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Financial turmoil and asymmetric information theory: evidence from e-MID platform

Claudio Porzio - University of Napoli Parthenope, claudio.porzio@uniparthenope.it

Francesca Battaglia - University of Napoli Parthenope, francesca.battaglia@uniparthenope.it

Antonio Meles - University of Napoli Parthenope, antonio.meles@uniparthenope.it

Maria Grazia Starita - University of Napoli Parthenope, mgrazia.starita@uniparthenope.it1

Preliminary draft April 2009

Abstract

The aim of the paper is to verify the adverse selection existence in the e-MID platform, a

specialized segment that can be considered a reference point for the overall interbank market

liquidity. According to the asymmetric information theory (Akerlof, 1970), in any transaction the

seller knows, about the quality of the exchanged good, more than the buyer; thus, similarly,

considering credit markets, academic literature points out an information gap which penalizes

lenders (Stiglitz and Weiss, 1981), unable to quantify and to price fairly the potential counterparties

risk.

By applying this reasoning to the interbank markets (Flannery, 1996), banks are uncertain about the

fundamentals of borrowing counterparties (for instance their exposure to structured financial

products) and about both their own and their competitors’ ability to evaluate credit quality.

Consequently, this would first lead to an increasing credit spread, then to a decrease in transaction

volumes and, finally, to market paralysis (Cassola et al, 2008; ECB, 2008). To this extent, in this

paper a analysis which focuses on overnight deposits of the e-Mid platform is provided.

In order to test the asymmetric information hypothesis, daily transaction volumes (borrowing side

and lending side) are analyzed referring to two different appropriately selected windows (pre and

post-crisis): the break date, marking the beginning of the crisis on the money market, is detected

through suitable statistical tests. The break date identification is instrumental to re-estimate

abnormal volumes, calculated as the difference between daily “normal volumes” (that would have

been recorded in the post-event window in absence of the financial turmoil) and daily volumes

actually observed in the same period. “Normal volumes” are estimated using an autoregressive

mixed model, in which, among possible explanatory variables, other interest money market rates -

such as Eonia, Eurepo, etc. - are considered.

Expected results should confirm the presence of adverse selection, namely a decrease in purchase

and sell volumes referring to a post-event window, with a maturities switch and a substantial

interest rates growth. The implications arising from these phenomena could be regarded as one of

the causes of the current financial turbulence and, therefore, considered as a misconduct signal on

the liquidity market.

Compared to the recent literature, this paper can be considered innovative because the asymmetric

information theory is applied to the money market and a new methodology is used for estimating

abnormal volumes.

Key words: money markets, asymmetric information theory

1 Acknowledgments

The authors want to thank Alessia Naccarato, Daniele Perla, Ornella Ricci and Giuseppe Squeo for their useful

suggestions and Sandro Rivo and Andrea Zappa from e-MID Sim for providing statistical data.

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JEL codes: E58, G20

1. Introduction

2. Literature review

3. The e-MID platform microstructure

4. The empirical analysis

5. Summary and conclusions

1. Introduction

The industrial countries have been severely affected by the financial crisis because of the high

degree of financial engineering of their economies. To understand the transmission mechanism of

the sub-prime mortgages default on the financial system it is necessary to retrace some key stages

(as in Cassola et al, 2008b).

The years prior to 2007 were characterized by low interest rates and stock price volatility, a reduced

risk premium and a large liquidity availability. The coexistence of these factors has triggered a

frantic search for investments with high returns and thus an excessive use of products arising from

the securitization. The asset backed securities, therefore, were included in the portfolios of

institutional investors and not: this situation implies a mechanism to distribute the risk that is both

complex and opaque. The ring that has failed and has caused the disintegration of the chain of

securitization is the sub-prime mortgage customers default. The fall of the prices on the real estate

market, the weakness of the U.S. economy, the absence of safeguards to protect the interest of the

bank have resulted in increased of credit default. This has caused both a rapid downgrading of the

mortgage backed securities and a rapid rise in the spreads of banks credit default swap, that have

been forced to grant ample liquidity lines to the vehicles. The re-intermediation caused by the

diffusion of the “originate to distribute” model and the direct exposure to sub-prime mortgages, has

created a climate of suspicion about the actual exposure to the crisis: that climate has unequivocally

infected the interbank market, in which banks exchange liquidity. The step from the interbank

market to the real economics is short if we consider the link between money market rates and bank

rates, in the bank-oriented systems, and the action of monetary authority.

The aim of the paper is to measure the effect of financial crisis on the e-Mid, that is the more

transparent and liquid money market segment. This estimate relates to the calculation of the

"normal" volumes, i. e. the transactions that would have been recorded in absence of the financial

turmoil, and the comparison with the actual volumes through abnormal volume. The working

hypothesis is that the "normal" volumes are determined by the autocorrelation and by interest rates

of the other money market segment, while the actual volumes recorded during the crisis have been

spoiled by asymmetric information about the real exposure of banks to US subprime mortgage

products.

The overnight segment incorporates only two types of informations: the information relating to

liquidity system and the information on the borrowers quality.

The remainder of the paper is organized as follows: in section 2 we analyzed the related literature,

in section 3 we indicate the relevant characteristics of e-Mid market, with reference to the overnight

segment, while in section 4 we illustrate the empirical verification (hypothesis basic objectives,

methodology, data and results). The work concludes with some considerations.

2. Literature review

Managing its asset and liabilities structure and trying to maximize its return, any bank is subjected

to two constraints: the first one related to liquidity, the second to solvency. In fact, banks, on one

side must realize, in the short term, a sustainable matching between cash-in flows and cash-out

flows, on the other side must be able to honor at any moment, both reimbursement obligations

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toward depositors and the promises of funds allocation related to the lending activity (Mottura,

2009).

