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Liquidity risk model implementation for Ucits and AIF Funds
Auteur : Gilson, Kevin
Promoteur(s) : Lambert, Marie
Faculté : HEC-Ecole de gestion de l'ULg
Diplôme : Master en ingénieur de gestion, à finalité spécialisée en Financial Engineering
Année académique : 2016-2017
URI/URL : http://hdl.handle.net/2268.2/2669
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LIQUIDITY RISK MODEL
IMPLEMENTATION FOR UCITS
AND AIF FUNDS
Jury:
Supervisor:
Marie LAMBERT
Reader:
Boris FAYS
Mentor:
Jennifer COLLIN
Project-dissertation presented by
Kevin GILSON
With a view to obtain the diploma of
Master’s Degree in Business Engineering
specialising in Financial Engineering
Academic year 2016/2017
1
ACKNOWLEDGEMENTS
I would like to thank my supervisor, Marie Lambert, for her feedback and recommendations
all along the project.
I would also like to thank my mentor, Jennifer Collin, for her guidance, alongside the whole
risk management team of Lemanik Asset Management S.A. for making me feel at home
inside the company.
Finally, I will always be grateful toward my family, especially my parents Alain and Nathalie,
who never failed to believe in me, and always supported me during all these years at the
university.
2
3
ABSTRACT
Liquidity is like pornography. Easy to identify when seen, but it is difficult to define.
(O'Hara, 1995)
In this paper, we analysed a given liquidity risk model, and tried to replicate and replace it
with a new, customised, and internal one.
We had to first understand the whole idea behind the risk of liquidity, and what were the legal
restrictions around it.
Then, we made sure to analyse deeply the actual model, and we replicated it as best as we
could, before trying to come up with a new one, that we also tried to implement.
In parallel, we analysed other models that, while useless in our situation, were of great help to
get a better all-around view of the problem.
We further draw conclusions towards our conflicting results regarding the potential
implementation of a new model, and its real utility considering its cost.
4
5
GLOSSARY
AIF: Alternative Investment Fund.
AIFM: Alternative Investment Fund Manager.
BBG: Bloomberg
CSSF: Commission de Surveillance du Secteur Financier.
CSV: Comma Separated Values.
FTP: File Transfer Protocol.
LAM: Lemanik Asset Management S.A.
L-VaR: Liquidity-adjusted Value at Risk.
MEUR: Million EUR.
MUSD: Million USD.
NAV: Net Asset Value.
RAMIS: Risk Asset Management Information System.
TNA: Total Net Asset.
TNAV: Total Net Asset Value.
TRACE: Trade Reporting and Compliance Engine.
TTL: Time to Liquidate.
UCITS: Undertakings for Collective Investments in Transferable Securities.
VaR: Value at Risk.
6
7
1. INTRODUCTION
1.1. BACKGROUND INFORMATION
Lemanik Asset Management S.A. (hereafter “LAM”), a member of the Lemanik Group, is a
Luxembourg-based UCITS V compliant Management Company, and an authorized AIFM
supported by more than 60 employees across Luxembourg, Ireland, and Switzerland. The
Lemanik group is a family owned business independent from banks or private equity houses,
with a financially healthy business.
Currently, the third party Management Company business accounts for 85% of the group’s
revenue, and LAM deserves more than 60 customers established in the US, Europe, UK,
Switzerland and Asia, and it supports the distribution in more than 25 countries, and
alongside, the Lemanik group has established relationships with 18 custodians and 14
administrators.
LAM provides several services, knowingly: start-up support, domiciliation services,
valuation, investment and portfolio management, and, the two departments concerned by the
different risk models, risk management and investment compliance.
Concretely, the risk management department is in charge of the identification, the assessment,
and the prioritization of the various risks, while trying to minimize the resources needed, and
while monitoring and controlling the probability and/or impact of unforeseen events.
The investment compliance department is responsible for the control of all orders after
trading, for the post-NAV compliance monitoring, and should set-up the fund’s investment
policy and monitor the breaches occurring.
1.2. REGULATORY REQUIREMENTS
After the 2008 crisis, the regulators started to get more and more interested in risk
management, and particularly in the risk of liquidity. Indeed, liquidity was often left apart
before the crisis, but after, it became obvious that the more liquid the assets, the less risk there
is of the market collapsing due to an impossibility of reimbursing redemptions.
In Mai 2011, the CSSF released its circular 11/512 (CSSF, CSSF Circular 11/512, 2011),
which now stands as the reference in matter of regulatory requirements regarding risk
management here in Luxembourg.
8
Concretely, all Management Companies in Luxembourg, including LAM, haves to define and
monitor several types of risks, depending on the type of fund (See Appendix 2).
But in our case, for the liquidity risk, no model is imposed by the regulator, as long as we
describe our policy in the annual Risk Management Process document submitted to the local
regulator, i.e. the CSSF, and demonstrate that we verify that our redemptions risk is covered,
that the funds are liquid in order to protect the shareholders, and that we realize stress tests on
our models (CSSF, CSSF Regulation 10-4, 2010) (See Appendix 1).
1.3. CURRENT LIQUIDITY MODEL AND INTERNALIZATION PROCESS
LAM is outsourcing the computation of its liquidity risk to an external provider, KPMG,
however, as a Management Company, LAM remains responsible for the model used and the
risk measures computed on its own funds as well as the analysis of the exceptions raised, and
the follow-up of any liquidity issue with its clients.
Nevertheless, considering the current growth, both in term of clients and assets under
management, the company has decided that it would probably be more profitable to develop
and internalize its own model of liquidity risk, in order to keep the expertise internally and
reduce the costs.
Indeed, the company is currently subject to double costs due to the nature of the service
provided by KPMG: while LAM is already paying circa $25,000 per month for Bloomberg
data (BBG), KPMG is also charging the company an average of $15,000 per month of
additional BBG costs, some being redundant, in parallel with the invoice of their own services
rising up to $7,500 per quarter.
Moreover, most of these costs are dependent on the number of instruments that have to be
analysed: it’s notably the case for most of the BBG costs, averaging, for KPMG, a cost of
$1.4 per instruments.
On top of that, LAM has faced different discussions with portfolio managers regarding the
KPMG’s model. Indeed, for the clients, the model is understood way too conservative, and
often, they don’t agree with the conclusions drawn by LAM regarding the illiquidity of a
given instrument, that the portfolio manager, would mainly base its judgment on volume and
price analysis, consider perfectly liquid. The model is therefore too theoretical to be fitted to
the reality of the market.
9
It is therefore obvious that the company is willing to change its model for one that would be
perfectly suited for its needs, and fully internalized. Indeed, not only would it be more
profitable from a financial point of view, but it would also allow LAM to be fully in charge of
the computation of the risk of liquidity from the moment the company receives the
information, to the moment it exports them into risk reports to the attention of the clients.
1.4. COST ANALYSIS
Currently, LAM is paying the following costs to KPMG and data provider:
Month July 2016 August 2016 September 2016
BBG Costs $12,702.88 $13,149,98 $13,388.44
Number of
instruments
9832 9571 9701
Cost per instrument $1.29 $1.37 $1.38
TABLE 1: COSTS ANALYSIS
In addition to the costs provided in Table 1, the company is also charged by BBG circa
$25,000 per months for the additional data needed for the various activities of the company,
such as portfolio management, the various other risks analysed, and the compliance.
1.5. INTERNAL DATA BASE AND RETRIEVING PROCESS
1.5.1. GLOBAL FEEDING PROCEDURE
RAMIS (Risk Asset Management Information System) is a homemade application created in
2012 composed of a database (warehouse) and a set of operational processes supporting the
activities of Risk, Compliance and Fund Reporting teams, as well as a reporting tool.
Concretely, RAMIS provide access to two types of information: static and dynamic ones.
Every day, the database is actualised according to the following process:
1. Several scripts are running constantly in order to fill the Staging database. The
Staging database is a temporary one where data are retrieved, filtered, and
formatted. Its lifespan is equal to one run. Data collected are coming under
different forms, whether they are retrieved from RBC, JP Morgan, or other
custodians: it goes from FTP server, to dedicated mailboxes and other
retrieving processes.
10
2. Retrieved data are transferred to the Work database. The Work database, with
a lifespan of one day, is where the quality checks controls are realised, and
where the missing information is completed.
3. Once the data have been completed and formatted correctly, three
simultaneous operations are conducted:
a. Data are encoded accordingly in the definitive database of RAMIS.
b. They are sent to KPMG for VaR and liquidity computations.
c. They are imported into Line Data for compliance controls.
Process is then repeated the next morning.
1.5.2. LIQUIDITY REPORTS FEEDING PROCEDURE
For liquidity risk management, the data retrieving process is based on the following steps:
1. First of all, LAM receives from the third party administrators, after they calculate the
Net Asset Value of each fund, several files containing the information about the
portfolio and their instruments. Each of these instruments will be either imported, or
merged if it already exists, in RAMIS, the internal LAM database and risk
management tool.
2. For each of these instruments, the company will have to complete the missing
information using the Bloomberg infrastructure. Indeed, there is a list of the
mandatory fields needed for the different risk analysis, based on each asset type.
3. At the end of the day, 5 different CSV files are exported to KPMG from RAMIS: one
containing the instruments details, one containing the positions, one for the
subscriptions and redemptions, one for the transactions, and one for the time series
(NAV values).
4. All the information are collected and stored in a CSV file formatted according to
specific standards defined with KPMG. In parallel, the same information is injected in
Line Data Compliance (a hosted application) for compliance controls.
5. Once KPMG is done computing the liquidity of each fund and sub-fund, LAM
receives the data as CSV files. These files are uploaded by KPMG on a given FTP
server, and automatically retrieved by the company.
6. LAM then import the computed liquidity risk figures into RAMIS and generates the
different risk reports.
7. At the end of the day, the process is repeated.
11
There is usually a delay of one day between the export of the raw data and the import of the
clean ones.
With the clean liquidity scores, various reports will be generated:
Board reports: monthly analysis of each part and risk of a sub-fund; available for
every sub-fund managed by LAM.
4C reports: daily risk dashboard, generated on demand by some of LAM clients.
Daily exception file: every day, exception reports are generated, and require further
analysis by the risk team (NAV various, new liquidity issue, VaR breaches …).
1.6. LEMANIK’S PORTFOLIO OVERVIEW
LAM currently holds several types and sub-types of assets, all of them being classified in the
internal data base of the company, RAMIS, according to the repartition available in Appendix
2.
The risk management department of LAM is responsible for the daily risk management and
investment compliance monitoring of 59 active umbrella funds (UCITS and AIF structures),
subdivided into 244 active compartments. Basically, it currently accounts for almost 30,000
instruments, out of which more than 15,000 are actives ones. As of today, this number is
constantly growing.
Concretely, each type, and sometimes even sub-type, of asset requires a different computation
in order to classify its risk of liquidity. This is probably the main difficulty of developing a
liquidity model suited for the company, as we will have to develop several smaller models to
include in a bigger one, all of this while respecting our constraints of profitability.
Currently, LAM’s actives instruments are distributed according to the following table:
12
Instruments Number
ABS/MBS 71
BOND 4459
CASH 4718
EQUITY 3850
FUND 926
FUTURE 154
FX Forward 1012
INDEX 96
MONEY MARKET INSTRUMENT 159
OPTION 176
STRUCTURED PRODUCT 27
SWAP 897
TERM LOAN 30
Total 16575
TABLE 2: INSTRUMENTS REPARTITION
As you can see in Table 2, the vast majority of LAM’s instruments is composed of cash and
cash equivalents, equities, and fixed incomes.
13
2. EXPOSITION
2.1. STRUCTURED PROJECT MANAGEMENT APPROACH
As part of my project thesis, I performed an internship at LAM from September 2016 to May
2017. From September to December, I had the occasion to work and discover the company
once a week. During this period, my main goal was to get familiar with the company, and in
particular the risk management and investment compliance department, in which I was going
to work later. Weeks after weeks, I became more familiar with the functioning of the team; it
was divided into two distinct sections: the risk management team, within which I was
affected, and the investment and compliance one.
The risk management team was responsible for the monitoring of every possible risk that
LAM’s clients were susceptible to face. Basically, the team has to make sure that each of its
clients is covered, both legally (regarding, mainly, the CSSF regulations), and from its
prospectus point of view (whether the fund respect its engagements to its clients or not).
Most of the risks monitored are internalised inside LAM, which use a combination of various
programs, such as Line Data Compliance (an external application), or RAMIS (the company
very own database that we briefly introduced above).
But there is still one risk that is outsourced, and it is the liquidity risk. Indeed, LAM provides
KPMG with information about their clients, and KPMG compute the Value at Risk and the
liquidity risk for LAM every single day. By the end of my internship, I had to determine
whether it is possible to internalise the computation of that risk, with which model, and at
which cost?
Obviously, I wouldn’t have been able to answer these questions with only one day per week
inside LAM, so I laid the foundations during these four months, until January 2017 where I
would finally be a full time intern until May.
Therefore, after a few weeks, once I was confident with the working processes of the team, I
started to read the documentation regarding the actual liquidity model. I wanted to understand
it completely, but I encountered my first problem: KPMG was deliberately holding most of
the information regarding their model. Of course, we have the formula, and the general
overview of their computation process. But the details behind it are much more complicated
than what they were willing to give us. Obviously, it is understandable that they don’t want
14
too much information regarding their methodology to leak: if we understood it completely,
what would refrain us to copy and adapt their model for our own needs, and therefore end our
commercial relationship with them? And they were right, that was exactly what we wanted.
We wanted to end the outsourcing. We wanted to get back in charge of the liquidity risk. We
wanted to be able to adapt it easily to our expectations, but most importantly, to the
expectation of our clients.
Weeks after weeks, months after months, I gathered more and more information regarding
KPMG’s model. Of course, they stayed reluctant to give me everything I asked for, and every
time they answered my questions, more interrogations were brought back. Nevertheless, by
the end of my internship, I was able to reproduce their model with a significant similarity,
while still not perfectly the same.
The results can be seen in Chapter 2.4.4.
At the same time, I started to research more and more about the liquidity risk, and that is
where I encountered my second problem: liquidity model are rare and not often suited for
practical applications.
Indeed, the liquidity risk is one of the less studied risk, and most models were useless in the
case of LAM. Various problems came with the theoretical models I found:
They required intraday data: while these models where often pretty representative of
the reality, intraday data are not often available, and from a financial point of view, a
real financial hole (see Appendix 7).
They computations consumed too many resources: some models where perfectly fine
when it came to retrieve and process data, but the computation needed were proven to
either require huge computation power, and therefore huge, costly, servers (which was
completely against the company objective of paying less), or required several hours if
not days to realise the computation on LAM whole portfolio, which was unbearable
considering the liquidity risk is computed daily.
The models were only theoretical and hadn’t been tested in a real life scenario before:
with a portfolio accounting for several millions, LAM couldn’t possibly rely on a
model without any empirical results
15
Most models were either a weak variations of one another, or they only accounted for
one dimension of the liquidity risk: we had to find a way to replicate the multi-
dimension model of KPMG. For further information, see Chapter 2.4.
I was struggling to find an appropriate model when I got my hands on the model of one of
LAM’s partners, which was based on market practices. After further discussions with the risk
department, we agreed that a variation of this model could be what the company was looking
for.
Now that we had decided which model could potentially be suited for the company, I had to
determine whether its implementation was realistic, and most importantly, whether the model
would give us the expected results. Indeed, LAM has no intentions to replace a fully
functioning model with a brand new one if it drives us towards poorly evaluated risks.
In order to do so, I based my tests on several funds dating from 2015, as it was the last period
where KPMG delivered us detailed liquidity computations. Two problems soon appeared:
while the new model was relatively similar to the old one for the equities, it still gave us lots
of exceptions; moreover, it was a complete failure when we applied it to the fixed incomes, as
the majority appeared as exceptions, and as most data were either inaccessible, or too costly.
Considering this, we tried to replicate the KPMG model, but once again, while we succeeded
for the equities, the bonds and every other type of assets where impossible to compute.
Moreover, the cost of retrieving and stocking the data would have been much higher than
what LAM currently pays to KPMG.
The more we tried, the more we came to the conclusion that, in the end, we wouldn’t be able
to replace the existing model, and therefore, the goal of my internship soon evolved into
justifying this lack of replacement possibility. That is why I conducted more tests, and why I
retrieved more documentation regarding the cost of implementation, and the human and time
factor needed.
In the following chapters, I will describe the models studied, and why they can’t be used
internally.
2.2. GENERALITIES ABOUT LIQUIDITY RISK
“Liquidity is the lifeblood of financial markets. Its adequate provision is critical for the
smooth operation of an economy. Its sudden erosion in even a single market segment or in an
16
individual instrument can stimulate disruptions that are transmitted through increasingly
interdependent and interconnected financial markets worldwide. Despite its importance,
problems in measuring and monitoring liquidity risk persist.” (Fernandez, 1999)
Out of all the researches I have done regarding liquidity risk, there was only one point where
all of the researchers agreed: liquidity risk is as important as it is hard to define.
Indeed, liquidity, and therefore liquidity risk, is now more than ever an important concept,
even though a pretty obscure one (Abankwa & Blenman, 2015). By definition, a liquid
security can be easily and rapidly traded, for a relatively low cost (Abankwa & Blenman,
2015). It is a complex subject that can be understood as the ease of trading (Amihud,
Mendelson, & Pedersen, Liquidity and Asset Prices, 2005).
For these reasons, liquid markets are often seen by investors as the interesting ones, as they
generally offer more advantages than disadvantages (Sarr & Lybek, 2002).
Usually, researches distinguish three dimensions responsible for liquidity, and therefore
liquidity risk. Based on the works of Von Wyss (Von Wyss, Zimmermann, & Keel, 2004),
Bervas (Bervas, 2006), and Nikolaou (Nikolaou, 2009), we know that liquidity is determined
by:
The tightness of the bid-ask spread, which represent the transaction cost for a standard
amount. Indeed, the bid-ask spread can be seen as a counter party for the immediacy
of the execution.
The market depth, accounting for the volume of transactions that could potentially be
immediately liquidated without any further consequences on the market prices.
The depth is also sometimes called breadth.
The resiliency or market resilience, which basically represent the time necessary for
the market prices to return to their equilibrium level, following a big transaction.
In opposition to the market depth, the resiliency also takes into account the elasticity
of the supply and demand (Von Wyss, Zimmermann, & Keel, 2004).
These three dimensions account for the fact that any amount of assets can be sold rapidly at
any given time for a minimal loss (Nikolaou, 2009).
17
Von Wyss (Von Wyss, Zimmermann, & Keel, 2004) goes even further in his analyse by
adding a fourth dimension, the trading time. According to him, trading time can be
represented by the relative number of trades per time unit.
2.3. TEST PORTFOLIO
As Baker stated (Baker, 1996), since liquidity has to be proxied to be observable (Von Wyss,
Zimmermann, & Keel, 2004), different liquidity measures lead to conflicting results.
Therefore, we had to come up with a test portfolio, one that would be representative of
LAM’s instruments, and one that would allow us to compare different models, while still
being able to catch up with KPMG’s results.
As stated before, KPMG send LAM its results of the computation of liquidity risk daily. Up to
September 2015, the results were detailed for each and every instrument in a portfolio, but
now it isn’t the case anymore. Therefore, for comparison purposes, we decided to base our
test portfolio on a few snapshots of LAM’s portfolios back in the 30th
of September 2015.
As we stated before, currently LAM has to manage above 15,000 active securities.
Considering Cash and Cash equivalent can always be liquidated within one day, and knowing
that the liquidity of OTC instruments won’t be easily determined (if determined at all), we
decided to mainly focus on Bonds and Equities, as they represent the vast majority of the
instruments.
We took 14 sub-funds snapshots of the 30th
of September 2015, mostly composed of Bonds
and Equities, and we used it for our comparison purposes.
The entirety of the sub-funds instruments used can be found in Appendix 3.
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2.4. KPMG MODEL
2.4.1. GENERATED REPORT
FIGURE 1: RISK REPORT
19
Before going into the theoretical aspect of KPMG’s model, It is important to understand how
are arranged its results, and what are the tests conducted on it.
Each month, one page of each board report (which can be found in Figure 1) forwarded to
LAM’s clients is dedicated to liquidity risk. While exported in PDF, the reports are
automatically generated through RAMIS and, upon further human verification, delivered to
the clients.
General information can be found in the top right counter of the report. It is important to note
that, for privacy reasons, theses information have been changed, and therefore, the
percentages below might not be adequate.
As you can see, the page of the board report regarding liquidity risk is divided into 4 parts.
From left to right, top to bottom, we have:
Exposure by liquidity score: this is a graphical representation of the time to liquidate
in MUSD for the portfolio, and for each asset types. The time to liquidate is the
estimated time required to liquidate a position with a minimal impact on the market.
Instruments are categorized based on their time to liquidate value, going from 1 day or
less, to 2-7 days period, and ending with the remaining instruments that require more
than 30 days to be liquidated.
Liquidity score per asset type: the liquidity score is divided into two parts, even
though the first one is the most used.
o On the top row, we can see what percentage of the portfolio NAV can be de
liquidate in a given time period. Each column is itemized according to each
involved instruments. In our example, we can see that, in one day or less, we
can liquidate 57.77% percent of the portfolio, out of which 34.98% account for
equities, 12.47% for government bonds …
o On the bottom row, the same summary is displayed, but this time in absolute
MUSD values.
Redemption Vs resources (Stressed conditions): this is where the computations
regarding stress test and liabilities are displayed. First, information in MUSD and
percentage of the NAV are given for normal conditions, and then for stressed ones.
Liquidity score in MUSD over the Net Assets: this is a simple graphical representation
of the liquidity score by asset type.
20
2.4.2. PRACTICAL TEST
2.4.2.1. ON THE ASSET SIDE
Although the time to liquidate is detailed for each asset type, the risk department will only
consider two data: the percentage of average resources of the portfolio under 7 days, and the
percentage of the portfolio available only after more than 30 days, as it is visible on Figure 2.
FIGURE 2: LIQUIDITY SCORES
Concretely, LAM use an internal limit of 70% in regards of the percentage of the portfolio
that should be available in 7 days or less. If this internal limit is not respected, it will affect
the fund risk profile calculated by the company, and a warning will be added in the summary
delivered to the client.
Secondly, the risk department will look for the percentage of assets that can only be liquidated
in 30 days or more. While there is no legal restrictions concerning the maximum percentage
of assets that can’t be liquidated within 30 days, it is a good market practice in Luxembourg
to restrict to a maximum of 10%, and therefore, if this percentage exceed this limit, once
again the fund will be informed of the problem, and the risk department will need to perform
further analysis.
2.4.2.2. ON THE LIABILITY SIDE
Concretely, once KPMG has computed redemptions for all the portfolio assuming a VaR at
level 99%, it compares it to the available resources by computer the redemption coverage
ratio using the following formula:
21
With the available resources being the ones with a time to liquidate of one or below.
Results can be seen on Figure 3.
FIGURE 3: REDEMTIONS
LAM employ an internal limit of 90% for its redemption coverage ratio: if it exceeds this
value, it means that the sub-fund would have to liquidate almost if not all of its portfolio in
order to go through a massive redemption, which is not acceptable, and therefore require
further analysis from the risk department.
2.4.2.3. STRESS TESTS
Once the redemption has been stressed by applying the arbitrary stress factor, and once the
arbitrary haircut has been applied to the available resources, a stressed redemption coverage
ratio is computed based on these values.
