Key determinants of the shadow banking system álvaro álvarez campana

BACHELOR THESIS
KEY DETERMINANTS OF THE
SHADOW BANKING SYSTEM
THE CASES OF EURO AREA, UNITED KINGDOM AND
UNITED STATES.
Author: Álvaro Álvarez-Campana Rodríguez
Tutor: David Martínez-Miera
Bachelor: Business Administration
Madrid, June 2016

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ABSTRACT
This paper presents two models of key determinants in the evolution of the shadow
banking system. First of all, a shadow banking measure is built from a European
perspective. Secondly, information on several variables is retrieved basing their selection
in previous literature. Thirdly, those variables are grouped in: 1) the base model: real
GDP, Institutional investors’ assets, term-spread, banks’ net interest margin and liquidity;
and 2) the extended model: the former five plus an indicator of systemic stress, an index
of banking concentration and inflation. Finally, regression analysis on those models is
conducted for different countries’ samples. Both OLS and panel data analysis is
undergone. Results suggest important and consistent geographical differences in relations
between shadow banking and key determinant variables’ effects. Thus, this essay
provides financial authorities with a valuable benchmark to which they should pay
attention before designing optimal policies seeking to reduce the financial risk that
shadow banking entails.
Keywords: shadow banking, financial stability, banking crisis, great recession, key
determinants.
JEL Classification: G21, G23
I am very grateful to David Martinez-Miera for his guidance and advice, Silvia Mayoral Blaya
for her assistance, and Mar García, Irene Rodríguez, Jesús Álvarez-Campana and Pablo Álvarez-
Campana for their inestimable support.

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Table of contents.
ABSTRACT...................................................................................................................................... 2
I. INTRODUCTION..................................................................................................................... 4
I.1 Why is Shadow Banking System so relevant?...................................................................... 4
I.2 What is this paper’s contribution?....................................................................................... 5
II. WHAT IS SHADOW BANKING?............................................................................................... 7
III. LITERATURE REVIEW........................................................................................................... 11
III.1 First research on SBS........................................................................................................ 11
III.2 SBS definition. .................................................................................................................. 11
III.3 SBS measure..................................................................................................................... 13
III.4 SBS determinants............................................................................................................. 15
III.5 Latest research on SBS. .................................................................................................... 16
IV. DATA AND METHODOLOGY ............................................................................................... 18
IV.1 Framework....................................................................................................................... 18
IV.2 Data description............................................................................................................... 19
IV.3 Models and extensions. ................................................................................................... 24
IV.4 Limitations........................................................................................................................ 26
V. RESULTS.............................................................................................................................. 28
V.1 Cross-sectional data approach.......................................................................................... 28
V.1.1 Whole-sample base and extended model analysis.................................................... 28
V.1.2 Core base model analysis........................................................................................... 29
V.2 Panel data approach. ........................................................................................................ 30
V.2.1 Whole-sample base and extended model analysis.................................................... 30
V.2.2 Core base model analysis........................................................................................... 31
V.2.3 Core extended model analysis................................................................................... 34
VI. CONCLUSIONS ................................................................................................................... 36
REFERENCES ................................................................................................................................ 38
ABBREVIATIONS .......................................................................................................................... 41
APPENDICES ................................................................................................................................ 42

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I. INTRODUCTION
I.1 Why is Shadow Banking System so relevant?
After the recent global financial crisis, authorities are concerned about the role that the
shadow banking plays in promoting the risk build up in the financial system. This process
of increasing the risk hinders financial stability, and it could facilitate the appearance of
spill-overs to other macroeconomic sectors. This worry within the international financial
institutions can be seen in some papers published by the European Central Bank (ECB),
the International Monetary Fund (IMF), or even the Federal Reserve Bank (FRB).
One of the most relevant claims of the ECB is that the interconnections between the
regulated and the non-regulated segments of the financial sector have increased.
Moreover, Basel III Accords on stringing capital and liquidity requirements for credit
institutions, would provide the former with more incentives to shift their financial
activities into the hardly regulated shadow banking system (SBS).1
According to this vein,
the Managing Director of the IMF, Christine Lagarde, shows her concern about the rapid
escalation of the less-regulated nonbank sector. She also states the necessity to unveil the
real functioning of this part of the financial system hidden in the shadows. However, she
mentioned the importance of SBS in the process of financing the economy which can
complement positively the traditional system.2
Credit intermediation has always existed outside the regulated banking system. The recent
rise of the concerns and the interest in sizing and understanding the shadow banking
system is closely related to the 2008 worldwide financial crisis (“great recession”). The
housing boom in the United States in early 2000s was mainly financed through the
originate-to-distribute model of banking. In this particular case, the majority of
securitization was made through asset-backed securities (ABS3
), and more precisely,
through a special type of ABS called mortgage backed securities (MBS4
) serving as
collateral.5
These securities’ usage as collateral was pretty new at that moment. It was
1
See Bakk-Simon et al. (2012).
2
See Lagarde (2014).
3
Deutsche Bundesbank defines an asset-backed security (ABS) as a security which is backed by credit claims, such as
building loans, car loans or credit card receivables.
4
Deutsche Bundesbank defines a mortgage-backed security (MBS) as securities that are backed by a pool of mortgage
loans. They are divided into commercial and residential depending on the type of underlying loan.
5
European Central Bank defines collateral as an asset or third-party commitment that is used by a provider to secure
an obligation vis-à-vis a taker.

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prompted and fostered due to the heavy demand for collateral in the repo market6
during
the years before the financial crisis. Previous to this increase on collateral demand in the
repo market, the main collateral instrument were Treasury securities, which means that
lenders did not have to worry about borrower´s default risk or the price of the underlying
collateral.7
But the optimism towards real estate prices changed all of a sudden in 2007,
when opposite to investors’ expectations, house prices fell for several consecutive
months. The fear appeared at first became panic. Repo lenders ran on repo borrowers,
including the riskless ones, and refused to renew their loans provoking the repo market
to freeze.8
This fact contributed to a systemic event of worldwide dimensions which
prompted a deep financial crisis.
As we can see above, shadow banking topic has become a central point of study for the
financial experts and institutions. The general aim seeks to monitor the behaviour of the
SBS and to control the potential negative outcomes, thus avoiding a new worldwide
contagion effect. This issue due to its novelty and relevance for the “great recession” has
positioned itself at the vanguard of the financial researching world. Such an importance
can be noticed in the lately proliferation of working papers dealing with the shadow
banking system. Apart from improving the definition of the SBS and the availability of
data to measure it, current research is following the path of studying the relationship of
SBS with some macroeconomic and financial indicators. This research is looking for
designing a group of indicators that could be able to signal the over-reliance of the credit
market on the SBS; and somehow warn in advanced this fact to reduce to a minimum the
potential negative impact.
I.2 What is this paper’s contribution?
This paper focuses on building a measure for the shadow banking system, and also
analyses the relations between the former and a set of financial and macroeconomic
indicators. Both the SBS measure and the indicators’ set have been carefully selected
based on previous research made by different experts on the field (as will be shown in the
methodological section of this paper). Furthermore, the analysis aims to discover different
6
See Gorton and Metrick (2012) for further repo market understanding.
7
See Sanches (2014) FRB Philadelphia.
8
See Yaron Leitner (2011) to know more about how markets freeze.

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trends among European countries and also between those and the United States. The
central question which is tried to solve in this essay is the following:
“What are the key determinants for the shadow banking system?”
And also, as a sub-question sourcing from the former:
“Are those key determinants consistent among countries?”
The analysis is conducted through two different models: the base model and the extended
model, which determine the relations between independent variables and the shadow
banking system ratio (dependent variable). Both linear regression and panel data analysis
is undergone. Data is retrieved for Euro area countries, United Kingdom and United
States, from 1999q1 to 2013q1 when available.
The first model is composed by the following variables: real Gross Domestic Product
(GDP), institutional investors, term-spread, margin and liquidity; whereas the extended
model (which is only ran for Europe due to data availability) is composed by the previous
variables plus a composite indicator of systemic stress (CISS), a measure of the banking
concentration (HHI) and the inflation. Information about the study’s temporal and
geographical framework and variables rationale and definition is provided in section IV.
Results show strong differences in the relations between shadow banking and the
independent variables within the Euro area. The study shows that the US shadow banking
behaviour is more similar to central-north Europe than to south-mediterranean countries.
Furthermore, the study is also consistent with the fact that those countries with a more
developed financial system, such as US and UK, also have higher levels of shadow
banking.
More precisely, under cross-sectional data approach, results suggest close similarities
between the central-north and the south-mediterranean areas. However, once the
regression is improved through fixed effects introduction, results suggest that for the first
group of countries, SBS grows when real GDP, term-spread and liquidity increase.
Whereas for the south-mediterranean, SBS diminishes if the same variables increase.
The relevance of this essay approach lays in 4 main points:
1) Adapting a European-based measure of SBS and replicating it for the US.