In such a context, the money market traditionally assumes a crucial function for an efficient

allocation of financial flows within the banking system with the main purpose to face unexpected

liquidity requirements and meds connected to the different specialization of banks (Bini Smaghi,

2008). In bank-oriented financial systems, money market rates represent the marginal cost of the

funding activity: through their maneuvers, monetary authorities succeeds in maintaining money

market rates near to the official rates and in influencing middle and long term rates to which lending

rates hooked. Literature is consequently detained on analyzing the money market structural

characteristics and its function of transmitting monetary policy impulses. The literature reviewed

for supporting the empirical analysis carried out in this paper can be divided into three groups: the

first one gathers the money market functions for banks and the financial system as a whole; the

second focuses on the recent financial crisis effects on the money market; the third one focuses on

the informative asymmetries and their effects on the money market malfunctioning during the

financial crisis.

The first group of studies focuses on the two most important functions played by the money market

for the banking system: an endogenous one, namely to reduce the existing mismatching gap

between cash-in and cash-out flows on different time span, from daily to yearly bucket (Battacharya

and Gale, 1987), and an exogenous one as it represents a fundamental mechanism for the monetary

policy transmission to interest rates charged by banks to their customers (ECB, 2008).

The correct and proper functioning of the interbank market strictly depends on its being an over the

counter market; furthermore, each segment of this market is closely related to another one even if it

is extremely difficult to reconstruct accurately the type of relationship (complementarities or

competition) among them (Bini Smaghi, 2008). For this purposes, it’s possible to make different

and alternative market sub-classifications based on their intrinsic liquidity, cost, degree of

transparency, ... (Delli Gatti, Verga, Hamaui, 2008). The e-MID, for example, is a market without

collateral where banks normally ex-change short-term funds in a transparent manner: in this

context, banks can exercise a continuous peer monitoring activity (Rochet and Tyrol, 1996).

From this point of view, in the literature lack studies trying to analyse all the variables explaining

the use, by banks, of the more representative money market segments and, at the same time,

modelling buyers’ and sellers’ behaviour. The model proposed in this paper has the aim to capture

the overnight (non guaranteed) market participants behaviour using interest rates representative of

the other main interbank segments.

The second group of studies is fairly recent, with one relevant exception (Flannery, 1996): in fact, it

includes studies analysing the present financial crises effects on the money market and the

"contamination" effects on bank rates. They can be found mainly in the economic research output of

the monetary authorities concerned by an economic slowdown due to a financial crisis and strictly

related to the banking system (Buiter, 2008; ECB, 2008; ECB 2009; Eisenschmidt and Tapking,

2009; Ewerhart and Tapking, 2008; Linzert and Schmidt, 2008; Michaud and Upper, 2008). From

this point of view, the analysis of the recent crisis effects can be useful to adjust forecasting models

in order to consider events usually not considered because their expected low frequency. Parameters

values recorded during the financial crisis can be used for the stress testing of existing liquidity and

treasury management models: among these parameters, the most significant can be considered the

Euribor rate, namely for its effects on other interest rates, and the transaction volumes on the

overnight market for estimating the liquidity daily requirement of the banking system.

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The third group includes studies that, starting from Akerlof’s contribution (1970), extend the

asymmetric information approach to the credit market (Stiglitz and Weiss, 1981) and to the

interbank market (Flannery, 1996); other studies consider the information asymmetry the main

rationale of the "contagion" in the money market and its subsequent paralysis (Cassola et al,

2008b). This group also includes studies modeling the banks behavior in the interbank market with

a peculiar focus on their information set and on the quality of their portfolios (Eisenschmidt and

Tapking, 2009; Heider et al, 2008). In particular, the first (Eisenschmidt and Tapking, 2009)

provides an interesting description of the behavior of banks providing liquidity, so explaining why

money market rates are different from overnight rates. These rates, in fact, depend both on the

credit risk and the liquidity risk where the liquidity risk premium strictly depends on the lending

banks liquidity. Anticipating a liquidity shock that may occur before the expiry date of term

deposits, banks reduce their lending activity or are available to deposit on the interbank forward

market only at higher interest rates. This behavior can be attributed to the suspicion of raising funds

at worst conditions or to the possible collateral worsening that corresponds for the lenders to an

increased probability of default.

The same behavior, in a different way, is included in another paper (Heider et al, 2008). The authors

identify, in the lifetime of the interbank market, three different phases explained by the counterpart

risk and the use of resources provided by the monetary authorities. In the first phase, volumes and

interbank interest rates are under control and this means that there are no liquidity shocks and banks

generally don’t make deposits at the Central Bank because it offers an interest rate lower than

market rates. In the second phase, the credit quality worsening produces an increasing spread

between interest rates for different maturities and the subsequent leaving of best lenders from the

interbank market; during this period, the increasing use of marginal deposits can be considered as

the first unambiguous signal of the lenders’ flight from the interbank market. The third phase is

characterized by a dramatic spread increase and by an extensive use of the marginal deposits that

culminates in the collapse of the overall interbank market: on the market still remain only the worst

borrowers (vacuum supply) while the lenders leave (vacuum demand) collecting cash from the

monetary authorities.

The subprime mortgage crisis, in fact, caused a general credit quality worsening involving the banks

and the interbank system as a whole. The first phase of Heider’s model corresponds to the period

before 2007, when low interest rates and little recourse to the interbank market; during the second

phase, from 9 August 2007 to 8 October 2008, the ECB temporarily and permanently injects

liquidity in the financial market in order to restore public confidence and to control interest rates

movements even in a context characterized by a low volume of exchange. The third phase

corresponds to the period after 8 October 2008: due to the scarcity of transactions in the money

market, the ECB decided to reduce the interest rate corridor (in order to more easily monitor the

interest rates) and to modify the auction procedure used for the main refinancing operations (open

market operations with fixed-rate and full allotment).

In this line of research, absent is a model able to directly link the banks behavior in the interbank

market to the typical descriptive parameters of the market functioning.

3. The e-MID platform microstructure

The e-Mid system is a multilateral electronic trading platform for interbank deposits. It belong to e-

Mid Sim Spa, e-Mid transactions are concentrated on overnight deposits denominated in euro which

account for 90% of the total volumes traded. Every trade proposal posted on the system is

transparent because the identity of the proponent is disclosed to all members.

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Starting from March 2007, the MIDER (for swap contracts based on the Eonia interest rate) and

starting from beginning of 2009, the e-MIC market (Mercato Interbancario Collateralizzato) operate

with the aim of further developing the interbank market using the e-MID platform also for

collateralized exchanges.