FIGURE 4: REDEMTIONS
Once again, the internal limit of LAM is set to 90%, but this time, if the stressed ratio exceeds
it, the company will only display a warning in its summary in order to let the fund adjust
itself.
2.4.3. THEORETICAL MODEL
2.4.3.1. ASSET SIDE
Currently, the model developed by KPMG is primarily based on the Bid-Ask spread.
22
It is important to note that the Bid-ask spread is a well-known liquidity measure. Indeed, the
lower its value, the easier it is for a transaction to appear, and therefore, the more liquid the
asset can be considered.
Thorough the years, the Bid-Ask spread has been widely studied by researches such as
Amihud (Amihud & Medelson, Asset pricing and the bid-ask spread, 1986) and others.
They have studied the impact of the spread on the liquidity, and while all of them agree that it
is an important indicator, it does not reflect every aspect of the liquidity risk. Indeed, the bid-
ask spread is highly correlated to liquidity (Corcuera, Guillaume, Madan, & Schoutens,
2010), and therefore to its risk, but it only represent one dimension of the liquidity, and it
doesn’t account for the market depth, nor the resiliency (Bao, Pan, & Wang, 2008).
In the KPMG’s model, the risk of liquidity is divided into two parts: the exogenous risk,
which doesn’t affect the market value, and the endogenous risk, which does affect the market
value. While the exogenous part barely changes in all the possible situations, the endogenous
part does change depending on the type of the asset. These two aspects of the spread have
already been studied, firstly by Muranaga (Muranaga & Ohsawa, 1999), who divided the
spread into order-processing costs and adverse selection cost, then by Amihud (Amihud,
Mendelson, & Pedersen, Liquidity and Asset Prices, 2005) in its current endogenous-
exogenous form.
With its approach, KPMG come up with four different formulas, based on the asset type.
2.4.3.1.1. EQUITIES
First, for the equities, we can obtain two different formulas: one for the instrument level, and
one for the position level. LAM only uses the position level, and therefore, we can come up
with the following liquidity cost:
(
)
EQUATION 1: EQUITIES ASSET SIDE
Where
Askt, Bidt, Midt, and Pt being the ask, bid, mid, and market prices of the asset at time t.
Qt being the number of shares in a particular position of the portfolio at time t.
23
being the price impact of all the assets that shall be liquidated at time t.
With
| |
EQUATION 2: EQUITIES PRICE IMPACT
Where
rt being the value of the asset’s percentage return at time t.
Volt being the market traded volume of the asset at time t.
Qt being the number of shares that shall be liquidated.
The definition of λ KPMG use in its model is heavily based on the work of Amihud (Amihud,
Illiquidity and stock returns: Cross-section and time series effects, 2002), itself based on
Kyle’s work (Kyle, 1985). Later on, the same measure has been proven to be a reliable one,
through its extended use in the financial world, as stated by Friewald (Friewald, Jankowitsch,
& Subrahmanyam, 2014).
As you can see, the relative bid-ask spread computed in the liquidity cost is divided by two.
Indeed, as the bid-ask spread is representative of a “buy then sell” relation, the spread must be
divided by two if we want it to account for only one side of the relation, here the selling part
(Bervas, 2006).
Another question one could ask himself is the utility of using two times the quantity: firstly
inside the computation of the price impact, and secondly once again when computing the
liquidity cost.
When asked, KPMG justified it by the fact that the quantity inside the price impact was
representative of the elasticity of the market, while the second one was only there to scale the
liquidity cost up to the position in portfolio.
“[…] to get a better interpretation of the formula one should look at the two coefficient
separately. More specifically, λ can be interpreted as a scaled return (the scaling factor
would be the quantity in portfolio over the quantity on the market) which aim to capture the
elasticity of the market. Afterwards, this scaled return can be multiplied by the value of the
position in the portfolio.” (KPMG contact mail, 2017).
24
Restarting from the liquidity cost formula, we can establish a relationship of dependency
between it and the maximum liquidity cost for equities:
(
)
EQUATION 3: MAXIMUM LIQUIDITY COST
By arbitrary defining the maximum risk, notably by using a percentage of the price of the
asset or a function of the Bid-Ask spread, this relation allows us to compute the quantity Qt
that we can liquidate in one day under this limit. It is then possible, using a prudential
approach, to come up with the time to liquidate of the asset.
We first need to compute the distribution of and
by using the means of the
instrument’s observed past prices, volumes, and Bid-Ask spreads.
Indeed, KPMG compute, for each trading days of the last year, the value of the price impact
relative to the position hold in portfolio.
Once it’s done, KPMG will take the worst scenario obtained for the first day, as it takes into
account that, if a trader wants to sell a huge position within only one day, he could experience
a no-depth market and therefore a large impact on prices. For the longer time periods, KPMG
will use a lower quantile of the distribution, mimicking a lower price impact.
With this approach, KPMG tries to account for all three dimension of liquidity: the spread, the
market depth with the volume, and the resiliency with the distribution of the price impact.
Now that we have a different price impact for each day thanks to our maximum liquidity cost,
we can compute the maximum amount that can be liquidated by solving the equation for Qt.
Once again, it is important to note that the general purpose of the maximum liquidity cost
policy is to avoid impacting the price of the asset we are trying to liquidate.
2.4.3.1.2. BONDS
Considering bonds’ volume data are usually not available, KPMG has determined that,
without a proxy approach, it would be impossible to use the same model as for the equities,
nor any model relying on volume data.
25
Therefore, KPMG has decided to rely on the data provided by FINRA TRACE (Trade
Reporting and Compliance Engine) as its proxy for the bonds. Indeed, from the population
represented in the whole proxy, it is possible to select an appropriate sample which will
accurately represent any portfolio for which we need to compute the risk of liquidity. In order
to select its sample, KPMG use the time to maturity and the rating as discriminating variables.
Based on the proxy’s data, we can compute the liquidity cost for the bonds:
(
)
EQUATION 4: BONDS ASSET SIDE
With
| |
EQUATION 5: BOND PRICE IMPACT
With respectively:
Askt, Bidt, Midt, and Pt being the ask, bid, mid, and market prices of the asset at time t.
Post being the size of a particular position in the portfolio at time t.
Qt being the number of shares in a particular position of the portfolio at time t.
Λt being the price impact of the asset at time t.
AmOutt representing the bond’s outstanding amount.
The same prudential approach already used for the equities will be needed for further
computations of the time to liquidate.
2.4.3.1.3. OTHER TYPE OF ASSETS
On the other hand, for every other type of assets, the company uses a custom made matrix of
liquidity: for each instruments, experts have estimated and fixed, probably based on empirical
results, a coefficient of liquidity and a given time to liquidate.
Concretely, the matrix (which can be found in Appendix 4), assign, to each instrument type, a
percentage of the position that can be liquidated after a given day, as it can be seen in the
example in Table 3:
26
Security/Day 1 2 3 4 5 6 7 8 9 10
Bond 0,4 0,6 0,8 0,9 1 0 0 0 0 0
Share 0,6 0,8 0,9 1 0 0 0 0 0 0
Cash 1 0 0 0 0 0 0 0 0 0
Fund 0,3 0,5 0,65 0,75 0,9 1 0 0 0 0
Commodity 0,3 0,5 0,65 0,8 0,9 1 0 0 0 0
CFD 0 0,1 0,3 0,5 0,65 0,75 0,85 0,95 1 0
BondFuture 0 0 0,2 0,45 0,75 0,85 0,9 1 0 0
BondFutureAdjustmen
tLeg
0 0 0,2 0,45 0,75 0,85 0,9 1 0 0
CDS 0 0 0 0,1 0,2 0,35 0,5 0,65 0,85 0,95
CDSAdjustmentLeg 0 0 0 0,1 0,2 0,35 0,5 0,65 0,85 0,95
FXForward 0,6 0,75 0,85 1 0 0 0 0 0 0
FXForwardLeg 0,6 0,75 0,85 1 0 0 0 0 0 0
CDSwaption 0 0 0 0 0 0,2 0,3 0,45 0,6 0,75
Forward 0,2 0,35 0,5 0,65 0,8 1 0 0 0 0
TABLE 3: KPMG MATRIX
2.4.3.2. LIABILITIES SIDE
In parallel, KPMG also compute the daily redemptions and compare them to the liquid assets,
using stress tests notably:
(
)
EQUATION 6: REDEMPTIONS
With respectively:
PercRedt being the share class redemptions at time t in percentage of the fund NAV.
scNRedt representing the number of redeemed shares for a specific share class at time
t.
scNSharet being the share class number of shares at time t.
scNAVt being the share class NAV at time t.
Fxt being the foreign exchange rate between share class and fund currency at time t.
27
fundNavt representing the fund NAV at time t.
This formula is applied for each share class, and then KPMG will synchronized and sum up
all of them in order to obtain the fund redemptions time series in percentage of the fund NAV.
Afterward, a frequency-severity calibration is performed on the time series: assuming the
independence between the two, how often do losses happen, and how big of an impact do they
have, assuming both are independent? Both questions are answered by finding suitable
probability distributions (a Bernoulli random variable for the frequency, and adjusted non
negative distributions for the severity), before being used in a Monte Carlo simulation that
will generate the simulated aggregate loss distribution, from which a desired quantile will be
selected, in order to get the expected level of redemptions that could occur over specific
timeframe, under a specific confidence level.
Currently, by considering only a confidence interval of 99%, the model can definitely be seen
as a rather conservative one.
2.4.3.3. STRESS TESTS
2.4.3.3.1. ON THE ASSET SIDE
For equities and corporate bonds, after defining a stress factor (or haircut), the daily market
volume time series is stressed by applying it to each data point. Afterwards, the available
liquidity is computed using the resulting stressed daily volume time series.
2.4.3.3.2. ON THE LIABILITY SIDE
The number of redemptions is multiplied by an arbitrary stress factor. They further compute
the expected level of stressed redemption based on the stressed redemption ratio.
2.4.4. REPLICATING THE MODEL BY OURSELVES
In order to be able to understand the model plainly, and in the possibility of bypassing KPMG
by computing it by ourselves, we decided to reproduced it based on the information we were
given, and see if you could come close to KPMG’s results. We first wanted to compute it for
an equity based sub-fund, and for a bond based one, much like KPMG, but as I will explain
later, we were only able to replicate the model in the case of the equities. Therefore, we
reproduced it on two different equities sub-funds, both whose securities can be found in
Appendix 3.
28
2.4.4.1. REPLICATION PROCEDURE
Several steps were required in order to faithfully reproduce the given model:
1. Collect the necessary data, from Bloomberg and from RAMIS.
2. Process the data and make them usable for the model.
3. Compute the required remaining data.
a. The maximum liquidity cost.
b. The price impact.
4. Compute the quantities that could be liquidated.
a. Quantities for one day.
b. Quantities for several days.
5. Assign them into buckets.
6. Compute the buckets percentages.
2.4.4.1.1. COLLECTING DATA
We first had to collect the necessary data:
The last price, from Bloomberg, under the mnemonic PX_LAST.
The Ask price, from Bloomberg, under the mnemonic PX_ASK.
The Mid price, from Bloomberg, under the mnemonic PX_MID.
The Bid price, from Bloomberg, under the mnemonic PX_BID.
The Volume, from Bloomberg, under the mnemonic PX_VOLUME.
The quantity in portfolio, from RAMIS.
The market value in portfolio, from RAMIS.
Each and every one of these data had to be retrieve for each security, but not only for the 30th
of September (date of our test portfolios), but also for every trading days between this date
and the 30th
of September 2014.
Therefore, for each instruments tested, 7 fields had to be retrieved for a total of 255 trading
days.
For the field coming from RAMIS, they were retrieved from the internal data base. For the
Bloomberg ones, we used the Bloomberg Request Builder, a paying tool which allowed us to
retrieved historical fields for the given securities.
29
2.4.4.1.2. PROCESSING THE DATA
Unfortunately, some of these data were unavailable for some dates, and therefore, we had to
find a way to process them, as they would be need for the computation of the distribution of
the price impact.
We proceeded differently following the fields:
For the fields missing in RAMIS, we excluded the security for the concerned dates.
Indeed, it would be illogical to proceed to computation for a date where a security
wasn’t hold by any of LAM’s clients.
For the fields missing in Bloomberg, we attributed them to missing trading days, and
therefore, we replaced them with their average over the 255 trading days.
Details regarding the processing formulas can be found in Appendix 5.1.
2.4.4.1.3. COMPUTING THE REMAINING DATA
Two needed data were left to compute. The first and easiest one was the Maximum liquidity
cost. As we stated before, the maximum cost is arbitrarily fixed, and therefore we started with
the same ratio as KPMG uses, which account for 0.25% of the market value in portfolio.
The second one was the trickiest. Indeed, we needed the distribution of the price impact, and
then needed the necessary quantiles used for the different liquidity buckets.
We first computed the distribution of the price impact accordingly to KPMG’s formula:
| |
We computed it for each instruments, and for each trading day where a quantity in portfolio
was available. The formulas used for the computations are available in Appendix 5.1.
Therefore, we were now left with 1 to 255 price impacts for each security, depending on the
number of trading days with available quantity in portfolio.
For each security, we needed four price impacts:
One for the “Below one day” liquidity bucket.
One for the “Between two and seven days” bucket.
One for the “Between eight and fifteen days” bucket.
30
One for the “Between sixteen and thirty days” bucket.
Thanks to KPMG’s documentation, we knew that, for the first bucket, a 99% quantile of the
distribution of the price impact was used. When we asked KPMG’s for further information
regarding the remaining buckets, we were confirmed that lower quantile were taken,
apparently decreasing at a constant pace in the standard configuration of their service:
[…] in our standard configuration, a maximum quantile of 0.99 and a minimum quantile of
0.05 are set. The quantiles selected for every step of the iteration depends on the time horizon
of the analysis selected (in the standard configuration it is 365).
More specifically evenly spaced quantiles (365 in the standard case) are generated which will
span from the minimum and the maximum quantile. On every iteration step, a lower quantile
is selected. (KPMG contact mail, 2017).
Nevertheless, after trials and error, we came to the conclusion that LAM wasn’t in the
standard configuration, as the quantiles that gave us the closest results to KPMG’s ones were
the following:
99% for the first bucket.
95% for the second one.
75% for the third one.
50% for the last one.
Indeed, if we had followed the standard configuration, we would have had to use the
following quantiles:
99% for the first bucket.
98.73% for the second one.
97.10% for the third one.
94.92% for the last one.
But after trials, it was evident that this configuration gave us results nowhere near the original
ones.
2.4.4.1.4. COMPUTING THE QUANTITIES
Now that we had all the necessary information, starting from this formula
31
(
)
we isolated the quantity, in order to compute the maximum quantity that could be liquidated
for each price impacts:
(
)
EQUATION 7: MAXIMUM QUANTITY
We had now four different quantities, for four different price impacts. Each quantities
representing the amount of the security that could be liquidated within one day, according to a
given price impact.
Therefore, in order to compute the total quantity that could be liquidated thorough a given
bucket, we had to multiple it by the number of days:
For the first bucket, one day and less, we multiplied it by one.
For the second, two to seven days, we multiplied it by six.
For the third one, eight to fifteen days, we multiplied it by seven.
For the last one, fifteen to thirty days, we multiplied it by fifteen.
We now had, for each security, the maximum quantity that could be liquidated for every
bucket.
2.4.4.1.5. ASSIGNING SECURITIES TO BUCKETS
The following step consisted in comparing the actual quantity hold in portfolio at the date of
the 30th
of September, to each buckets, and therefore determine whether they could be entirely
liquidated within thirty days or less, or whether some instruments were left.
This way, for each security, took the quantity in portfolio and subtracted the quantity that
could be liquidated in the first bucket. If some instruments were remaining, we subtracted the
second bucket, then the third, and finally the last one. Either the security was entirely
liquidated in one of these buckets, or a remaining quantity was assign to the thirty-plus days
bucket.
32
2.4.4.1.6. COMPUTING BUCKETS PERCENTAGES
The final step consisted in computing, for each bucket, the percentage of the portfolio that
could be liquidated.
For each security, we needed two data:
Their relative position in portfolio as a percentage of the Total Net Assets (TNA).
This information could be easily retrieved in RAMIS.
For each liquidity bucket, what percentage of the security was liquidated?
This was a simple computation of the relation between the quantities liquidated and
the quantity in portfolio, for each bucket.
With these values, we were now able to compute the percentage of the sub-fund that was
liquidated for each bucket by taking, for each security, the sum of the product of these two
percentages.
Obviously, considering we didn’t applied the replication on Cash and Cash equivalent, since
they are always liquidated in one day or less, the total of the percentages computed didn’t sum
up to 100%, but only to the total percentage without cash.
2.4.4.2. RESULTS
The full results of the replication can be found in Appendix 5.2.
2.4.4.2.1. FOR THE EQUITIES
As I mentioned before, the trickiest part of the replication consisted in selecting the quantiles
used. After trials and errors, I decided to stick with the four abovementioned, as they were
giving me the closest results to KPMG’s model.
With these quantiles, the results were satisfactory. Indeed, while the results weren’t exactly
the same as KPMG’s ones, they were still relevant.
Most of the time, when the replication gave us a different bucket than KPMG, our model
proved to be a bit less restrictive, which is one of LAM’s goal, and therefore allowed us to
liquidate our position faster.
Nevertheless, some exceptions also appeared where our model was more restrictive. Most of
the time, these cases were linked to securities that, at the date of the test portfolio, LAM’s
clients had recently acquired.
33
Indeed, since some of those securities had only be into the sub-fund for less than a month, the
computation of the distribution of the price impact was restricted to these dates, and
therefore, less occurrences were computed, which led us to less reliable quantiles of the price
impact.
For those types of securities, I suspect KPMG to either use a distribution of the price impact
based on other clients, or based on a generic model.
2.4.4.2.2. FOR THE BONDS
In opposition to the equities, the replication of the model for the bonds was a complete failure.
Indeed, while the model itself was replicable, the information needed were either inaccessible
(through FINRA Trace), or too costly (through Bloomberg), leading us to give up on the idea
of replicating KPMG’s model for the bonds.
2.4.4.3. COST ANALYSIS
From a more financial point of view, the replication costs could be divided into two groups:
The costs of retrieving the data, both historically for a starter, then daily for the
maintenance.
The actual costs of implementation, both financially and humanly.
Considering the growth of the company, it is easy to round up the number of bonds, equities,
and other assets for which the bid-ask spread and volume are available, to 10,000 active
instruments for computation purposes.
2.4.4.3.1. DATA COSTS
As we stated before, we need seven data fields in order to run the model:
The quantities in portfolios, and their relative market value, both available in RAMIS.
The last price, ask, mid, bid, and volume for each securities, available through
Bloomberg.
While the two information already encoded in RAMIS are easily and freely accessible, the
five others must be bought to Bloomberg, and each come at a cost.
In order to estimate this cost, Bloomberg forwarded us detailed information regarding their
billing procedure. The billing table can be found in Appendix 7.
34
It is important to note that, while LAM already pays for several Bloomberg services, any of
these potential field requests would account as additional costs. Moreover, the Bloomberg
field are classified into different categories, with, in our case, two interesting ones: the pricing
ones, and the security master ones, which are far more expensive.
In the case of the replication of KPMG’s model, only pricing information are needed.
Therefore, for an approximate of 10,000 active securities, assuming half of them as equities
like, and the other half as fixed incomes, the annual additional costs would went up to
$10,000 for the equities, and $25,000 for the fixed incomes annually for our new model.
Nevertheless, we also need historical pricing information, at least the year of the
implementation, and therefore, additional costs of $17,325 for the equities, and $44,550 for
the fixed income, would be necessary.
On top of that, an additional access fee of $0.01for the pricing information, and $0.03 for the
snapshot ones, is billed for each request.
In our case, assuming 255 trading days and 5 requested fields for 10,000 securities, we would
first have a $382,500 access fee for the historical pricing, following by a daily $500 access fee
for the requested prices, which, once again assuming an average of 255 trading days, account
for $127,500 access fee per year.
In conclusion, LAM would have to invest a total amount of $606,875 the year of the
implementation, followed by an annual cost of at least $162,500, and probably more
considering the constant growth of the company.
As you can understand, this far above the current cost of KPMG’s model, and is therefore in
complete contradiction with the objective of cost reduction.
2.4.4.3.2. IT COSTS
In order to get a better view of the troubles an internal implementation would bring us, I
worked in pair with the IT department. That’s where I learned that not only would the model
have humongous data costs, but I would also have a huge IT impact.
Indeed, considering the IT department is composed of 4 people currently, the estimated
implementation time of such a model would be higher than an hundred days.
35
According to the IT department, they first would have to clean and filter the database, in order
to optimise it for the latter implementation. For each data to retrieve, they would have to make
sure LAM hasn’t already this information, and then they would have to code the data
retrieving system in order to deal with the multiple exceptions possible:
What if the information is inaccessible?
What if it is unavailable?
What if it’s equal to zero?
What if the ISIN doesn’t match the Bloomberg code?
…
All those possible exception would make the implementation a nightmare, justifying an
implementation time above a hundred days.
Moreover, the retrieving in itself and the storage of the information would bring a lot of
troubles.
Indeed, retrieving 3,000 fields (whether the last price of 3,000 securities or 30 fields for 100
securities) and encoding them into RAMIS takes an average of an hour.
As we already stated above, we can count on a 10,000 active instruments basis. Therefore, we
would need, on a daily basis, to retrieve 5 fields for each instruments (the last price, ask, mid,
bid, and volume), leading us to not less than 50,000 request every day, which would take us,
assuming no optimisation of the retrieving process has been done, approximately sixteen to
seventeen hours every day, only to retrieve the necessary fields.
This is obviously without accounting for the historical data required by the model for the
computations of the price impact: 10,000 securities, 5 fields, and an average of 255 trading
days, accounting therefore for not less than 12,750,000 requests.
The approximate time that would be needed to retrieve this information goes up to 4,250
hours, and therefore more than 177 days, assuming a constant process!
Moreover, LAM’s servers are nowhere near sufficient to handle such a massive amount of
stored data, which would therefore lead to additional costs for the company, as we would
need to upgrade our IT infrastructure.
36
This is clearly inapplicable in a small company like LAM, especially when the main goal is to
reduce the costs.
2.5. LAM’S PARTNER MODEL
As we stated before, the vast majority of LAM’s managed instruments is distributed between
the cash and its equivalents, the equities, and the fixed incomes. Currently, KPMG deals with
every other type of assets as exceptions, and considering the difficulties of finding an
appropriate model for every instruments type, we decided to once again focus on the fixed
incomes and the equities. Indeed, evaluating liquidity is already hard in itself, but most of the
studies have focused on equities and bond markets, leading to a lack of model for the other
types of instruments, whether we are talking about FX market (Abankwa & Blenman, 2015),
or different types of fixed incomes such as the MBS (Kitsul & Ochoa, 2016).
The cash and its equivalent staying the most liquid assets any investors can hold, and
therefore, the evaluation of their liquidity is never a problem.
Obviously, if the model is proven to be efficient, we will be able to adapt it in order to
accommodate further and further instruments, until only the OTC are left as exceptions.
Indeed, it is always certain that OTC product will be and will stays as exceptions, considering
the overall lack of information about them (Jankowitsch, Nashikkar, & Subrahmanyam,
2008).
