market liquidity. We are not aware of previous papers
studying the liquidity premium in the interbank market using transaction
volume data.4Following Bank of England (2007), Poskitt calculated the default related component
of the spread from CDS data and estimate the e¤ects of liqudiity etc on the remaning part
of the spread.
11
3 Data
Most international studies of the interbank market risk premium de�ne it
as the (average) interbank rate minus the expected average short money
market rate as measured by the Overnight Index Swap Rate, OIS. For in-
stance, U.S. OIS rates are calculated based on the daily federal funds rate.
Unfortunately, such as �nancial intrument does not exist in Sweden. The
instrument closest to the desired measure of expected future daily policy
rates is the STINA swap, where the �oating leg is the average overnight
intebank rate. Following the Riksbank, the Swedish interbank risk premium
is calculated as the STINA swap rate plus the spread between the shortest
interbank rate and the repo rate. These data are collected from Thomson
Reuters.
The credit risk of domestic banks is proxied by an equally weighted
average of the credit default swap (CDS) spreads of the 4 largest Swedish
banks. These data are also collected from Thomson Reuters, as are US and
EURO zone interbank market risk premia de�ned as the average three month
interbank rate minus the corresponding OIS rate. Data on the deviations
from covered interest rate parity (CIP) between the EURO and the USD,
and betwen the SEK and the EURO, are calculated using three months T-
bill rates and matching three months currency futures.5 We also use data
on the implied volatility of �ve major international exchange rates (not
including the SEK), the Swedish stock market and the U.S. stock market.
As a robustness check, the Swedish interbank market risk premium is proxied
by the TED spread (the three month interbank market rate minus the three
month T-bill rate) in Section x.y. All data are daily and collected from
5Formula for CIP.
12
Thompson Reuter. The sample period is January 2, 2007 to November 22,
2011, see Table 1 for details.
Two di¤erent measures of interbank market liquidity are investigated.
First, following Schwartz (2010) and Valenzuela (2010), we calculate the
interest rate di¤erential between Swedish government bonds and bonds of
the same maturity (two years and �ve years) that are guarenteed or covered
by the Swedish government. This measure of market liquidity is compared
to the volume of transactions on the Swedish interbank market. The data
on overnight interbank market trade are based on records of loan advances
and repayments processed in the Swedish large value payment system, RIX,
managed by the Swedish Riksbank.
In the RIX-system, large value payments are recorded one by one by
the participants themselves. In that process the banks indicate the type
of transaction that the payment originates from, e.g. if the transaction is
an overnight deposit with another participant bank. The data includes all
transactions registered as an overnight deposit in the RIX-system, made
between two banks and with a maturity from one day to the next. Hence,
the transactions contain bilaterally unsecured overnight interbank deposits
between the banks members in RIX. However, to the extent that the banks�
make overnight deposits in the Riksbank in addition to the transactions
made to manage their own liquidity position, these are most likely not caught
in the data. Indicatively, such transactions are rare. In addition, the banks
also make overnight transactions in the form of repos and swaps which are
not captured in the data either. Finally, not all banks are members in the
13
RIX-system.6 Speci�cally, only a few foreign banks are members in RIX.7
These banks instead balance their liquidity by taking overnight loans with
their correspondent banks, which are members in RIX. Such transactions
are captured only indirectly, as they a¤ect the correspondent banks� net
balances and thus the correspondent banks� overnight loan requirements.
The sample period for the data on interbank market transaction volume is
July 16, 2007 to November 11, 2010.8
Several studies have shown that the deviations from covered interest
parity (CIP) are strongly correlated to the interbank market risk premium
(REFs). We include the deviations from CIP between the EURO and the
USD as a control variable given the assumption that Sweden is a small coun-
try that does not a¤ect international developments. The deviation from CIP
between the SEK and the EURO is however only included in a robustness
speci�cation due to the unclear causal relationship between this variable and
the interbank market risk premium. During the �nancial crisis, a shortage
6The following banks are members of RIX: Bankgirocentralen, Citibank, CLS Bank,
Crédit Agricole. Danske Bank. DnB NOR Bank, EMCF, Euroclear Sweden, Fortis Bank
SA/NV, Kommuninvest, Landshypotek AB, Länsförsäkringar Bank, NASDAQ OMX,
Nordea Bank, Nordnet Bank, Nykredit Bank A/S, Riksbanken, Riksgälden, Royal Bank
of Scotland, SBAB, SEB, Skandiabanken, Svensk Exportkredit, Svenska Handelsbanken,
Swedbank AB, Ålandsbanken7The following banks are not RIX members but have some activity on the Swedish
interbank market: Deutsche Bank AG, JP Morgan Chase Bank, UBS AG.8The Riksbank has detailed raw data on Swedish interbank market volumes contained
in one excel �le for each day also after November 2010. Unfortunetaly, we have not been
able to pursuade them to convert this information to time series data or grant us access to
the original �les to update the data set. For the sample period that we have, the data on
transaction volumes had already been collected manually from each excel �le by Riksbank
sta¤ for a di¤erent purpose.