Trading volumes

During the 2003-2008 period, on the e-Mid market has been transferred liquidity for almost €

31,000 million, with a maximum of € 6,000 million in 2006 (graph 1). About 75% of total

transactions are buy initiated transactions (i.e. trades initiated by a bank posting a quote for taking a

deposit in the e-Mid trading platform). A part from this market side consideration, it can be

observed the strong decline in the overall trading volume during the year 2008; the decline during

the year 2007 is lower considering that the effects of the financial crisis on the money markets have

been revealing beginning from August of 2007 (ECB, 2008).

Graph 1 – e-Mid: Trading volumes 2003-2008 (€ million)

Source: e-Mid, data processed by the authors

Graphs 2 and 3 report, respectively for the buy initiated transactions and the sell initiated

transactions, the daily average trading volumes sub-divided according to the contractual maturity.

Although the maturities traded vary from overnight to one year, the e-Mid activity is concentrated

on overnight deposits which account for about 90% (91,6 % for the buy initiated transactions and

87,8% for the sell initiated transactions). of the total.

Graph 2 – e-Mid: Daily trading volumes 2003-2008 - maturity breakdown (€ million)Buy initiated transactions

Source: e-Mid, data processed by the authors

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The maturity breakdown of the daily average volume show that during the financial crisis the total

volumes sharply reduced but the operators’ preferences didn’t unchanged on both market sides

(borrowers and lenders).

Graph 3 – e-Mid: Daily trading volumes 2003-2008 maturity breakdown (€ million) Sell initiated transactions

Source: e-Mid, data processed by the aothors

The analysis of the intraday volumes distribution (graph 4) shows the permanence of the liquidity

trading during the day, although it’s possible to verify a strong activity concentration during the first

hours of the morning and a physiological reduction between the 13,30 and the 14,30, and at the end

of the day. The introduction of Express II increased overall transactions volume on the e-Mid,

particularly at the beginning of the day amplifying the so-called "morning effect" (Banca d’Italia,

2004): about 45% of the liquidity is exchanged during the first two hours of the morning (8:30-

10:30): this is due both to the adjustment of unbalances from transactions not regulated in the night-

time cycle and to the European Banking Federation deadline for posting lending quotes at the

Euribor rate.

Graph 4 – e-Mid: Trading intraday overnight volumes 2003-2008

Source: e-Mid, data processed by the authors

Legend: A=8:30-9:00; B=9:00-9:30; C= 9:30-10:00;D=10:00-10:30;E=10:30:11:00;F=11:00-11:30;

G= 11:30-12:00; H= 12:00-12:30; I=12:30-13:00; J= 13:00-13:30; K= 13:30-14:00; L= 14:00-14:30;

M=14:30-15:00; N= 15:00-15:30; O= 15:30-16:00; P= 16:00-16:30; Q=16:30-17:00; R= 17:00-17:30;

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The market structure

The statistics on the market structure (graph 5) show the international character of the e-Mid which

can be considered a reference segment for the bank liquidity management; between 2003 and 2008,

had access to the platform about 247 banking counterparties, from inside and outside the euro area:

146 Italian banks (more than 60% of the total), 71 EMU banks, 20 European non EMU banks.

Graph 5 - e-Mid banks – country break down (2003-2008)

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Source: e-Mid, data processed by the authors

The 58,68% of the liquidity and the 91,5% of the number of transactions have been exchanged

following an order introduced by an Italian bank (table 1).

Table 1 – Total transactions bidding – country break down (2003-2008)

Source: e-Mid, data processed by the authors

It can be argued than domestic banks widely and continuously use the e-Mid market concluding a

high number of transactions of small amount (in average € 27,5 million); on the contrary, the

foreign banks behavior finally disprove the hypothesis that they use the e-Mid market to drain

liquidity.

The interest rate level on the e-Mid is to be analyzed considering: 1) the Eonia corridor to which the

e-Mid rate is strictly related (see Graph 6); 2) interest rates on the other money market segments,

obviously excluding short term securities interest rates (see Graph 7).

2 As data on foreign currency transactions are not available, the number of foreign banks members of the –Mid market

can be considered an underestimated proxy of operators in money market.

Transactions (n°)

Traded volumes

(€ bil)

Average transaction

volume (€ mln)

Italian Banks 656.507 18.057 27,5

EMU Banks 48.298 9.971 206,5

Non EMU Banks 11.110 2.746

247,0

Total 715.915 30.774 43,0

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Graph 6 - The corridor of standing facilities rates and EONIA (2003-2008)

Source: ECB, data processed by the authors

During the period 2003-2008, the Eonia interest rate, and consequently the e-Mid overnight rate is

approximately in the middle of the interest rate corridor established by the ECB for standing

facilities operations: the cap is the marginal refinancing rate and the floor is the marginal deposit

rate.

Graph 7 - Eonia and other money market rates (2003-2008

Source: ECB, data processed by the authors

Moreover, it must be noted that all money market interest rate move in the same manner, but it’s

different the degree of sensitivity to monetary policy.

4. The empirical analysis

4.1 Hypothesis and adopted methodology

In the Eurosystem, all the money market transactions can be grouped into four segments: unsecured,

secured, derivatives and short-term securities; for each of them, it is possible to identify signaling

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indexes of volumes, interest rates, supply and demand structure and to determine their relative

position in respect of the monetary authority activity.

The correct understanding of prices and volumes in the money market should consider the structural

features of the monetary policy and the money market itself. The overnight market instability can

also result from traders' expectations about the increase or the cutting of interest rates by the

monetary authority. Within the Eurosystem, however, since 2004 the overnight rates have not been

incorporating inflation or interest rates expectations (Cassola et al; ECB, 2004); any decision about

interest rates is taken by the ECB Executive Council before the reserve maintenance period: by this

way, the effects induced do not create shocks in the overnight market and its functioning is driven

only by reserve requirements and banks liquidity. The information on the expected monetary

maneuvers are therefore perfectly distributed among the money market participants and similarly

the information about the borrowers and lenders quality, thanks to the mechanisms of direct (direct

exposure) and indirect (rating on CDS spreads, ...) peer monitoring.

