The Influence of Systemic Risk on Sovereign Stability

The inﬂuence of systemic risk on sovereign stability: evidence from the Eurozone.

Marcin Wolski

Master Thesis

Prof. Ron Berndsen, Supervisor Prof. Jakob de Haan, Supervisor

Tilburg University De Nederlandsche Bank

July 2011

The inﬂuence of systemic risk on sovereign stability: evidence from the Eurozone.

Marcin Wolski Master Thesis Tilburg University and De Nederlandsche Bank

The financial crisis 2007/09 shed a new light on the importance of Systemic Risk (SR) (Schwarcz, 2008). In short, SR can be recognised as a risk of disruption to financial services that results from an impairment of the financial system (BIS, 2011). This Thesis quantifies SR in the Eurozone in years 2006-2010 using the methodology proposed by Acharya et al.(2010). The results prove that over this period SR was fluctuating. On the basis of that three sub periods were distinguished: the uncertainty period, between first quarter 2006 and second quarter 2007, the crisis period, until second quarter 2009 and the calm period, until the end of the time span. This classification is in line with the literature (Getter et al., 2007).

The influence of SR on sovereign stability have been measured using the system GMM estimator. The results show that SR was positively and significantly associated with the longer term Sovereign Credit Default Swaps (SCDS). Shorter term SCDS are also positively affected by SR, however, its coefficients are not significant. This was mainly caused by the vulnerability to short term shocks, bail out programs and ”humped” yield curve observed in the dataset. Moreover, the analysis shows that the effect of SR on SCDS changes when a certain threshold level is being reached.

Keywords: systemic risk, credit default swap, Eurozone ACKNOWLEDGMENTS

This Thesis would have not been written without help from my closest relatives, friends and coworkers. I would like to thank my parents for their support in pursuing my academic degree at the Tilburg University, Caroline for her time and patience in reading the draft versions of this

Thesis and for the warm word, my supervisors and De Nederlandsche Bank for the opportunity to have an internship at the Research Department and crucial comments on my work, and the Popov for the best sandwiches I have ever tasted that served as a quick bite between the paragraphs.

The opinions expressed in this Thesis are personal views of the author and should not be associated with De Nederlandsche Bank. Contents

Table of Contents iv

1 Introduction1

2 Systemic Risk and its consequences5 2.1 Systemic Risk...... 5 2.2 Evolution of Systemic Risk...... 7 2.3 Modelling Systemic Risk...... 9 2.3.1 Marginal Expected Shortfall...... 10 2.3.2 Economic model...... 12 2.4 Empirical analysis...... 14 2.4.1 Calculation methods...... 14 2.4.2 Data and motivation...... 16 2.4.3 Results...... 18 2.4.4 Benchmarking...... 22

3 The inﬂuence on countries’ stability 25 3.1 When is a country stable?...... 25 3.2 Systemic Risk and a country’s stability...... 26 3.3 Data...... 27 3.4 Methodology...... 30 3.5 Results...... 32

4 Conclusions 44

Appendices 46

iv Chapter 1

Introduction

The European Economic and Monetary Union (EMU) reshaped economic conditions and political realities in Europe substantially (Wyplosz, 2006). Together with bringing extraordinary benefits, it also reduced the availability of macroeconomic policy instruments - governments are no longer able to fight the recession by manipulating exchange rates or controlling their monetary supply and credit expansion (Enderlein, 2006). Moreover, as proved by the recent global financial crisis, the stability of the entire Eurozone is very susceptible to the internal difficulties of individual Member

States (EUC, 2011). Nevertheless, the origins of those troubles have not been fully explored yet.

The contribution of this Thesis is to investigate the role of ﬁnancial market instability in bringing

(at least) some of the EMU’s countries to the edge of bankruptcy.

Financial markets, currently driven by the model ”originate and distribute”, posses the internal threat that some institutions will not be able to repay their obligations (IISD, 2010). In case of a financial crisis, such uncertainty may easily translate into liquidity shocks to other financial counter-parties, even turning them into insolvency (Carletti, 2008). Nowadays financial intermediaries are heavily interconnected (IISD, 2010) so that the default of one company could eventually lead to the bankruptcy of the whole financial sector (the so-called ”domino effect”). The probability of collapse of the whole financial system is often called Systemic Risk (SR) and reflects the 1 2 stability (or rather instability) of financial markets.