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2) The division of the Euro area part of the sample between two different groups:
the south-mediterranean, which have also suffered from financial turmoil during
the recent years, and the central-north, characterised for have been stronger and
more consistent facing the global financial downturn.
3) The construction of two new models based on well-known indicators which
have not been grouped together before.
4) The implementation of a fixed effects approach to study the shadow banking
system controlling for time or country differences.
The essay is structured as follows: Section II defines shadow banking and shows its
functioning and weight within the financial system. Section III presents the literature
review of this topic from the first papers until the latest ones. This part addresses
definition, measure and determinants knowledge development through successive
working papers of renowned authors. Section IV explains in detail the methodology
followed in designing and conducting the analysis, the data retrieval process and the
models’ construction. Complementary information and replication steps are provided in
the Appendices. Section V deals with the exposition of the results obtained and the
plausible rationale behind those findings. Finally, section VI serves as a summary of the
conclusions drawn from the essay and the specific research made.
II. WHAT IS SHADOW BANKING?
The accelerated innovation, regulation and competition accomplished during this last
decade in the banking sector, have reshaped banking activities from the traditional
banking model to the originate-to-distribute model. This new model has change
dramatically credit intermediation and risk absorption in the financial system. It prompted
increasing reliance on capital markets instead of those functions happening within banks’
balance sheets. This means that banks in place of borrowing short, lending long and
keeping loans as investment (traditional banking model); after generating loans they sell
them to broker-dealers who pool, securitize9
and distribute them to investors satisfying
9
See Jobst (2008) for further information on the securitization process, which is key to the comprehension of the
shadow banking performance.

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their unique risk desires (originate-to-distribute model, as known as the shadow banking
system).10
Exhibit 1 shows information about the tendency of the traditional and shadow
banking financial assets during the period 2010-2014.
Exhibit 1. Assets of financial intermediaries.
Notes: Banks = broader category of “deposit-taking institutions”; OFIs = Other Financial Intermediaries;
Shadow Banking = measure of shadow banking based on economic functions. These are not mutually
exclusive categories, as shadow banking is largely contained in OFIs.
Source: Global Shadow Banking Monitoring Report 2015. Financial Stability Board (FSB).
From a general point of view, the shadow banking system (SBS) can be defined as all the
activities related to credit intermediation, liquidity and maturity transformation that
happens outside the regulated banking system.11
Despite the diverse names authors give
to the shadow banking system, it is clear that they talk about the same concept, although
the existence of slight nuances explained in Section II of this essay.
The size of the SBS depends on the approach taken in its definition and measuring
process. It was showed that around 1995, the level of shadow banking liabilities in the
US overtook the traditional bank level. The distance between liabilities types widened
during the following decade reaching a maximum in 2008-2009.12
In the same vein, the
Financial Stability Board (FSB) has conducted a recent worldwide research on SBS
10
See Pozsar (2008) for deeper explanation in evolution between banking models.
11
See Bakk-Simon et al. (2012). This is the most commonly used shadow banking system definition by the European
Central Bank. Liquidity transformation refers to investing in illiquid assets while acquiring funding through liquid
liabilities. Maturity transformation relates to the use of short-term liabilities to fund investment in long-term assets.
12
See Pozsar et al. (2010).

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demonstrating its growing importance compared to banks (see Exhibit 1). In this study
made for a 26 jurisdiction sample, it is shown that SBS has been steadily increasing at a
rate of 6% in average during 2011-2014 period. Furthermore, it is demonstrated how this
financial segment’s growth in 2014 was 10.1%, whereas the pace of traditional banks was
6.4%.
Exhibit 2. Shadow Banking assets share per country
Note: CA = Canada; CN = China; DE = Germany; EMEs ex CN = Argentina, Brazil, Chile, India,
Indonesia, Mexico, Russia, Turkey, Saudi Arabia, South Africa; FR = France; IE = Ireland; JP = Japan; KR
= Korea; NL = Netherlands; UK = United Kingdom; US = United States.
Source: Global Shadow Banking Monitoring Report 2015. Financial Stability Board (FSB).
In Exhibit 2 it is showed the countries’ podium in percentage of shadow banking assets
as a fraction of the total: US (40%), UK (11%), China (8%), Ireland (8%), Germany (7%),
Japan (7%) and France (4%). It is remarkable the impressive increase in the case of China.
This country went from a 2% of the share in 2010 to an 8% in 2014.13
Actually, it is
relevant the fact that, from this FSB sample, more than a half of shadow banking assets
are in hands of US and UK nowadays, revealing the financial power of both countries.
Although the traditional banking system (TBS) and the SBS are thought as mutually
exclusive, they have a high degree of interconnectedness. In Exhibit 3, the reader can
discern what the International Monetary Fund (IMF) presented as a simple explanatory
scheme of the diverse players, the functioning and the flow of funds among SBS players
and also between those and the TBS.
13
See FSB (2015) for methodological process behind those results.

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Exhibit 3. Traditional versus Shadow Banking credit intermediation.
Note: lenders category includes institutional investors (ICPFs), central banks and sovereign wealth funds.
Source: International Monetary Fund (2014).
Blue boxes represent the typical players in a traditional banking-based economy where
financial intermediation is made by banks. They raise funds from lenders’ deposits and
give loans to borrowers, making business from interest rates differentials between both
flows. All the grey boxes depict the SBS. Dark grey box called “Finance companies and
other nonbank lenders” represents shadow banking in less developed economies, whereas
inner boxes represents, in broad terms, a developed country non-regulated banking
system. Whereas the main trade in the TBS is capturing deposits (providing loans) in
exchange of interest rate payments (collection), in the SBS system the principal trade is
between securities and money or loans. Thus the process of securitization earns
tremendous relevance in this credit intermediation model.14
14
See footnote 1.

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III. LITERATURE REVIEW.
III.1 First research on SBS.
The definition of the Shadow Banking System is not straightforward and it depends on
the point of view adopted by the researcher. The term of shadow banking was first coined
by McCulley (2007), as he referred to all the combination of unregulated and heavily
levered up non-bank structures and vehicles. Those unregulated shadow banks fund
themselves with un-insured commercial paper which is not backstopped in case of
liquidity problems. Opposite to the regulated banking system, which has the tools to
overcome liquidity stress such as FRB discount window, the SBS is much more prone to
suffer from runs. First working papers and articles dealing with a deep assessment of the
SBS were made by Pozsar (2008) and Adrian and Shin (2009). A comprehensive
overview of the SBS can be found in Pozsar, Adrian, Ashcraft, and Boesky (2010). In
that paper, the authors foster further discussion on SBS topic while presenting the
features, economic role and relation of it with the traditional banking system. As
McCulley claimed, they also highlight the attractiveness generated by SBS as an
“inexpensive” credit funding source during early 2000s’ real estate boom. Investors’
blindness made them unable to realise the inherent dangerous lack of guarantee schemes
against a possible capital and liquidity shortfall.
III.2 SBS definition.
In one of the first academic papers dealing with SBS, Pozsar (2008), the author defines
SBS as a network of highly levered off-balance sheet credit intermediation vehicles which
is at the heart of the financial crisis. The author differentiates between the traditional
model of banking and the originate-to-distribute model. This distinction is also made by
other authors, as Martinez-Miera and Repullo (2015). In a later paper, Pozsar (2013), the
author improved the definition. He refers to shadow banking as the credit intermediation
chain composed by specialized financial intermediaries, called shadow banks, which are
in charge of traditional banking activities (credit, maturity and liquidity transformation).
These intermediaries perform banking activities without the safety net of having direct
and explicit access to public sources of liquidity or credit backstops.

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Other authors, as Shin and Shin (2010), focus their attention on the counterpart of the
liability itself. In order to consider a liability as core (estimate of TBS) or non-core
(estimate of SBS) it is necessary to know whom the liability is due to. It will be a core
liability if its counterpart is an ultimate domestic creditor whereas it will be classified as
non-core if its counterpart is an intermediary or a foreign creditor.
Another approach to the SBS is made by Harutyunyan, Massara, Ugazio, Amidzic and
Walton (2015). In this paper the authors claim that the institutional perspective taken by
Pozsar in his first essays, defining shadow banks as institutions outside the banking
system’s regulatory framework, is not accurate enough. They defend the fact that shadow
banking-like liabilities can be potentially issued by all financial institutions involved in
credit intermediation. Authors also give relevance to the counterpart of that kind of
liabilities as in Shin and Shin (2010). However, their essay’s main contribution to the
SBS scope is the focus on the financial instrument’s nature. They distinguish between
core and non-core liabilities. The former are the traditional banking sources of regular
deposits from ultimate domestic creditors and the latter are the SBS-like liabilities.
Furthermore, noncore liabilities can be considered as narrow or broad depending on
whether the measure encompasses intra-SBS positions (asset of one financial corporation
represents the liability of another) or not.
Finally, from an institutional point of view, Bakk-Simon et al. (2012) and the IMF (2014)
define SBS in broader terms. Both use the same definition: SBS are all the activities
related to credit intermediation, liquidity and maturity transformation that happens
outside the regulated banking system, and therefore lacking a formal safety net. However,
it is noticeable the position taken by the IMF of trying to run away from the completely
negative connotation of the SBS. This bad image is mainly due to the interconnectedness
between SBS and the financial crisis. To overcome this idea, the IMF states that SBS can
complement the traditional banking system by expanding access to credit, or by
supporting market liquidity, maturity transformation and risk sharing. As an example,
Gosh, Gonzalez del Mazo and Otker (2012) explained that in developing economies, the
SBS provides a vital service of giving access to credit and investments to under-banked
communities, subprime customers and low-rated firms.