In other words, referring to the European context, an anomalous trend of the overnight market

signaling parameters may depend only on two informations’ "flaws", ie the information about: 1)

the liquidity’s availability in the system (Gaspar et al, 2008); 2) the borrowers’ credit risk in the

segment without collateral (Cassola et al, 2008). Referring to the first aspect, the example is

represented by the large banks’ behavior during crisis: in order to drain liquidity, they leave the

domestic market for cross-border markets; the effects of such modus operandi can be found in the

market microstructure, in term of daily trading volumes’ distribution and interest rates’ volatility.

Referring to the second point, during crisis the borrowers’ average credit quality becomes worse,

by causing, on one side, a sharp increase in spreads and, on the other side, a sudden decline in

trading volumes.

As the overall liquidity in the Eurosystem is continuously monitored by ECB and the liquidity’s

system is available to banks through the Reuters platform, the only potential “disequilibrium” in

information availability influencing the overnight market can be related only to borrowers credit

quality.

Before explaining the methodology used to answer the three research question, we have to specify

that the period analyzed was divided into two temporal windows, one referring to pre-crisis period

and an other referring to financial crisis period.

The choice of the 9th august 2007 as a watershed date between the two periods above mentioned is

not random: in fact, the choice of that date is supported not only by the marginal lending facility of

the ECB we have discussed before, but also by two other important elements certainly not

negligible. First, as demonstrated by the buy-initiated daily average volumes’ analysis (that is

transactions started on the initiative of those banks which needed liquidity) implemented by Cassola

Drehmann, Hartmann, Lo Duca and Scheicher (2008b), it is clear that, by grouping the daily

volumes in intervals of half an hour on that date, volumes gradually reduced until they completely

reset between the two intervals 13.00-13.30and 13.30-14.00.

Moreover, the statistical analysis of overnight volumes’ time series reveal the presence of a

structural break at the same date. We investigate the presence of that break through the

implementation of one of the methods ad hoc used. Generally, these techniques test the presence of

changes in the regressions’coefficients through the use of statistics F. As we have a quite probable

date, we decide to use the Chow test3, which tests the null hypothesis of breaks’ lack

4. For each of

3 Chow G., 1960, “Tests of equality between sets of coefficients in two linear regressions”, Econometrica 28 (3).

4 The F statistic for the Chow test is distribuited F ~ (k,N_1+N_2-2*k). The formula for the Chow test is:

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the two models we intend to exploit, we divided all daily observations (volumes and observations

about all other regressors) in two groups: the first one from observation number 1 to observation

number 1180 (from January the 2nd 2003 to August the 8

th 2008) and the second one from

observation number 1181 to observation number 1537 (from August the 9th 2007 to December the

31th 2008). The value of F statistics, created as indicated in note number 4, allowed us to reject the

null hypothesis of breaks’ lacks for both the models with a significant level lower than 1%.

With reference to the first research question, we used two autoregressive models with multiple

predictors: one for the buy-side and an other for the sell-side, in which we included the lagged

values of the dependent variable and other regressors5. In particular, the independent variables we

use for the creation of buy-side (1) and sell-side models (2) are the following:

(1)

shockspreadspread

spreadspreadspread

spreadVolVolVolVolVol

twoistaxwEuribortaxtbuytaxwoistax

tbuytaxweurepotaxtbuytaxneurepottaxteoniataxrifmartax

teoniataxbuytaxtbuytbuytbuytbuytbuy

11),1_1_(10),_1_(9

),_1_(8),_/_(7),__(6

),__(54,43,32,21,10,

βββ

βββ

ββββββ

+++

++++

++++++=

−−

−−−

−−−−−

(2)

shockspreadspread

spreadspreadpread

spreadVolVolVolVolVol

twoistaxwEuribortaxtselltaxwoistax

tselltaxweurepotaxtselltaxneurepottaxtdepmartaxeoniatax

teoniataxselltaxtselltselltselltselltsell

11),1_1_(10),_1_(9

),_1_(8),_/_(7),__(6

),__(54,43,232,21,10,

βββ

βββ

ββββββ

+++

++++

++++++=

−−

−−−

−−−−−

The use of the two models is, therefore, instrumental to the identification of those variables that can

explain the evolution of the volumes recorded during pre-and post-shock periods.

In order to assess the turmoil’s effect on overnight transactions, we estimated abnormal volumes.

The estimate of normal volumes, that is the volumes that would have been expected from August to

December 2007 if the credit market turmoil had not risen, is instrumental to the assessment of the

above mentioned variable.

The expected daily volumes for the post-shock period were obtained by using the significant

coefficients, estimated with the two previously mentioned models, and their regressors.

We estimated abnormal daily volumes (ie. volumes in excess) as the difference between the

expected volumes and the effective ones. The creation of this new variable is crucial not only for

the second research question, but also for verifying the possible presence of asymmetric

information.

In the end, worth of mentioning is the fact that methodology held to estimate expected volumes is

quite different from those exploited by Cassola, Drehmann, Hartmann, Lo Duca and Scheicher

(2008a) and by Cassola, Holthausen and Lo Duca (2008b).

Particularly, in both papers normal volumes were calculated by regressing average daily

volumes on the basis of a set of annual dummies (to capture trends) and monthly dummies (to

capture seasonal factors).

kNN

essess

k

essesscess

*22_1_

2_1_

)2_1_(_

−+

+

+−

where ess_1 and ess_2 are the error sum of squares from the separate regressions (group 1 e group 2), ess_c is the error

sum of squares from the pooled regression, k is the number of parameters, N_1 and N_2 are the number of observations

in the two groups. 5 The lagged values of these regressors are not significant.

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4.2 Data and variables used in buy-side and sell-side models

The data, relating to transactions occurring on the e-Mid platform, provided by e-MID SIM SpA,

refer to the period from January the 2nd 2007 to December the 31

st 2008, consisting in 1537 working

days. They contain information for each standard transaction. In order to verify the presence of

asymmetric information we have removed from the dataset the days corresponding to the first and

last day of reserves maintenance period, and those in which the ECB carried out open market

operations.