Therefore, we decided to explore a new possibility, based on one of LAM’s partner model.
According to them, the best way to compute the liquidity risk of each asset is probably to
divide the task into four major steps:
1. First of all, we need to compute and assign a liquidity score to each asset.
2. Secondly, in order to satisfy our regulatory requirements, we will need to come up
with the time to liquidate of each asset, and therefore of each portfolio.
3. Thirdly, we will need to compute the liability part.
4. Finally, we will compute stress test scenarios based on the liquidity level calculated in
steps 2 and 3.
As we already said, this approach has many advantages. Indeed, each asset type will be
evaluated according to multiple liquidity indicators, each of them selected according to the
instrument. Indeed, unlike the previous model, this one will be suited for LAM’s whole
37
portfolio, and will take into consideration the remarks of the portfolio managers who found it
to be too conservative for their needs.
Moreover, it will be fully internalized, meaning LAM will be able to update and upgrade it
anytime the company want or need to, allowing us to always perfect it.
2.5.1. STEP 1: THE SCORING
Several paths can be considered to compute the various liquidity scores. Basically, we will,
for each category, compute several liquidity scores using different techniques when the data is
available, and then use the weighted average of these scores.
Based on other external models developed by LAM’s partners, alongside our own researches,
we think that it would be a practical approach in line with the market practice, to classify each
asset into 5 ranking categories, based on their liquidity score:
0: Undefined. Used for assets lacking information or special cases.
[0.1;1.5]: Illiquid.
[1.6;2.5]: Relatively liquid.
[2.6;3.5]: Liquid.
[3.6;4.5]: Very liquid.
Concretely, each asset would be scored based on one or more tests/models. Depending on the
results of each test, the asset would be given a temporary score going from 0 to 4.5. Its final
score would be the average of each test. We could also consider using bonus/malus score
points in some specific cases, such as the Bloomberg rating of the instrument or the issuer.
Obviously, each model uses its own thresholds for every single test, but it would be a
competitive approach to develop our own thresholds by trials and error. Nevertheless, it is
important to note that we won’t be able to do whatever we want with the threshold, as it
wouldn’t give us rigorous results, and would therefore lead us to an incoherent model.
A detailed version of the scoring tables can be found in Appendix 6.1.
2.5.1.1. EQUITIES AND EQUITIES LIKE
2.5.1.1.1. BID-ASK SPREAD
Various forms of Bid-ask spreads could be foreseen, going from the simplest ones to a variant
of the KPMG model.
38
Some of the models analysed used the following thresholds, with the following formula:
EQUATION 8: BID-ASK SPREAD
Below 15 bps, very liquid.
Between 15 and 55, liquid.
Between 55 and 80, relatively liquid.
Above 80, illiquid.
Nevertheless, considering the work of Amiram place the median bid-ask spread at around 85
basis points (Amiram, Cserna, & Levy, 2016), it is important to stay critical in regards to
theses thresholds.
Obviously, the bid-ask spread is representative of the first dimension of liquidity, the spread.
2.5.1.1.2. TIME TO LIQUIDATE
Considering 20% of the Average Traded Volume of all markets (on a 6 months basis), we can
compute the Time to Liquidate by using the following formula:
EQUATION 9: TIME TO LIQUIDATE
They later assign the liquidity score using custom thresholds:
TTL <= 0.01: 4.5
TTL <= 1: 3.5.
…
By taking into account the volume, the time to liquidate is representative of the second
dimension of liquidity, the depth.
2.5.1.2. CASH AND CASH EQUIVALENTS
Due to its nature, cash should always be at the top of the liquidity score. Indeed, much like
with KPMG’s model, the cash and its equivalent can always be liquidated within one trading
day.
39
2.5.1.3. BONDS AND FIXED INCOMES
2.5.1.3.1. BID-ASK SPREAD
On the same basis as the equities, we can classify the bonds based on their Bid-ask spread by
using the following thresholds:
Below 25 bps, very liquid.
Between 25 and 75, liquid.
Between 75 and 150, relatively liquid.
Above 150, illiquid.
Once again, it is representative of the first dimension of liquidity.
2.5.1.3.2. PERCENTAGE OF THE TOTAL BOND ISSUE
EQUATION 10: BOND ISSUE
Below 1%, very liquid.
Between 1 and 3%, liquid.
Between 3 and 10%, relatively liquid.
Above 10%, illiquid.
This liquidity proxy is in correlation with the work of Bao, who determined that the higher the
original issue size of bond was, the smaller was its illiquidity, and therefore, the higher was its
liquidity (Bao, Pan, & Wang, 2008).
With the quantity, this measure is representative of the second measure of liquidity, the depth.
2.5.1.3.3. RATINGS
The scoring could be influenced by the ratings in some kind of bonus/malus points on the
global rating of the asset:
AA+ and higher: +0.75.
A and higher: +0.5.
BBB+ and higher: +0.25.
BBB- and higher: no shift.
40
BB- and higher: -0.5.
B- and higher: -0.75.
CCC+ and below: -1.
Once again, we can link this estimation with Bao, who proved that the higher the rating of a
bond, the higher its liquidity (Bao, Pan, & Wang, 2008).
Furthermore, one can consider ratings as a representation of the third measure of liquidity, the
resiliency.
2.5.1.4. EXCEPTIONS
While we could replicate KPMG’s matrix for all the given exceptions, we could also try and
link a few instrument types to the equities and bonds. Indeed, for several of them, the bid-ask
spread not only stays a relevant liquidity measure, but it also is easily available through
Bloomberg. It is the case of the options, for which the bid-ask spread is also often used
(Chaudhury, 2014).
Obviously, the same principle applies to every asset with available bid-ask spread, such as,
according to trials on the Bloomberg Request Builder:
The ABS/MBS.
The Funds.
The Future on:
o Bonds.
o Commodities.
o Index.
The Treasury bills.
The Options (non-OTC ones).
The Structured products.
The Term loans.
2.5.2. STEP 2: THE TIME TO LIQUIDATE
Once the scoring of each asset is done, we will need to compute the time to liquidate for each
of them.
41
When it comes to cash, equities and closed-ended target funds, it is relatively simple to
compute the time to liquidate for each of them through the classical formula:
For the remaining assets, much like for the computation of our thresholds, it could be a good
idea to develop our own custom time to liquidate matrix for each asset type, based on its
already computed liquidity score, and based on the partner’s already computed TTL-table,
and later tweak it through trials and errors based on the historical time to liquidate generated
by the KPMG model.
A detailed table of the TTL-Scoring table can be found in Appendix 6.1.
2.5.3. STEPS 3 AND 4: LIABILITY SIDE AND STRESS TESTS
Concerning the liability side, most of the KPMG model could potentially be conserved, with
just a few adaptations. Indeed, in the end, we primarily need to evaluate the redemption level
and its coverage ratio, both in normal and stressed situations, which is already done by with
the current method.
2.5.4. COMPARISON WITH KPMG’S MODEL
In comparison with the actual model, we can find several advantages, alongside
disadvantages, to the partner’s model. Indeed, they both have their pros and cons.
2.5.4.1. KPMG’S MODEL
Pros Cons
Efficient model. Too complex.
Automatically updated by KPMG. Too conservative: there is no exception, not
even for half trading days such as the 24th
of
December.
Expensive.
Externalized: considering it isn’t internalised,
delays could potentially occurs.
TABLE 4: KPMG MODEL PROS AND CONS
42
2.5.4.2. PARTNER’S MODEL
Pros Cons
Fully internalized. Simpler.
Fitted to LAM’s needs. No support: it has to be developed and
maintained.
Representative of the market. Brand new: there is no empirical backup.
More understandable.
TABLE 5: PARTNER'S MODEL PROS AND CONS
Nevertheless, theses points stayed theoretical until the practical tests, where some of them
changed the results, as you will see.
2.5.5. IMPLEMENTATION
In order to have a practical view of the model, we tried to run it on the same test portfolios
than we used with the replication of KPMG’s model. The result was irrevocably a complete
failure. Not only did it brought back every single problems of KPMG’s model, whether we
were talking about the costs, the IT implementation, or the lack of relevant results, but it also
removed the only positive result of KPMG’s replication: the equities. Indeed, with the
partner’s model, not even the equities results were close to what we wait, as the vast majority
of them fell into buckets of exceptions, resulting in a time to liquidate often above hundreds
of days.
Moreover, it is important to note that we couldn’t even perform all of the models correctly.
Indeed, out of all the required data, only the bid-ask spread and the volumes were available
through the Bloomberg Request Builder tool. The rest of the information, such as the ratings,
were only accessible in exchange of an upgrade of our services, according to the costs found
in Appendix 7, which was obviously too expensive for a simple testing phase. Therefore, we
had to restrict our computations to the bid-ask spread mostly, which in a sense, greatly limit
the results, although in the end, the partner model proved to be a dead end, as we will explain
in the costs analysis.
This, obviously, isn’t suitable for the company. While we could have understand the necessity
of handling a few errors (indeed, information aren’t always perfect, and neither are the
markets), the fact that most of the securities appeared as ones is definitely a no-go.
43
2.5.6. COST ANALYSIS
Much like the KPMG’s model, the costs emerging from an internal model are way too high to
be profitable, even on a long run.
Indeed, as we stated before, not only are the costs of retrieving all the needed information on
Bloomberg humongous, but once again, the implementation in itself isn’t profitable.
Moreover, this approach doesn’t only rely on pricing information, but also on security master
information, which are, as we stated above, way more expensive. Indeed, for the same
assumption as the one we used with the KPMG model, 5,000 equities and 5,000 fixed
incomes, we would have to expect the following annual costs:
For the pricing information:
o $10,000 for the equities.
o $25,000 for the fixed incomes.
o $0.01 of access fee per transaction.
For the security master information:
o $50,000 for the equities.
o $70,000 for the fixed incomes.
o $0.01 of access fee per transaction.
Once again, even without computing the access fees, it is easy to see that the model is already
more expensive than the existing one.
2.6. OTHER EXISTING MODELS
During the process, other alternatives were foreseen, but in the end, none of them were worth
the tests or the implementation. Still, here is a few of the abovementioned model, and why
they weren’t suited for the company.
2.6.1. BLOOMBERG LQA
One of the other options was the Bloomberg LQA service. Basically, Bloomberg offered to
compute the liquidity risk in place of KPMG, and therefore integrate all its computations,
scores, time to liquidate, and so on, inside our package.
This way, we would benefit from an extensive liquidity analysis, available and importable in
the same way we import prices and other data.
44
As you can see below, Bloomberg attribute a liquidity score for each asset, and provide
information regarding the liquidation horizon for a given volume (the time to liquidate):
FIGURE 5: LQA GLOBAL
Moreover, several data are computed, such as the bid-ask spread, and can therefore be
retrieved directly. Here is an incomplete list of all the computed data:
FIGURE 6: LQA CODES
45
As you can see, with the current state of LAM’s Bloomberg services, none of these
information are available.
While the service in itself could potentially be a good replacement for KPMG’s model, two
main problems appeared and talked us out of even testing it: the lack of information, and the
cost.
Indeed, this model is even more closed than KPMG’s one. We don’t know anything about the
formulas used, neither the computation, nor the works on which the results are based. The
only justification that Bloomberg is willing to provide is that the model was develop by
experts in order to accommodate the vast majority of listed assets.
Moreover, the services is billed not less than $200,000 per years, as additional costs from
what LAM already pays for its pricing information.
For such a huge price, it was obvious that LAM wasn’t willing to pay without at least more
explanations on the model. Indeed, let’s not forget that one of the main objectives of an
internalisation was to be able to understand the model better, and therefore be more at ease
when talking about it with the clients.
The Bloomberg model was therefore ditched without a doubt.
2.6.2. THEORETICAL MODELS
Several other models could have been considered, but most of the time, theses theoretical
models were far away from being applicable in a real life scenario, at least in the case of a
small company looking to reduce its costs. Here are a few of the different model analysed.
2.6.2.1. MODELS TRYING TO UPGRADE THE BID-ASK SPREAD APPROACH
2.6.2.1.1. L-VAR UPGRADE
In his work, Al Janabi (Al Janabi, 2009) tries to update the classical L-VaR approach of the
bid-ask spread in order to account for the volatility of the spread. Usually, when one wants the
L-VaR to account for a consecutive period of days, we use the square root of the time
multiplier (the number of days). According to him, this methodology assumes that the
liquidation only occurs at the end of the period, all at once. Obviously, in a real life scenario,
the quantity would be progressively liquidated all along the period. That’s what Al Janabi
tried to represent with his new model.
46
While his work is worth the attention, the application of an L-VaR model in our case
wouldn’t have worked. Indeed, his model assume that we want to know the potential risk
based on a liquidation horizon, while our clients want the opposite: we want to know in how
many days the position can be liquidated while minimising the risks.
2.6.2.1.2. HIGH AND LOW BID-ASK SPREAD
Based on the fact that high prices are almost always buy orders, and low prices almost always
sell orders, Corwin (Corwin & Schultz, 2011) came up with the idea that we could estimate
the bid-ask spread based on the high and low prices of an asset. According to him, the ratio of
high-to-low prices for a day account for both the volatility of the asset, and its bid-ask spread.
While its idea is interesting and applicable, considering we already have access to the ask,
mid, and bid quantities for most of our assets, we didn’t see the utility of using an estimator
when we can compute them directly instead.
2.6.2.2. THE LOT VARIABLE
Another well-known liquidity proxy is the LOT variable, which stands for its creators,
Lesmond, Ogden, and Trzcinka (Lesmond, Ogden, & Trzcinka, 1999).
Basically, the authors argue that the proportion of zero return days can be used as a measure
of liquidity for equities markets. Indeed, according them, stocks with lower liquidity are more
likely to have days with no volume, leading to zero return days. Moreover, stocks with higher
transaction costs have less private information acquisition, leading once again to zero return
days. While this measure has been studied and modified extensively, notably by Chen (Chen,
Lesmond, & Wei, 2007), and by Zhao (Zhao & Wang, 2015), it was also proven to be pretty
hard to implement, as the resources needed for its computation are humongous (Goyenko,
Holden, & Trzcinka, 2009), and therefore not easily applicable in a real life scenario,
especially on a daily basis like our case.
2.6.2.3. MODEL COMPARISONS
As we stated before, there is three major dimensions of liquidity: the spread, the depth, and
the resilience. Nevertheless, most proxies developed in order to estimate the liquidity only
account for one of these dimensions. That’s why some authors decided to compare the most
used proxies, with the goal of finding which one was the most accurate, while still being
applicable.
47
The first one to talk about is Ernst, who decided to compare several models (Ernst, Stange, &
Kaserer, 2009), classifying them based on which data they required (spread, volume, or
weighted spread). According to her, lots of models weren’t even worth the studies, as most
models are either purely theoretical and without any empirical evidences, or relying on
intraday data, which most companies can’t afford. She finally came to the conclusion that
models are heavily influenced by the data they rely on, and that a few models should, such as
Cosandey (Cosandey, 2001) and Berkowtiz (Berkowitz, 2000) ones, should only be used if no
others is available.
The second work worth mentioning is the one realised by Goyenko, who tried to prove that
liquidity proxies are truly representative of liquidity (Goyenko, Holden, & Trzcinka, 2009). In
his paper, Goyenko compare and benchmark various well-known models, such as Amihud
(Amihud & Medelson, Asset pricing and the bid-ask spread, 1986) and the LOT variable
(Lesmond, Ogden, & Trzcinka, 1999), in order to prove that they are efficient enough to be
used. With a distinction between high frequency proxies (intraday data), and low frequency
ones, Goyenko classifies each of its measure according to the benchmark he runs. He notably
express the need of high capacity calculators for the computation of the LOT variable, but
also prove that overall, the Amihud measure stays relevant after all these years.
Finally, Schestag focused on comparing models on bond markets (Schestag, Schuster, &
Uhrig-Homburg, 2015). Once again, the authors decided to split his analysis between high
and low frequency proxies. When we focus on the low frequency ones, the one that could
have been useful in our case, Schestag find that proxies based on TRACE pricing source, the
one KPMG currently use, are the better choice.
48
49
3. CONCLUSIONS
As we stated initially, liquidity risk is an important, yet sensible subject. Indeed, we analysed
several models, in order to understand the main troubles, first from a theoretical point of view,
then from a practical point of view.
We first found that nobody agrees on a specific, universal, method to evaluate the liquidity of
an asset and the risks resulting of it. Indeed, there is several models, all claiming to be more
suited than the previous ones, but nobody agrees on them. The only constant we found was
regarding the three dimension of liquidity, and especially the bid-ask spread.
Indeed, the vast majority of the models were all partially, if not completely, focussed on the
spread. It was also the case of the actual model developed by KPMG. Therefore, it was
logical to try and found a new model that would accurately represent the dimensions, and
especially the bid-ask spread.
We now had to find the appropriate one. Indeed, with our objective of costs minimization,
high frequency proxies relying on intraday data were obviously excluded. But moreover, we
had to find a model that would suit the entirety of LAM’s portfolio. This wasn’t an easy case
considering most models either focuses on the equities or on the fixed incomes.
We then came up with the idea of a score-based model that one of LAM’s partners already
used. Nevertheless, it was important for us to first completely understand the actual model,
and to try and replicate it.
After analysing KPMG’s model and asking several questions to their staff, we replicate it the
best we could, and that’s how we encountered our first disappointment. Indeed, most of the
information needed for the model was either unavailable or way too expensive, regarding of
cost minimisation policy.
Moreover, regarding the actual implementation, the quantity of data to retrieve was
humongous, and the implementation time and costs, from an IT point of view, were
proportional and in complete opposition to our goal.
We therefore decided to try and compute the partner’s model. And once again, it was a
failure. Again, the data needed where either unavailable or too expensive. Again, the
implementation in itself was extremely expensive from an IT point of view. But in addition,
the model wasn’t even working properly. With a vast majority of exception on the bonds
50
market, and even some regarding the equities, the model was a complete failure, and certainly
not a viable solution to our problem.
Obviously, we tried to find other alternatives, but usually, the results were the same, and none
of them could satisfy our cost minimisation policy, especially not Bloomberg.
The ultimate conclusion of this project is therefore, unfortunately, that it can’t be realised. At
least not if we want to satisfy all of our objectives.
Indeed, we can either come up with our own, customized, model for equities, bonds, and
every other types of assets, or we can try to minimise our costs, but we certainly can’t do
both.
If I had to take a guess at why we can’t humanly do both, I would certainly talk about the
economies of scales.
Indeed, LAM is a relatively small company, but with lots of assets to manage. Therefore, the
company is subject to huge information costs, while, most of the time, it basically need the
data for a one time computation.
In regards, companies like Bloomberg or KPMG have multiple clients around the world, and
therefore it is not difficult for them to distribute their data costs all around their clients.
51
APPENDICES
1. LEGISLATIONS
1.1. CSSF 11/512
1.1.1. RISK MANAGEMENT
The risk management policy shall permit to evaluate market risks (including global
exposure), liquidity, counterparty as well as all other risks (including operational risks)
which may be significant for UCITS, considering the investment objectives and strategies, the
styles or methods of management (e.g. management based on an algorithm) adopted for
managing the UCITS and the processes of assessment, and which may thus directly affect the
interests of the unitholders of the managed UCITS.
(Circular 11/512, 2010).
1.1.2. LIQUIDITY RISK
Regarding more specifically liquidity risk, management companies shall, in accordance with
Article 45(3) and (4) of the CSSF Regulation, employ an appropriate liquidity risk
management process, supported, where appropriate, by a programme of stress tests, in order
to ensure that all UCITS they manage are able to comply at any time with the repurchase
obligation laid down by the 2010 Law. To this end, management companies shall ensure in
particular that the liquidity profile of the investments of the UCITS is in conformity with the
redemption policy mentioned in the fund regulation, the instruments of incorporation or the
prospectus.
[…]
The risk management policy shall be described and shall specify the risks covered. Article 43
of the CSSF Regulation refers to market, liquidity and counterparty risks, as well as all other
risks, including operational risks, which may be material for UCITS (including risks which
may be material for UCITS and which are not specifically addressed in the following sections
of this Appendix).
Determination and monitoring of liquidity risk:
1. The liquidity risk management policy shall be described.
52
2. It shall be demonstrated that the liquidity risk management policy ensures compliance
with the repurchase obligation laid down in the Law of 2010 and explained how the
liquidity profiles of the investments of the UCITS are in conformity with the
redemption policy of these UCITS.
3. Where appropriate, a description of the stress tests carried out shall be made in order
to assess the liquidity risk which UCITS are subject to in exceptional circumstances.
(CSSF, CSSF Circular 11/512, 2011)
1.2. CSSF 10-04
Article 43
Risk management policy
1. Management companies shall establish, implement and maintain an adequate
and documented risk management policy which identifies the risks the UCITS
they manage are or might be exposed to. The risk management policy shall
comprise such procedures as are necessary to enable the management company
to assess for each UCITS it manages the exposure of that UCITS to market,
liquidity and counterparty risks, and the exposure of the UCITS to all other risks,
including operational risks, which may be material for each UCITS it manages.
Management companies shall address at least the following elements in the risk
management policy:
a. the techniques, tools and arrangements that enable them to comply with the
obligations set out in Articles 45 and 46 of this Regulation;
b. the allocation of responsibilities within the management company pertaining
to risk management.
2. Management companies shall ensure that the risk management policy referred to in
paragraph (1) states the terms, contents and frequency of reporting of
the risk management function referred to in Article 13 of this Regulation to the
board of directors and to senior management and, where appropriate, to the
supervisory function.
53
3. For the purposes of paragraphs (1) and (2), management companies shall take
into account the nature, scale and complexity of their business and of the UCITS they
manage.
[..]
Article 45
Measurement and management of risk
[…]
3. Management companies shall employ an appropriate liquidity risk management
process in order to ensure that each UCITS they manage is able to comply at
any time with Article 11, paragraph (2) or Article 28, paragraph (1), point b)
of the Law of 17 December 2010 concerning undertakings for collective
investment. Where appropriate, management companies shall conduct stress tests
which enable assessment of the liquidity risk of the UCITS under exceptional
circumstances.
4. Management companies shall ensure that for each UCITS they manage the liquidity
profile of the investments of the UCITS is appropriate to the redemption policy laid
down in the management regulations or the instruments of incorporation or the
prospectus.
(CSSF, CSSF Regulation 10-4, 2010)
54
2. LAM’S INSTRUMENTS
Currently, LAM holds the following types of instruments:
ABS/MBS.
Asset backed securities.
Collateralized mortgage obligation.
Mortgage backed securities.
Bonds.
Catastrophe bond.
Convertible bond.
Corporate bond.
Cum warrant.
Government bond.
Promissory note.
Cash.
Accrued interest.
Aggregated fee.
Aggregated payable.
Aggregated receivable.
Broker cash collateral.
Broker excess cash.
Broker initial margin.
Broker variation margin.
Cash at sight.
Loan.
Spot exchange.
Term deposit.
Equity.
ADR/GDR.
Common stock.
Equities linked certificates.
Preferred share.
Unit share.
55
Fund.
Closed fund.
ETF (open).
MMF (open).
Open fund.
REIT (closed).
Future.
Future on basket.
Future on bonds.
Future on commodities.
Future on currencies.
Future on equity.
Future on index.
Future on interest rate.
Future on option.
FX Forward.
Forward exchange transaction.