14
of dollars developed in Europe.9 Swedish banks and �rms were constrained
and could not borrow at the US market interest rates. This resulted in siz-
able deviations from covered interest parity all over Europe. The Riksbank
stepped in and started lending US dollar to Swedish banks and �rms to
alleviate their liquidity problems. Data on the amount of dollar loans to the
Swedish economy allows us to evaluate the e¤ects of this monetary policy
action as well. It turns out that the dollar loans reduced the deviations
from CIP signi�cantly but do not have a signi�cant direct e¤ect on the risk
premium.
Table 1 shows descriptive statistics, sample periods and unit root tests
for all included time series. According to the ADF unit root test, the main
variables are clearly I(0): Swedish and US risk premia, traded volume in the
interbank market, international stock volatility, international exchange rate
volatility. This is an unusal �nding, but our sample period is also unusually
long and covers calm periods both before and after the �nancial crisis. The
null hypotheses of the presence of unit roots is marginally rejected for sev-
eral other variables, including the EURO area risk premium and liquidity
measured as the spread between sovereign and covered bonds. Given the
well known low power of the ADF test to reject the null hypothesis of a unit
root in small samples (as documented by Kwiatkowski et al. (1992), De-
Jong et al., (1992), and others), the variables are treated as I(0) in most of
the empirical tests. The main speci�cation is however estimated also using
�rst di¤erences of the data. The Riksbank �nancial crisis interventions or
extraordinary lending to domestic banks displays a much high p-value than
the other variables. This is not surprising since the interventions are zero
9The dollar shortage was not unique to Eiurope. For instance, xx studies the deviations
from CIP between the US dollar and the Australian c during the �nancial crisis.
15
for most observations, with a single peak during the �nancial crisis. The ef-
fect of the interventions is investigated using several di¤erent speci�cations
including dummy variables.
Table 1: Descriptive statistics
Sample period Mean Max Min Std ADFInterbank spread, SE 07-01-01 to 11-12-30 33.34 146.30 1.00 22.43 -3.038Interbank spread, US 07-01-02 to 11-11-22 49.76 363.88 4.14 54.01 -2.964Interbank spread, EURO 07-01-02 to 11-11-22 54.00 195.80 5.30 34.44 -2.184CDS spread, SE 07-01-01 to 11-11-21 98.58 226.74 19.81 46.68 -2.210CDS theoretical premium 07-01-01 to 10-11-29 17.67 61.18 1.01 12.93 -1.826Liq: Bond Spread, 2 y 07-01-02 to 11-11-22 27.37 95.53 -6.55 23.00 -2.296Liq: Bond Spread, 5 y 07-01-02 to 11-11-22 56.00 111.35 -11.20 36.79 -2.234Liq: Trade Vol., SEK*109 07-07-16 to 10-12-30 17.7 79.2 0.051 18.4 -4.438RB interventions, SEK*109 07-01-02 to 11-11-22 48.2 290.1 0 60.9 -1.352Stock volatility, int. 07-01-02 to 11-11-22 0.237 2.198 -0.224 0.406 -2.990Stock volatility, SE 06-06-02 to 11-06-20 25.93 77.92 11.52 10.36 -2.381XR volatility, int 07-01-02 to 11-11-22 0.419 2.250 -0.430 0.449 -2.747CIP dev, SEK 07-01-02 to 11-06-17 0.361 1.412 -0.778 0.498 -2.027CIP dev, EURO 07-01-01 to 11-06-17 0.331 1.483 -0.737 0.544 -2.081VIX 07-01-03 to 11-06-30 25.19 80.86 9.89 11.52 -3.592
Daily data. ADF is the Augmented Dickey Fuller unit root test, where the number of lagsis chosen according to the AIC. The 5
The cross correlations between the variables are shown in Table 2. The
three interbank riskpremia for Sweden, the US, and the EURO area are
highly correlated. Volatility measures also display correlations above 0.7
with the risk premia. Figures 1 through 5 show the variables in questions.