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III.3 SBS measure.
The most extended measures used in the literature about the SBS so far, have been
computed only from an institutional perspective, due to data availability constraints.
However, the trend is changing from institutional to activity-based approach once enough
valuable data is gradually being obtained (as it is explained in sub-section III.2). There
are two main measures in the literature that deserve to be brought to the review.
Exhibit 4. Shadow Bank Liabilities vs Traditional Bank Liabilities, $ trillion.
Source: Flow of Funds Accounts of the United States as of 2010 Q1 (FRB) and FRBNY.
On one hand, the first measure deals with the SBS assessment process basing its
construction on the official data retrieved by the Federal Reserve and was presented by
Pozsar15
in 2010 (see Exhibit 4). It is remarkable how shadow bank liabilities overtook
the traditional ones in 1995 and they were growing at a higher rate until 2009. The crash
of the great recession was, by far, more damaging for SBS than for TBS. These findings
were align with SBS procyclicality results appeared in Shin and Shin (2010) and Hahm,
Joon-Ho, Shin and Shin (2012).
15
See Pozsar (2010) to get technical information about the accounts forming the measure.

14
On the other hand, the second measure deals with the SBS estimation from an European-
data based model and was presented by Bakk-Simon et al.16
in 2012 (see Exhibit 5). This
is the source from which the measure built for this paper arise, whose explanation the
reader can find under the methodological sub-section IV.2. European-based measure is
also consistent with the idea of procyclicality of the SBS with the economic cycle, as it is
display in Exhibit 5 graph b).
When comparing the two measures, the most relevant finding is the huge difference in
the extent to which the financial market resort to shadow banking system to fund its
operations. As an example, in 2008, European “banks” assets almost triplicate the
corresponding to “other intermediaries”. Meanwhile, in 2008 in the US, an opposite
situation was observed, with SBS liabilities accounting for around the double than TBS
liabilities.
Exhibit 5. Assets of banks and other intermediaries in the euro area.
Source: Euro Area Accounts (ECB and Eurostat) and monetary statistics (ECB)
16
Assets of “banks” are estimated as the assets of the MFI sector (EAA) minus Eurosystem assets (monetary statistics)
and money market fund shares issued by MFIs (EAA). Assets of “other intermediaries” are equal to EAA OFIs assets
plus money market fund shares issued by MFIs minus mutual fund shares issued by investment funds other than MMFs
(EAA).

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III.4 SBS determinants.
The study of possible determinants or contributors to the growth of the shadow banking
system is widespread along the literature. The collection of different sources dealing with
variables related to the SBS will serve as a benchmark for this essay. Moreover, it will
support the main essay’s idea of developing quantitative models to study the non-
regulated segment of the financial system.
Researchers have realised the fact that during periods of fast economic development,
traditional banking, as the main source of credit, is not enough to cover the demand of the
market. This scarcity of credit is mainly due to the rigidity of the traditional banking
system (monitoring and legal costs and constraints) which is vanquish by banks and non-
financial credit institutions shifting to “non-traditional” sources. Following this idea,
Hahm, Joon-Ho, Shin and Shin (2012) developed an innovative model of credit supply.
The model supported the hypothesis of procyclicality of non-regulated sector with the
expansion of the balance sheet during a credit boom. Moreover, other authors as Shin and
Shin (2010) have also shown the positive correlation between the non-traditional
liabilities and the business cycle. This business cycle can be measured in terms of Gross
Domestic Product (GDP).
The authors in IMF (2014) established that the search for yield effect17
, tighter bank
regulation and grow of the rest of the financial system can be variables that contribute to
the SBS development. Moreover, they conducted a panel regression to quantify for the
effects of some macro-financial variables (e.g. real GDP growth, banking sector size,
institutional investors’ size and term-spread) and some regulatory variables (e.g. overall
capital stringency and global liquidity indicators). The search for yield effect also appears
in other research papers as Martinez-Miera and Repullo (2015). The latter refers to the
search for yield effect in the banking sector, due to gap reduction between interest rates
paid on deposits and interest rates earned on loans; whereas the former focus its attention
on the term-spread squeeze.
17
See Rajan (2005). “Search for yield effect” is defined as an increase in investment risk-taking as a manner to obtain
higher expected return during periods with low interest rates.

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Duca (2014) studied the drivers of the SBS in the short and the long run. On one hand, he
demonstrated that, in the long-run, SBS is negatively correlated to information costs and
positively correlated to bank reserve requirements and the relative burden of capital
requirements on commercial versus shadow bank credit. On the other hand, he showed
that the shadow banking system share in the short run fell when liquidity premia were
high, and when term premia reflected expectations of economic scenario improvements;
however, it rose when deposit rate ceilings were more stringent and regulatory changes
benefit nonbank compared to bank finance. This SBS procyclicality and vulnerability to
liquidity shocks are consistent with Adrian and Shin (2009a, 2009b, 2010), Brunnermeier
and Sannikov (2013), Geankoplos (2010), and Gorton and Metrick (2012).
III.5 Latest research on SBS.
During last years, the focus has been mainly pointed towards overcoming the shadow
banking institutional-based measure granularity problem. Moreover, it is clearly signalled
the intention to change the approach from institutional to activity-based definition and
measure, once enough data will be retrieved. Apart from that, monitoring process,
financial stability and the Chinese SBS escalation are also heavily attracting researchers´
attention.
The ECB (2015) showed how the decomposition of the SBS broad measure into different
institutional sub-aggregates has been evolving lately. Light has been shed on those
categories from 2008 until today, once relevant data has started to be computed by
financial authorities (see Exhibit 6). Despite this advances in the SBS assessment, there
is still a 50% of shadow banking assets for which a breakdown is not available. However,
it is known that two thirds of this “residual” part of Euro area SBS assets are held in
Netherlands and Luxembourg.
In the report from FSB (2015), it has been designed a SBS measure based in five different
economic functions, each of which involves non-bank credit intermediation that
potentially pose some financial risks (activity-based approach). Those risks may raise

17
financial stability concerns demanding a policy response.18
Furthermore, the activity
based approach implemented in this report accomplishes two main goals: it allows policy
makers to better focus on the activities of the shadow banking entities and risks; and also,
it allows a refinement of the SBS measure, putting aside non-bank entities that are not
involved in significant maturity and liquidity transformation or leverage.
Exhibit 6. Total assets of the shadow banking sector by the broad measure.
Note: A breakdown of statistical data for MMFs, other funds, and FVCs is available only from the indicated
dates onwards.
Source: Report on Financial Structures, October 2015.
Finally, the powerful new trend of SBS in China has been increasing in importance among
researcher during recent years due to the rapid credit creation rate since 2010 and the lack
of transparency in non-banking activities. Moreover, the control that the state provides to
banks through regulations, foster incentives in the banking sector to shift activities
towards less known and regulated shadow banking. This shift has its advantages for the
Asian giant, as SBS serves as a lubricant of corporate financing promoting development
and expansion of small and medium-sized enterprises. Nevertheless, it has brought a lot
of uncertainty to Chinese financial system stability. Those findings are consistent with
the studies conducted by Elliott, Kroeber and Qiao (2015) and Liu, Shao and Gao (2016).
18
Through the FSB’s shadow banking information-sharing exercise, authorities from a number of jurisdictions have
noted that some entity-types classified as shadow banking are highly regulated through a range of policy tools available
to address and mitigate shadow banking risks.

18
IV. DATA AND METHODOLOGY
This section presents the data retrieved and the different regression analysis conducted
during the research. The methodological explanation describes the following: 1) the
study’s temporal and geographical framework, 2) the measure computed and the variables
analysed, 3) model types, implementation and their extensions 4) some limitations to have
in mind for this essay in particular, and also, some of them can be extrapolated to the
literature written about shadow banking so far.
IV.1 Framework.
The framework in which the analysis is embedded is divided in two different sources of
variation: the temporal and the geographical.
The first source of variation defines the time limits of the research. It sets the quarterly-
based computation of the SBS measure and the independent variables from 1999q1 to
2013q1 (for the countries with the highest data availability of the sample: “core sample”).
The period’s length selection has been made considering two main factors 1) data
availability for some countries of the Euro area (EA) before 1999 is very limited; and 2)
the fact that the analysis is based on comparing Euro area with the United States (US),
and also analysing the trends within the Euro area, it does make sense to establish the
starting point from the very beginning of the euro adoption. Furthermore, the quarterly
periodicity chosen is aligned with the most commonly used in SBS’s economic research.
It allows the analysis to compute for more accurate and detailed variation than an annual
approach. The variables retrieved on an annual basis have been transformed to quarterly
using simple linear interpolation, whereas the monthly ones have been converted using
the pertinent quarter average, as explained in Appendix 1.
The second source of variation is established as the country for which information on the
SBS and the other variables has been computed. The countries selected for data retrieval
are the US, UK and the members of Euro area (19 countries): Austria, Belgium, Cyprus,
Germany, Estonia, Spain, Finland, France, Greece, Ireland, Italy, Lithuania,
Luxembourg, Latvia, Malta, Netherlands, Portugal, Slovakia and Slovenia. The US has
been selected due to its role of most powerful economy nowadays and also because the

