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Financial turmoil and asymmetric information theory: evidence from e-MID platform
Claudio Porzio - University of Napoli Parthenope, [email protected]
Francesca Battaglia - University of Napoli Parthenope, [email protected]
Antonio Meles - University of Napoli Parthenope, [email protected]
Maria Grazia Starita - University of Napoli Parthenope, [email protected]
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)
2
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;
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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.
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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).
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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
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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
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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.
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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
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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.
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20
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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
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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.
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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
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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
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26