Non deliverable forward.
MMI.
Certificate of deposit.
Commercial paper.
Treasury bill.
Option.
Exotic option.
LEPO warrants.
Option on basket.
Option on bond.
Option on commodities.
Option on currencies.
Option on equity.
Option on future.
Option on index.
56
Option on interest rate.
Rights.
Swaption.
Warrants.
Structured product.
Credit linked note.
Exchange traded commodity.
Exchange traded note.
Structured product.
SWAP.
Asset swap.
Contract for difference.
Credit default swap.
Credit default swap index.
Currency swap.
Equity swap.
Inflation linked swap.
Interest rate swap.
Structured swap.
Total return swap.
Variance swap.
Term Loan.
Term loan.
Unknown asset.
57
3. TEST PORTFOLIO SECURITIES
Name Type ISIN
Itv Plc Equity GB0033986497
Canadian Natl Railway Co Equity CA1363751027
Natura Cosmeticos Sa Equity BRNATUACNOR6
Berkeley Group Holdings Equity GB00B02L3W35
Lincoln Electric Holdings Equity US5339001068
Infosys Ltd-sp Adr Equity US4567881085
Csl Ltd Equity AU000000CSL8
Albemarle Corp Equity US0126531013
Sanofi Equity FR0000120578
Fast Retailing Co Ltd Equity JP3802300008
Kddi Corp Equity JP3496400007
Sgs Sa-reg Equity CH0002497458
Sundrug Co Ltd Equity JP3336600006
Bank Rakyat Indonesia Perser Equity ID1000118201
Beiersdorf Ag Equity DE0005200000
Close Brothers Group Plc Equity GB0007668071
Henkel Ag & Co Kgaa Vorzug Equity DE0006048432
Sap Se Equity DE0007164600
Coloplast-b Equity DK0060448595
Smith & Nephew Plc Equity GB0009223206
Factset Research Systems Inc Equity US3030751057
Wipro Ltd-adr Equity US97651M1099
Shoprite Holdings Ltd Equity ZAE000012084
Ramsay Health Care Ltd Equity AU000000RHC8
Unilever Nv-cva Equity NL0000009355
Singapore Telecommunications Equity SG1T75931496
Experian Plc Equity GB00B19NLV48
Broadridge Financial Solutio Equity US11133T1034
Geberit Ag-reg Equity CH0030170408
Smiths Group Plc Equity GB00B1WY2338
Vinda International Holdings Equity KYG9361V1086
Axis Bank Ltd- Gdr Reg S Equity US05462W1099
Cvs Health Corp Equity US1266501006
Capita Plc Equity GB00B23K0M20
Rockwell Automation Inc Equity US7739031091
Reed Elsevier Nv Equity NL0006144495
Truworths International Ltd Equity ZAE000028296
Bidvest Group Ltd Equity ZAE000117321
China State Construction Int Equity KYG216771363
Roche Holding Ag-genusschein Equity CH0012032048
Novartis Ag-reg Equity CH0012005267
Hennes & Mauritz Ab-b Shs Equity SE0000106270
58
Accenture Plc-cl A Equity IE00B4BNMY34
Merck & Co. Inc. Equity US58933Y1055
Christian Dior Equity FR0000130403
Sonova Holding Ag-reg Equity CH0012549785
Banco Bradesco Sa-pref Equity BRBBDCACNPR8
Sodexo Equity FR0000121220
Societe Bic Sa Equity FR0000120966
Metro Inc Equity CA59162N1096
Novozymes A/s-b Shares Equity DK0060336014
Bank Of Montreal Equity CA0636711016
Can Imperial Bk Of Commerce Equity CA1360691010
Barclays Africa Group Ltd Equity ZAE000174124
National Bank Of Canada Equity CA6330671034
Toronto-dominion Bank Equity CA8911605092
Taiwan Semiconductor-sp Adr Equity US8740391003
Fuji Heavy Industries Ltd Equity JP3814800003
Nitto Denko Corp Equity JP3684000007
Astellas Pharma Inc Equity JP3942400007
3m Co Equity US88579Y1010
Telekomunikasi Indonesia Per Equity ID1000129000
Alfa Laval Ab Equity SE0000695876
Novo Nordisk A/s-b Equity DK0060534915
Kasikornbank Pcl Equity TH0016010009
Medtronic Plc Equity IE00BTN1Y115
Next Plc Equity GB0032089863
Air Products & Chemicals Inc Equity US0091581068
Analog Devices Inc Equity US0326541051
Baxter International Inc Equity US0718131099
Becton Dickinson And Co Equity US0758871091
Campbell Soup Co Equity US1344291091
Clorox Company Equity US1890541097
Colgate-palmolive Co Equity US1941621039
Cummins Inc Equity US2310211063
Ecolab Inc Equity US2788651006
Gap Inc/the Equity US3647601083
General Mills Inc Equity US3703341046
Ww Grainger Inc Equity US3848021040
Hasbro Inc Equity US4180561072
Hershey Co/the Equity US4278661081
Intl Business Machines Corp Equity US4592001014
Intl Flavors & Fragrances Equity US4595061015
Weir Group Plc/the Equity GB0009465807
Johnson & Johnson Equity US4781601046
Kimberly-clark Corp Equity US4943681035
Mccormick & Co-non Vtg Shrs Equity US5797802064
59
Nike Inc -cl B Equity US6541061031
Northern Trust Corp Equity US6658591044
Praxair Inc Equity US74005P1049
Qualcomm Inc Equity US7475251036
Wells Fargo & Co Equity US9497461015
Robert Half Intl Inc Equity US7703231032
Sherwin-williams Co/the Equity US8243481061
Steris Corp Equity US8591521005
Sysco Corp Equity US8718291078
Texas Instruments Inc Equity US8825081040
Tiffany & Co Equity US8865471085
Tjx Companies Inc Equity US8725401090
Walt Disney Co/the Equity US2546871060
Lupatech Sa-sponsored Adr Equity US5504387094
Road King 2012 Ltd Bond XS0828764133
Pacific Emerald Pte Ltd Bond XS0955613228
Republic Of Argentina Bond US040114GM64
Itau Unibanco Hldg Sa/ky Bond US46556MAJ18
Halyk Savings Bank-kazak Bond US46627JAB08
Petrobras Global Finance Bond US71645WAN11
Trad & Dev Bank Mongolia Bond US89253YAA01
Br Malls Intl Finance Bond USG1593PAB43
Gtl Trade Finance Inc Bond USG24422AA83
Country Garden Hldg Co Bond USG24524AG84
Csn Islands Xii Bond USG2585XAA75
Gcx Ltd Bond USG37767AA13
Magnesita Finance Ltd Bond USG5768TAA81
Odebrecht Oil & Finance Bond USG6712EAA67
Yingde Gases Invstmt Ltd Bond USG9844KAB55
Andrade Gutier Int Sa Bond USL01795AA80
Mhp Sa Bond USL6366MAC75
Minerva Luxembourg Sa Bond USL6401PAC79
Minerva Luxembourg Sa Bond USL6401PAD52
Qgog Constellation Sa Bond USL7877XAA74
Cimpor Financial Opertns Bond USN20137AD23
Jababeka International Bond USN4717BAC02
Marfrig Holding Europe B Bond USN54468AD05
Aeropuertos Dominicanos Bond USP0100VAA19
Andino Invest Holding Bond USP0323NAC67
Aes El Salvador Trust Ii Bond USP06076AA49
Banco Bonsucesso Sa Bond USP07041AA72
Banco Est Rio Grande Sul Bond USP12445AA33
Camposol Sa Bond USP19189AA04
Cent Elet Brasileiras Sa Bond USP22854AG14
Credito Real Sab De Cv Bond USP32506AC43
60
Co Minera Ares Sac Bond USP3318GAA69
Empresas Ica Sociedad Bond USP37149AR55
Banco Do Brasil (cayman) Bond USP3772WAA01
Banco Do Brasil (cayman) Bond USP3772WAC66
Banco Do Brasil (cayman) Bond USP3772WAE23
Banco Do Brasil (cayman) Bond USP3772WAF97
Financiera Independencia Bond USP4173SAE48
Masisa Bond USP6460HAA34
Petroleos De Venezuela S Bond USP7807HAT25
Sixsigma Networks Mexico Bond USP8704LAA63
Usj Acucar E Alcool Sa Bond USP9634CAA91
Votorantim Cimentos Sa Bond USP98088AA83
Alam Synergy Pte Ltd Bond USY00371AA53
Alam Synergy Pte Ltd Bond USY00371AB37
Golden Legacy Pte Ltd Bond USY2749KAA89
Ottawa Holdings Pte Ltd Bond USY6589AAA44
Vingroup Jsc Bond USY9383WAB64
Yanlord Land Group Ltd Bond USY9729AAD38
Provincia De Buenos Aire Bond XS0270992380
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Gabonese Republic Bond XS1003557870
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Cap Gemini Equity FR0000125338
Carrefour Sa Equity FR0000120172
Bayer Ag-reg Equity DE000BAY0017
Ing Groep Nv-cva Equity NL0000303600
Compagnie De Saint Gobain Equity FR0000125007
Ubisoft Entertainment Equity FR0000054470
Ingenico Equity FR0000125346
L'oreal Equity FR0000120321
73
Axa Sa Bond XS0503665290
Lvmh Moet Hennessy Louis Vui Equity FR0000121014
Adidas Ag Equity DE000A1EWWW0
Intl Consolidated Airline-di Equity ES0177542018
Ubi Banca Scpa Equity IT0003487029
Unione Di Banche Italian Bond IT0001197083
Opera Software Asa Equity NO0010040611
Intl Bk Recon & Develop Bond XS0094374872
Banca Intesa Spa Bond IT0001271649
Goldman Sachs Group Inc Bond XS0212843352
Dexia Crediop Spa Bond IT0001264792
Dexia Crediop Spa Bond IT0001277406
European Investment Bank Bond XS0219808549
Depfa Acs Bank Bond DE000A0GHGN0
Deut Pfandbriefbank Ag Bond XS0083585595
Citigroup Inc Bond XS0236075908
Ayt Cedulas Cajas Global Bond ES0312298070
Assicurazioni Generali Bond XS0257010206
Assicurazioni Generali Bond XS0283627908
Real Estate Credit Inves Equity GG00B4ZRT175
Saes Getters Spa Equity IT0001029492
Ayt Cedulas Cajas Global Bond ES0312298120
Banca Popolare Di Milano Bond XS0597182665
Old Mutual Plc Bond XS0632932538
Global Bond Series 3 Bond XS0556289394
Csg Guernsey Iv Ltd Bond CH0181115681
Unicredit Spa Bond IT0004780562
Svg Capital Plc Equity GB0007892358
Republic Of Austria Bond XS0216258763
Banco Bilbao Vizcaya Arg Bond ES0413211121
Bolzoni Spa Equity IT0004027279
Elica Spa Equity IT0003404214
Bpmo 2007-1 A2 Bond IT0004215320
Mediocredito Lombardo Bond IT0001307286
D'amico International Shippi Equity LU0290697514
3i Group Plc Equity GB00B1YW4409
Enel Finance Intl Nv Bond USL2967VCY94
Royal Bk Of Scotland Plc Bond XS0357281046
Investor Ab-b Shs Equity SE0000107419
Groupama Sa Bond FR0010815464
Lbg Capital No.2 Plc Bond XS0459087986
Cert Di Credito Del Tes Bond IT0004584204
Banca Delle Marche Bond XS0302580880
Banca Monte Dei Paschi S Bond IT0004702251
Eurazeo Equity FR0000121121
74
Unipol Gruppo Finanziario Sp Equity IT0004810054
European Investment Bank Bond IT0006527052
Barclays Bank Plc Bond US06740L8C27
Citigroup Inc Bond XS0550611494
Unicredit Spa Bond IT0004803505
Unicredit Spa Bond IT0004767577
Royal Bk Scotlnd Grp Plc Bond US780097AY76
Unione Di Banche Italian Bond IT0004632862
Unicredit Spa Bond IT0004803497
Global Bond Srs Ix Sa Bond XS0619513269
Unicredit Spa Bond IT0004762586
Merrill Lynch & Co Bond IT0006602871
Citigroup Inc Bond XS0497249184
Deutsche Bank Ag London Bond DE000DB08ME7
Cajas Rurales Unidas Bond ES0215316029
Banca Nazionale Lavoro Bond IT0004920374
Banca Nazionale Lavoro Bond IT0004776438
Unicredit Spa Bond IT0004806748
Unione Di Banche Italian Bond IT0001267381
Deutsche Bank Ag Milan Bond IT0004937816
Global Bond Series Xiii Bond XS0768280751
Banco Popolare Sc Bond IT0004866551
Global Bond Series X Sa Bond XS0768280322
Global Bond Series Xiv S Bond XS0779340495
Unicredit Spa Bond IT0004806730
Axa Sa Bond XS1069439740
Unicredit Spa Bond IT0004725914
Banca Imi Spa Bond IT0001271003
Finecobank Spa Equity IT0000072170
Mediobanca Spa Bond IT0004955685
Unicredit Spa Bond IT0004918543
Unicredit Spa Bond IT0004633001
Global Bond Srs Vii Sa Bond XS0607790077
Banca Monte Dei Paschi S Bond IT0004983612
Banca Nazionale Lavoro Bond IT0004727613
Banca Nazionale Lavoro Bond IT0004854672
Global Bond Srs Viii Sa Bond XS0617319032
Ge Capital Interbanca Sp Bond IT0001304010
Banca Monte Dei Paschi S Bond IT0005029282
Buoni Poliennali Del Tes Bond IT0004917958
Unicredit Banca Spa Bond IT0004825029
Lv Friendly Soc Ltd Bond XS0935312057
Mediobanca Spa Equity IT0000062957
Mediobanca Spa Bond XS0801456244
Sogefi Equity IT0000076536
75
Buoni Poliennali Del Tes Bond IT0004969207
Banca Nazionale Lavoro Bond IT0004873151
Royal Bk Of Scotland Plc Bond NL0009289321
Assicurazioni Generali Bond XS0399861326
Buoni Poliennali Del Tes Bond IT0005012783
Ubs Ag Bond CH0244100266
Banca Imi Spa Bond IT0004845084
Officine Maccaferri Spa Bond XS1074596344
Pgh Capital Ltd Bond XS1081768738
Ams Ag Equity AT0000A18XM4
76
4. KPMG MATRIX
committm
entType
12
34
56
78
910
1112
Bon
d0,
40,
60,
80,
91
00
00
00
0
Shar
e0,
60,
80,
91
00
00
00
00
Cas
h1
00
00
00
00
00
0
Fund
0,3
0,5
0,65
0,75
0,9
10
00
00
0
Com
mod
ity
0,3
0,5
0,65
0,8
0,9
10
00
00
0
CFD
00,
10,
30,
50,
650,
750,
850,
951
00
0
Bon
dFut
ure
00
0,2
0,45
0,75
0,85
0,9
10
00
0
Bon
dFut
ureA
djus
tmen
tLeg
00
0,2
0,45
0,75
0,85
0,9
10
00
0
CD
S0
00
0,1
0,2
0,35
0,5
0,65
0,85
0,95
10
CD
SAdj
ustm
entL
eg0
00
0,1
0,2
0,35
0,5
0,65
0,85
0,95
10
FXFo
rwar
d0,
60,
750,
851
00
00
00
00
FXFo
rwar
dLeg
0,6
0,75
0,85
10
00
00
00
0
CD
Swap
tion
00
00
00,
20,
30,
450,
60,
750,
91
Forw
ard
0,2
0,35
0,5
0,65
0,8
10
00
00
0
Futu
re0,
30,
450,
650,
81
00
00
00
0
Futu
reA
djus
tmen
tLeg
0,3
0,45
0,65
0,8
10
00
00
00
TRS
00,
10,
30,
50,
650,
750,
850,
951
00
0
TRSA
djus
tmen
tLeg
00,
10,
30,
50,
650,
750,
850,
951
00
0
ILS
00
0,1
0,3
0,5
0,65
0,75
0,85
0,95
10
0
ILSA
djus
tMen
tLeg
00
0,1
0,3
0,5
0,65
0,75
0,85
0,95
10
0
IRSL
eg0,
20,
350,
50,
650,
81
00
00
00
Opt
ion
00
00
00,
20,
30,
450,
60,
750,
91
Rig
ht0
00
00
0,2
0,3
0,45
0,6
0,75
0,9
1
Swap
0,2
0,35
0,5
0,65
0,8
10
00
00
0
Swap
tion
00
0,2
0,35
0,5
0,65
0,8
10
00
0
Synt
heti
cCD
O0
00
00,
20,
350,
50,
650,
81
00
Con
vert
ible
Bon
d0,
10,
30,
50,
650,
750,
850,
951
00
00
Stru
ctur
edPr
oduc
t0
00
00,
10,
30,
50,
650,
750,
850,
951
OTC
Bon
d0
00,
20,
450,
750,
850,
91
00
00
MB
S0
00
00
0,2
0,3
0,45
0,6
0,75
0,9
1
77
5. KPMG REPLICATION
5.1. FORMULAS
5.1.1. CONVERTING RAW DATA TO USABLE ONES
=IF(RAW_ASK!B2=" ";AVERAGE(RAW_ASK!B$2:B$256);RAW_ASK!B2)
As you can see, if the value of the price (here, the Ask), is equal to a space (the value returned
by Bloomberg Request Builder when it’s missing), we will take the average of every other
values, for a given security.
5.1.2. COMPUTING THE PRICE IMPACTS
=IF(Qt!B3="Unavailable";"";((ABS(Returns!B2)*Qt!B3)/(PX_VOLUME!B3)))
If the quantity in portfolio is unavailable for a given period, the price impact won’t be
computed. If it is, it will be computed according to the following formula:
| |
5.1.3. FINDING THE QUANTILES OF THE PRICE IMPACTS
=PERCENTILE(B$2:B$255;$A262)
The quantiles are computed according to the different price impacts, and a given probability
(in $A262).
5.1.4. COMPUTING THE MAXIMUM QUANTITIES
=(SumUp!$R2)/((((SumUp!$B2-SumUp!$D2)/(SumUp!$C2*2))+SumUp!N2)*SumUp!$E2)
The maximum quantities are computed regarding the proper price impact, and according to
the following formula:
(
)
5.1.5. COMPUTING THE QUANTITIES LIQUIDATED AFTER X DAYS
=$O3+(7-1)*$P3+(15-7)*$Q3+(30-15)*$R3
78
The above example represent the computation of the quantities liquidated after 30 days: each
maximum quantity is multiplied by the number of days present in their given buckets (1 day
or less, 2-7 days, 8-15 days, 16-30 days).
5.1.6. COMPUTING THE QUANTITIES LIQUIDATED PER BUCKETS
=MIN($T3;$U3)
The quantities liquidated in one day or less are equals to the minimum between the maximum
liquidated quantities, and the quantity in portfolio at a given period of time.
=MAX(0;IF($T3>V3;V3-U3;$T3-U3))
For each buckets going from 2 to 30 days, the quantities are equals to the maximum between
0, and the difference between either:
If the total volume at a given period of time exceed the maximum quantities that could
be liquidated in X days (calculated the step before), then the difference between the
actual bucket and the previous one.
If the total volume is inferior, then the difference between it and the previous bucket.
=MAX(0;$T3-$X3)
The quantities liquidated in more than 30 days are equals to the maximum between 0, and the
remaining quantities.
79
5.2. RESULTS
Usi
ng
PER
CEN
TILE
Qu
anti
tie
s li
qu
idat
ed
of
X d
ays
Qu
anti
tie
s li
qu
idat
ed
in e
ach
bu
cke
t
Secu
rity
Qt
Max
1D
Qt
Max
7D
Qt
Max
15D
Qt
Max
30D
Vo
lum
eQ
tLi
qu
idat
ed
in 1
DLi
qu
idat
ed
in 7
DLi
qu
idat
ed
in 1
5DLi
qu
idat
ed
in 3
0DK
PM
GB
uck
ets
In 1
DIn
2-7
DIn
8-1
5DIn
16-
30D
In 3
0+D
1D %
2-7D
%8-
15D
%16
-30D
%30
+D %
GB
0033
9864
971.
275,
551.
301,
591.
397,
921.
416,
2715
.284
.962
,00
8.99
0,00
1.27
5,55
9.08
5,10
20.2
68,4
541
.512
,48
1 d
ay o
r le
ss2-
7 d
ays
1.27
5,55
7.71
4,45
0,00
0,00
0,00
14,1
9%85
,81%
0,00
%0,
00%
0,00
%
CA
1363
7510
2711
.034
,53
12.3
94,5
614
.212
,75
14.5
07,5
42.
048.
333,
0060
0,00
11.0
34,5
385
.401
,89
199.
103,
9241
6.71
7,08
1 d
ay o
r le
ss1
day
or
less
600,
000,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
BR
NA
TUA
CN
OR
65.
779,
656.
497,
128.
338,
228.
863,
722.
354.
900,
005.
000,
005.
779,
6544
.762
,35
111.
468,
0924
4.42
3,89
1 d
ay o
r le
ss1
day
or
less
5.00
0,00
0,00
0,00
0,00
0,00
100,
00%
0,00
%0,
00%
0,00
%0,
00%
GB
00B
02L3
W35
56,0
094
,59
106,
4911
7,42
555.
686,
0061
0,00
56,0
062
3,54
1.47
5,50
3.23
6,84
1 d
ay o
r le
ss2-
7 d
ays
56,0
055
4,00
0,00
0,00
0,00
9,18
%90
,82%
0,00
%0,
00%
0,00
%
US5
3390
0106
810
.868
,91
11.1
32,9
912
.288
,01
14.7
82,9
754
5.54
6,00
700,
0010
.868
,91
77.6
66,8
217
5.97
0,90
397.
715,
501
day
or
less
1 d
ay o
r le
ss70
0,00
0,00
0,00
0,00
0,00
100,
00%
0,00
%0,
00%
0,00
%0,
00%
US4
5678
8108
521
.224
,61
22.3
85,1
624
.091
,71
24.4
35,4
610
.472
.432
,00
2.91
0,00
21.2
24,6
115
5.53
5,60
348.
269,
2671
4.80
1,18
1 d
ay o
r le
ss1
day
or
less
2.91
0,00
0,00
0,00
0,00
0,00
100,
00%
0,00
%0,
00%
0,00
%0,
00%
AU
0000
00C
SL8
635,
1163
6,08
637,
5763
9,21
1.26
9.59
9,00
960,
0063
5,11
4.45
1,56
9.55
2,09
19.1
40,2
31
day
or
less
2-7
day
s63
5,11
324,
890,
000,
000,
0066
,16%
33,8
4%0,
00%
0,00
%0,
00%
US0
1265
3101
312
.245
,47
12.6
80,4
514
.732
,79
15.0
15,9
11.
366.
843,
0080
0,00
12.2
45,4
788
.328
,20
206.
190,
5043
1.42
9,14
1 d
ay o
r le
ss1
day
or
less
800,
000,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
FR00
0012
0578
22.9
50,0
623
.633
,81
24.6
07,0
425
.259
,05
3.65
3.00
4,00
620,
0022
.950
,06
164.
752,
9436
1.60
9,22
740.
494,
921
day
or
less
1 d
ay o
r le
ss62
0,00
0,00
0,00
0,00
0,00
100,
00%
0,00
%0,
00%
0,00
%0,
00%
JP38
0230
0008
1,13
1,13
1,13
1,13
814.