We underline that due to peculiarity and importance of information contained in this database, we

intend to comment table 2, which represents an example of the data we processed for the purposes

of the analysis.

Table 2.- Example of e-Mid data

Data Time Product Verb Price Q.ty Num

contr

Cod

Proposal

Cod

Ordering

Start

date

Mat

date 01-12-08 9:14:52 ONL Sell 2.87 1000.00000 08336000029 IT0159 IT0237 01-12-08 02-12-2008

01-12-08 10:53:48 ON Buy 3.00 5.00000 08336000150 IT0244 IT0245 01-12-08 02-12-08

Source: e-MID SIM s.p.a.

Starting from the left, the table’s columns show for each transaction an identification code

("contract number"), the day ("date"), the time ("time"), the type of contract ( " product "), the rate

(" price "), the size of the transaction (" q.ty "), and the maturity of the contract identified by the

start date (“start date”) and the expiry date (" maturity date "). We didn’t consider the three columns

"verb", "CodProposal" and "Ordering Code", because of the relevance of their content, which

needs a particular attention. Particularly, the "Proposal Cod" represents the identification code

(from the two initial letters it is possible to infer the nationality of the bank) of the bank that

inserted the proposal , the "Ordering Code" identifies , however, the “bank-aggressor”, ie the bank

capturing the proposal and that, therefore, enters the order “buy” or “sell”, corresponding on what

one reads in the column "verb”.

The first example illustrated in table 2 shows that the proposal comes from a borrower and that,

therefore, the “aggressor” is represented by a lender, who then proceeded to enter the order. The

opposite occurred in the second transaction shown in table 2. Basically, every time we read sell in

the “verb” column, it means that it is a "buy-initiated transactions", ie that the transaction started

on the initiative of a borrower; every time we read buy in the "verb " column, this means that the

transaction is sell-initiated.

For the purposes of this analysis, we take into account only overnight6 transactions in euro

7, which

currently represents about 85% of all exchanges taking place on the market. On this item, it is stated

that we analysed overnight deposits with standard sizes (minimum lot of 1.5 million euros) and the

so-called "large overnight" (ONL), characterized by a very high minimum lot (at least 100

million)8.

With reference to the other variables used in both models, that is the EONIA rate, Eurepo t/n

Eurepo 1 week9, OIS 1 week

10 and Euribor 1 week, the source of the daily data, referring to the

analysed period, is represented by Bloomberg. However, about the marginal lending and the

6 We did not analyse the other types of e-Mid contracts: tomorrow next (T/N), spot next (S/N), broken date deposits and

time deposits (1 week, 2 week, 1 month, 3 months, etc.). 7 Actually, on the e-Mid platform you can negotiate interbank deposits in euros, American dollars, British pounds and

polish Zlotys. 8 Cassola et al (2008a) and Cassola et al.(2008b) analyzed only standard overnight deposits.

9 We refer to Eurepo General Collateral transactions (ECB, 2009).

10 The daily time series of the Ois segment is available from June

20th 2005.

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deposits rates we used the official data available on the ECB's website. Table 3 presents the

descriptive statistics of variables used in the construction of two models.

Table 3 – Descriptive statistics of the variables used in the two models

PANEL A – Variables used in the “buy-side”model

Variables Observataion Mean Dev. std Min Max

Volume 1537 13452.12 4747.852 2539.6 38375

Sp_e-mid/eonia 1537 -.0156125 .0416494 -.5719873 .0724096

Sp_marginal lending facility/eonia 1537 .938488 .1117193 .23 1.66

Sp_eurepot/n/e-mid 1537 .0231686 .0886716 -.6912324 .8959945

Sp_eurepo1w/e-mid 1537 -.0027351 .1007425 -.7247875 .6525832

Sp_ois1w/ e-mid 905 .0257351 .1140428 -.7198552 .7002991

Sp_euribor1w/ois1w 905 .0933913 .1307715 -.1397014 1.007

PANEL B – Variables used in the“sell-side”model

Variables Observations Mean Dev. std Min Max

Volume 1537 4387.749 1836.988 312.5 14800

Sp_e-mid/eonia 1537 .0094053 .0354412 -.3199009 .2605942

Sp_eonia/deposit facility 1537 1.023776 .1804939 .117 1.77

Sp_eurepot/n/e-mid 1537 -.0011632 .0787248 -.7147875 .6425831

Sp_eurepo1w/e-mid 1537 -.0027351 .1007425 -.7247875 .6525832

Sp_ois1w/ e-mid 905 -.0082333 .1072977 -.753014 .5492432

Sp_euribor1w/ois1w 905 .0933913 .1307715 -.1397014 1.007

Note: volumes are expressed in euro millions; the spreads are expressed in basis points.

Data source: e-Mid, Bloomberg and ECB data processed by the authors

About the variables used in the creation of the buy-side and sell-side models, we need to clarify that

the estimates of normal volumes, finalized to verify the existence of imperfect information, take

into account the descriptive parameters of the banks’ behaviour on overnight compartment. In

particular, the modus operandi of these intermediaries can be approximated by the response of the

e-Mid volumes to the changes of the reference rates of the other money market’s segments.

There it follows a summary of the variables used in the construction of the buy-side and sell-side

models:

- lagged volumes (Vt-p): literature (ECB, 2009) suggests that volumes at t time are strongly

related with the volumes recorded in the previous days. Specifically, also the correlogram

test, which confirmed the existence of a significant autocorrelation until the fourth day

before the date t, verified this relationship. Therefore, we excluded from the two models, as

not significant, lags after the 4th day;

- spread between e-Mid rate/ Eonia rate: the base hypothesis that justifies the inclusion of

such regressor, depending on the weight taken by the e-Mid (overnight unsecured market)

referring to the unsecured money markets (Bank of Italy , 2002), is that the volumes’ pattern

is strictly connected with the price of liquidity (ECB, 2009);

- spread between marginal lending rate / Eonia rate: this variable is an indicator of the

opportunity-cost of the last resort rather than the unsecured money market;

- spread between Eonia rate / deposit rate: the regressor is a proxy of the opportunity-cost of

investing in ECB risk-free deposits rather than in the unsecured money market;

13

- spread between Eurepo t/n rate or Eurepo 1 week rate / e-Mid rate: this differential

represents the ratio between the unsecured overnight market and the secured market segment

(ECB, 2009). The choice of these maturities was determined by their proximity to the

segment facility;

- spread between Ois 1week rate / e-Mid-rate: the use of this variable is justified by its

assumed alternative function referring to e-mid segment and its supposed complementary

referring to the eurepo segment;

- spread between Euribor 1week rate /Ois 1 week rate: the differential is an expression of the

money market’s tensions;

- shock: the inclusion of the dummy variable, justified by the findings of the Chow test,

which takes value 0 before the crisis and value 1 during the turmoil, captures the effect of

the financial turbulence on average daily volumes11.