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bigger bulk of research about Shadow Banking topic is related to it. On the other hand,
the selection of all the countries of the Euro area (EA) and UK, has been made for enable
the study to address a faithful sample on reflecting European shadow banking system
evolution and current situation. However, and due to data limitations in some variables
of the study, five countries of the sample have been dropped in all the models: Cyprus,
Estonia, Lithuania, Luxembourg and Malta. Apart from these countries, many others from
EA and also the US are not considered under the robustness tests nor in the extended
model for the same reason.
IV.2 Data description.
The majority of variables selected and analysed in this study have been based on previous
research and literature written by highly recognized experts in the shadow banking and
credit intermediation field. As it can be seen in Section III of this paper, the process of
searching for relations between shadow banking system and macro-financials is widely
share among researchers. Along the following paragraphs, all the variables used will be
defined and also the effect that the former seek to capture will be explained. Furthermore,
it is indicated which of the variables are based on previous studies and, on the contrary,
which have risen from my intuition and knowledge.
The dependent variable used in every model of this essay is the shadow banking measure,
which is called sbsratio. The measure has been computed from a European perspective
based on EA data availability, and it has been replicated for the US making the pertinent
adaptations. As it is not straightforward to compute the same measure in two widely
different financial environments and histories, it is suggested to look into this replication
critically. Differences in building the same measure for EA and US is one of the
limitations of this paper. Technical descriptions of the accounts considered in building
sbsratio are explained in Appendix 2.
In the case of the EA, the ratio is defined as Other Financial Institutions (OFIs, measuring
the shadow banking system) divided by Monetary Financial Institutions (MFIs,
measuring the traditional banking system), as computed by the ECB in the Euro Area
Accounts (EAA). As stated in the FSB (2015), shadow banking activities are largely run
by OFIs. A similar approach of the close relation of OFIs and SBS is made in

20
Harutyunyan et al. (2015) and Bakk-Simon et al. (2012). More precisely, the measure
used in this paper is based on the last-mentioned work. In this report the author estimates
the traditional banking system (TBS) as MFIs assets minus Eurosystem assets and Money
Market Funds (MMFs) shares issued by MFIs; whereas the shadow banking system is
estimated as OFIs assets plus MMFs shares issued by MFIs minus mutual fund shares
issued by investment funds other than MMFs.
The explanation of taking a broader approach in this essay distinguishing only between
OFIs (SBS) and MFIs (TBS) is due to several reasons. First of all, the fact of not having
available MMFs data breakdown before 2008. Secondly, the low MMFs level showed in
general terms in EA once the breakdown was available, with the only exception of some
SBS outliers such as Netherlands and Luxembourg. Moreover, it is arguable subtracting
non-MMFs shares from OFIs, since in the ECB report 2015 it is showed that non-MMFs
contributes to the 40% of the shadow banking whereas MMFs only to the 4%. These are
the reasons why final measure built is considered fair enough to assess shadow banking
level within the study, bearing in mind the huge amount of data availability limitations.
Exhibit 7. Ratio levels for base model sample.
Source: calculations made by the author. Data from EAA.
0.000
0.001
0.001
0.002
0.002
0.003
0.000
0.000
0.000
0.000
0.000
0.001
0.001
0.001
0.001
0.001
Ratio (OFI/MFI), base model sample.
Belgium Germany
Spain Finland
France Portugal
United Kingdom 'United States (secondary axis)

21
In the case of US, the measure is replicated in the following way: first, OFIs assets are
calculated as total financial assets minus central banks, credit institutions, monetary
market mutual funds, property-casualty insurance companies, life insurance companies
and pension funds; second, MFIs assets are computed as central banks plus credit
institutions and money market mutual funds.
As it can be seen in Exhibit 7, the ratio for US is considerably bigger (right y-axis) than
for the rest of the sample, which can be biased due to replication method. Besides, the
existence of two different trends in SBS evolution is distinguishable. Belgium, United
Kingdom and Germany have followed an increasing trend, whereas the rest have suffered
from an up and down peaking process around year 2007.
The independent variables used in this study are the following: 1) GDP at constant prices
(realgdp), 2) institutional investors’ assets (instinv), 3) term-spread (tspread), 4) net
interest margin for banks (margin), 5) liquidity (liquidity), 6) banking concentration index
(hhi), 7) composite indicator of systemic stress (ciss), and 8) inflation (inflation). A
complete explanation on the technical definition, sources and data retrieval process can
be found in Appendix 2.
1) GDP at constant prices, also known as real GDP (realgdp), is the inflation adjusted
value of the goods and services produced by labour and property located inside
corresponding country borders. This variable has been considered as an approach
to measure the procyclicality trait existing between SBS and economic booms and
bursts. The rationale of introducing this variables is based on several studies
already conducted by other researchers.
2) Institutional investors (instinv) are the insurance corporations and pension funds’
assets (ICPFs). This variable has been taken from the IMF (2014). In this report,
the authors related stronger growth of ICPFs with higher growth of SBS and also
a general trend in financial development. Instinv is used to capture
complementarities with SBS and demand-side effects.
3) Term-spread (tspread) is computed as the difference between long-term interest
rate (LTIR) and short-term interest rate (STIR). The idea of introducing this
variable has been taken from IMF (2014), and it tries to capture the search for
yield effect related to government-based securities. When government bonds
yields are low and investors are looking for higher yield assets, it is the SBS that
often supply those assets. Furthermore, the tspread gives a sense of stability in the

22
economy and the higher the spread, the more investors want to borrow for the long
term. In Exhibit 8, it can be observed the similar trend for all the countries before
2010. After that year, in the countries which suffered from a deeper financial
turmoil the tspread triggered. The case of Portugal clearly stands out.
Exhibit 8. Term-spread evolution.
Source: calculations made by the author. Data from OECD.
4) Bank net interest margin (margin) is defined as the difference between the interest
rate paid on borrowing deposits from savers and the interest rate received from
loans to borrowers. It is computed through the accounting value of bank's net
interest revenue as a share of its average interest-bearing (total earning) assets.
This variable selection is based on Martinez-Miera and Repullo (2015) and tries
to capture the search for yield effect in its private banking approach. They defend
that the lower the margin, the more the incentives for banks to shift their
operations towards the SBS and get higher returns being exposed to higher risks.
From Exhibit 9 three main points can be highlighted: 1) the big margin volatility
in Europe in early 2000s and the recent trend towards convergence; 2) another
outlier peak of Portugal around 2006; and 3) the stability presented by the US
banks’ net interest margin.
-4
-2
0
2
4
6
8
10
12
14
Term-spread (%)
Belgium Germany Spain Finland
France Portugal United Kingdom United States

23
Exhibit 9. Margin evolution.
Source: calculations made by the author. Data from FRB.
5) Liquidity (liquidity) is measured as total reserves minus gold. This variable
comprises mainly the reserves of IMF members held by the IMF and also holdings
of foreign exchange under the control of monetary authorities. The idea of taking
into account one variable to measure the liquidity of the countries has been
obtained mainly from Bernanke (2012). The author stressed the role of the
liquidity of the country as a safety net to backstop liquidity shortfalls in the
traditional banking system.
6) Banking concentration index (hhi) is defined as the Herfindahl-Hirschman index
for Credit Institutions (as defined in European Community Law) total assets. It
has been introduced in the extended model to compute for the effect of TBS
financial structure on SBS development.
7) Composite Indicator of Systemic Stress (ciss) is defined as a combination of
financial stress measures of five important segments of an economy’s financial
system. This variable has been introduced in the extended model to capturing
certain symptoms of financial stress such as increases in agents’ uncertainty,
investor disagreement or information asymmetries.
8) Inflation (inflation) is defined as the growth rates on the consumer price index.
The idea to analyse this variable is taken from the IMF (2014). The aim is to
0
1
2
3
4
5
6
7
8
Banks' net interest margin (%)
Belgium Germany Spain Finland
France Portugal United Kingdom United States

24
capture the effect of the loss in money purchasing power among investors on their
decisions to shift their investments towards the SBS.
IV.3 Models and extensions.
Once the previous parts of this section have clarified the limits and variables of the
research, it is time to present how the regression models have been built. The models shed
light on the relations existing between independent variables and the SBS through a
multiple regression analysis. The structure of the study is divided in two approaches:
cross-sectional data and panel data, depending on whether Ordinary Least-Squares (OLS)
regression or fixed effects (FE) regression is considered. FE control for some specific
characteristic within the country or the year that may bias the predictor variables outcome.
They remove the effect of those features so it is possible to assess the net effect of the
predictors on the outcome variable.
The cross sectional and panel data studies are composed by two models: the base model
and the extended model. The first one computes the relations between five independent
variables (realgdp, instinv, tspread, margin, and liquidity) and the SBS measure. The
extended one adds to the former another three independent variables (ciss, hhi and
inflation). As data availability varies depending on the considerations taken, a detailed
explanation on the countries composing each of the samples is presented in Appendix 3.
In the cross-sectional part, first an analysis encompassing all the sample is conducted
(whole-sample analysis) for both the base model [Equation 1] and the extended model
[Equation 2]. These regressions serve as a benchmark to compare how the results change
once groups of countries are analysed separately. Second, a “core” base model regression
is run [Equation 1]. The “core” sample is composed by all the countries of the whole-
sample for which data is available from 1999q1 to 2013q1. Moreover, this sample is
divided in four sub-samples depending on both geographical distribution and also on
whether the countries have suffered from a big financial turmoil during the great
recession. The subsamples are the following: the two first are from the EA, being 1)
Belgium, Germany, Finland and France (countries from central and north Europe (C-N)
which have not suffer big financial turmoil) and 2) Spain and Portugal (countries from
south-mediterranean (S-M) Europe which have felt big financial turmoil); 3) United
Kingdom and 4) United States.