000,
0010
0,00
1,13
7,89
16,9
233
,86
1 d
ay o
r le
ssm
ore
th
an_3
0 d
ays
1,13
6,77
9,03
16,9
466
,14
1,13
%6,
77%
9,03
%16
,94%
66,1
4%
JP34
9640
0007
22,3
222
,61
22,6
822
,72
13.0
93.2
00,0
02.
400,
0022
,32
158,
0033
9,44
680,
251
day
or
less
mo
re t
han
_30
day
s22
,32
135,
6718
1,44
340,
811.
719,
750,
93%
5,65
%7,
56%
14,2
0%71
,66%
CH
0002
4974
5814
3,39
143,
8214
7,24
149,
4336
.956
,00
20,0
014
3,39
1.00
6,32
2.18
4,25
4.42
5,71
1 d
ay o
r le
ss1
day
or
less
20,0
00,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
JP33
3660
0006
28,3
340
,63
42,6
144
,72
627.
400,
001.
000,
0028
,33
272,
1261
2,96
1.28
3,81
1 d
ay o
r le
ss16
-30
day
s28
,33
243,
7834
0,85
387,
040,
002,
83%
24,3
8%34
,08%
38,7
0%0,
00%
ID10
0011
8201
5,38
6,12
6,57
6,77
75.0
84.9
00,0
066
.000
,00
5,38
42,1
294
,65
196,
131
day
or
less
mo
re t
han
_30
day
s5,
3836
,74
52,5
310
1,49
65.8
03,8
70,
01%
0,06
%0,
08%
0,15
%99
,70%
DE0
0052
0000
079
9,22
799,
3680
3,14
807,
2562
7.56
1,00
410,
0079
9,22
5.59
5,39
12.0
20,5
324
.129
,25
1 d
ay o
r le
ss1
day
or
less
410,
000,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
GB
0007
6680
7112
0,10
124,
1313
7,18
143,
9535
3.66
9,00
1.59
0,00
120,
1086
4,88
1.96
2,32
4.12
1,63
1 d
ay o
r le
ss8-
15 d
ays
120,
1074
4,78
725,
120,
000,
007,
55%
46,8
4%45
,61%
0,00
%0,
00%
DE0
0060
4843
21.
590,
911.
599,
941.
621,
121.
631,
7996
5.61
0,00
590,
001.
590,
9111
.190
,54
24.1
59,5
148
.636
,42
1 d
ay o
r le
ss1
day
or
less
590,
000,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
DE0
0071
6460
01.
596,
701.
597,
491.
600,
321.
602,
453.
109.
834,
0082
0,00
1.59
6,70
11.1
81,6
423
.984
,19
48.0
20,9
01
day
or
less
1 d
ay o
r le
ss82
0,00
0,00
0,00
0,00
0,00
100,
00%
0,00
%0,
00%
0,00
%0,
00%
DK
0060
4485
9579
8,24
829,
3889
8,34
918,
6847
1.04
9,00
630,
0079
8,24
5.77
4,53
12.9
61,2
126
.741
,43
1 d
ay o
r le
ss1
day
or
less
630,
000,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
GB
0009
2232
0620
1,26
205,
6821
0,52
213,
132.
724.
084,
002.
780,
0020
1,26
1.43
5,35
3.11
9,49
6.31
6,37
1 d
ay o
r le
ss8-
15 d
ays
201,
261.
234,
101.
344,
650,
000,
007,
24%
44,3
9%48
,37%
0,00
%0,
00%
US3
0307
5105
79.
216,
319.
565,
0411
.111
,41
12.7
87,5
636
0.46
1,00
200,
009.
216,
3166
.606
,55
155.
497,
8634
7.31
1,31
1 d
ay o
r le
ss1
day
or
less
200,
000,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
US9
7651
M10
996.
510,
0010
.592
,33
13.4
46,2
815
.516
,83
1.12
6.48
0,00
3.51
0,00
6.51
0,00
70.0
63,9
517
7.63
4,18
410.
386,
691
day
or
less
1 d
ay o
r le
ss3.
510,
000,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
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ays
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45,3
538
.854
,65
0,00
0,00
0,00
22,2
9%77
,71%
0,00
%0,
00%
0,00
%
IT00
0106
3210
34.3
03,5
665
.919
,84
78.6
21,9
687
.298
,83
11.4
65.6
68,0
020
.000
,00
34.3
03,5
642
9.82
2,58
1.05
8.79
8,28
2.36
8.28
0,67
1 d
ay o
r le
ss1
day
or
less
20.0
00,0
00,
000,
000,
000,
0010
0,00
%0,
00%
0,00
%0,
00%
0,00
%
IT00
0102
9492
443,
7955
0,47
1.07
4,73
1.99
8,00
11.3
53,0
010
.000
,00
443,
793.
746,
5912
.344
,40
42.3
14,4
62-
7 d
ays
8-15
day
s44
3,79
3.30
2,80
6.25
3,41
0,00
0,00
4,44
%33
,03%
62,5
3%0,
00%
0,00
%
IT00
0147
7402
1.44
8,78
3.53
5,97
6.59
4,56
11.0
09,1
132
.720
,00
60.0
00,0
01.
448,
7822
.664
,61
75.4
21,0
824
0.55
7,68
mo
re t
han
_30
day
s8-
15 d
ays
1.44
8,78
21.2
15,8
337
.335
,39
0,00
0,00
2,41
%35
,36%
62,2
3%0,
00%
0,00
%
IT00
0126
8561
6,58
256,
1562
4,40
1.05
9,64
14.2
72,0
06.
000,
006,
581.
543,
516.
538,
7022
.433
,26
8-15
day
s8-
15 d
ays
6,58
1.53
6,92
4.45
6,49
0,00
0,00
0,11
%25
,62%
74,2
7%0,
00%
0,00
%
IT00
0426
9723
1.73
7,17
3.13
7,23
10.3
71,5
820
.249
,53
33.7
64,0
090
.000
,00
1.73
7,17
20.5
60,5
710
3.53
3,24
407.
276,
25m
ore
th
an_3
0 d
ays
8-15
day
s1.
737,
1718
.823
,40
69.4
39,4
30,
000,
001,
93%
20,9
1%77
,15%
0,00
%0,
00%
IT00
0444
1603
1.04
1,26
2.52
5,39
4.58
9,47
6.64
9,54
104.
147,
0060
.000
,00
1.04
1,26
16.1
93,6
352
.909
,35
152.
652,
528-
15 d
ays
16-3
0 d
ays
1.04
1,26
15.1
52,3
636
.715
,72
7.09
0,65
0,00
1,74
%25
,25%
61,1
9%11
,82%
0,00
%
IT00
0458
5243
353,
421.
894,
305.
668,
7313
.336
,11
76.9
90,0
060
.000
,00
353,
4211
.719
,25
57.0
69,1
025
7.11
0,69
16-3
0 d
ays
16-3
0 d
ays
353,
4211
.365
,82
45.3
49,8
52.
930,
900,
000,
59%
18,9
4%75
,58%
4,88
%0,
00%
IT00
0490
0160
41,1
212
0,05
381,
1364
4,25
10.0
18,0
07.
000,
0041
,12
761,
413.
810,
4713
.474
,27
2-7
day
s16
-30
day
s41
,12
720,
293.
049,
063.
189,
530,
000,
59%
10,2
9%43
,56%
45,5
6%0,
00%
IT00
0107
7780
3,50
99,8
469
7,88
2.00
7,66
650,
0027
.851
,00
3,50
602,
526.
185,
5336
.300
,48
mo
re t
han
_30
day
s16
-30
day
s3,
5059
9,02
5.58
3,01
21.6
65,4
70,
000,
01%
2,15
%20
,05%
77,7
9%0,
00%
IT00
0006
2072
40.1
11,0
141
.814
,18
44.0
10,7
545
.125
,34
10.1
02.1
97,0
012
.000
,00
40.1
11,0
129
0.99
6,07
643.
082,
071.
319.
962,
231
day
or
less
1 d
ay o
r le
ss12
.000
,00
0,00
0,00
0,00
0,00
100,
00%
0,00
%0,
00%
0,00
%0,
00%
IT00
0006
4482
141.
304,
8777
9.99
5,66
1.20
8.34
7,13
1.48
3.11
9,03
51.6
09.5
31,0
025
0.00
0,00
141.
304,
874.
821.
278,
8114
.488
.055
,87
36.7
34.8
41,3
42-
7 d
ays
2-7
day
s14
1.30
4,87
108.
695,
130,
000,
000,
0056
,52%
43,4
8%0,
00%
0,00
%0,
00%
IT00
0492
3022
16.5
25,9
323
.088
,97
34.3
53,1
441
.547
,72
417.
728,
0013
0.00
0,00
16.5
25,9
315
5.05
9,72
429.
884,
821.
053.
100,
698-
15 d
ays
2-7
day
s16
.525
,93
113.
474,
070,
000,
000,
0012
,71%
87,2
9%0,
00%
0,00
%0,
00%
IT00
0500
2883
17.4
93,4
367
.451
,03
94.5
45,6
911
0.37
7,16
3.17
0.46
6,00
15.0
00,0
017
.493
,43
422.
199,
621.
178.
565,
142.
834.
222,
561
day
or
less
1 d
ay o
r le
ss15
.000
,00
0,00
0,00
0,00
0,00
100,
00%
0,00
%0,
00%
0,00
%0,
00%
IT00
0501
2908
522,
601.
340,
533.
468,
297.
488,
4748
.000
,00
60.0
00,0
052
2,60
8.56
5,79
36.3
12,1
514
8.63
9,23
mo
re t
han
_30
day
s16
-30
day
s52
2,60
8.04
3,19
27.7
46,3
523
.687
,85
0,00
0,87
%13
,41%
46,2
4%39
,48%
0,00
%
PTB
ES0A
M00
070,
000,
000,
000,
0042
2.72
2,55
350.
000,
000,
000,
000,
000,
001
day
or
less
mo
re t
han
_30
day
s0,
000,
000,
000,
0035
0.00
0,00
0,00
%0,
00%
0,00
%0,
00%
100,
00%
Sam
e a
s K
PM
GN
ew
is b
elo
wN
ew
is a
bo
veP
ort
foli
o11
,27%
5,18
%4,
51%
2,80
%0,
19%
82
6. PARTNER’S MODEL
6.1. SCORING TABLE
6.1.1. EQUITIES
Score BA TTL
4.5 0.9 0.01
4.4 1.8 0.02
4.3 2.7 0.04
4.2 3.6 0.05
4.1 4.5 0.06
4 5.4 0.07
3.9 7.3 0.26
3.8 9.2 0.44
3.7 11.2 0.63
3.6 13.1 0.81
3.5 15 1
3.4 17.8 1.3
3.3 20.6 1.5
3.2 23.5 1.8
3.1 26.3 2
3 29.1 2.3
2.9 34.3 2.8
2.8 39.5 3.4
2.7 44.6 3.9
.6 49.8 4.5
2.5 55 5
2.4 57.2 5.2
2.3 59.3 5.3
2.2 61.5 5.5
2.1 63.6 5.7
2 65.8 5.9
1.9 68.6 6.1
1.8 71.5 6.3
1.7 74.3 6.5
1.6 77.2 6.8
1.5 80 7
1.4 129.1 252.4
1.3 178.2 497.7
1.2 227.3 743.1
1.1 276.4 988.5
1 325.5 1233.8
0.9 358.4 5328.1
0.8 391.4 9422.3
0.7 424.3 13516.5
0.6 457.3 17610.8
0.5 490.2 21705
0.4 523.2 25799.3
0.3 556.1 29893.5
0.2 589.1 33987.8
83
0.1 622 38082
6.1.2. BONDS
Score BA Bonds Original issue size TTL (from score)
4.5 1.8 0.03 0.01
4.4 3.5 0.06 0.02
4.3 5.3 0.06 0.04
4.2 7.1 0.12 0.05
4.1 8.8 0.15 0.06
4 10.6 0.18 0.07
3.9 11.5 0.34 0.26
3.8 12.4 0.51 0.44
3.7 13.2 0.67 0.63
3.6 14.1 0.84 0.81
3.5 15 1 1
3.4 21.2 1.1 1.3
3.3 27.5 1.2 1.5
3.2 33.7 1.3 1.8
3.1 40 1.5 2
3 46.2 1.6 2.3
2.9 52 1.9 2.8
2.8 57.7 2.1 3.4
2.7 63.5 2.4 3.9
.6 69.2 2.7 4.5
2.5 75 3 5
2.4 79.4 3.6 5.2
2.3 83.9 4.1 5.3
2.2 88.3 4.7 5.5
2.1 92.8 5.3 5.7
2 97.2 5.9 5.9
1.9 107.8 6.7 6.1
1.8 118.3 7.5 6.3
1.7 128.9 8.3 6.5
1.6 139.4 9.2 6.8
1.5 150 10 7
1.4 162.6 13.9 252.4
1.3 175.2 17.9 497.7
1.2 187.7 21.8 743.1
1.1 200.3 25.7 988.5
1 212.9 29.7 1233.8
0.9 234 37.5 5328.1
0.8 255.1 45.3 9422.3
0.7 276.3 53.1 13516.5
0.6 297.4 60.9 17610.8
0.5 318.5 68.7 21705
0.4 339.6 76.6 25799.3
0.3 360.8 84.4 29893.5
0.2 381.9 92.2 33987.8
84
0.1 403 100 38082
6.2. RESULTS
6.2.1. EQUITIES
ISIN KPMG TTL PX_ASK PX_MID PX_BID BAS Score TTL
GB0033986497 1 day or less 246,1 246,05 246 4 4,1 0,06
CA1363751027 1 day or less 75,79 75,785 75,78 1,3 4,4 0,02
BRNATUACNOR6 1 day or less 19,51 19,505 19,5 5,1 4 0,07
GB00B02L3W35 1 day or less 3340 3339,5 3339 2,9 4,2 0,05
US5339001068 1 day or less 52,44 52,435 52,43 1,9 4,3 0,04
US4567881085 1 day or less 19,08 19,075 19,07 5,2 4 0,07
AU000000CSL8 1 day or less 89,3 89,09 88,88 47,2 0,6 4,5
US0126531013 1 day or less 44,09 44,085 44,08 2,2 4,3 0,04
FR0000120578 1 day or less 84,89 84,885 84,88 1,1 4,4 0,02
JP3802300008 1 day or less 48500 48420 48340 33 2,9 2,8
JP3496400007 1 day or less 2667,5 2662,25 2657 39,5 2,8 3,4
CH0002497458 1 day or less 1700 1699,5 1699 5,8 3,9 0,26
JP3336600006 1 day or less 6290 6285 6280 15,9 3,4 1,3
ID1000118201 1 day or less 8650 8638 8625 28,9 3 2,3
DE0005200000 1 day or less 79,337 79,237 79,137 25,2 3,1 2
GB0007668071 1 day or less 1494 1493,5 1493 6,6 3,9 0,26
DE0006048432 1 day or less 91,941 91,859 91,776 17,9 3,3 1,5
DE0007164600 1 day or less 58,034 57,96 57,886 25,5 3,1 2
DK0060448595 1 day or less 472,7 472,6 472,5 4,2 4,1 0,06
GB0009223206 1 day or less 1153 1152,5 1152 8,6 3,8 0,44
US3030751057 1 day or less 159,84 159,835 159,83 0,6 4,5 0,01
US97651M1099 1 day or less 12,29 12,285 12,28 8,1 3,8 0,44
ZAE000012084 1 day or less 15716 15657 15598 75,6 1,6 6,8
AU000000RHC8 1 day or less 58,75 58,5 58,25 85,8 1,4 252,4
NL0000009355 1 day or less 35,905 35,8925 35,88 6,9 3,9 0,26
SG1T75931496 1 day or less 3,6 3,595 3,59 27,8 3 2,3
GB00B19NLV48 1 day or less 1058 1057,5 1057 9,4 3,7 0,63
US11133T1034 1 day or less 55,36 55,355 55,35 1,8 4,4 0,02
CH0030170408 1 day or less 297,7 297,65 297,6 3,3 4,2 0,05
GB00B1WY2338 1 day or less 1005 1004,5 1004 9,9 3,7 0,63
KYG9361V1086 2-7 days 13,96 13,95 13,94 14,3 3,5 1
US05462W1099 1 day or less 37,9 37,8 37,7 53 2,5 5
US1266501006 1 day or less 96,49 96,485 96,48 1 4,4 0,02
GB00B23K0M20 1 day or less 1198 1197,5 1197 8,3 3,8 0,44
US7739031091 1 day or less 101,47 101,465 101,46 0,9 4,5 0,01
NL0006144495 1 day or less 14,575 14,5725 14,57 3,4 4,2 0,05
ZAE000028296 1 day or less 8502 8483 8464 44,8 0,6 4,5
85
ZAE000117321 1 day or less 10152,77 10143,27 10133,78 18,7 3,3 1,5
KYG216771363 1 day or less 11,1 11,03 10,96 127,7 1,4 252,4
CH0012032048 1 day or less 257 256,9 256,8 7,7 3,8 0,44
CH0012005267 1 day or less 89,45 89,425 89,4 5,5 3,9 0,26
SE0000106270 1 day or less 305,7 305,6 305,5 6,5 3,9 0,26
IE00B4BNMY34 1 day or less 98,19 98,185 98,18 1 4,4 0,02
US58933Y1055 1 day or less 49,37 49,365 49,36 2 4,3 0,04
FR0000130403 1 day or less 167,15 167 166,85 17,9 3,3 1,5
CH0012549785 1 day or less 125,4 125,35 125,3 7,9 3,8 0,44
BRBBDCACNPR8 1 day or less 19,473 19,464 19,455 9,2 3,8 0,44
FR0000121220 1 day or less 74 73,905 73,81 25,7 3,1 2
FR0000120966 1 day or less 138,8 138,75 138,7 7,2 3,9 0,26
CA59162N1096 1 day or less 36,42 36,39 36,36 16,5 3,4 1,3
DK0060336014 1 day or less 290,9 290,8 290,7 6,8 3,9 0,26
CA0636711016 1 day or less 72,89 72,82 72,75 19,2 3,3 1,5
CA1360691010 1 day or less 96,09 95,985 95,88 21,9 3,2 1,8
ZAE000174124 1 day or less 17020 16965,5 16911 64,4 2 5,9
CA6330671034 1 day or less 42,71 42,655 42,6 25,8 3,1 2
CA8911605092 1 day or less 52,62 52,58 52,54 15,2 3,4 1,3
US8740391003 1 day or less 20,76 20,755 20,75 4,8 4 0,07
JP3814800003 1 day or less 4283 4276,5 4270 30,4 2,9 2,8
JP3684000007 1 day or less 7126 7114 7102 33,7 2,9 2,8
JP3942400007 1 day or less 1545,5 1543 1540,5 32,4 2,9 2,8
US88579Y1010 1 day or less 141,76 141,75 141,74 1,4 4,4 0,02
ID1000129000 2-7 days 2645 2643 2640 18,9 3,3 1,5
SE0000695876 1 day or less 136,9 136,85 136,8 7,3 3,9 0,26
DK0060534915 1 day or less 358,3 358,25 358,2 2,7 4,3 0,04
TH0016010009 1 day or less 171,5 171,25 171 29,2 2,9 2,8
IE00BTN1Y115 1 day or less 66,94 66,935 66,93 1,4 4,4 0,02
GB0032089863 1 day or less 7610 7605 7600 13,1 3,6 0,81
US0091581068 1 day or less 117,9483 117,9437 117,939 0,7 4,5 0,01
US0326541051 1 day or less 56,4 56,395 56,39 1,7 4,4 0,02
US0718131099 1 day or less 32,85 32,845 32,84 3 4,2 0,05
US0758871091 1 day or less 132,65 132,64 132,63 1,5 4,4 0,02
US1344291091 1 day or less 50,69 50,685 50,68 1,9 4,3 0,04
US1890541097 1 day or less 115,54 115,535 115,53 0,8 4,5 0,01
US1941621039 1 day or less 63,48 63,475 63,47 1,5 4,4 0,02
US2310211063 1 day or less 108,57 108,56 108,55 1,8 4,4 0,02
US2788651006 1 day or less 109,72 109,71 109,7 1,8 4,4 0,02
US3647601083 1 day or less 28,5 28,495 28,49 3,5 4,2 0,05
US3703341046 1 day or less 56,12 56,115 56,11 1,7 4,4 0,02
US3848021040 1 day or less 215,01 215,005 215 0,4 4,5 0,01
US4180561072 1 day or less 72,15 72,145 72,14 1,3 4,4 0,02
US4278661081 1 day or less 91,88 91,865 91,85 3,2 4,2 0,05
US4592001014 1 day or less 144,9 144,885 144,87 2 4,3 0,04
86
US4595061015 1 day or less 103,24 103,225 103,21 2,9 4,2 0,05
GB0009465807 1 day or less 1170 1169,5 1169 8,5 3,8 0,44
US4781601046 1 day or less 93,35 93,345 93,34 1 4,4 0,02
US4943681035 1 day or less 109,04 109,035 109,03 0,9 4,5 0,01
US5797802064 1 day or less 82,17 82,165 82,16 1,2 4,4 0,02
US6541061031 1 day or less 61,48 61,475 61,47 1,6 4,4 0,02
US6658591044 1 day or less 68,17 68,155 68,14 4,4 4,1 0,06
US74005P1049 1 day or less 101,83 101,82 101,81 1,9 4,3 0,04
US7475251036 1 day or less 53,73 53,72 53,71 3,7 4,1 0,06
US9497461015 1 day or less 51,31 51,305 51,3 1,9 4,3 0,04
US7703231032 1 day or less 51,16 51,155 51,15 1,9 4,3 0,04
US8243481061 1 day or less 223 222,97 222,94 2,6 4,3 0,04
US8591521005 1 day or less 64,98 64,975 64,97 1,5 4,4 0,02
US8718291078 1 day or less 38,97 38,965 38,96 2,5 4,3 0,04
US8825081040 1 day or less 49,52 49,51 49,5 4 4,1 0,06
US8865471085 1 day or less 77,23 77,225 77,22 1,2 4,4 0,02
US8725401090 1 day or less 71,42 71,415 71,41 1,4 4,4 0,02
US2546871060 1 day or less 102,24 102,225 102,21 2,9 4,2 0,05
US5504387094 1 day or less 0,799 0,7255 0,652 2254,6 0,1 38082
CH0011432447 1 day or less 93,7 93,5 93,3 42,8 2,7 3,9
GB00B03MLX29 1 day or less 1554 1553,75 1553,5 3,2 4,2 0,05
FR0010208488 1 day or less 14,445 14,4375 14,43 10,3 3,7 0,63
FR0010242511 1 day or less 14,618 14,6017 14,5855 22,2 3,2 1,8
GB00B1XZS820 1 day or less 551,4 551,15 550,9 9 3,8 0,44
US1510201049 1 day or less 108,17 108,1 108,03 12,9 3,6 0,81
CH0038863350 1 day or less 73,3 73,275 73,25 6,8 3,9 0,26
GB0005405286 1 day or less 498,75 498,725 498,7 1 4,4 0,02
US25179M1036 1 day or less 37,09 37,085 37,08 2,6 4,3 0,04
DE0007236101 1 day or less 80,125 80,065 80,005 14,9 3,5 1
DE0005190003 1 day or less 79,489 79,354 79,22 33,9 2,9 2,8
NL0000235190 1 day or less 52,93 52,915 52,9 5,6 3,9 0,26
US92343V1044 1 day or less 43,49 43,485 43,48 2,2 4,3 0,04
CH0011075394 1 day or less 239,2 239,15 239,1 4,1 4,1 0,06
CH0025751329 1 day or less 12,75 12,725 12,7 39,3 2,8 3,4
DE000ENAG999 1 day or less 6,817 6,809 6,801 23,5 3,2 1,8
CH0043238366 1 day or less 41,27 41,255 41,24 7,2 3,9 0,26
CH0012032048 1 day or less 257 256,9 256,8 7,7 3,8 0,44
CH0012005267 1 day or less 89,45 89,425 89,4 5,5 3,9 0,26
CH0012221716 1 day or less 17,23 17,225 17,22 5,8 3,9 0,26
FR0000120172 1 day or less 26,445 26,44 26,435 3,7 4,1 0,06
DE000BAY0017 1 day or less 114,776 114,593 114,41 31,9 2,9 2,8
IT0000784154 1 day or less 6,3 6,2925 6,285 23,8 3,1 2
CH0012255151 1 day or less 361,1 361,05 361 2,7 4,3 0,04
CH0012138530 1 day or less 22,763 22,758 22,753 4,3 4,1 0,06
CA13645T1003 1 day or less 192 191,65 191,3 36,5 2,8 3,4
87
US1667641005 1 day or less 78,88 78,875 78,87 1,2 4,4 0,02
CH0126881561 1 day or less 83,65 83,625 83,6 5,9 3,9 0,26
GB0000566504 1 day or less 1005 1004,75 1004,5 4,9 4 0,07
FR0000120271 1 day or less 40,2 40,1625 40,125 18,6 3,3 1,5
FR0000120354 1 day or less 4,7607 4,7598 4,7589 3,7 4,1 0,06
AT0000A18XM4 1 day or less 36,3 36,28 36,25 13,7 3,5 1
CH0014852781 1 day or less 217,4 217,35 217,3 4,6 4 0,07
CH0008742519 1 day or less 486,2 485,95 485,7 10,2 3,7 0,63
US0325111070 1 day or less 60,39 60,385 60,38 1,6 4,4 0,02
US0378331005 1 day or less 110,29 110,285 110,28 0,9 4,5 0,01
US1491231015 1 day or less 65,36 65,355 65,35 1,5 4,4 0,02
US1912161007 1 day or less 40,12 40,115 40,11 2,4 4,3 0,04
AU000000S320 1 day or less 1,37 1,365 1,36 73,5 1,7 6,5
US3755581036 1 day or less 98,15 98,09 98,03 12,2 3,6 0,81
US4062161017 1 day or less 35,34 35,335 35,33 2,8 4,2 0,05
US4128221086 1 day or less 54,9 54,89 54,88 3,6 4,2 0,05
US7170811035 1 day or less 31,41 31,405 31,4 3,1 4,2 0,05
US7427181091 1 day or less 71,84 71,835 71,83 1,3 4,4 0,02
US7475251036 1 day or less 53,73 53,72 53,71 3,7 4,1 0,06
US9130171096 1 day or less 88,95 88,94 88,93 2,2 4,3 0,04
IT0001069902 16-30 days 6,34 6,3375 6,335 7,8 3,8 0,44
IT0001223277 more than_30 days 1,479 1,468 1,457 150,9 1,3 497,7
IT0001237053 8-15 days 0,409 0,4069 0,4048 103,7 1,4 252,4
IT0001467577 2-7 days 0,8025 0,7963 0,79 158,2 1,3 497,7
IT0003697080 2-7 days 2,842 2,804 2,766 274,7 1,1 988,5
IT0001063210 1 day or less 3,764 3,753 3,742 58,7 2,3 5,3
IT0001029492 2-7 days 4,112 4,11 4,108 9,7 3,7 0,63
IT0001477402 more than_30 days 7,155 7,1075 7,06 134,5 1,3 497,7
IT0001268561 8-15 days 0,7895 0,7867 0,784 70,1 1,8 6,3
IT0004269723 more than_30 days 7,4 7,35 7,3 136,9 1,3 497,7
IT0004441603 8-15 days 0,685 0,6827 0,6805 66,1 1,9 6,1
IT0004585243 16-30 days 0,8396 0,8248 0,8101 364,1 0,8 9422,3
IT0004781412 1 day or less 0,6935 0,6922 0,691 36,1 2,8 3,4
IT0001077780 more than_30 days 5,8194 5,7296 5,6397 318,6 1 1233,8
IT0000062072 1 day or less 1,99 1,9745 1,959 158,2 1,3 497,7
IT0000064482 2-7 days 16,36 16,35 16,34 12,2 3,6 0,81
IT0004923022 8-15 days 0,8835 0,8832 0,883 5,6 3,9 0,26
IT0005002883 1 day or less 103,745 103,685 103,625 11,5 3,6 0,81
IT0005012908 more than_30 days 9,8158 9,8121 9,8084 7,5 3,8 0,44
PTBES0AM0007 1 day or less 0,3 0,2985 0,297 101 1,4 252,4
N.A. 2-7 days 0,004 0,0033 0,0025 6000 0,1 38082
88
FR0010208488 1 day or less 14,445 14,4375 14,43 10,3 3,7 0,63
IT0003153621 more than_30 days 3,41 3,407 3,404 17,6 3,4 1,3