Results

This section summarizes results obtained with respect to each of the three research questions we

have underlined in par. 4.1.

To identify the significant elements of trading volumes, you can proceed at first by illustrating the

results from by the buy-side model, then those from the sell-side one.

Table 4 – Coefficients and p-value of significant regressors

PANEL A – Coefficients and p-value in the“buy-side”model

Coefficient p-value

Constant 3450.812 0.000

Volumet-1 .4329581 0.000

Volumet-2 .1189062 0.001

Volumet-3 .1638742 0.000

Volumet-4 .0754236 0.024

Spread(e-Mid rate/Eonia rate),t 7796.081 0.002

Spread(Eurepot/n rate/Eonia are),t 3573.723 0.038

Shock -836.0938 0.001

R2

0.5982

Adjusted R2 0.5946

PANEL B– Coefficients and p-value in the “sell-side” model

Coefficient p-value

Constant 1473.184 0.000

Volumet-1 .270416 0.000

Volumet-2 .1256012 0.001

Volumet-3 .0759873 0.004

Volumet-4 .0965341 0.000

Spread(e-Mid rate/Eonia rate),t -3999.9 0.000

11 With this regard, we underline that the new variables created by multiplying the regressors of both models for the

dummy, aren’t significant.

14

Spread(e-Mid rate /deposit facility rate),t 655.2479 0.004

Spread(Eurepot/n rate/Eonia rate),t -981.1193 0.0040

Shock 0.5982 0.000

R2

0.3919

Adjusted R2 0.3887

Data source: our elaborations on the e-Mid data

From table 4 it’s clearly shown as buy-initiated volumes recorded during t time depend in a

significant and positive way on those registered until the fourth previous day.

With reference to the spread between the e-Mid rate and the Eonia rate, we point out a significant

and positive relationship: while the spread between the above mentioned rates increase, situation

that underlines the existence of a tension in the overnight market, then we recorded a rise of buy-

initiated volumes in the e-Mid platform. This result seems to show that, on one side, buyers, in

order to take liquidity, are willing to pay an higher rate, becoming price-takers; on the other side,

sellers remain in stand by, becoming “aggressors” of buyers’ proposal only in case they consider the

offered rates appropriate to the operation’s risk.

With reference to the spread between the Eurepo t/n rate and the e-Mid rate, we can underline a

significant and positive relationship, to show that also borrowers consider the secured segment as an

alternative source of liquidity; so, when the Eurepo t/n rate increases, they concentrate transactions

on the unsecured overnight segment.

Buy-initiated volumes are correlated in a negative and significant way with the variable “shock”,

that is buy-initiated transactions undergo a significant decrease in the post-shock window.

It may be interesting to underline that even if characterized by the expected positive sign, the

relationship between volumes and the spread between the marginal lending rate and the Eonia rate

is not statistically significant. Basically, the sign of this relationship confirms the original

hypothesis, that is the ECB refinancing operations represent a residual source of liquidity, but it’s

not taken in consideration by borrowers even when the spread between the e-Mid and the Eonia

rates increases sharply.

In the end, worth of mentioning is the fact that the spread between the Euribor 1week and the Ois 1

week rates doesn’t explain in a significant way the volumes’ evolution, that, however, are positively

related to that variable.

The empirical analysis shows clearly as sell-initiated volumes, recorded during t time, depend in a

significant and positive way on those registered until the fourth previous day. These volumes are

negatively correlated with the spread between the e-Mid rate and the Eonia, and with the differential

between the e-Mid rate and the Eurepo t/n rate. In particular, the decrease in sell-initiated

transactions, associated to the increase of the spread between the e-Mid rate and Eonia, is due to the

lenders’ behaviour in presence of money market’s tensions; on one hand, they become price-takers

(as confirmed by the positive relationship existing between the spread e-Mid/Eonia and the buy-

initiated volumes), on the other, they have to leave the overnight compartment in search of

alternative and safer liquidity investments (Eurepo t/n segment). This behaviour is consistent with

the dynamics of the money market’s functioning, described in the literature (CASSOLA et al, 2008;

HEIDER et al, 2008; Eisenschmidt and Tapking, 2009).

When the overnight rates increase, lenders move to other money market compartments (firstly,

secured segment with shorter maturities) and liquidity’s sources (intergroup cash and transactions

with the monetary authority ) (ECB, 2008 ECB, 2009).

An other sign of the lenders’ behaviour is the relationship with the floor of the corridor rates: the

relationship between the spread e-Mid/ marginal deposit and e-Mid-volume is positive, indicating

that the more the money’s rate outperforms the deposits’one, the more the sales increase on

overnight compartment (HEIDER et al, 2008).

15

The relationship with the dummy variable is significant and negative: in the post-shock window the

sell-initiated volumes fall sharply.

With reference to the second aim of the research, we must specify that normal volumes have been

obtained by eliminating the not significant regressors, so the abnormal volumes, calculated as

difference between the expected volumes and the actual ones, are statistically significant.

Graph 8 illustrates the evolution of normal and actual volumes from April 2007 to December 2008

for buy-initiated transactions.

Graph 8. – Average actual and normal buy-initiated volumes from April 2007 to December 2008

Note: The monthly volumes, expressed in millions of euros, are obtained as a simple average of daily volumes

Data source: our elaborations on the e-Mid data

The choice of the previous time horizon is not random, as in August 2007 we record the reversal of

the patterns of actual monthly volumes and normal monthly volumes: from that month, normal

volumes, until then lower than the actual ones, start to outperform the real volumes, as you can see

also from Graph 9, which shows abnormal positive volumes.