25
Equation 1. Formula of the base model regression for cross-sectional data.
𝐬𝐛𝐬𝐫𝐚𝐭𝐢𝐨𝒊,𝒕 = 𝛃 𝟎 + 𝛃 𝟏 𝐫𝐞𝐚𝐥𝐠𝐝𝐩𝒊,𝒕 + 𝛃 𝟐 𝐢𝐧𝐬𝐭𝐢𝐧𝐯𝒊,𝒕 + 𝛃 𝟑 𝐭𝐬𝐩𝐫𝐞𝐚𝐝𝒊,𝒕 + 𝛃 𝟒 𝐦𝐚𝐫𝐠𝐢𝐧𝒊,𝒕 +
𝛃 𝟓 𝐥𝐢𝐪𝐮𝐢𝐝𝐢𝐭𝐲𝒊,𝒕 +𝛆𝒊,𝒕

The following equation is based on Equation 1, but now ciss, hhi and inflation are
introduced as additional independent variables.
Equation 2. Formula of the extended model regression for cross-sectional data.
𝛃 𝟓 𝐥𝐢𝐪𝐮𝐢𝐝𝐢𝐭𝐲𝒊,𝒕 + 𝛃 𝟔 𝐜𝐢𝐬𝐬𝒊,𝒕 + 𝛃 𝟕 𝐡𝐡𝐢𝒊,𝒕 + 𝛃 𝟖 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐨𝐧𝒊,𝒕 + 𝛆𝒊,𝒕
In both equations, i indicates the country and t the quarter. The 𝛃 𝟎 coefficient indicates
the value of the ratio in the hypothetical case when all the independent variables’ values
are equal to zero. The rest of 𝛃 coefficients show the change in ratio relative to a one unit
change in the respective independent variable. ε is the error term.
In the panel data approach the two first steps are the same that in the cross-sectional part,
but now considering fixed effects. Moreover, two further improvements are implemented:
introducing the “core” extended model, and conducting a robustness test for both base
and extended “core” models. Robustness test shows how the regression outcomes change
in value, sign and statistical significance once more countries are added to the ”core
sample”.
The regressions formulae for the whole-sample base and extended model, accounting for
fixed effects, are presented in [Equation 3] and [Equation 4]. Furthermore, those models
are analysed also for the core-sample as in the cross-sectional part explained above and
also for an extension of the core-sample called robustness-sample.

26
Equation 3. Formula of the base model regression for panel data.
𝛃 𝟓 𝐥𝐢𝐪𝐮𝐢𝐝𝐢𝐭𝐲𝒊,𝒕 + 𝜸𝒊 + 𝜸 𝒕 + 𝛆𝒊,𝒕
The following equation is based in Equation 1 but now ciss, hhi and inflation are
introduced as additional independent variables.
Equation 4. Formula of the extended model regression for panel data.
𝛃 𝟓 𝐥𝐢𝐪𝐮𝐢𝐝𝐢𝐭𝐲𝒊,𝒕 + 𝛃 𝟔 𝐜𝐢𝐬𝐬𝒊,𝒕 + 𝛃 𝟕 𝐡𝐡𝐢𝒊,𝒕 + 𝛃 𝟖 𝐢𝐧𝐟𝐥𝐚𝐭𝐢𝐨𝐧𝒊,𝒕 + 𝜸𝒊 + 𝜸𝒕 + 𝛆𝒊,𝒕
In both equations, i indicates the country and t the quarter. The 𝛃 𝟎 coefficient indicates
the value of the ratio in the hypothetical case when all the independent variables’ values
are equal to zero. The rest of 𝛃 coefficients show the change in ratio relative to a one unit
change in the respective independent variable. 𝜸𝒊 refers to country fixed effect and 𝜸𝒕 for
time fixed effects. ε is the error term.
The results obtained from the regression models analysis will be presented and explained
in section V of this essay.
IV.4 Limitations
In order to conclude with the methodological part of this study, it has been decided to
present as a sum-up, all the relevant limitations that have been faced along the fulfilment
of the data retrieval and preparation process. In broad terms, the main limitations can be
grouped in five categories: data definition, data availability, data granularity, data
homogeneity and data periodicity.
 Data availability limitation is related to the lack of information on the variables
for the whole period between 1999 and 2013. As the SBS studies are pretty recent,
then there is valuable data computed only for a very small breakdown of the

27
developed countries. This fact reduce the possibility to infer reliable
generalizations from the results obtained in regressions.
 Data definition issue presents the problem of measuring SBS with data which has
not been designed with that aim. It goes together with the previous limitation.
 Data granularity limitation deals with the fact that data is grouped in different
categories by the source, but those are not enough broken down. This fact favours
the reduction on the accuracy of the phenomenon measure because very different
entities in charge of taking diverse credit intermediation activities can be grouped
under the SBS when actually do not contribute to it, or vice versa.
 Data homogeneity handicap verse about differences to extrapolate the SBS
measure from the European perspective to the US perspective. Besides, the
impossibility to compute for the three new variables under the extended model
approach has appeared. The heterogeneity in the Euro Area and the so-different
economic evolution path makes it difficult to establish the same accounts
classification for both.
 Data periodicity is not actually a big deal. The variables computed monthly,
quarterly and annually can be adapted and transformed to other periodicity, as it
has been done in this research. However, it is true that some precision is lost in
the process. Loss of accuracy depends on the degree of intra-period volatility of
the variable adjusted.
The recent literature developed by governing authorities and international financial
institutions is mainly focused on overcome the three first limitations. In broad terms, the
fourth is more difficult to accomplish due to the widely different historical path follow
by the most relevant countries or group of countries in terms of SBS (US, China, Japan
and EA). The last one depends on the main use for which the data is designed. There are
some variables that are worthless to compute more often because the information added
does not deserve the costs entailed.

28
V. RESULTS
Along this section of the essay the results obtained after conducting the regression
analysis are presented to the reader. The structure explained in the methodology section
of this essay will be followed. It should be reminded that causality cannot be inferred
directly from this results. The main objective of this essay is to discover relevant relations
between each variable and the shadow banking system. The number of observations for
each regression and goodness of fit are showed in Appendix 4.
V.1 Cross-sectional data approach.
Along the following paragraphs, the results analysed are those obtained through OLS
regression method. An OLS multiple regression model consist in using OLS for
predicting the value of a dependent variable (regressand) from the values of two or more
independent variables (regressors). The coefficients corresponding to each regressor
measures its partial effect on the regressand, holding the other variables fixed.
V.1.1 Whole-sample base and extended model analysis.
Before narrowing down the sample to address the two central models of the study, a
regression analysis for each of them has been run taking into account whole countries’
sample in order to have a general view of the raw analysis results. The tables are plotted
in Exhibit 10. Results obtained from this regression are not very significant due to the big
heterogeneity of the sample. However, they are useful as benchmarks to compare the
improvement of the analysis along the process of segmentation of the sample and fixed
effect introduction.
For both models, when institutional investors increase, the SBS ratio increase too
(positive correlation); whereas when the margin widens and the liquidity measure grows,
the SBS ratio decrease (negative correlation). Another reflection on the results of this
approach is the fact that the outcome from the regression signals that in the case of the
base model (m1), the real GDP level and term-spread are not statistically significant for

29
the study, meanwhile in the case of the extended model (m2) the new three variables and
the term-spread are not relevant neither.
Exhibit 10. Base and extended model results for the whole-sample (Cross-sectional)
Note: ***, **, * indicate significance at the 1 percent, 5 percent and 10 percent levels, respectively.
m1 = whole-sample base model; m2 = whole-sample extended model; b = β coefficient; se = standard error.
V.1.2 Core base model analysis.
Now the base model will be studied for the core-sample in order to discover different
trends that cannot be distinguished under the whole-sample approach. This is a double-
change step from the previous analysis in IV.1.1.19
The analysis is made for C-N (m3)
and S-M (m4) groups, United Kingdom (m5) and United States (m6). As we can see in
Exhibit 11, the statistical relevance of model m4 is the highest. From the point of view of
the coefficients, C-N and S-M are pretty similar with the only difference that term-spread
increases when SBS increases in C-N and does the opposite in S-M. In addition, real GDP
is negatively correlated with SBS in the Euro Area and positively correlated in UK and
US. In the case of the banks’ margin, it has a positive relation with SBS in C-N (0.062)
19
See Appendix 5 for obtaining more information on the intermediate step results. This step refers to analysing the
core base model as a unique group (single-change step), before dividing it in sub-samples (double-change step).