It is clear that the Swedish interbank market risk premium is closely related
to the corresponding premia in both the US and the EURO zone. During
the 2008 crisis, Swedish markets reacted stronger to the US development
that the EURO interbank market did. The recent EURO debt crisis has
however a¤ected Sweden more than the US. Liquidity measures as the yield
16
spread between sovereign and covered bonds display a similar pattern as the
interbank market risk premia and the volatility measures, while the volume
of trade on the interbank market falls during the �nancial crisis and does
not recover within the sample period. The Riksbank interventions are large
relative to the volume of trade between banks, but only occur during the
�nancial crisis.
4 Empirical results
In order to capture the e¤ects of domestic credit risk, liquidity risk, and
central bank interventions on the Swedish interbank market risk premium,
we need to control for other factors in�uencing the risk premium and use ap-
propriate econometric techniques and/or instruments to avoid endogeneity.
For instance, it is not appriori clear whether higher credit risk as measure by
the CDS spread of Swedish banks result in a higher interbank market risk
premium, whether a higher risk premium results in higher CDS spreads, or
whether both variables simply react to international shocks. The interbank
markets display a high degree of international integration. In the case of
Sweden, international variables can be assumed to be exogenous. Sweden
is a small, reasonably stable country and domestic events have had negli-
gable e¤ect on international �nancial variables. Endogeneity is obviously a
problem in the case of domestic variables and the central bank interventions
in particular. The Riksbank�s liquidity interventions may decrease the risk
premium, but it is likely that the Riksbank decides to intervene when the
market risk premium is perceived to be excessive. In addition, all these vari-
ables react to the same shocks. News, for instance information about the
Greek haircut negotiations halting again, are likely to a¤ect both credit risk,
17
market liquidity, the Riksbank credit provisions, and the interbank market
risk premium. Hence instrumental variables are used to estimate these rela-
tionships. The choice of instruments is discussed in some detail below, but
the main idea is to use lagged variables. Current shocks do not a¤ect lagged
variables, and a �nancial market shock in t+1 is by de�nition unpredictable
given the information available at t.
4.1 Main �ndings
Several papers have studied the determination of interbank market risk pre-
mia using factor models. However, since the factors are not identi�ed, these
studies do not answer questions such as whether credit risk or liquidity are
more important, or whether central bank interventions are e¤ective. Ide-
ally, one would obviously like to have a pure measure of e.g. domestic credit
risk which is una¤ected by other variables. Given data on the CDS spreads
of domestic banks, controlling for other variables (international risk premia
and international exchange rate risk), and estimating the relationship using
instrumental variables, the estimated coe¢ cient is interpreted as the e¤ect
of domestic credit risk on the interbank market risk premium:
(1) rpSEt = �0 +Pki=1 �iXit + "t;
where rpSEt is the Swedish interbank market risk premium and X is a
set of variables includes the US and EURO area interbank market risk pre-
mia, international exchange rate risk (measured as the implicit volatility of z
major exchange rates, and the deviations from covered interest parity, CIP,
between the EURO and the US dollar), and domestic credit risk, liquid-
ity, and the Riksbank interventions. The implicit volatility of international
18
variables can be assumed to be una¤ected by shocks to the small Swedish
economy. The relationship nevertheless has to be estimated using instru-
mental variable techniques since international shocks a¤ect both Swedish
and international variables simultanously.