FR0000121485 1 day or less 146 145,975 145,95 3,4 4,2 0,05
DE0005190003 1 day or less 79,489 79,354 79,22 33,9 2,9 2,8
DE000ENAG999 1 day or less 6,817 6,809 6,801 23,5 3,2 1,8
DE000A1EWWW0 1 day or less 72,258 72,115 71,971 39,8 2,7 3,9
FR0000120271 1 day or less 40,2 40,1625 40,125 18,6 3,3 1,5
IT0000072170 2-7 days 5,945 5,9425 5,94 8,4 3,8 0,44
IT0000072618 1 day or less 3,156 3,155 3,154 6,3 3,9 0,26
IT0003324024 more than_30 days 19,9 19,475 19,05 446,1 0,6 4,5
AU000000CHC0 1 day or less 4,56 4,385 4,21 831,3 0,1 38082
US20451N1019 1 day or less 78,4 78,385 78,37 3,8 4,1 0,06
AU000000FLT9 1 day or less 36,5 36 35,5 281,6 1 1233,8
AU000000HVN7 1 day or less 3,9 3,885 3,87 77,5 1,5 7
US29364G1031 1 day or less 65,09 65,085 65,08 1,5 4,4 0,02
NO0003078800 1 day or less 157,4 157,3 157,2 12,7 3,6 0,81
HK0006000050 1 day or less 72,75 72,675 72,6 20,6 3,3 1,5
CA87971M1032 1 day or less 42,15 42,1 42,05 23,7 3,1 2
HK0066009694 2-7 days 33,65 33,6 33,55 29,8 2,9 2,8
CH0011037469 1 day or less 312,1 312 311,9 6,4 3,9 0,26
CA2935701078 1 day or less 8,24 8,205 8,17 85,6 1,4 252,4
US0028962076 1 day or less 21,2 21,195 21,19 4,7 4 0,07
US45774N1081 1 day or less 39,67 39,655 39,64 7,5 3,8 0,44
CA87971M1032 1 day or less 42,15 42,1 42,05 23,7 3,1 2
CH0012032048 1 day or less 257 256,9 256,8 7,7 3,8 0,44
CA1254911003 1 day or less 30,33 30,275 30,22 36,3 2,8 3,4
CA82028K2002 1 day or less 25,88 25,84 25,8 31 2,9 2,8
CA6252841045 1 day or less 17,89 17,87 17,85 22,4 3,2 1,8
US0012041069 1 day or less 61,04 61,03 61,02 3,2 4,2 0,05
AU000000WPL2 1 day or less 28,97 28,835 28,7 94 1,4 252,4
CA73755L1076 1 day or less 27,45 27,43 27,41 14,5 3,5 1
GB0031698896 2-7 days 350,8 350,7 350,6 5,7 3,9 0,26
AU000000ORI1 1 day or less 15,1 14,95 14,8 202,7 1,2 743,1
US2091151041 1 day or less 66,87 66,865 66,86 1,4 4,4 0,02
GB0002374006 1 day or less 1770,5 1770 1769,5 5,6 3,9 0,26
GB00BMHTPY25 1 day or less 523,5 523,25 523 9,5 3,7 0,63
US1912161007 1 day or less 40,12 40,115 40,11 2,4 4,3 0,04
US5770811025 1 day or less 21,06 21,055 21,05 4,7 4 0,07
US7170811035 1 day or less 31,41 31,405 31,4 3,1 4,2 0,05
US7427181091 1 day or less 71,84 71,835 71,83 1,3 4,4 0,02
US8101861065 2-7 days 60,83 60,825 60,82 1,6 4,4 0,02
US9134561094 1 day or less 49,61 49,595 49,58 6 3,9 0,26
IT0003497168 2-7 days 1,102 1,101 1,1 18,1 3,3 1,5
89
ES0113211835 1 day or less 7,581 7,5805 7,58 1,3 4,4 0,02
DE0007100000 1 day or less 65,328 65,244 65,16 25,7 3,1 2
IT0003849244 2-7 days 7,12 7,115 7,11 14 3,5 1
IT0003856405 2-7 days 11,2 11,195 11,19 8,9 3,8 0,44
DE000BASF111 1 day or less 68,646 68,518 68,39 37,4 2,8 3,4
FR0000120578 1 day or less 84,89 84,885 84,88 1,1 4,4 0,02
ES0113900J37 1 day or less 4,744 4,7425 4,741 6,3 3,9 0,26
ES0178430E18 1 day or less 10,835 10,8325 10,83 4,6 4 0,07
DE0007236101 1 day or less 80,125 80,065 80,005 14,9 3,5 1
FR0004024222 1 day or less 20,8273 20,6773 20,5273 146,1 1,3 497,7
FR0000131104 1 day or less 52,46 52,455 52,45 1,9 4,3 0,04
IT0001029492 16-30 days 7,155 7,1075 7,06 134,5 1,3 497,7
DE0005557508 1 day or less 15,866 15,857 15,847 11,9 3,6 0,81
DE0008404005 1 day or less 140,385 140,242 140,098 20,4 3,3 1,5
DE0005190003 1 day or less 79,489 79,354 79,22 33,9 2,9 2,8
DE0006048432 1 day or less 91,941 91,859 91,776 17,9 3,3 1,5
CH0010645932 1 day or less 1585 1584,5 1584 6,3 3,9 0,26
GB0059822006 1 day or less 35,9 35,8 35,7 56 2,4 5,2
DE0007164600 1 day or less 58,034 57,96 57,886 25,5 3,1 2
NL0000235190 1 day or less 52,93 52,915 52,9 5,6 3,9 0,26
DE0007664039 1 day or less 99,22 99,051 98,882 34,1 2,9 2,8
NL0000009538 1 day or less 21,045 21,0425 21,04 2,3 4,3 0,04
IT0001050910 1 day or less 34,6 34,59 34,58 5,7 3,9 0,26
BE0003793107 1 day or less 94,96 94,935 94,91 5,2 4 0,07
NL0000009355 1 day or less 35,905 35,8925 35,88 6,9 3,9 0,26
NL0000395903 1 day or less 27,515 27,495 27,475 14,5 3,5 1
IT0004027279 more than_30 days 3,47 3,425 3,38 266,2 1,1 988,5
FR0000127771 1 day or less 21,13 21,125 21,12 4,7 4 0,07
LU0290697514 more than_30 days 0,702 0,6988 0,6955 93,4 1,4 252,4
NL0006144495 1 day or less 14,575 14,5725 14,57 3,4 4,2 0,05
FR0000130007 2-7 days 3,28 3,2785 3,277 9,1 3,8 0,44
FR0000120628 1 day or less 21,635 21,6325 21,63 2,3 4,3 0,04
IT0003132476 1 day or less 14,05 14,045 14,04 7,1 3,9 0,26
FR0000125338 1 day or less 79,63 79,57 79,51 15 3,5 1
FR0000120172 1 day or less 26,445 26,44 26,435 3,7 4,1 0,06
DE000BAY0017 1 day or less 114,776 114,593 114,41 31,9 2,9 2,8
NL0000303600 1 day or less 13,96 13,76 13,55 302,5 1 1233,8
FR0000125007 1 day or less 38,715 38,7075 38,7 3,8 4,1 0,06
FR0000054470 2-7 days 18,12 18,115 18,11 5,5 3,9 0,26
FR0000125346 1 day or less 107,8 107,6 107,4 37,2 2,8 3,4
FR0000120321 1 day or less 155,05 155,025 155 3,2 4,2 0,05
FR0000121014 1 day or less 152,25 152,2 152,15 6,5 3,9 0,26
DE000A1EWWW0 2-7 days 72,258 72,115 71,971 39,8 2,7 3,9
90
ES0177542018 1 day or less 588 587,5 587 17 3,4 1,3
IT0003487029 1 day or less 6,34 6,3375 6,335 7,8 3,8 0,44
NO0010040611 1 day or less 45,9 45,875 45,85 10,9 3,7 0,63
GG00B4ZRT175 1 day or less 106 105 104 192,3 1,2 743,1
IT0001029492 2-7 days 7,155 7,1075 7,06 134,5 1,3 497,7
GB0007892358 8-15 days 467,1 467,05 467 2,1 4,3 0,04
IT0004027279 1 day or less 3,47 3,425 3,38 266,2 1,1 988,5
IT0003404214 2-7 days 1,843 1,828 1,813 165,4 1,3 497,7
LU0290697514 2-7 days 0,702 0,6988 0,6955 93,4 1,4 252,4
GB00B1YW4409 1 day or less 466,3 465,95 465,6 15 3,5 1
SE0000107419 1 day or less 287,3 287,2 287,1 6,9 3,9 0,26
FR0000121121 1 day or less 56,6666 56,6095 56,5523 20,2 3,3 1,5
IT0004810054 1 day or less 3,928 3,923 3,918 25,5 3,1 2
IT0000072170 2-7 days 5,945 5,9425 5,94 8,4 3,8 0,44
IT0000062957 1 day or less 8,79 8,7875 8,785 5,6 3,9 0,26
IT0000076536 2-7 days 1,832 1,8305 1,829 16,4 3,4 1,3
AT0000A18XM4 1 day or less 36,3 36,28 36,25 13,7 3,5 1
6.2.2. BONDS
ISIN KPMG TTL PX_ASK PX_MID PX_BID BAS Score TTL
XS0828764133 2-7 days 104,818 104,475 104,131 65,9 0,6 4,5
XS0955613228 2-7 days 95,249 94,59 93,93 140,4 1,5 7
US040114GM64 2-7 days 8,205 8,083 7,96 307,7 0,5 21705
US46556MAJ18 1 day or less 86,929 86,491 86,052 101,9 1,9 6,1
US46627JAB08 2-7 days 102,475 101,999 101,522 93,8 2 5,9
US71645WAN11 2-7 days 83,547 82,855 82,163 168,4 1,3 497,7
US89253YAA01 2-7 days 98,391 97,841 97,291 113 1,8 6,3
USG1593PAB43 2-7 days 85,044 82,407 79,769 661,2 0,1 38082
USG24422AA83 2-7 days 83,724 83,062 82,4 160,6 1,4 252,4
USG24524AG84 2-7 days 100,497 100,109 99,721 77,8 2,4 5,2
USG2585XAA75 8-15 days 38,829 37,537 36,244 713,2 0,1 38082
USG37767AA13 2-7 days 95,655 94,984 94,313 142,2 1,5 7
USG5768TAA81 8-15 days 70,225 67,774 65,324 750,2 0,1 38082
USG6712EAA67 16-30 days 19 19 19 0 4,5 0,01
USG9844KAB55 2-7 days 74,978 73,959 72,94 279,4 0,6 4,5
USL01795AA80 8-15 days 63,015 61,509 60,003 501,9 0,1 38082
USL6366MAC75 2-7 days 85,833 85,048 84,263 186,3 1,2 743,1
USL6401PAC79 2-7 days 88,783 88,28 87,777 114,6 1,8 6,3
USL6401PAD52 16-30 days 88,917 88,493 88,069 96,2 2 5,9
USL7877XAA74 1 day or less 47,998 45,502 43,005 1161 0,1 38082
USN20137AD23 8-15 days 69,718 68,177 66,636 462,5 0,1 38082
USN4717BAC02 2-7 days 89,166 88,644 88,122 118,4 1,7 6,5
USN54468AD05 2-7 days 86,961 86,3 85,638 154,4 1,4 252,4
USP0100VAA19 2-7 days 101,106 100,383 99,659 145,1 1,5 7
91
USP0323NAC67 2-7 days 93,063 91,784 90,504 282,7 0,6 4,5
USP06076AA49 2-7 days 91,774 90,955 90,135 181,8 1,2 743,1
USP07041AA72 2-7 days 83,293 82,12 80,947 289,8 0,6 4,5
USP12445AA33 2-7 days 78,231 76,31 74,388 516,6 0,1 38082
USP19189AA04 2-7 days 88 88 88 0 4,5 0,01
USP22854AG14 2-7 days 76,309 75,583 74,857 193,9 1,1 988,5
USP32506AC43 2-7 days 100,533 99,804 99,075 147,1 1,5 7
USP3318GAA69 2-7 days 96,194 94,957 93,719 264 0,7 13516,5
USP37149AR55 8-15 days 45,132 44,162 43,193 448,9 0,1 38082
USP3772WAA01 2-7 days 82,918 82,121 81,323 196,1 1,1 988,5
USP3772WAC66 2-7 days 61,54 60,838 60,135 233,6 0,9 5328,1
USP3772WAE23 2-7 days 82,238 81,55 80,862 170,1 1,3 497,7
USP3772WAF97 2-7 days 59,819 59,105 58,391 244,5 0,8 9422,3
USP4173SAE48 2-7 days 95,493 94,412 93,331 231,6 0,9 5328,1
USP6460HAA34 8-15 days 90,78 90,039 89,298 165,9 1,3 497,7
USP7807HAT25 2-7 days 32,886 32,471 32,056 258,9 0,7 13516,5
USP8704LAA63 2-7 days 95,418 94,487 93,556 199 1,1 988,5
USP9634CAA91 2-7 days 38,887 37,535 36,182 747,6 0,1 38082
USP98088AA83 8-15 days 75,094 74,128 73,162 264 0,7 13516,5
USY00371AA53 2-7 days 80,249 79,624 78,998 158,3 1,4 252,4
USY00371AB37 2-7 days 90,337 89,629 88,92 159,3 1,4 252,4
USY2749KAA89 2-7 days 89,317 88,81 88,304 114,7 1,8 6,3
USY6589AAA44 2-7 days 44,193 42,805 41,417 670,2 0,1 38082
USY9383WAB64 2-7 days 105,731 105,238 104,746 94 2 5,9
USY9729AAD38 2-7 days 105,09 104,666 104,241 81,4 2,3 5,3
XS0270992380 2-7 days 98,598 97,736 96,873 178 1,2 743,1
XS0290125391 2-7 days 95,178 94,6 94,022 122,9 1,7 6,5
XS0294364103 2-7 days 45,391 44,894 44,396 224,1 0,9 5328,1
XS0524610812 2-7 days 101,193 100,795 100,396 79,3 2,4 5,2
XS0552679879 2-7 days 98,119 97,635 97,15 99,7 1,9 6,1
XS0584493349 2-7 days 97,652 97,046 96,439 125,7 1,7 6,5
XS0638572973 2-7 days 100,834 100,435 100,035 79,8 2,3 5,3
XS0751016865 2-7 days 85,129 84,126 83,123 241,3 0,8 9422,3
XS0833000861 2-7 days 104,598 104,115 103,632 93,2 2 5,9
XS0872777122 2-7 days 78,82 77,969 77,118 220,7 0,9 5328,1
XS0877742105 2-7 days 100,469 100,035 99,6 87,2 2,2 5,5
XS0888948717 2-7 days 108,996 108,498 108 92,2 2,1 5,7
XS0922301717 2-7 days 95,326 94,879 94,432 94,6 2 5,9
XS0922883318 1 day or less 95,911 95,39 94,868 109,9 1,8 6,3
XS0937236783 2-7 days 95,126 94,805 94,484 67,9 0,6 4,5
XS0973119273 2-7 days 106,275 105,896 105,517 71,8 2,5 5
XS0992162635 1 day or less 92,652 91,66 90,668 218,8 0,9 5328,1
XS1003557870 2-7 days 84,092 83,291 82,489 194,3 1,1 988,5
XS1008223858 2-7 days 94,694 94,32 93,946 79,6 2,3 5,3
XS1014156274 1 day or less 103,613 103,143 102,673 91,5 2,1 5,7
92
XS1017606853 1 day or less 95,253 94,832 94,41 89,2 2,1 5,7
XS1040726587 2-7 days 94,929 94,49 94,051 93,3 2 5,9
XS1045993208 2-7 days 107,247 106,743 106,239 94,8 2 5,9
XS1057929645 2-7 days 91,905 91,017 90,129 197 1,1 988,5
XS1063367509 2-7 days 104,583 104,081 103,579 96,9 2 5,9
XS1076700175 2-7 days 100,996 100,575 100,154 84 2,2 5,5
XS1078208334 2-7 days 86,547 85,425 84,303 266,1 0,7 13516,5
XS1079702079 2-7 days 96,278 95,687 95,095 124,4 1,7 6,5
XS1080330704 2-7 days 71,236 70,87 70,503 103,9 1,9 6,1
XS1086808570 2-7 days 103,5 103,5 103,5 0 4,5 0,01
XS1090370104 2-7 days 78,161 76,979 75,796 312 0,5 21705
XS1106299586 2-7 days 104,435 103,95 103,464 93,8 2 5,9
XS1175854923 1 day or less 93,404 92,872 92,339 115,3 1,8 6,3
XS1199929826 2-7 days 76,355 75,667 74,979 183,5 1,2 743,1
XS1261826355 1 day or less 87,001 86,152 85,302 199,1 1,1 988,5
XS1273033719 1 day or less 87,693 86,923 86,154 178,6 1,2 743,1
XS0559641146 1 day or less 103,402 102,709 102,016 135,8 1,6 6,8
NL0010060257 2-7 days 112,895 112,88 112,865 2,6 4,4 0,02
XS0835302513 2-7 days 103,238 103,107 102,975 25,5 3,3 1,5
XS0873432511 1 day or less 105,926 105,467 105,007 87,5 2,2 5,5
IT0004898034 2-7 days 121,96 121,94 121,92 3,2 4,4 0,02
FR0011441831 1 day or less 91,976 91,342 90,708 139,7 1,5 7
XS0235535035 1 day or less 100,492 100,249 100,006 48,5 2,9 2,8
XS0260057285 1 day or less 102,969 102,517 102,064 88,6 2,1 5,7
PTOTEAOE0021 2-7 days 121,135 121,07 121,005 10,7 3,9 0,26
DE000A1MA9H4 1 day or less 103,916 103,729 103,541 36,2 3,1 2
US00101JAF30 1 day or less 89,426 88,815 88,203 138,6 1,6 6,8
ES00000123X3 2-7 days 120,825 120,793 120,76 5,3 4,3 0,04
CH0222437011 1 day or less 104 102,25 102 196 1,1 988,5
XS1078234330 1 day or less 94,777 93,992 93,207 168,4 1,3 497,7
XS1114452060 1 day or less 94,36 93,828 93,296 114 1,8 6,3
XS1190663952 1 day or less 96,389 96,07 95,751 66,6 0,6 4,5
XS1197351577 1 day or less 94,705 94,514 94,322 40,6 3 2,3
XS1199968998 1 day or less 100,036 99,746 99,455 58,4 2,7 3,9
XS1203941775 1 day or less 94,131 93,756 93,38 80,4 2,3 5,3
XS1214673722 1 day or less 86,592 86,042 85,491 128,7 1,7 6,5
XS1214673565 1 day or less 93,433 92,693 91,953 160,9 1,4 252,4
XS1237519571 1 day or less 99,737 99,417 99,096 64,6 0,6 4,5
XS0985874543 1 day or less 104,148 103,809 103,47 65,5 0,6 4,5
XS1020952435 1 day or less 107,995 107,697 107,399 55,4 2,8 3,4
DE000A1R0410 1 day or less 102,044 101,657 101,27 76,4 2,4 5,2
FR0011769090 1 day or less 105,098 104,796 104,494 57,8 2,7 3,9
XS1046851025 1 day or less 97,064 96,526 95,987 112,2 1,8 6,3
FR0011801596 1 day or less 84,988 84,475 83,962 122,1 1,7 6,5
XS1048568452 1 day or less 101,335 100,857 100,379 95,2 2 5,9
93
XS1061608300 1 day or less 102,943 101,955 100,966 195,8 1,1 988,5
XS1055530304 2-7 days 103,855 103,601 103,346 49,2 2,9 2,8
XS1062900912 1 day or less 101,394 101,011 100,628 76,1 2,4 5,2
FR0011965177 1 day or less 98,493 98,026 97,559 95,7 2 5,9
XS1077882121 1 day or less 101,625 101,23 100,835 78,3 2,4 5,2
XS1135334800 1 day or less 99,112 98,878 98,643 47,5 2,9 2,8
US375558AY93 1 day or less 101,038 100,826 100,614 42,1 3 2,3
XS1134780557 1 day or less 99,125 98,812 98,498 63,6 0,6 4,5
XS1167308128 1 day or less 84,908 84,348 83,788 133,6 1,6 6,8
XS1171914515 1 day or less 100,105 99,819 99,533 57,4 2,8 3,4
XS1195056079 1 day or less 96,162 95,872 95,582 60,6 2,7 3,9
XS1195202822 1 day or less 88,684 88,24 87,796 101,1 1,9 6,1
XS1204154410 1 day or less 92,099 91,78 91,461 69,7 2,5 5
XS1211040917 1 day or less 94,638 94,31 93,981 69,9 2,5 5
DE000A14J9N8 1 day or less 89,504 89,248 88,991 57,6 2,8 3,4
IT0004969207 2-7 days 0,3153 0,3134 0,3115 121,9 1,7 6,5
XS0222524372 1 day or less 92,149 91,477 90,804 148,1 1,5 7
XS0879569464 1 day or less 93,752 93,125 92,498 135,5 1,6 6,8
XS0893205186 1 day or less 96,625 96,17 95,715 95 2 5,9
XS0309688918 1 day or less 94,638 92,765 90,892 412,1 0,1 38082
XS0462994343 1 day or less 77,616 77,125 76,634 128,1 1,7 6,5
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XS1028954367 1 day or less 89,788 88,783 87,778 228,9 0,9 5328,1
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XS1079108160 1 day or less 103,519 102,673 101,827 166,1 1,3 497,7
XS1107890847 2-7 days 94,683 94,327 93,971 75,7 2,4 5,2
XS1185941850 1 day or less 92,501 92,012 91,523 106,8 1,9 6,1
XS0919581982 1 day or less 87,948 87,538 87,127 94,2 2 5,9
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XS0955232854 1 day or less 97,969 97,599 97,229 76,1 2,4 5,2
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XS0957736480 2-7 days 98,925 98,779 98,633 29,6 3,2 1,8
XS0995045951 1 day or less 101,633 100,72 99,807 182,9 1,2 743,1
XS1014703851 1 day or less 98,542 98,084 97,626 93,8 2 5,9
XS1048657800 1 day or less 42,27 40,975 39,679 652,9 0,1 38082
XS1070363343 2-7 days 91,269 90,42 89,571 189,5 1,1 988,5
DE000DB7XHP3 1 day or less 95,297 95 94,702 62,8 2,7 3,9
XS1087760648 1 day or less 105,565 105,002 104,438 107,9 1,8 6,3
XS1107291541 1 day or less 93,663 93,303 92,943 77,4 2,4 5,2
XS1115498260 1 day or less 98,956 98,484 98,012 96,3 2 5,9
XS1134541306 1 day or less 94,457 94,015 93,572 94,5 2 5,9
XS1171914515 1 day or less 100,105 99,819 99,533 57,4 2,8 3,4
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IT0005126989 2-7 days 99,649 99,64 99,631 1,8 4,5 0,01
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IT0005067266 2-7 days 100,005 100,005 100,004 0 4,5 0,01
IT0005070609 2-7 days 100,01 100,009 100,007 0,2 4,5 0,01
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IT0005089955 2-7 days 99,837 99,828 99,82 1,7 4,5 0,01
XS0645669200 2-7 days 110,51 110,373 110,235 24,9 3,3 1,5
XS0718395089 2-7 days 101,486 101,442 101,397 8,7 4,1 0,06
XS0173501379 2-7 days 111,835 111,668 111,501 29,9 3,2 1,8
XS0759014375 2-7 days 105,022 104,849 104,849 16,4 3,4 1,3
XS0746276335 2-7 days 109,718 109,581 109,444 25 3,3 1,5
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XS0803479442 2-7 days 103,22 103,17 103,12 9,6 4 0,07
XS0829209195 8-15 days 108,162 108,02 107,878 26,3 3,3 1,5
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IT0004890882 2-7 days 105,71 105,67 105,63 7,5 4,1 0,06
XS0544695272 2-7 days 107,67 107,595 107,52 13,9 3,6 0,81
IT0004734973 2-7 days 104,04 103,74 103,41 60,9 2,7 3,9
XS0563739696 2-7 days 107,105 106,951 106,796 28,9 3,2 1,8
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XS0974373515 2-7 days 103,937 103,778 103,618 30,7 3,2 1,8
XS0968913268 1 day or less 92,312 91,919 91,526 85,8 2,2 5,5
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XS0995039806 2-7 days 93,373 93,024 92,675 75,3 2,4 5,2
XS0995417846 2-7 days 101,682 101,528 101,373 30,4 3,2 1,8
XS1046272420 2-7 days 102,977 102,803 102,629 33,9 3,1 2
XS1002977103 2-7 days 103,488 103,375 103,261 21,9 3,3 1,5
XS1055725730 2-7 days 100,548 100,467 100,386 16,1 3,4 1,3
IT0005012783 2-7 days 105,595 105,545 105,495 9,4 4 0,07
XS0211637839 2-7 days 103,332 102,838 102,344 96,5 2 5,9
XS0213927667 8-15 days 99,656 99,575 99,494 16,2 3,4 1,3
XS0270800815 2-7 days 104,318 104,267 104,216 9,7 4 0,07
IT0004380546 2-7 days 109,18 109,14 109,1 7,3 4,1 0,06
XS0353643744 1 day or less 112,421 112,275 112,129 26 3,3 1,5
XS0342289575 2-7 days 111,13 111,004 110,878 22,7 3,3 1,5
IE00B28HXX02 2-7 days 113,875 113,845 113,815 5,2 4,3 0,04
XS0944435121 2-7 days 107,029 106,741 106,453 54,1 2,8 3,4
XS0494996043 2-7 days 105,92 105,74 105,63 27,4 3,3 1,5
XS0463509959 2-7 days 104,552 104,45 104,348 19,5 3,4 1,3
XS0754846235 2-7 days 105,095 103,588 104,935 15,2 3,4 1,3
XS0866278921 2-7 days 103,377 103,265 103,153 21,7 3,3 1,5
95
XS0863482336 2-7 days 105,557 105,448 105,339 20,6 3,4 1,3
XS0741942576 2-7 days 110,398 110,263 110,128 24,5 3,3 1,5
IT0004760655 2-7 days 109,022 108,897 108,772 22,9 3,3 1,5
IT0004576978 2-7 days 101,373 101,335 101,296 7,6 4,1 0,06
ES00000121L2 2-7 days 115,135 115,115 115,095 3,4 4,4 0,02