30
and in S-M (0.028); however, its relation with SBS is negative and stronger in the United
States (-0.278).
Exhibit 11. Core base model results (Cross-sectional).
m3 = C-N base model; m4 = S-M base model; m5 = UK base model; m6 = US base model; b = β coefficient;
se = standard error.
V.2 Panel data approach.
In this part of the results’ section, fixed effects will be introduced using panel data. Panel
data allows the study to control for variables that cannot be observed or measured like
cultural factors; or variables that change over time but not across countries. The rationale
behind using panel data derive from the cross-sectional approach results. The difference
in outcomes between countries (see Exhibit 11), suggest the existence of not controlled
factors. Those underlying characteristic of each country can affect and bias the predictor
variables’ result. The analysis will overcome this negative impact by introducing FE.
V.2.1 Whole-sample base and extended model analysis.
Comparing the results obtained in this analysis for cross-sectional (Exhibit 10) and panel
data (Exhibit 12), it is possible to distinguish some differences. First, real GDP has

31
become significant for both models, although the sign is negative for the base model (-
0.023) and positive for the extended (1.413). Second, the banks’ interest margin has lost
its significance for the base model and now is positively related with shadow banking.
Finally, the banking concentration variable (hhi) has improved its significance and shows
negative relation to shadow banking development (-1.081).
Exhibit 12. Base and extended model results for the whole-sample (Panel)
m7 = whole-sample base model; m8 = whole-sample extended model; b = β coefficient; se = standard error.
V.2.2 Core base model analysis
Once again, a double-change step is taken from the previous analysis in IV.2.1.20
The
approach to narrowing the base model analysis is divided in two steps: first, analysing the
results from the “core base model” for both European countries’ groups, UK and US.
Secondly, test the robustness of the model in the cases of the European groups by adding
new countries to the sample. Exhibit 13 presents the results of “core base model” analysis
20
See Appendix 6 for obtaining more information on the intermediate step results. This step refers to analysing the
core base model as a unique group (single-change step), before dividing it in sub-samples (double-change step).

32
in the cases of C-N (m9), S-M (m10), UK (m5) and US (m6). We keep the outcomes for
UK and US from the cross-sectional part as they are not grouped within other countries.
It is relevant to see the opposite relations between the variables and the SBS ratio for C-
N and S-M. This fact is not seen when the same analysis is made under cross-sectional
approach. In the case of the north and central Europe, the shadow banking grows when
real GDP, term-spread and liquidity increase; and, on the contrary, it decreases when the
interest margin for banks widens and institutional investors assets grow. In the case of the
south-mediterranean area, the correlations signs are the inverse, which supports the
decision of dividing the EA sample in this two groups of countries. Moreover, for the UK
and US, the base model is not as statistically significant as for the previous two groups.
In the case of UK, it is proved that the SBS grows when the term-spread (0.012) and
liquidity increase (3.206). However, the evidence shows that for the US, liquidity is
negatively correlated with SBS (-4.131), as it is the banks’ interest margin (-0.278).
Exhibit 13. Core base model results. (Panel).
m9 = C-N base model; m10 = S-M base model; m5 = UK base model; m6 = US base model; b = β
coefficient; se = standard error.
V.2.2.1 Robustness test on base model
The robustness test is made in order to find whether the results are consistent once more
countries are considered. Due to the extension of the general sample computed for this

33
paper and the rationale behind countries’ pooling process, robustness test only makes
sense in the cases of C-N and S-M groups. Exhibit 14 shows a comparison between the
respective core base model (m1, m2) and robustness test (m5, m6) for each group: m1 vs
m5 for C-N, and m2 versus m6 for S-M.
The outcome of the robustness test must be analysed in terms of both the statistical
relevance of the variables under study and stability of the coefficient signs. In the case of
the C-N, term-spread has lost its statistical relevance whereas liquidity and institutional
investors have improved and worsen theirs, respectively. From the point of view of the
signs, it is noticeable that they have stayed stable. Analysing the outcome of the S-M
group, it is possible to realize the lower extent of the change introduced by the test. The
main reason for such a low variation is the poor observations availability on the new
countries introduced, Italy, Ireland and Greece.21
Exhibit 14. Robustness test for base model. (Panel).
m9 = C-N base model; m11 = C-N robustness base model; 10 = S-M base model; m12 = S-M robustness
base model; b = β coefficient; se = standard error.
21
S-M robustness-sample coincides with the so-called PIIGS countries, i.e. Portugal, Ireland, Italy,
Greece and Spain.

34
V.2.3 Core extended model analysis.
The second model to analyse is the extended model. The approach taken is the same as
in the base model: first, explaining the results for the “core extended model” for both
European countries’ groups (m13 for C-N and m14 for S-M) and UK (m15). Afterwards,
testing the robustness of this model. As it is explained in the methodology section, US is
dropped from this model’s sample due to no data availability in terms of the three new
variables. Exhibit 15 shows the results obtained from this analysis.
Exhibit 15. Core extended model results. (Panel).
m13 = C-N extended model; m14 = S-M extended model; m15 = UK extended model; b = β coefficient;
se = standard error.
Results suggest that the core extended model outcome maintains the same signs for the
common variables with the core base model in the case of C-N and S-M groups. However,
some variables such as liquidity and realgdp have lost their significance. For UK, the
significance of term spread has been lost but liquidity correlation with SBS ratio keeps
consistently positive and statistically relevant. In terms of the three new variables
introduced, it is remarkable that ciss is only relevant in the case of UK in which when it
decreases, SBS increases. The hhi variable is negatively correlated with SBS ratio for C-

35
N group (-0.769) and positively correlated for S-M group (5.314). Finally, the variable
inflation is positively correlated with shadow banking in the cases of C-N and UK.
V.2.3.1 Robustness test on extended model
The robustness test for the extended model has been conducted for C-N (m16) and S-M
(m17). Results are presented in Exhibit 16. The outcome shows that variable signs are
consistent with the core extended model analysis for each of the groups. It is fair to
highlight the fact that, apart from UK case, it is the first occasion in which the banks’ net
interest margin lose its statistical relevance. This loss is perceived in the robustness test
results of C-N extended model. However, it has still negative correlation with shadow
banking.
Exhibit 16. Robustness test for extended model. (Panel).
m13 = C-N extended model; m16 = C-N robustness extended model; m14 = S-M extended model;
m17 = S-M robustness extended model; b = β coefficient; se = standard error.

36
VI. CONCLUSIONS
Shadow banking system (SBS) has played a central role in the recent financial crisis,
though it is a current discussion whether it was a mere amplifier or an originator. This
banking segment provided easy and “inexpensive” access to credit intermediation during
the economic boom of early 2000s. This fact has been argued to contribute to the risk
build-up in the economy. Some countries such as UK and US have a higher, more
developed and deeper rooted shadow banking system than other financially powerful
countries analysed (e.g. Germany and France).
It is certain that shadow banking system is not as negative as its pejorative name implies.
Shadow banking system supplies the financial world with flexible mechanisms to
overcome a rigid and restricted traditional banking system. It even allows under-banked
communities, subprime customers and low-rated firms to get access to credit. However,
its negative impact on financial stability has heavily attracted the attention of governing
authorities. International financial institutions and governments seek to monitor and
regulate shadow banking system to avoid a new worldwide financial catastrophe. In this
vein, the institutional SBS measure used so far, is being changed towards an activity-
based approach, which fits better to shadow banking behaviour. At the same time that
authorities are analysing the real functioning of SBS, a new threat has materialized: the
Chinese shadow banking system escalation.
This paper analyses how the shadow banking level varies when some key determinant
variables change. Those variables are the real GDP, institutional investors’ assets, term-
spread, banks’ net interest margin, liquidity, indicator of systemic stress, banking
concentration measure and inflation. It has been proven that, once controlling for fixed
effects, the SBS and the determinants show opposite relations for central-north (C-N)
than for south-mediterranean (S-M) Euro area. As an example, real GDP has positive
relation with SBS for C-N and negative relation with the S-M sub-sample; and banks’
margin and banking concentration are negatively correlated to SBS in C-N but positively
in S-M. Another conclusion is that the behaviour of the shadow banking system in US is
more similar to C-N Europe than to S-M Europe. Moreover, it has been shown that
robustness tests confirm the consistency of this model when analysing a wider sample of
countries.

37
For all the above-mentioned, this paper suggest that the financial authorities should focus
their attention on these key determinants. Policy making decisions to monitor and control
for shadow banking must be based on the performance observation of these indicators.
Despite the fact that both models analysed are relevant in estimating shadow banking
behaviour, the base model reveals better S-M than C-N fit. The opposite occurs in the
case of the extended model. This information altogether with the opposite sign relations
depending on geography revealed in the previous paragraph, pose European authorities a
big challenge: Which is the best model approach to adopt? Which is the part of the Euro
area that deserves further resources allocation to overcome shadow banking risks? And,
in case that area-specific policies could be implemented to control opposite SBS trends,
how to keep these policies independent between areas in a common monetary and
economic environment?