Graph 9 shows the evolution of monthly average abnormal volumes for the period from April 2007

to December 2008. We clarify that, for construction, the presence of positive abnormal volumes

gives evidence for the existence of actual volumes lower than the expected ones, that is the volumes

that would be recorded in the same temporal window without shock. On the contrary, the presence

of negative in excess volumes gives evidence for the outperformance of the actual volumes

compared to the normal ones.

Graph 9.- Evolution of the average monthly abnormal buy-initiated volumes for the period from

April 2007 to December 2008

16

Note: The monthly volumes, expressed in millions of euros, are obtained as a simple average of daily volumes

Data source: our elaborations on the e-MID data

It’s also interesting to observe the evolution of the cumulative abnormal volumes, which, as you can

see from Graph 10, shows a growing trend. Once again, the graph indicates in August 2007 a

reversal of the decreasing trend, although in that month the cumulative residual volumes are still

negative; we highlight that the increasing pattern of cumulative abnormal volumes characterizes the

entire post-shock temporal window (from August 2007 to December 2008).

Graph 10.- Evolution of the monthly cumulative abnormal buy-initiated volumes in the period from

June 2007 to December 2008

Note: The cumulative volume referring to the tth month, expressed in millions of euros, is obtained as the sum of t-1

thmonth average volume and the t

th month average volume .

Data Source: our elaborations on the e-Mid data

Graph 11 exhibits a comparison between the evolution of the average cumulative buy-initiated and

sell-initiated transactions. Particularly, the graph shows that the abnormal buy-initiated volumes are,

in absolute terms, higher than those estimated for the sell-initiated transactions. In particular, actual

17

buy-side volumes suffer a significant decrease from August 2007 and move in this direction for the

entire post-shock period; by contrast, transactions on the initiative of borrowers begin to decline

with a lag, if compared to the buy-initiated ones, as you can be see from the sign of the

corresponding abnormal volumes, which become positive only in the last two months of 200712.

This situation changed in the latter period13, when the decline in sell-initiated volumes exceeds, in

relative terms, the decrease in actual buy-initiated volumes and the abnormal sell-initiated volumes

become positive (Cassola et al, 2008a)14.

Graph 11.- Evolution of the monthly cumulative abnormal buy-initiated and sell-initiated volumes

in the period from June 2007 to December 2008

Note: The t-th month cumulative volume, expressed in millions of euros, is obtained as a sum of t-1 th month average

volume and t-th month average volume

Data Source: our elaborations on the e-MID data

In order to answer the third research question, we systematically consider the results referring to the

first and second research questions.

The analysis of the determinants of “normal” volumes shows an unexpected behavior on buy-side

and sell-side operators: the increase in the Eonia / e-Mid’s spread causes a decrease in volumes

traded on the lenders’ initiative and an increase in sell-initiated transactions.

These behaviors are consistent with the characteristics of the money market: on one side, the

increase in the spread between rates pushes lenders to leave the unsecured overnight market and to

go towards more secure segments (with General Collateral segment, for example) or to follow ways

consistent with the their risk tolerance (appetite for risk); on the other side, it pushes borrowers to

accept an increase in the cost of funding, in order to meet their undelayable needs of liquidity.

The lack of the overall volumes implies liquidity rationing with further raising of interest rates: the

"good" borrowers are constrained to leave the market.

At the same time, the analysis of abnormal volumes shows a change in the sign of the differential

between normal and actual buy volumes referring to the break month (August 2007).

12 See Table 1 and 2 in Appendix.

13 Generally, on the e-Mid platform, in absolute terms, the volumes of buy-initiated transactions are higher than those of

the sell-initiated ones. 14 See Table 2 in Appendix.

18

This point deserves further investigations: the decrease in buy-initiated volumes, i. e. the transaction

started on the initiative of the buy-side and ended with an order from the sell-side, doesn’t support a

decline in the borrowers’ liquidity needs or a waiting behavior about the possible cuts in the cost of

money by the authority.

Information on both points above mentioned are available to the banking system, therefore, the only

factor that can affect trading volumes is the unfair distribution of information referring to the

borrowers’ actual exposure at risk. In this scenario, lenders cope with two sets of problems: 1) the

estimation of the borrowers’ creditworthiness, because of the counterparty risk on the unsecured

overnight market; 2) the self-assessment of their actual degree of exposure at risk with reference to

a possible unexpected shock.

Because of the difficulty in assessing their own creditworthiness and the borrowers’ one, lenders

leave the overnight market, causing a lack of supply: buy-initiated volumes begin to decrease from

August 2007. As a consequence, also the “good” borrowers, following the lenders’ behavior,

decide to leave the overnight market: the liquidity rationing and the interest rates’ increase cause a

lack of demand.

The end of 2008 marks the final collapse of the money market: ECB changed the open market

operations’ procedures and narrowed the corridor of standing facilities’ rates. As a consequence, the

ECB replaces the money market as liquidity’s provider (the e-Mid volumes are about one third of

those recorded in the first months of 2007).

5.Summary and conclusions

The analysis offers a series of useful instruments for interpreting what happened on the interbank

market as a result of the spread of the sub-prime mortgages’ crisis.

The review of the literature allowed us to reconstruct a model of banks’ behaviour in the money

market considering the important functions that such market provides for banks and financial

system.

The Eurosystem interbank market’s functioning is linked to flows of funds arising from the

management of minimum reserve and the needs of cash management. The abnormal patterns of

volumes and rates can be attributed only to "worries" about the overall level of liquidity in the

system or the degree of risk of the counterparties.

The e-Mid is one of the "streams" of liquidity market but it offers an important advantage in the

analysis: it’s a transparent market in the sense that the operators know the other side of the market

when the trading proposal is satisfied. Consequently, it represents the most information-sensitive

market, regarding to the degree of counterparty risk.