A suitable set of instruments for estimating (1) consists of variables that
are uncorrelated with the error term and su¢ ciently correlated with the
X-variables. From theory, the realization of �nancial variables in period
t should be impossible to predict using the information available at t � 1,
lagged variables are valid instruments. Given that the residuals typically
display �rst order autocorrelation but not higher order autocorrelation, we
use lags two and above. Table x report the p-value of the J-test for overiden-
tifying restrictions and the Crabb-Donald test for instrument validity. The
latter approximately a multivariate equivalent to the F-test of the �rst stage
regression in a 2SLS in the univariate case. The Crabb-Donald test falls rad-
ically when higher lags than lag three are included. Hence the standard set
of instruments in Table x consists of the second and third lag of all variables
in the speci�cation in question. In case of the Riksbank interventions, one
and two month lags are used, since the volume of extraordinay loans to the
banks is not altered on a daily basis. The e¤ects of this variable is studied
more extensively in Section z. More details about the set of instruments
used in each speci�cation are provided in the footnotes of Table x.
Table c shows the main results. Interbank markets are highly integrated
across borders and two main determinants of the Swedish interbank market
risk premium are the US and EURO zone interbank market risk premia.
These two variables are highly correlated (0.89) and their relative in�uence
varies across the speci�cations. In column two, only international variables
are included. US or international stock volatility has been excluded since it
19
lacks signi�cant e¤ects on the Swedish interbank market risk premium once
we control for the US and EURO interbank market risk premia. The Swedish
interbank market appears to react more than the US and EURO markets to
international exchange rate factors since both international exchange rate
volatility and the deviations from CIP between the EURO and the US are
signi�cant. This is consistent with the exchange rate being more important
to a small open economy than to the larger and more closed economies. In
the third column of Table z, domestic variables are added to the regression.
Credit risk of Swedish banks as measured by the CDS premium, domestic
liquidity measured by the yield spread betweem sovereign and covered bonds,
and the implicit volatility of Swedish stocks are estimated with negative,
insigni�cant coe¢ cients. Hence these domestic variables that are available
for the full sample do not appear to have independent e¤ects on the Swedish
interbank market risk premium. Since several studies use CDS data to
extract a theoretical credit
Columns four and �ve includes the traded volume on the interbank mar-
ket. This is a separate regression because data on this variable is only
available up to 2010-12-30 and we do not want to use this reduced sample
period for the other speci�cations. Traded volume in the interbank market is
expected to be negatively related to the risk premium and this parameter is
indeed signi�cantly negative. Section z shows that this �nding is reasonably
robust, but also that it is possible to �nd speci�cations where the volume
of trade does not have a signi�cant negative e¤ect on the interbank market
risk premium.
Columns x and z contains standardized coe¢ cients, where all variables
have been divided by their standard deviations. This results in coe¢ cients
of comparable sizes in the sense that the relative importance of the di¤erent
20
variables for movements in the Swedish interbank market risk premium is
captured. The relative sizes of the unstandardized variables do not cap-
ture how important the variables are, since the variables are measured in
di¤erent units and have di¤erent variances. For instance, judging from the
non-standardized coe¢ cients in e.g. column s one wiould conclude that the
Eurozone risk premium is more important to the Swedish risk premium,
because its estimated e¤ect is larger. However, even though all riskpremia
are measured in the same unit (basis points, annualized), the US risk pre-
mium has a much larger variance and therefore accounts for more of the
variation in the Swedish risk premium. Traded volume and the amount of
Riksbank lending to banks is measured in SEK, which renders an evaluation
of the relative importance of the variables based on estimated coe¢ cients
that are not standardized even more hazardous. Columns x and z show that
the US risk premium is the most important determinant of the Swedish
interbank market risk premium, followed by the implicit volatility of inter-
national exchange rates, the EURO/USD deviations from CIP, the covered
bonds spread (although the estimated e¤ect is negative and insigni�cant),
interbank market transactions volume, Swedish CDS premium and then the
EURO interbank market risk premium.
During the �nancial crisis in 2009, many central banks attempted to
alleviate the credit crunch and keep the �nancial markets xx. The Riksbank
initiated major interventions (lending to domestic banks) in October 2008
and phased these loans out during 2010. As discussed above, we have data
set covering the daily volume of credit (loans) from the Riksbank to domestic
banks. This amount peaked at 300 000 000 000 SEK or x percent of GDP in
July 2009. The results in columns six and seven of Table x indicate that the
Riksbank interventions in the interbank market lowered the risk premium
21
signi�cantly. The robustness of this �nding is investigated in Section x.
Since the unit of measurement is millions of SEK, the magnitude of the e¤ect
of the interventions on the interbank market risk premium is rather modest,
around 60 basis points or 0.6 percent in July 2009, when the interventions
peaked.