38
REFERENCES
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41
ABBREVIATIONS
ABS = Asset-backed security
CISS = Composite Indicator of Systemic Stress
C-N = Central-North
EA = Euro Area
EAA = Euro Area Accounts
ECB = European Central Bank
FE = Fixed Effects
FRB = Federal Reserve Bank
FSB = Financial Stability Board
FVC = Finance Vehicle Corporation
GDP = Gross Domestic Product
HHI = Herfindahl-Hirschman Index
ICPF = Insurance Companies and Pension Funds
IMF = International Monetary Fund
LTIR = Long-term interest rate
MBS = Mortgage-backed security
MFI = Monetary Financial Institution
MMF = Money Market Fund
OFI = Other Financial Institution
OLS = Ordinary Least-Squares
SBS = Shadow banking system
S-M = South-Mediterranean
STIR = Short-term interest rate
TBS = Traditional banking system

42
APPENDICES
Appendix 1. Methods of data transformation.
Source: made by the author
Interpolation: Data transformation from annually to quarterly. The formulae used are as
follows:
𝑄1(𝑌𝑡) = 𝐴 𝑡
𝑄 𝑛(𝑌𝑡) = 𝑄 𝑛−1(𝑌𝑡) +
(𝐴 𝑡+1 − 𝐴 𝑡)
3
, 𝑛 = 2,3,4
Where 𝑄1 refers to the first quarter and 𝑄 𝑛 to the second, third and fourth (depending on
n value). 𝐴 𝑡 is the annually computed value of data in year t (𝑌𝑡).
Average: Data transformation from monthly to quarterly. It consists of computing a
simple arithmetic mean of the monthly values corresponding to each quarter.
Model Variables Periodicity Adjustments
realgdp
instinv
tspread
margin
liquidity
inflation
hhi
ciss Monthly Average
Annually Interpolation
Extended
Base Quarterly No adjustments

43
Appendix 2. Data. Descriptive statistics, correlation tables, definitions and sources.
Table 2.1 Variables’ descriptive statistics.
Source: calculations made by the author.
Table 2.2 Variables’ correlation coefficients.
Source: calculations made by the author.
Variable Obs Mean Std. Dev. Min Max
sbsratio 997 0.733 1.013 0.033 7.821
realgdp 1072 0.912 2.856 0.003 14.777
instinv 997 1.895 3.884 0.000 23.291
tspread 1126 1.664 2.717 -16.070 24.704
margin 1254 2.312 1.435 0.125 14.196
liquidity 1407 0.025 0.048 0.000 0.299
ciss 712 0.207 0.229 0.011 0.978
hhi 1220 0.118 0.086 0.014 0.407
inflation 1365 2.470 1.892 -1.700 15.300

44
Definitions and sources.
OFI
A corporation or quasi-corporation other than an insurance corporation and pension fund that
is engaged mainly in financial intermediation by incurring liabilities in forms other than currency,
deposits and/or close substitutes for deposits from institutional entities other than MFIs, in
particular those engaged primarily in long-term financing, such as corporations engaged in
financial leasing, financial vehicle corporations created to be holders of securitised assets,
financial holding corporations, dealers in securities and derivatives (when dealing for their own
account), venture capital corporations and development capital companies.
https://www.ecb.europa.eu/home/glossary/html/index.en.html
Euro Area and United Kingdom.
Source: European Central Bank Statistical Data Warehouse
Euro area accounts > Financial balance sheets and non-financial assets
https://sdw.ecb.europa.eu/browse.do?node=2019184
Frequency: Quarterly (Q); Geographic Area: Euro Area 19 (fixed composition), Austria, Belgium,
Cyprus, Germany, Estonia, Spain, Finland, France, Greece, Ireland, Italy, Lithuania, Luxembourg,
Latvia, Malta, Netherlands, Portugal, Slovakia, Slovenia, United Kingdom; Counterpart area:
[W0] World (all entities, including reference area, including IO (international organizations));
Reference sector: [S12P] Other Financial Institutions (financial corporations other than MFIs,
insurance corporations and pension funds); Counterpart sector: [S1] Total economy; Accounting
entries: [A] Assets (Net acquisition of); Stocks, Transactions, Other Flows: [LE] Closing Balance
Sheet/Positions/Stocks; Instruments and assets classification: [F] Total financial
assets/liabilities; Unit of measure: [XDC] Domestic currency (incl. conversion to current currency
made using a fixed parity), Million euros.
Explained label: World (all entities, including reference area, including IO), Total economy,
Assets (Net Acquisition of), Total financial assets/liabilities
Technical label: QSA.Q.N.AT.W0.S12P.S1.N.A.LE.F._Z._Z.XDC._T.S.V.N._T
Note: country name in grey.
United States.
Source: Z1 Financial Accounts of the United States
Flow of Funds, Balance Sheets, and Integrated Macroeconomics Accounts.
https://www.federalreserve.gov/releases/z1/
Frequency: Quarterly (Q); Unit of measure: Million euros (data transformed using the ECB
reference exchange rate, US dollar/Euro, 2:15 pm (C.E.T.), EXR.Q.USD.EUR.SP00.A)
OFI= Domestic financial sectors; total financial assets [L108] - Monetary authority; total financial
assets [L109] - Private depository institutions; total financial assets [L110] - Money market
mutual funds; total financial assets [L121] - Property-casualty insurance companies; total
financial assets [L115] - Life insurance companies; total financial assets [L116] - Private and
Public Pension funds; total financial assets [L117].

45
MFI
Financial institutions which together form the money-issuing sector of the euro area. These
include the Eurosystem, resident credit institutions (as defined in EU law) and all other resident
financial institutions whose business is to receive deposits and/or close substitutes for deposits
from entities other than MFIs and, for their own account (at least in economic terms), to grant
credit and/or invest in securities. The latter group consists predominantly of money market
funds.
Reference sector: [S12K] Monetary Financial Institutions; Counterpart sector: [S1] Total
economy; Accounting entries: [A] Assets (Net acquisition of); Stocks, Transactions, Other Flows:
[LE] Closing Balance Sheet/Positions/Stocks; Instruments and assets classification: [F] Total
financial assets/liabilities; Unit of measure: [XDC] Domestic currency (incl. conversion to current
currency made using a fixed parity), Million euros.
Technical label: QSA.Q.N.I8.W0.S12K.S1.N.A.LE.F._Z._Z.XDC._T.S.V.N._T
United States.
Frequency: Quarterly (Q); Unit of measure: Million euros (data transformed using the ECB
reference exchange rate, US dollar/Euro, 2:15 pm (C.E.T.), EXR.Q.USD.EUR.SP00.A)
MFI= Monetary authority; total financial assets [L109] + Private depository institutions; total
financial assets [L110] + Money market mutual funds; total financial assets [L121].

46
REAL GROSS DOMESTIC PRODUCT.
Real gross domestic product is the inflation adjusted value of the goods and services produced
by labour and property located in a country.
https://research.stlouisfed.org/fred2/series/GDPC1
Source: Federal Reserve Bank of St. Louis. (retrieved from Eurostat).
Home > FRED® Economic Data > Releases > Gross Domestic Product
https://research.stlouisfed.org/fred2/
Frequency: Quarterly (Q); Geographic Area: Austria, Belgium, Germany, Spain, Finland, France,
Greece, Ireland, Italy, Latvia, Netherlands, Portugal, Slovakia, Slovenia, United Kingdom; Unit of
measure: Thousand Billion of Chained 2010 Euros; Seasonally and calendar adjusted data.
Technical label:
Eurostat unit ID: CLV10_MNAC
Eurostat item ID = B1GQ
Eurostat country ID: XX
For euro area member states, the national currency series are converted into euros using the
irrevocably fixed exchange rate. This preserves the same growth rates than for the previous
national currency series. Both series coincide for years after accession to the euro area but
differ for earlier years due to market exchange rate movements.
United States.
Source: Federal Reserve Bank of St. Louis.
Home > FRED® Economic Data > Releases > Gross Domestic Product
https://research.stlouisfed.org/fred2/series/GDPC1
Frequency: Quarterly (Q); Geographic Area: United States; Unit of measure: Thousand billion
of Chained 2009 Euros (data transformed from Thousand Billion of Chained 2009 Dollars using
the ECB reference exchange rate, US dollar/Euro, 2:15 pm (C.E.T.), EXR.Q.USD.EUR.SP00.A);
Seasonally and calendar adjusted data.
Technical label: BEA Account Code: A191RX1

47
INSTITUTIONAL INVESTORS.
This variable is considered due to its introduction in the paper IMF October 2014 as “Assets of
insurance companies and pension funds”. According to the ESA 2010, the insurance corporations
subsector consists of all financial corporations and quasi-corporations which are principally
engaged in financial intermediation as a consequence of the pooling of risks mainly in the form
of direct insurance or reinsurance; the pension funds subsector consists of all financial
corporations and quasi-corporations which are principally engaged in financial intermediation
as the consequence of the pooling of social risks and needs of the insured persons (social
insurance). Pension funds as social insurance schemes provide income in retirement, and often
benefits for death and disability.
Reference sector: [S12Q] Insurance corporations and Pension Funds; Counterpart sector: [S1]
Total economy; Accounting entries: [A] Assets (Net acquisition of); Stocks, Transactions, Other
Flows: [LE] Closing Balance Sheet/Positions/Stocks; Instruments and assets classification: [F]
Total financial assets/liabilities; Unit of measure: [XDC] Domestic currency (incl. conversion to
current currency made using a fixed parity). Thousand Billion Euros.
Technical label: QSA.Q.N.SK.W0.S12Q.S1.N.A.LE.F._Z._Z.XDC._T.S.V.N._T
Note: country name in grey
United States.
Frequency: Quarterly (Q); Unit of measure: Thousand Billion Euros (data transformed using the
ECB reference exchange rate, US dollar/Euro, 2:15 pm (C.E.T.), EXR.Q.USD.EUR.SP00.A)
INSTITUTIONAL INVESTORS= Property-casualty insurance companies; total financial assets
[L115] + Life insurance companies; total financial assets [L116] + Private and Public Pension
funds; total financial assets [L117].