The empirical analysis is based on daily transactions recorded on the overnight segment without

collateral from the e-Mid platform from January 2003 to December 2008; this is a sufficiently wide

range to neutralize the effects of the seasonal components and trend.

The employed methodology, i. e. the autoregressive model, allowed us to isolate the significant

explanatory variables on the buy-side and on the sell-side and to calculate the "normal" volumes, i.

e. the volumes that were recorded in the absence of shock (the date of structural break in time series

is August the 9th 2007).

Results show that the e-Mid market’s behavior is contrary to the law of supply and demand (if the

prices - expressed in terms of rates’ differential – increase, the sales decrease and the purchases

increase) because of the structure of the interbank market (sellers move towards other interbank

market segments or accumulate liquidity in anticipation of a shock) and because of the undelayable

needs of liquidity (the bad borrowers don’t consider price as relevant factor for the provision of

financial resources).

To support this behaviour, the abnormal volumes’ analysis allowed us to highlight the lack of

supply (in the buy-initiated volume) in August 2007, which was followed by the gradual withdrawal

of the good borrowers (lack of demand) in remainder of 2007 until the definitive collapse in

19

October 2008, when the ECB replaces the interbank market in its fundamental function of release

and absorption of intraday liquidity. From a systemic point of view it is of utmost importance to

find a solution to this attitude: the monetary policy, in fact, at least in the strategic framework of

Eurosystem, cannot pursue its objective of maintaining price without going through the money

market. The ECB must simultaneously think about an exit strategy for the lenders (to prevent the

spreading behaviour of moral hazard) and re-start the interbank market by promoting greater

transparency and a real flow of information on banks’ exposure to toxic securities.

The next step of work is, therefore, to seek additional instruments of measuring asymmetric

information on the interbank market in order to recalibrate the models of liquidity risk management,

more generally, the stress testing, and to increase transparency.

20

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22

Appendix

Tables Table 1:

Average monthly buy-initiated actual, normal and abnormal volumes from April 2007 to December

2008.

Note: the average monthly volumes, expressed in millions euros, are estimated as the average of the

daily ones, in turn obtained by aggregating intra-daily standard and large overnight deposits’

transactions. The normal daily volumes are obtained using the significant coefficients, returned by

the buy-side model, and their regressors. The abnormal volumes are calculated as the difference

between normal and actual volumes.

Date Actual Volumes

Normal

Volumes

Abnormal

Volumes

apr-07 18491.2337 16608.8468 -1882.3868

may-07 19981.9605 18557.3145 -1424.6459

jun-07 20784.6057 19576.8200 -1207.7857

jul-07 15591.6950 15298.7068 -292.9882

aug-07 13740.5865 13762.4939 21.9074

sep-07 13026.3810 13080.3185 53.9375

oct-07 12448.5300 12595.2474 146.7174

nov-07 14044.7900 13586.3836 -458.4064

dec-07 11648.4905 12326.5161 678.0256

jan-08 11940.4045 11982.8035 42.3989

feb-08 10606.2948 10985.8933 379.5986

mar-08 10015.7611 10295.1851 279.4241

apr-08 11206.8881 11069.9936 -136.8945

may-08 10568.1757 11010.9573 442.7816

jun-08 11841.7549 12400.6876 558.9328

jul-08 11111.0530 11690.5130 579.4600

aug-08 8311.1114 9221.1047 909.9933

sep-08 8915.7700 9348.2583 432.4883

oct-08 5910.7543 5073.4684 -837.2859

nov-08 5608.9210 5897.7557 288.8347

dec-08 5780.9886 6691.7087 910.7201

23

Table 2:

Average monthly sell-initiated actual, normal and abnormal volumes from April 2007 to December

2008.

Date Actual Volumes

Normal

Volumes

Abnormal

Volumes

apr-07 4117.6084 3762.0537 -355.5547

may-07 3656.1323 3438.0723 -218.0600

jun-07 4408.5371 3967.3060 -441.2312

jul-07 5397.6323 4445.0210 -952.6113

aug-07 3857.8922 3494.0017 -363.8905

sep-07 3705.2970 3264.7507 -440.5464

oct-07 3328.0274 3122.1881 -205.8393

nov-07 2821.4305 2884.4837 63.0533

dec-07 2081.4589 2260.0877 178.6287

jan-08 3571.4650 3323.0285 -248.4365

feb-08 2318.2457 2794.4732 476.2275

mar-08 2450.5474 2840.0645 389.5172

apr-08 2058.3136 2411.7744 353.4608

may-08 2330.7952 2804.4006 473.6053

jun-08 1914.8367 2434.4739 519.6372

jul-08 2438.3370 2688.5670 250.2300

aug-08 2718.5586 2975.2673 256.7087

sep-08 2455.8927 2676.9380 221.0452

oct-08 2642.1674 2940.0717 297.9043

nov-08 2530.6450 2382.8696 -147.7755

dec-08 2422.2514 2410.5806 -11.6708

Note: the average monthly volumes, expressed in millions euros, are estimated as the average of the

daily ones, in turn obtained by aggregating intra-daily standard and large overnight deposits’

transactions. The normal daily volumes are obtained using the significant coefficients, returned by

the sell-side model, and their regressors. The abnormal volumes are calculated as the difference

between normal and actual volumes.

24

Graphs Graph 1:

Average monthly sell-initiated actual and normal volumes from April 2007 to December 2008.

Note: the average monthly volumes, expressed in millions euros, are estimated as the average of the

daily ones, in turn obtained by aggregating intra-daily standard and large overnight deposits’

transactions. Note that sell-initiated volume means the volume relating to the transactions on the

lenders’ initiative. In essence, the proponent of the transaction is the lender-bank, while the

“aggressor” is the borrower-bank

25

Graph 2:

Average monthly sell-initiated abnormal volumes, from April 2007 to December 2008.

Note: Abnormal volume are calculated as the difference betwwen the normal volumes and the

actual ones (see notes to figure 1).

Graph 3:

Cumulative abnormal monthly volumes of sell-initiated transactions from June 2007 to December

2008.

Note: The t-th month cumulative volume, expressed in millions of euros, is calculated as a sum of t-

1 th month average volume and t-th month average volume

26