Since several previous studies have treated these variables as integrated
of order one and hence used di¤erenced data, the corresponding regression
is performed here as well. The Cragg-Donald test statistic for instrument
relevance falls below 2 if two lags of the di¤erenced variables are used as in-
struments, but is even lower for more lags or only one lag. The residuals do
not display �rst or higher order autocorrelation according to LM and Ljung-
Box tests, but are heteroscedastic according to the Engel () test. Hence the
White () heteroscedasticity consistent covariance matrix is used. Further-
more, the xx GMM estimator where both the covariance matrix and the
coe¢ cient vector are updated between iterations does not converge. Hence,
the yy GMM estimator where only the weighting matrix is updated is em-
ployed. Several of the main results from Table z still stand when di¤erenced
data are used. For instance, the US interbank market risk premium remains
signi�cantly positive and neither domestic CDS spreads or domestic liquid-
ity measured as the yield spread between sovereign and covered bonds are
signi�cant. On the other hand, traded volume on the interbank market is
now insigni�cant, as are the Riksbank interventions and all other variables.10
Instrument relevance is so low for the regressions using di¤erenced data that
10The Riksbank interventions typically only move marginally between the days within
each week, so creating a daily di¤erenced series of this variable is actually not a very
sensible operation. However, we still replicate the full Table z also for �rst di¤erences for
completeness.
22
they are not reliable - we only included them to enable comparison to other
studies.
4.2 Robustness of the e¤ectiveness of the Riksbank�s inter-
ventions
According to the main �ndings in Section c, the credit provisions by the
Riksbank to domestic banks resulted in a signi�cant reduction of the inter-
bank market risk premium. Table z shows two types of robustness tests of
the e¤ects of the interventions. First, the interventions variable is de�ned in
di¤erent ways: as a continous variable simply measuring the amount lended
to the banks each day, the logarithm of the same variable (to capture de-
creasing e¤ects of the amount of credit), as a discrete dummy variable taking
the value 1.0 when a positive amount is lent to the market, and as a discrete
dummy variable that covers the days from the announcement of the �rst
loans to the announcement of the discountinuation of the loans. We also
include the square of the amount.
The fact that this type of interventions where the central bank provides
liquidity to the interbank market has signi�cant e¤ects on the risk premium
is often taken as evidence of a market liquidity premium. For instance,
Fukuda�s conclusions about the e¤ects of market liquidity are based on the
size and signi�cance of the e¤ects of central bank liquidity injections. Simi-
larily, ... . The
funding liquidity is de�ned as the ability to raise cash at short notice
either via asset sales or new borrowing �
The potential shortage of funding liquidity is often called rollover risk,
because it may be di cult or impossible for a bank to �roll over� its short-
23
term borrowings
4.3 Comparing the two liquidity measures
The two measures of market liquidity used in this paper are (i) the relative
returns to a more liquid and a less liquid bonds with (ideally) the same
expected payo¤s and (ii) the turnover or traded volume on the interbank
market. As shown in Table x, they have a correlation of -0.22. The negative
sign is expected since a high premium on the illiquid bond signi�es a negative
liquidity shock, as does low traded volume on the interbank market. This
correlation is however rather low. The bond spread displays high correlations
to C
Table x summarizes the observable charachteristics on the two measures
of liquidity. First, it is clear that the interest rate spread between covered
bond and government bonds is much more correlated to measures of credit
risk (CDS spreads) and to Swedish and international market risk than the
transaction volume data. The correlation coe¢ cients range between 0.67
and 0.8 in the former case and between 0.04 and 0.4 for the volume data.
The volume data are much more variable as shown by the standard devia-
tions of x and y in Table c. The correlation coe¢ cient of the two measures is
only -0.22 but nevertheless highly signi�cant with a p-value of 0.000. Inter-
estingly, the volume variable is positively correlated with the interbank risk
premium (0.22), while the theoretical relationship would imply that more
trade reduced the risk premium. The regression coe¢ cients from previous
tables are also summarized in Table z. The trade volume consistently has
the expected negative sign, but is statistically signi�cant only in about half
of the cases. The covered bond spread has negative coe¢ cients in 3 of x
24
cases and is positive and signi�cant in c. It is also highly colinear with the
other variables in the regressions. The VIF measure of collinearity for this
variable is sm , d, across the speci�cations in Table z, compared to only s
,df, for the volume variable.