XS0456477578 2-7 days 105,366 105,058 104,75 58,8 2,7 3,9
XS0548805299 2-7 days 105,77 105,662 105,554 20,4 3,4 1,3
XS0273766732 2-7 days 104,325 104,28 104,235 8,6 4,1 0,06
XS1273507100 1 day or less 93,164 92,864 92,564 64,8 0,6 4,5
XS0452187916 1 day or less 123,595 123,284 122,972 50,6 2,9 2,8
XS0453908377 1 day or less 119,512 119,348 119,183 27,6 3,2 1,8
BE6243179650 1 day or less 108,723 108,379 108,034 63,7 0,6 4,5
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XS0629626663 2-7 days 105,502 105,143 104,783 68,6 0,6 4,5
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IT0004760655 2-7 days 109,022 108,897 108,772 22,9 3,3 1,5
XS0748187902 1 day or less 119,95 119,731 119,512 36,6 3,1 2
XS0674277933 2-7 days 105,467 105,191 104,914 52,7 2,8 3,4
XS0741942576 2-7 days 110,398 110,263 110,128 24,5 3,3 1,5
XS0806449814 2-7 days 113,637 113,465 113,293 30,3 3,2 1,8
FR0011401736 2-7 days 100,402 100,1 99,797 60,6 2,7 3,9
XS0866278921 2-7 days 103,377 103,265 103,153 21,7 3,3 1,5
XS0459410782 2-7 days 116,259 116,113 115,966 25,2 3,3 1,5
XS1004874621 2-7 days 111,644 111,3 110,955 62 2,7 3,9
FR0011694033 2-7 days 110,166 109,903 109,639 48 2,9 2,8
XS1018032950 2-7 days 109,136 108,901 108,665 43,3 3 2,3
XS1028600473 2-7 days 101,426 101,011 100,596 82,5 2,3 5,3
XS1050917373 2-7 days 97,73 97,349 96,968 78,5 2,4 5,2
FR0011462571 2-7 days 105,489 105,205 104,921 54,1 2,8 3,4
XS0954946926 2-7 days 106,206 106,057 105,908 28,1 3,2 1,8
XS0933604943 2-7 days 103,909 103,624 103,339 55,1 2,8 3,4
XS1172947902 1 day or less 87,463 86,975 86,486 112,9 1,8 6,3
XS1200670955 2-7 days 95,043 94,816 94,589 47,9 2,9 2,8
XS1169353254 2-7 days 98,142 97,962 97,782 36,8 3,1 2
XS0319639232 2-7 days 106,128 105,701 105,274 81,1 2,3 5,3
XS0306644344 2-7 days 108,339 108,208 108,077 24,2 3,3 1,5
XS0259604329 2-7 days 104,75 103,997 104,5 23,9 3,3 1,5
XS0266838746 1 day or less 103,672 103,06 103,05 60,3 2,7 3,9
XS1292988984 1 day or less 99,959 99,714 99,469 49,2 2,9 2,8
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XS1293087703 2-7 days 100,572 100,437 100,302 26,9 3,3 1,5
XS1292384960 1 day or less 99,868 99,655 99,443 42,7 3 2,3
XS1290729208 2-7 days 98,729 98,523 98,316 42 3 2,3
XS1291004270 1 day or less 100,153 99,817 99,48 67,6 0,6 4,5
96
XS1288894691 1 day or less 100,591 100,367 100,142 44,8 3 2,3
XS1075218799 1 day or less 104,819 104,568 104,317 48,1 2,9 2,8
XS1061697568 2-7 days 104,252 104,08 103,908 33,1 3,2 1,8
XS1055725730 2-7 days 100,548 100,467 100,386 16,1 3,4 1,3
XS1057486471 2-7 days 93,959 93,632 93,304 70,2 2,5 5
XS1057822766 2-7 days 100,285 100,18 100,075 20,9 3,4 1,3
XS1054528457 2-7 days 105,753 105,523 105,292 43,7 3 2,3
XS1050547857 1 day or less 105,351 105,209 105,068 26,9 3,3 1,5
XS1002977103 2-7 days 103,488 103,375 103,261 21,9 3,3 1,5
XS1040422526 2-7 days 101,33 101,219 101,107 22 3,3 1,5
XS1014627571 2-7 days 107,144 106,927 106,71 40,6 3 2,3
XS1046276504 2-7 days 101,202 101,129 101,056 14,4 3,5 1
XS1046272420 2-7 days 102,977 102,803 102,629 33,9 3,1 2
XS0974372467 2-7 days 106,352 106,208 106,064 27,1 3,3 1,5
DE000A1YCQ29 1 day or less 104,255 103,814 103,373 85,3 2,2 5,5
XS0966598061 2-7 days 109,97 109,473 108,976 91,2 2,1 5,7
XS0996354956 2-7 days 107,918 107,621 107,323 55,4 2,8 3,4
XS1001749107 1 day or less 107,498 107,267 107,035 43,2 3 2,3
XS1014674227 2-7 days 102,8 102,66 102,519 27,4 3,3 1,5
XS1014759648 1 day or less 107,091 106,843 106,594 46,6 2,9 2,8
XS1016720853 2-7 days 104,986 104,861 104,735 23,9 3,3 1,5
CH0236733827 1 day or less 105,667 105,344 105,02 61,6 2,7 3,9
XS0968913268 2-7 days 92,312 91,919 91,526 85,8 2,2 5,5
XS0974373515 2-7 days 103,937 103,778 103,618 30,7 3,2 1,8
FR0011560077 2-7 days 111,555 111,256 110,957 53,8 2,8 3,4
XS0953219416 2-7 days 106,534 106,372 106,21 30,5 3,2 1,8
XS0951216083 2-7 days 106,739 106,527 106,315 39,8 3,1 2
FR0011531631 2-7 days 106,852 106,632 106,411 41,4 3 2,3
XS1114477133 2-7 days 100,237 99,971 99,706 53,2 2,8 3,4
XS1115208107 2-7 days 100,384 100,173 99,963 42,1 3 2,3
DE000A11QR73 1 day or less 98,62 98,195 97,77 86,9 2,2 5,5
DE000A11QR65 1 day or less 98,661 98,279 97,896 78,1 2,4 5,2
XS1088129660 2-7 days 99,936 99,608 99,28 66 0,6 4,5
XS1135276332 1 day or less 98,443 98,235 98,027 42,4 3 2,3
XS1135334800 1 day or less 99,112 98,878 98,643 47,5 2,9 2,8
XS1143163183 1 day or less 99,921 99,651 99,38 54,4 2,8 3,4
FR0012939841 1 day or less 100,487 100,34 100,193 29,3 3,2 1,8
XS1288903278 1 day or less 100,436 100,289 100,141 29,4 3,2 1,8
XS1280783983 2-7 days 99,533 99,381 99,228 30,7 3,2 1,8
XS1266734349 1 day or less 99,552 99,3 99,048 50,8 2,9 2,8
XS1234248919 2-7 days 100,346 100,083 99,819 52,7 2,8 3,4
XS1241581179 2-7 days 99,734 99,528 99,321 41,5 3 2,3
XS1188117391 2-7 days 97,006 96,848 96,689 32,7 3,2 1,8
XS1188094673 2-7 days 96,842 96,643 96,445 41,1 3 2,3
XS1173845436 2-7 days 97,879 97,686 97,493 39,5 3,1 2
97
AT0000A1C741 2-7 days 100,061 99,807 99,552 51,1 2,9 2,8
XS1169353338 2-7 days 96,724 96,507 96,29 45 3 2,3
XS1167204699 1 day or less 96,145 95,911 95,678 48,8 2,9 2,8
XS1168003900 2-7 days 98,565 98,424 98,283 28,6 3,2 1,8
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XS1225004461 2-7 days 93,879 93,531 93,183 74,6 2,5 5
FR0012601367 2-7 days 93,281 93,006 92,731 59,3 2,7 3,9
XS1203854960 2-7 days 95,083 94,84 94,596 51,4 2,9 2,8
XS0540449096 2-7 days 93,235 92,522 91,809 155,3 1,4 252,4
XS0521000975 1 day or less 113,958 113,724 113,489 41,3 3 2,3
XS0608392550 2-7 days 116,99 116,657 116,323 57,3 2,8 3,4
XS0579847673 2-7 days 119,7 119,514 119,328 31,1 3,2 1,8
XS0930010524 2-7 days 99,39 99,033 98,676 72,3 2,5 5
XS0883614231 2-7 days 108,332 108,108 107,884 41,5 3 2,3
XS0903531795 2-7 days 104,863 104,517 104,17 66,5 0,6 4,5
XS0874840845 2-7 days 109,303 109,053 108,803 45,9 3 2,3
IT0004869985 2-7 days 109,231 109,017 108,802 39,4 3,1 2
XS0802638642 2-7 days 130,176 129,642 129,108 82,7 2,3 5,3
XS0834386228 2-7 days 104,741 104,448 104,155 56,2 2,8 3,4
XS0820547825 1 day or less 108,318 108,116 107,914 37,4 3,1 2
XS0826634874 1 day or less 110,596 110,316 110,035 50,9 2,9 2,8
FR0011318658 1 day or less 110,075 109,777 109,479 54,4 2,8 3,4
XS0795877454 2-7 days 112,916 112,637 112,359 49,5 2,9 2,8
IT0004808421 2-7 days 109,547 109,272 108,996 50,5 2,9 2,8
XS0767977811 1 day or less 111,334 111,057 110,779 50 2,9 2,8
XS0741137029 2-7 days 114,13 113,905 113,681 39,4 3,1 2
IT0004794142 2-7 days 110,024 109,9 109,776 22,5 3,3 1,5
XS0733696495 2-7 days 112,329 112,114 111,9 38,3 3,1 2
XS0426738976 2-7 days 120,435 120,251 120,066 30,7 3,2 1,8
XS0497179035 1 day or less 113,452 113,209 112,965 43,1 3 2,3
XS0499243300 2-7 days 113,542 113,355 113,167 33,1 3,2 1,8
BE6221503202 1 day or less 115,263 115,022 114,781 41,9 3 2,3
XS0458748851 2-7 days 104,471 104,395 104,318 14,6 3,5 1
XS0479945353 2-7 days 104,873 104,818 104,763 10,4 4 0,07
ES0000012411 2-7 days 144,11 144,045 143,98 9 4 0,07
IT0004594930 2-7 days 115,405 115,39 115,375 2,6 4,4 0,02
IT0004604671 2-7 days 110,32 110,27 110,22 9 4 0,07
XS0424787926 2-7 days 116,427 116,303 116,179 21,3 3,3 1,5
ES0000012783 2-7 days 109,795 109,783 109,77 2,2 4,4 0,02
IT0003256820 2-7 days 147,46 147,418 147,375 5,7 4,2 0,05
IT0003535157 2-7 days 137,77 137,718 137,665 7,6 4,1 0,06
XS1266734349 2-7 days 99,552 99,3 99,048 50,8 2,9 2,8
XS0429114530 2-7 days 114,288 114,182 114,076 18,5 3,4 1,3
IE0034074488 2-7 days 119,295 119,248 119,2 7,9 4,1 0,06
FR0000571150 2-7 days 149,17 149,152 149,135 2,3 4,4 0,02
98
DE0001135176 2-7 days 164,205 164,163 164,12 5,1 4,3 0,04
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IT0004793474 2-7 days 107,315 107,305 107,295 1,8 4,5 0,01
DE0001135481 2-7 days 129,135 129,087 129,04 7,3 4,1 0,06
IT0004848831 2-7 days 127,84 127,82 127,8 3,1 4,4 0,02
FI4000047089 2-7 days 108,27 108,258 108,245 2,3 4,4 0,02
XS0820548716 2-7 days 106,111 106,027 105,942 15,9 3,4 1,3
XS0802638642 2-7 days 130,176 129,642 129,108 82,7 2,3 5,3
XS0874841066 2-7 days 104,59 104,39 104,19 38,3 3,1 2
IT0004889033 2-7 days 129,78 129,733 129,685 7,3 4,1 0,06
IT0004890882 2-7 days 105,71 105,67 105,63 7,5 4,1 0,06
BE0000328378 2-7 days 112,84 112,823 112,805 3,1 4,4 0,02
IT0004898034 2-7 days 121,96 121,94 121,92 3,2 4,4 0,02
FR0011486067 2-7 days 108,825 108,815 108,805 1,8 4,5 0,01
XS0933540527 2-7 days 107,592 107,382 107,172 39,1 3,1 2
AT0000A105W3 2-7 days 109,435 109,413 109,39 4,1 4,3 0,04
XS0934983999 2-7 days 103,987 103,697 103,406 56,1 2,8 3,4
IT0004734973 2-7 days 104,04 103,74 103,41 60,9 2,7 3,9
XS0525982657 2-7 days 107,05 106,35 106,4 61 2,7 3,9
XS0671138377 2-7 days 115,557 115,397 115,237 27,7 3,2 1,8
XS0605958791 2-7 days 118,105 118,048 117,99 9,7 4 0,07
XS0540449096 2-7 days 93,235 92,522 91,809 155,3 1,4 252,4
DE0001102374 2-7 days 99,745 99,738 99,73 1,5 4,5 0,01
XS1169353338 2-7 days 96,724 96,507 96,29 45 3 2,3
FR0012517027 2-7 days 96,315 96,305 96,295 2 4,4 0,02
ES00000127H7 2-7 days 101,285 101,262 101,24 4,4 4,3 0,04
AT0000A1FAP5 2-7 days 103,02 102,998 102,975 4,3 4,3 0,04
XS1266734349 1 day or less 99,552 99,3 99,048 50,8 2,9 2,8
XS1280783983 2-7 days 99,533 99,381 99,228 30,7 3,2 1,8
XS1288903278 1 day or less 100,436 100,289 100,141 29,4 3,2 1,8
XS1146282634 2-7 days 97,585 97,315 97,044 55,7 2,8 3,4
XS1135276332 2-7 days 98,443 98,235 98,027 42,4 3 2,3
XS1139091372 1 day or less 98,424 98,257 98,089 34,1 3,1 2
XS1143163183 2-7 days 99,921 99,651 99,38 54,4 2,8 3,4
XS1077631635 2-7 days 98,297 98,03 97,764 54,5 2,8 3,4
XS0951216083 2-7 days 106,739 106,527 106,315 39,8 3,1 2
ES00000124B7 2-7 days 110,24 110,218 110,195 4 4,3 0,04
ES00000124C5 2-7 days 132,205 132,138 132,07 10,2 4 0,07
IT0004957574 2-7 days 109,85 109,84 109,83 1,8 4,5 0,01
XS0966598061 2-7 days 109,97 109,473 108,976 91,2 2,1 5,7
IT0004966401 2-7 days 114,92 114,903 114,885 3 4,4 0,02
IT0005004426 2-7 days 113,145 113,085 113,025 10,6 4 0,07
XS1054528457 2-7 days 105,753 105,523 105,292 43,7 3 2,3
XS1054418196 2-7 days 102,13 101,75 101,371 74,8 2,5 5
IT0005012783 2-7 days 105,595 105,545 105,495 9,4 4 0,07
99
FR0011883966 2-7 days 114,365 114,338 114,31 4,8 4,3 0,04
ES00000126A4 2-7 days 107,45 107,358 107,265 17,2 3,4 1,3
BE0000304130 2-7 days 156,69 156,623 156,555 8,6 4,1 0,06
XS1292384960 1 day or less 99,868 99,655 99,443 42,7 3 2,3
ES0000012932 2-7 days 122,32 122,255 122,19 10,6 4 0,07
XS0266838746 2-7 days 103,672 103,06 103,05 60,3 2,7 3,9
IT0004380546 2-7 days 109,18 109,14 109,1 7,3 4,1 0,06
ES00000121A5 2-7 days 110,47 110,453 110,435 3,1 4,4 0,02
IT0004243512 2-7 days 115,195 115,148 115,1 8,2 4,1 0,06
XS1200670955 2-7 days 95,043 94,816 94,589 47,9 2,9 2,8
XS1196373507 2-7 days 95,21 94,983 94,756 47,9 2,9 2,8
XS1238901166 1 day or less 99,506 99,288 99,07 44 3 2,3
ES00000123U9 2-7 days 126,96 126,925 126,89 5,5 4,2 0,05
XS1071713470 2-7 days 100,391 100,022 99,652 74,1 2,5 5
XS1111559768 2-7 days 99,626 99,406 99,185 44,4 3 2,3
XS1116263325 2-7 days 100,226 99,994 99,762 46,5 2,9 2,8
XS0505157965 1 day or less 137,435 137,193 136,95 35,4 3,1 2
FR0010773192 2-7 days 155,69 155,65 155,61 5,1 4,3 0,04
EU000A1GRVV3 2-7 days 118,465 118,4 118,335 10,9 3,9 0,26
XS0841073793 2-7 days 116,513 116,177 115,841 58 2,7 3,9
IE00B4TV0D44 2-7 days 137,06 136,973 136,885 12,7 3,7 0,63
IT0004760655 2-7 days 109,022 108,897 108,772 22,9 3,3 1,5
ES00000121O6 2-7 days 114,65 114,625 114,6 4,3 4,3 0,04
XS0452314536 1 day or less 124,551 124,088 123,624 74,9 2,5 5
BE0000324336 2-7 days 135,355 135,325 135,295 4,4 4,3 0,04
IT0004536949 2-7 days 115,565 115,553 115,54 2,1 4,4 0,02
BE6243179650 2-7 days 108,723 108,379 108,034 63,7 0,6 4,5
XS0544936817 2-7 days 113,119 112,969 112,818 26,6 3,3 1,5
NL0000113587 2-7 days 68,596 67,934 67,272 196,8 1,1 988,5
FR0010161026 1 day or less 64,461 63,303 62,145 372,6 0,2 33987,8
XS0747231362 2-7 days 104,15 104,15 104,15 0 4,5 0,01
XS0903872355 2-7 days 104,323 103,994 103,664 63,5 2,7 3,9
XS0906420574 1 day or less 106,02 105,476 104,931 103,7 1,9 6,1
IT0004604671 2-7 days 110,32 110,27 110,22 9 4 0,07
IT0004863608 2-7 days 102,105 102,073 102,04 6,3 4,2 0,05
XS0849517650 1 day or less 115,204 114,831 114,457 65,2 0,6 4,5
IT0004957574 2-7 days 109,85 109,84 109,83 1,8 4,5 0,01
XS1050460739 1 day or less 100,522 100,147 99,771 75,2 2,4 5,2
XS1150673892 2-7 days 95,228 94,737 94,245 104,3 1,9 6,1
XS1190663952 1 day or less 96,389 96,07 95,751 66,6 0,6 4,5
XS1219498141 2-7 days 80,132 79,561 78,989 144,7 1,5 7
IT0004917958 2-7 days 102,73 102,685 102,64 8,7 4,1 0,06
DE000A1YCQ29 2-7 days 104,255 103,814 103,373 85,3 2,2 5,5
FR0011606169 1 day or less 91,458 91,007 90,556 99,6 1,9 6,1
IT0004969207 2-7 days 103,745 103,685 103,625 11,5 3,9 0,26
100
XS1048568452 1 day or less 101,335 100,857 100,379 95,2 2 5,9
DE0001030559 2-7 days 111,97 111,795 111,62 31,3 3,2 1,8
IT0005012783 2-7 days 105,595 105,545 105,495 9,4 4 0,07
USF8586CXG25 2-7 days 92,971 92,624 92,276 75,3 2,4 5,2
XS1107291541 1 day or less 93,663 93,303 92,943 77,4 2,4 5,2
US05579T5G71 2-7 days 99,645 99,365 99,085 56,5 2,8 3,4
XS1190655776 2-7 days 95,906 95,528 95,15 79,4 2,4 5,2
XS1194054166 2-7 days 94,957 94,609 94,261 73,8 2,5 5
XS0161100515 1 day or less 129,008 128,457 127,906 86,1 2,2 5,5
US465410BS63 2-7 days 107,079 106,86 106,64 41,1 3 2,3
IT0004863608 2-7 days 102,105 102,073 102,04 6,3 4,2 0,05
IT0004669575 1 day or less 102,48 102,29 101,52 94,5 2 5,9
XS0559644322 more than_30 days 101 100,62 100,24 75,8 2,4 5,2
XS0619513269 2-7 days 99,09 98,23 97,52 160,9 1,4 252,4
IT0004854060 2-7 days 97,757 97,587 97,417 34,9 3,1 2
IT0004937816 16-30 days 102 101,73 101,15 84 2,2 5,5
IT0001267217 1 day or less 105,7 105,34 104,77 88,7 2,1 5,7
IT0004725914 1 day or less 103,556 103,318 103,079 46,2 3 2,3
IT0004774987 1 day or less 100,9 100,855 100,809 9 4 0,07
IT0004690092 16-30 days 103,27 103,23 103,18 8,7 4,1 0,06
XS0607790077 more than_30 days 100,14 100,01 99,93 21 3,4 1,3
IT0004983612 2-7 days 96,72 96,72 95,07 173,5 1,3 497,7
XS0617319032 2-7 days 97,25 96,67 96,01 129,1 1,6 6,8
IT0006718750 1 day or less 103,45 102,38 102,39 103,5 1,9 6,1
IT0004703317 2-7 days 103,8 103,4 103,4 38,6 3,1 2
IT0004719032 16-30 days 102,86 102,3 101,91 93,2 2 5,9
IT0005029282 8-15 days 95,8 95,8 95,5 31,4 3,2 1,8
IT0004738081 8-15 days 101,6 101,46 100,75 84,3 2,2 5,5
XS0564046786 2-7 days 99,35 98,79 98,31 105,7 1,9 6,1
GB00B6HZ5375 2-7 days 102,48 101,87 101,25 121,4 1,7 6,5
IT0006721630 1 day or less 102 98,95 98,45 360,5 0,3 29893,5
IT0004845084 2-7 days 104,99 103,84 103,84 110,7 1,8 6,3
IT0004991961 1 day or less 102,44 102,114 101,788 64 0,6 4,5
IT0004656275 2-7 days 100,26 100,253 100,245 1,4 4,5 0,01
IT0004712748 2-7 days 102,01 102,005 102 0,9 4,5 0,01
IT0004917792 2-7 days 101,39 101,385 101,38 0,9 4,5 0,01
XS0503665290 1 day or less 110,093 109,801 109,509 53,3 2,8 3,4
IT0001197083 more than_30 days 96,28 96,12 96,12 16,6 3,4 1,3
XS0094374872 2-7 days 163,481 160,132 160,233 202,7 1 1233,8
IT0001271649 2-7 days 111,49 111,25 111,25 21,5 3,3 1,5
XS0212843352 8-15 days 101,15 98,95 98,95 222,3 0,9 5328,1
IT0001264792 8-15 days 105,69 105,69 105,52 16,1 3,4 1,3
101
IT0001277406 16-30 days 105,98 105,69 105,7 26,4 3,3 1,5
XS0219808549 2-7 days 150 103,466 60 15000 0,1 38082
DE000A0GHGN0 16-30 days 100,315 100,3 100,285 2,9 4,4 0,02
XS0083585595 16-30 days 128,29 128,2 127,35 73,8 2,5 5
XS0236075908 2-7 days 100,082 99,821 99,559 52,5 2,8 3,4
ES0312298070 1 day or less 110,545 110,36 110,175 33,5 3,2 1,8
XS0257010206 2-7 days 101,766 101,245 100,723 103,5 1,9 6,1
XS0283627908 2-7 days 101,83 101,385 100,94 88,1 2,2 5,5
ES0312298120 1 day or less 129,905 129,575 129,245 51 2,9 2,8
XS0597182665 1 day or less 112,166 111,593 111,019 103,3 1,9 6,1
XS0632932538 1 day or less 111,526 111,1 110,674 76,9 2,4 5,2
XS0556289394 16-30 days 98,76 98,64 98,55 21,3 3,3 1,5
CH0181115681 2-7 days 105,45 105,45 105,4 4,7 4,3 0,04
IT0004780562 2-7 days 109,31 109,15 109,1 19,2 3,4 1,3
XS0216258763 2-7 days 102,561 101,351 101,361 118,3 1,8 6,3
ES0413211121 1 day or less 116,01 115,865 115,72 25 3,3 1,5
IT0004215320 2-7 days 98,86371 98,86371 98,86371 0 4,5 0,01
IT0001307286 more than_30 days 110,54 110,55 110,42 10,8 3,9 0,26
USL2967VCY94 2-7 days 108,6 108,506 108,412 17,3 3,4 1,3
XS0357281046 2-7 days 117,721 115,958 114,195 308,7 0,5 21705
FR0010815464 1 day or less 105,348 104,987 104,625 69,1 0,6 4,5
XS0459087986 2-7 days 122,209 121,781 121,353 70,5 2,5 5
IT0004584204 2-7 days 100,34 100,328 100,315 2,4 4,4 0,02
XS0302580880 1 day or less 27,831 25,082 22,333 2461,8 0,1 38082
IT0004702251 2-7 days 104,318 104,179 104,039 26,8 3,3 1,5
IT0006527052 1 day or less 129,09 129,09 127,52 123,1 1,7 6,5
US06740L8C27 2-7 days 112,991 112,584 112,177 72,5 2,5 5
XS0550611494 8-15 days 106,8 106,14 106,13 63,1 2,7 3,9
IT0004803505 16-30 days 102,004 101,937 101,869 13,2 3,7 0,63
IT0004767577 2-7 days 101,865 101,642 101,418 44 3 2,3
US780097AY76 2-7 days 107,852 107,419 106,985 81 2,3 5,3
IT0004632862 1 day or less 102,51 102,37 101,68 81,6 2,3 5,3
IT0004803497 16-30 days 101,187 101,134 101,081 10,4 4 0,07
XS0619513269 16-30 days 99,09 98,23 97,52 160,9 1,4 252,4
IT0004762586 2-7 days 100,221 100,031 99,84 38,1 3,1 2
IT0006602871 more than_30 days 101,95 101,93 101,5 44,3 3 2,3
XS0497249184 2-7 days 101,201 100,896 100,591 60,6 2,7 3,9
DE000DB08ME7 more than_30 days 99,5 99,47 99,44 6 4,2 0,05
ES0215316029 2-7 days 99,18 98,546 97,911 129,6 1,6 6,8
IT0004920374 16-30 days 99,1 98,98 98,87 23,2 3,3 1,5
IT0004776438 more than_30 days 102,05 101,07 101,07 96,9 2 5,9
IT0004806748 16-30 days 104,09 103,8 103,55 52,1 2,8 3,4
102
IT0001267381 more than_30 days 110,49 110,5 110,31 16,3 3,4 1,3
IT0004937816 more than_30 days 102 101,73 101,15 84 2,2 5,5
XS0768280751 2-7 days 94,95 92,15 92,14 304,9 0,5 21705
IT0004866551 2-7 days 104,239 103,909 103,578 63,8 0,6 4,5