48
TERM SPREAD.
This variable is computed as the difference between LTIR and STIR.
LONG TERM INTEREST RATE.
Long-term interest rates refer to government bonds maturing in ten years. Rates are mainly
determined by the price charged by the lender, the risk from the borrower and the fall in the
capital value. Long-term interest rates are generally averages of daily rates, measured as a
percentage. These interest rates are implied by the prices at which the government bonds are
traded on financial markets, not the interest rates at which the loans were issued. In all cases,
they refer to bonds whose capital repayment is guaranteed by governments. Long-term interest
rates are one of the determinants of business investment. Low long-term interest rates
encourage investment in new equipment and high interest rates discourage it. Investment is, in
turn, a major source of economic growth.
https://data.oecd.org/interest/long-term-interest-rates.htm
Euro Area, United Kingdom and United States.
Source: OECD Data
Interest rates.
https://data.oecd.org/interest/long-term-interest-rates.htm
Germany, Spain, Finland, France, Greece, Ireland, Italy, Latvia, Netherlands, Portugal, Slovakia,
Slovenia, United Kingdom, United States; Unit of measure: Percent per annum.
SHORT TERM INTEREST RATE.
Short-term interest rates are the rates at which short-term borrowings are effected between
financial institutions or the rate at which short-term government paper is issued or traded in the
market. Short-term interest rates are generally averages of daily rates, measured as a
percentage. Short-term interest rates are based on three-month money market rates where
available. Typical standardised names are "money market rate" and "treasury bill rate".
https://data.oecd.org/interest/short-term-interest-rates.htm
Source: OECD Data
Interest rates.
https://data.oecd.org/interest/short-term-interest-rates.htm
Germany, Spain, Finland, France, Greece, Ireland, Italy, Latvia, Netherlands, Portugal, Slovakia,
Slovenia, United Kingdom, United States; Unit of measure: Percent per annum.

49
NET INTEREST MARGIN.
Accounting value of bank's net interest revenue as a share of its average interest-bearing (total
earning) assets.
https://research.stlouisfed.org/fred2/search?st=NET+INTEREST+MARGIN
Source: Federal Reserve Economic Data.
https://research.stlouisfed.org/fred2/search?st=NET+INTEREST+MARGIN
Frequency: Annual (A) (transformed into Quarterly by linear interpolation); Geographic Area:
Austria, Belgium, Cyprus, Germany, Estonia, Spain, Finland, France, Greece, Ireland, Italy,
Lithuania, Luxembourg, Latvia, Malta, Netherlands, Portugal, Slovakia, Slovenia, United
Kingdom, United States; Unit of measure: percentage.
LIQUIDITY.
Liquidity measured as total reserves. Total reserves minus gold comprise special drawing rights,
reserves of IMF members held by the IMF, and holdings of foreign exchange under the control
of monetary authorities. Gold holdings are excluded. Data are in current U.S. dollars.
http://data.worldbank.org/indicator/FI.RES.XGLD.CD
Source: International Financial Statistics (IFS)
Reserves selected indicators
http://data.imf.org/regular.aspx?key=60998126
Frequency: Quarterly (Q); Geographic Area: Euro Area 19(fixed composition), Austria, Belgium,
Latvia, Malta, Netherlands, Portugal, Slovakia, Slovenia, United Kingdom, United States; Unit of
measure: Thousand Billion Euros (data transformed using the ECB reference exchange rate, US
dollar/Euro, 2:15 pm (C.E.T.), EXR.Q.USD.EUR.SP00.A).

50
COMPOSITE INDICATOR OF SYSTEMIC STRESS (CISS).
The CISS (Composite Indicator of Systemic Stress) comprises the five arguably most important
segments of an economy’s financial system: the sector of bank and non-bank financial
intermediaries, money markets, securities (equities and bonds) markets as well as foreign
exchange markets. The current level of stress in each of these five segments is measured on the
basis of three raw stress indicators capturing certain symptoms of financial stress such as
increases in agents’ uncertainty, investor disagreement or information asymmetries. Certain
raw stress indicators shall also capture flight-to-quality and flight-to-liquidity effects,
respectively. The CISS measures such stress symptoms mainly on the basis of securities market
indicators which are quite standard in the literature (such as volatilities, risk spreads and
cumulative valuation losses).
https://www.ecb.europa.eu/pub/pdf/scpwps/ecbwp1426.pdf?6d36165d0aa9ae601070927f3a
b799fc
Home > Economic Concepts > Monetary and financial statistics > Composite Indicator of
Systemic Stress
Frequency: Monthly (M) (transformed into Quarterly by linear interpolation); Geographic Area:
Austria, Belgium, Germany, Spain, Finland, France, Greece, Ireland, Italy, Netherlands, Portugal,
United Kingdom; Financial market instrument: [EC] Economic indicator; Financial market
provider identifier: [SOV_CI] Sovereign Systemic Stress Composite Indicator; Financial market
data type: [IDX] Index; Unit of measure: pure number.
Technical label: CISS.M.AT.Z0Z.4F.EC.SOV_CI.IDX

51
HERFINDAHL-HIRSCHMAN INDEX (HHI).
A measure of market concentration, it depends on the number of firms and their size relative to
the market. It is calculated by summing up the squares of market shares of each firm.
http://www.nasdaq.com/investing/glossary/h/herfindahl-hirschman-index
Home > Economic Concepts > Monetary and financial statistics > Structural Financial Indicators
Kingdom; Structural statist indicator: [H10] Herfindahl index for Credit Institutions (CIs) total
assets; Unit of measure: percentage.
Technical label: SSI.A.AT.122C.H10.X.U6.Z0Z.Z
INFLATION RATE.
The measure of the variation of the increase in the general price level.
Home > Economic Concepts > Prices, output, demand and labour market > Prices > Consumer
price indices.
Kingdom; Classification-ICP context: [000000] HICP- Overall Index; Series variation-ICP context:
[AVR] Annual average rate of change; Unit of measure: percentage change.
Technical label: ICP.A.AT.N.000000.4.AVR

52
Appendix 3. Sample categories for regression analysis.
Table 3.1 Countries divisions
base model extended model
whole-sample
Austria, Belgium, Germany,
Spain, Finland, France,
Greece, Ireland, Italy,
Netherlands, Portugal,
Slovakia, Slovenia, United
Kingdom and United States.
Austria, Belgium,
Germany, Spain,
Finland, France,
Greece, Ireland, Italy,
Netherlands, Portugal
and United Kingdom.
core-sample
C-N: Belgium, Germany,
Finland and France; S-M:
Spain and Portugal; United
Kingdom; and United States.
C-N: Belgium,
Germany, Finland and
France; S-M: Spain and
Portugal; and United
Kingdom.
robustness-sample
C-N: Austria, Belgium,
Germany, Finland, France,
Netherlands, Slovakia and
Slovenia; S-M: Spain,
Portugal, Greece, Ireland
and Italy;
C-N: Austria, Belgium,
Germany, Finland,
France and
Netherlands; S-M:
Spain, Portugal,
Greece, Ireland and
Italy;

53
Appendix 4. Regressions number of observations and goodness of fit.
Table 6.1. Cross-sectional data.
Table 6.2. Panel data.
Regression Obs Adj. R^2
m1 549 0.6663
m2 396 0.2246
m3 228 0.2400
m4 114 0.8315
m5 57 0.8996
m6 57 0.5659
m15 42 0.9620
m19 456 0.9200
Cross-sectional data
within between overall
m7 549 0.1351 0.0006 0.0497
m8 396 0.2531 0.0662 0.0559
m9 228 0.2612 0.2224 0.1842
m10 114 0.7462 1.0000 0.5903
m11 309 0.1230 0.0158 0.0451
m12 126 0.6982 0.5482 0.3849
m13 200 0.4914 0.1664 0.1334
m14 100 0.8084 1.0000 0.7782
m16 242 0.3337 0.1260 0.0838
m17 112 0.7397 0.0266 0.0977
m18 456 0.1403 0.1997 0.0944
Panel data
R^2
ObsRegression

54
Appendix 5. Base model for whole-sample and one-group core-sample. (Cross-
sectional).
m1 = whole-sample base model; m19 = one-group core-sample base model; b = β coefficient; se = standard
error.
As it can be seen in the table, some results change if the base model regression is run for
the whole sample (m1) or for the core-sample as a unique group (m19). The three most
relevant variations are 1) the increase in statistical significance of realgdp, and also its
change of sign; 2) the change in the sign of margin; and 3) decrease in absolute value of
the liquidity coefficient.

55
Appendix 6. Base model for whole-sample and one-group core-sample. (Panel).
m7 = whole-sample base model; m18 = one-group core-sample base model; b = β coefficient; se = standard
error.
As it can be seen in the table, the results does not change significantly if the base model
regression is run for the whole sample (m7) or for the core-sample as a unique group. The
two most relevant variations are: 1) the fall in tspread significance; and 2) the increase in
absolute value of the liquidity coefficient.

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