Di¤erent measures of market liquidity could have di¤erent e¤ects be-
cause that they capture di¤erent aspects of liquidity rather than because
one measure captures liquidity better than the other measure. We have not
seen theoretical models of the interaction or covariance between credit risk
and liquidity. Most formal models of the pricing of liquidity focus on equity
rather than �xed income assets that are a icted by credit risk.
The results from repeating the exercise using the spread between covered
and sovereign 2 year bonds as measure of market liquidity are reported in
Table z. The expected sign of the estimated coe¢ cient is positive here, since
a high liquidity risk premium on the less liquid covered bond should indicate
in a higher interbank market risk premium. The covered bond spread has
a signi�cant positive e¤ect in two out of six speci�cations: when no control
variables at all are included (column two) and when only the EURO inter-
bank risk premium is added (columns three). In all other speci�cations with
more control variables, the estimated coe¢ cient is either insigni�cantly pos-
itive or signi�canlty negative. This can be interpreted in two ways: either
market liquidity does not a¤ect the Swedish interbank market risk premium
or the interest rate spread between these two bonds with similar expected
cash �ows but di¤erent liquidity charachteristics is not a suitable measure
of market liquidity.
The �nal column shows the results from replacing the original data on the
yield spread between Swedish sovereign bonds and Swedish covered bonds
by a puri�ed or orthogonal measure, obtained as the residuals from an OLS
25
with the same independent variables as in the previous column. Ideally,
other forms of risk than liquidity risk a¤ecting the data have been removed
by this procedure. The results are however not a¤ected as this measure of
liqudity still enters with a signi�cant negative sign rather than the expected
positive coe¢ cient.
The �nding that the interest rate spread between covered and uncovered
bonds is highly correlated to market returns and credit risk but not to the
volume of trade in the interbank market supports the view that it does not
really measure interbank market liquidity.
4.4 Domestic credit risk
As evident from Table 3, the Swedish interbank market risk premium is
mainly determined by international variables. The credit risk of domestic
banks is one of the domestic variables that could be expected to in�uence it,
but the estimated coe¢ cients in Table 3 are all negative and insigni�cant.
This result is somewhat surprising, but not unique. Several previous studies
have documented signi�cant e¤ects of credit risk measured as domestic bank
credit default spreads on the interbank risk premium (Taylor and Williams,
2009, Fukuda 2011, ). However, Michaud and Upper () �nd a statistically
and economically insigni�cant coe¢ cient on CDS spreads on interbank risk
premia in a very thorough study of a cross section of banks. In addition,
Angelini et al (2009) document insigi�cant e¤ects of observable measures of
borrower banks�creditworthiness.
When the robustness exercise above is repeated for this variable the CDS
premium actually has a positive signi�cant e¤ect on the interbank risk pre-
mium in several speci�cation with only few control varialbes. These results
26
are shown in Table x. In columns two to four, the only included control
variables are international interbank risk premia. This results in positive
signi�cant estimates of the e¤ect of domestic credit risk. Signi�cance is
then lost as international exchange rate risk is included in columns x and
y. Adding Swedish variables but excluding liquidity measured as the spread
between covered and sovereign bonds still yields positive estimated coe¢ -
cients. The �nal column shows that including this spread yields a negative
coe¢ cient.
Given that the CDS spread itself is theoretically expected to have a
non-linear e¤ect on the interbank market risk premium while the estimated
model is linear, we also calculate the theoretical credit premium as in xxx.
This transformed variable should have a linear relationship to the interbank
market risk premium. As shown in column z, credit risk remains insignif-
icant. A quick look at the results for the �rst di¤erences of the data in
Table x also c a negative estimated coe¢ cient for the Swedish CDS pre-
mium. Hence there is little evidence that the credit risk of domestic banks
a¤ect the Swedish interbank market risk premium.