XS0768280322 16-30 days 98,15 97,78 97,31 86,3 2,2 5,5
XS0779340495 16-30 days 101,32 100,98 100,61 70,5 2,5 5
IT0004806730 1 day or less 108,72 108,375 108,029 63,9 0,6 4,5
XS1069439740 1 day or less 94,414 93,959 93,503 97,4 1,9 6,1
IT0004725914 1 day or less 103,556 103,318 103,079 46,2 3 2,3
IT0001271003 16-30 days 146,95 146,95 146,61 23,1 3,3 1,5
IT0004955685 1 day or less 105,29 105,031 104,771 49,5 2,9 2,8
IT0004918543 2-7 days 103,483 103,15 102,816 64,8 0,6 4,5
IT0004633001 8-15 days 104,17 103,81 103,45 69,5 2,5 5
XS0607790077 more than_30 days 100,14 100,01 99,93 21 3,4 1,3
IT0004983612 2-7 days 96,72 96,72 95,07 173,5 1,3 497,7
IT0004727613 more than_30 days 103 102,54 102,09 89,1 2,1 5,7
IT0004854672 16-30 days 99,9 99,7 99,3 60,4 2,7 3,9
XS0617319032 2-7 days 97,25 96,67 96,01 129,1 1,6 6,8
IT0001304010 1 day or less 124,45 124 124 36,2 3,1 2
IT0005029282 2-7 days 95,8 95,8 95,5 31,4 3,2 1,8
IT0004917958 2-7 days 102,73 102,685 102,64 8,7 4,1 0,06
IT0004825029 1 day or less 111,931 111,625 111,318 55 2,8 3,4
XS0935312057 2-7 days 100,103 99,657 99,211 89,9 2,1 5,7
XS0801456244 2-7 days 96,99 97 96,5 50,7 2,9 2,8
IT0004969207 2-7 days 103,745 103,685 103,625 11,5 3,9 0,26
IT0004873151 more than_30 days 100,15 100,15 99,83 32 3,2 1,8
NL0009289321 2-7 days 109,498 108,934 108,37 104 1,9 6,1
XS0399861326 2-7 days 111,14 110,594 110,047 99,3 1,9 6,1
IT0005012783 2-7 days 105,595 105,545 105,495 9,4 4 0,07
CH0244100266 1 day or less 98,902 98,551 98,2 71,4 2,5 5
IT0004845084 1 day or less 104,99 103,84 103,84 110,7 1,8 6,3
XS1074596344 1 day or less 97,265 96,61 95,955 136,5 1,6 6,8
XS1081768738 2-7 days 109,621 109,356 109,09 48,6 2,9 2,8
103
7. BLOOMBERG BILLING TABLE
7.1. SECURITY MASTER
104
7.2. PRICING
105
7.3. SNAPSHOT
106
7.4. DERIVED FIELDS
107
7.5. HISTORICAL TIME SERIES
108
7.6. OTHERS
109
7.7. AD HOC FEE
110
111
RESOURCE PERSONS
UNIVERSITY
Marie Lambert, supervisor.
Boris Fays, reader.
COMPANY
Jennifer Collin, risk manager.
Jean-Philippe Claessens, head of risk.
Gilles Roland, IT department.
112
113
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117
TABLE OF CONTENTS
Acknowledgements .................................................................................................................... 1
Abstract ...................................................................................................................................... 3
Glossary ...................................................................................................................................... 5
1. Introduction ............................................................................................................................ 7
1.1. Background information ................................................................................................. 7
1.2. Regulatory requirements ................................................................................................. 7
1.3. Current liquidity model and internalization process ....................................................... 8
1.4. Cost analysis .................................................................................................................... 9
1.5. Internal data base and retrieving process ........................................................................ 9
1.5.1. Global feeding procedure ......................................................................................... 9
1.5.2. Liquidity reports feeding procedure ....................................................................... 10
1.6. Lemanik’s portfolio overview ....................................................................................... 11
2. Exposition ............................................................................................................................. 13
2.1. Structured project management approach ..................................................................... 13
2.2. Generalities about liquidity risk .................................................................................... 15
2.3. Test portfolio ................................................................................................................. 17
2.4. KPMG model ................................................................................................................ 18
2.4.1. Generated report ..................................................................................................... 18
2.4.2. Practical test ........................................................................................................... 20
2.4.2.1. On the asset side .............................................................................................. 20
2.4.2.2. On the liability side ......................................................................................... 20
2.4.2.3. Stress tests ....................................................................................................... 21
2.4.3. Theoretical model ................................................................................................... 21
2.4.3.1. Asset side ......................................................................................................... 21
2.4.3.1.1. Equities ..................................................................................................... 22
2.4.3.1.2. Bonds ........................................................................................................ 24
118
2.4.3.1.3. Other type of assets .................................................................................. 25
2.4.3.2. Liabilities side ................................................................................................. 26
2.4.3.3. Stress tests ....................................................................................................... 27
2.4.3.3.1. On the asset side ....................................................................................... 27
2.4.3.3.2. On the liability side .................................................................................. 27
2.4.4. Replicating the model by ourselves ....................................................................... 27
2.4.4.1. Replication procedure ..................................................................................... 28
2.4.4.1.1. Collecting data .......................................................................................... 28
2.4.4.1.2. Processing the data ................................................................................... 29
2.4.4.1.3. Computing the remaining data ................................................................. 29
2.4.4.1.4. Computing the quantities ......................................................................... 30
2.4.4.1.5. Assigning securities to buckets ................................................................ 31
2.4.4.1.6. Computing buckets percentages ............................................................... 32
2.4.4.2. Results ............................................................................................................. 32
2.4.4.2.1. For the equities ......................................................................................... 32
2.4.4.2.2. For the bonds ............................................................................................ 33
2.4.4.3. Cost analysis .................................................................................................... 33
2.4.4.3.1. Data costs ................................................................................................. 33
2.4.4.3.2. IT costs ..................................................................................................... 34
2.5. LAM’s Partner model .................................................................................................... 36
2.5.1. Step 1: The scoring ................................................................................................. 37
2.5.1.1. Equities and equities like ................................................................................. 37
2.5.1.1.1. Bid-ask spread .......................................................................................... 37
2.5.1.1.2. Time to liquidate ...................................................................................... 38
2.5.1.2. Cash and cash equivalents ............................................................................... 38
2.5.1.3. Bonds and fixed incomes ................................................................................ 39
2.5.1.3.1. Bid-ask spread .......................................................................................... 39
119
2.5.1.3.2. Percentage of the total bond issue ............................................................ 39
2.5.1.3.3. Ratings ...................................................................................................... 39
2.5.1.4. Exceptions ....................................................................................................... 40
2.5.2. Step 2: The Time to Liquidate ................................................................................ 40
2.5.3. Steps 3 and 4: Liability side and Stress tests .......................................................... 41
2.5.4. Comparison with KPMG’s model .......................................................................... 41
2.5.4.1. KPMG’s model ............................................................................................... 41
2.5.4.2. Partner’s model ............................................................................................... 42
2.5.5. Implementation ....................................................................................................... 42
2.5.6. Cost analysis ........................................................................................................... 43
2.6. Other existing models .................................................................................................... 43
2.6.1. Bloomberg LQA ..................................................................................................... 43
2.6.2. Theoretical models ................................................................................................. 45
2.6.2.1. Models trying to upgrade the bid-ask spread approach ................................... 45
2.6.2.1.1. L-VaR upgrade ......................................................................................... 45
2.6.2.1.2. High and Low bid-ask spread ................................................................... 46
2.6.2.2. The LOT variable ............................................................................................ 46
2.6.2.3. Model comparisons ......................................................................................... 46
3. Conclusions .......................................................................................................................... 49
Appendices ............................................................................................................................... 51
1. Legislations ...................................................................................................................... 51
1.1. CSSF 11/512 ............................................................................................................. 51
1.1.1. Risk management ............................................................................................... 51
1.1.2. Liquidity risk ...................................................................................................... 51
1.2. CSSF 10-04 ............................................................................................................... 52
2. LAM’s instruments .......................................................................................................... 54
3. Test portfolio securities .................................................................................................... 57
120
4. KPMG Matrix .................................................................................................................. 76
5. KPMG replication ............................................................................................................ 77
5.1. Formulas .................................................................................................................... 77
5.1.1. Converting raw data to usable ones .................................................................... 77
5.1.2. Computing the price impacts .............................................................................. 77
5.1.3. Finding the quantiles of the price impacts ......................................................... 77
5.1.4. Computing the maximum quantities .................................................................. 77
5.1.5. Computing the quantities liquidated after X days .............................................. 77
5.1.6. Computing the quantities liquidated per buckets ............................................... 78
5.2. Results ....................................................................................................................... 79
6. Partner’s model ................................................................................................................ 82
6.1. Scoring table .............................................................................................................. 82
6.1.1. Equities ............................................................................................................... 82
6.1.2. Bonds .................................................................................................................. 83
6.2. Results ....................................................................................................................... 84
6.2.1. Equities ............................................................................................................... 84
6.2.2. Bonds .................................................................................................................. 90
7. Bloomberg billing table .................................................................................................. 103
7.1. Security master ........................................................................................................ 103
7.2. Pricing ..................................................................................................................... 104
7.3. Snapshot .................................................................................................................. 105
7.4. Derived fields .......................................................................................................... 106
7.5. Historical time series ............................................................................................... 107
7.6. Others ...................................................................................................................... 108
7.7. Ad Hoc Fee .............................................................................................................. 109
Resource persons .................................................................................................................... 111
University ........................................................................................................................... 111
121
Company ............................................................................................................................ 111
Bibliography ........................................................................................................................... 113
RÉSUMÉ
Ce papier a pour but d’analyser un modèle de risque de liquidité existant, et de tenter de le
reproduire puis de le remplacer par un nouveau, développé sur mesure et en interne.
Tout d’abord, il fallut comprendre l’idée générale se cachant derrière le risque de liquidité, et
qu’elles en étaient les contraintes légales.
Ensuite, nous avons étudié en détail le modèle existant, afin de le reproduire le plus
fidèlement possible, avant de développer un nouveau que nous avons tenté d’implémenter.
En parallèle, nous avons analysé d’autres modèles qui, bien qu’inutiles dans notre cas, nous
ont étés d’une grande aide dans la compréhension du problème.
Enfin, nous tirons des conclusions de nos résultats pour le moins nuances, en ce qui concerne
la mise en place potentielle d’un tel modèle, et son utilité vis-à-vis de ses coûts.
MOTS CLÉS
Bid-ask spread.
Bloomberg.
Bond.
Equity.
Fixed income.
Horizon de liquidation.
Liquidité.
Mémoire projet.
Risque de liquidité.
Time to liquidate.
Value at Risk.
EXECUTIVE SUMMARY
In this paper, we analysed a given liquidity risk model, and tried to replicate and replace it
with a new, customised, and internal one.
We had to first understand the whole idea behind the risk of liquidity, and what were the legal
restrictions around it.
Then, we made sure to analyse deeply the actual model, and we replicated it as best as we
could, before trying to come up with a new one, that we also tried to implement.
In parallel, we analysed other models that, while useless in our situation, were of great help to
get a better all-around view of the problem.
We further draw conclusions towards our conflicting results regarding the potential
implementation of a new model, and its real utility considering its cost.
KEYWORDS
Bid-ask spread.
Bloomberg.
Bond.
Equity.
Fixed income.
Liquidation horizon.
Liquidity risk.
Liquidity.
Project thesis.
Time to liquidate.
Value at Risk.