5 Conclusions
Analyzing the interbank market risk premium is important for several rea-
sons. The recent surge in attention is mainly due to the fact that during the
�nancial crisis, the exploding risk premium on the interbank market drove
a wedge between policy rates and the interest rates paid by consumers and
investor, rendering the expansionary monetary policy measures pursued dur-
ing the crisis much less e¤ective. There is a sizable and rather inconclusive
literature on the relative importance of credit risk versus liquidity risk in the
27
determination of interbank market risk premia. The most common measure
of market liquidity is the interest rate spread between similar bonds with
di¤erent liquidity charachteristics. This captures the di¤erential between
two asset speci�c liquidity premia, which according to the theoretical model
of Acharya and Pedersen (2005) is a function of the covariance between
asset speci�c liquidity shocks and market returns. In this paper we use ac-
tual interbank market transaction volume data collected by the Riksbank
to measure interbank market liquidity. We �nd that the transaction vol-
ume has a reasonably robust negative e¤ect on the interbank market risk
premium. In contrast, the interest di¤erential between similar bonds with
di¤erent liquidity charachteristics enters with the wrong sign in most full
speci�cations, implying that a higher liquidity premium on covered bonds
relative to sovereign bonds is associated with a lower risk premium.
Domestic credit risk as measured by the credit default spread of do-
mestic bank does not have a signi�cant e¤ect on the interbank market risk
premium in the benchmark speci�cations. This result is however not fully
robust as several speci�cations with fewer control variables result in the
expected positive signi�cant e¤ect. Instead, the main determinants of the
Swedish interbank market risk premium are international variables. Using
standardized coe¢ cients, it turns out that the US interbank risk premium is
the single most in�uential factor. International exchange rate risk measured
as the implicit volatility of major exchange rates has a major in�uence over
and above the e¤ects through the interbank market risk premia in the ma-
jor countries. Hence the small open Swedish economy appears to be more
sensitive to exchange rate risk or the factors determining exchange rate risk
than the larger and less open US and EURO zone economies. The sam-
ple period includes the international �nancial crisis of 2008-2009 and the
28
European debt crisis in 2011, when the domestic shocks generated by the
Swedish economy or Swedish �nancial markets were relatively minor. Hence
domestic factors could well have a larger in�uence during periods with more
domestic disturbances.
Finally we study the e¢ casy of the Riksbank�s interventions in the inter-
bank market. Using data on the amount lent to Swedish banks during the
�nancial crisis, we �nd that the Riksbank was able to reduce the interbank
market risk premium signi�cantly. Since the logarithm of the interventions
is the statistically most appopriate transformation to use, the e¤ect appears
to be non linear and decreasing in the size of the interventions. A simple
dummy variable is however enough to document that the interventions had
signi�cant e¤ects, as long as several control variables are included. The
e¤ect of liquidity provided by the Riksbank does not di¤er signi�cantly in
magnitude from the e¤ect of increased transactions between banks, although
the point estimate is several times larger. Finding valid and relevant instru-
ments for the interventions is obviously troublesome. We use very long lags
to instrument for the level of the Riksbank interventions. Furthermore the
endogeneity bias would generate a positive coe¢ cient since the Riksbank
intervenes when the interbank market risk premium is high.
References
Angelini et al. (2011),"The interbank market after August 2007: What has
changed, and Why?", Journal of Money, Credit and Banking 43(5), 923-958.
Acharya, V. and Pedersen, L., (2005), "Asset Pricing with Liquidity
Risk", Journal of Financial Economics, 77, 375-410.
Bank of England, 2007. "An indicative decomposition of Libor spreads",
29
in Markets and operations, Quarterly Bulletin, 490-510.
Brunetti, C., Filippo, M., and Harris, J., (2011), "E¤ects of Central
Bank intervention on the interbank market during the Subprime crisis",
The Review of Financial Studies 24(6), 2053-2083.
Constantinides, G., (1986), "Capital market equilibrium with transac-
tion costs," Journal of Political Economy 94(4), 842-862.
DeJong, D, Nankervis, J., Savin, N. E. and Whiteman, C, (1992), "Inte-
gration versus trend stationarity in time series", Econometrica 60(2), 423-
433
Du¢ e, D., Garleanu, N. and Pedersen, L., (2005), "Over-the-Counter
Markets", Econometrica 73(6), 1815-1847.
Du¢ e, D., Garleanu, N. and Pedersen, L.,(2007), "Valuation in Over-
the-Countermarkets", The Review of Financial Studies 20(5), 1865-1900.
Fukuda, S., (2011), "Market-speci�c and currency-speci�c risk during
the global �nancial crisis: Evidence from the interbank markets in Tokyo
and London", NBER Working Paper 16962.
Goyenko, R., Holden, C., and Trzinka, C., "Do liquidity measures mea-
sure liquidity?"
Heaton J., and Lucas, D, (1996)"Evaluating the e¤ects of incomplete
markets on risk sharing and asset Pricing.", Journal of Political Economy
104(3), 443-487.
Heijmans, R., Heuver, R., and Walraven, D., (2010), "Monitoring the
unsecured interbank money market using TARGET2 data", DNB Working
Paper 276.
Korajczyk, R., and Sadka, R., (2008), "Pricing the commonality across
alternative measures of liquidity", Journal of Financial Economics 87(1),
The Crabb-Donald statistics is the multivariate equivalent of the (mimimum) �rst stageF-test. Prob(J) is the marginal probability of the J-test for overidentifying restrictions. 1The instrument set includes the second and third lags of all variables in our data set thatare available for the full sample period. 2 The instrument is the same as above plus thesecond and third lags of traded volume in the interbank market. 3 The instrument set isthe same as in 1 plus the one and two months lag of the Riksbank interventions. 4 Theinstrument set includes the �rst lags of all variables in our data set and the one month lagof the Riksbank interventions. (5) The instruments include the seond lag of the variables inthe regression, except for the Riksbank intervention, where the one month lag is used. Theresults are similar for longer lags of the interventions, but the Crabb-Donald test for weakinstruments falls drastically..
32
Table 3: Regression results
2 lags all dito IV IVConstant 0.398653 0.042062 0.224747 -0.100125
1.441222 0.331229 1.109924 -0.615190Interbank, US 1.044992 0.291515 0.270507 0.336351
2.117426 2.330640 2.267999 4.564589Interbank, EURO -0.078707 0.294811 0.286906 0.200415
-0.111136 0.842694 0.844618 1.010814SVol, int
FX Vol, int -80.43951 22.20056 15.72421 4.510564-1.690667 1.547859 1.252796 0.683718
CIP dev E -21038.10 -35.29447 -194.2340 2558.414-1.894211 -0.010865 -0.075332 1.677352
CDS SE -0.693611 -0.667564 -0.316889-1.850002 -1.692632 -1.369474
Covered bond spread -0.123351 -0.007919 -0.045563-0.345403 -0.028440 -0.210873
The Crabb-Donald statistics is the multivariate equivalent of the (mimimum) �rst stage F-test. Prob(J) is the marginal probability of the J-test for overidentifying restrictions. 1 Theinstrument set includes the second and third lags of all variables in our data set that areavailable for the full sample period (i.e. not the trade volume). 2 The instrument is the sameas above plus lag 42 (the two months lag) of the Riksbank interventions. 3 The instrumentset is the same as in 2 but lag 3 of all variables are excluded. 4 The instrument set is thesame as in 1 plus the �ve and seven months lags of the Riksbank interventions.
34
Table 5: The e¤ects of the Riksbank interventions: Robustness
Weekly data on returns to investments in U.S. and German ten-year zero coupon governmentbenchmark bonds from Dahlquist et al. (2000).
Regression: st+� � st = �+ ��rdt+� � rft+�
�+ ut+� .
t-statistics in brackets, p-values in parentheses.Columns four and �ve contain Wald test statstics of the null hypotheses.The �nal column contains the Sargan test of overidentifying restrictions.
35
Table 6: The e¤ects of the Riksbank interventions: Robustness
Weekly data on returns to investments in U.S. and German ten-year zero coupon governmentbenchmark bonds from Dahlquist et al. (2000).
Regression: st+� � st = �+ ��rdt+� � rft+�
�+ ut+� .
t-statistics in brackets, p-values in parentheses.Columns four and �ve contain Wald test statstics of the null hypotheses.The �nal column contains the Sargan test of overidentifying restrictions.
36
Table 7: The e¤ects of the Riksbank interventions: Robustness
Weekly data on returns to investments in U.S. and German ten-year zero coupon governmentbenchmark bonds from Dahlquist et al. (2000).
Regression: st+� � st = �+ ��rdt+� � rft+�
�+ ut+� .
t-statistics in brackets, p-values in parentheses.Columns four and �ve contain Wald test statstics of the null hypotheses.The �nal column contains the Sargan test of overidentifying restrictions.