The exact formula to calculate SR has not been given in the literature yet (Schwarcz, 2008).

The main reason for that is the complexity of the topic. Therefore to quantify SR, ﬁrst some crucial assumptions have to be made. This research quantiﬁes SR by the methodology proposed by

Acharya et al.(2010) which measures the propensity of a ﬁnancial company to be undercapitalised when the whole system is undercapitalised. In fact, this approach fully matches the deﬁnition of

SR stated above. It captures the contribution to the risk of contagion of each financial intermediary during the stress. By aggregating those quantities over the whole financial sector within the given time span, one may get the overall financial fragility measure.

The main advantages of this concept are twofold: (i) it connects two approaches of measuring

SR: structural and reduced-form and (ii) is has been successfully applied to predict the 2007-

2009 crisis, capturing well the behaviour of the ﬁnancial companies, i.e., their leverage, volatility, correlation and tail-dependence of the returns (Acharya et al., 2010).

The scope of the research is narrowed to the EMU countries. The main reason for that is the high level of integration of the Eurozone’s ﬁnancial markets and their development (ECB, 2009).

Financial integration accelerates efficiency in financial intermediation. It creates competitive pressure on financial companies, generates economies of scale, extends the scope of risk diversifi- cation and improves overall market liquidity (ECB, 2009). A high level of financial development reduces asymmetric information, improves the completeness of markets, lowers transaction costs and increases competition. Henceforth, running the analysis on the Eurozone’s sample yields more credible results as the threat of market inefficiency bias is minimised1.

Moreover, with monetary policy centralised, the Eurozone is not affected by asymmetric monetary actions. Since the introduction of the EMU in 1999, all the countries lost their ability to

1In fact, IISD(2010) points out that efficient financial markets should have the ability to identify, assess and price SR more accurately. The error is limited as SR is a kind of ”slow failure” or ”creeping risk” which is not always reflected in inefficient markets (WEF, 2010) 3 stimulate the economy by running lax monetary policies (Eichengreen and Wyplosz, 1988). The

European Central Bank (ECB) is the only monetary authority in the Eurozone thereafter. Since monetary decisions affect the ﬁnancial sector (and therefore SR) directly (ECB, 2009), narrow- ing the sample to the EMU’s Member States limits the bias of individual monetary shocks. The provision of money is spread across the entire Euro area evenly2.

This research takes the Sovereign Credit Default Swaps (SCDS) as a proxy for a country’s stability. CDS are a kind of protection contracts where one party agrees to make a contingent pay- ment in the case of a speciﬁed credit event in exchange for a premium (Packer and Suthiphongchai,

2003). In SCDS this event is a country’s default. For the Eurozone, the price of the SCDS reﬂects how much one has to pay to insure 10 million EUR in government bonds of a Member State.

Baum and Wan(2010) suggest that Credit Default Swaps are a good representation of macroeconomic uncertainty. The advantage of using this measure is twofold. First, it reflects a country’s political as well economic stability. In fact, since financial markets are heavily interconnected with other sectors of the economy, their fragility can affect not only the economic performance of a country (IISD, 2010). Given the circumstances, the SCDS allows for a broader view on the impact of the financial sector’s difficulties on a country’s stability. Second, since the SCDS is a tradable product, data in the form of time series is available for almost every country in the sample. An analysis carried out on a panel dataset yields more credible results as it limits the (i) endogeneity

(ii) country-speciﬁc omitted variable bias3 (Beck, 2008).

The direct impact of SR on a country’s performance within the Eurozone has not been examined

2Indeed, the reference value of the broad monetary aggregate’s (M3) growth rate was set at 4.5% annualy for the whole Euro area (De Haan et al., 2005). 3In fact, endogeneity and omited variable bias proved to be an important topic in financial econometrics recently (Beck, 2008). The former arrises when the independent variable is correlated with the error term. The latter occurs if an important variable that can significantly alter the results has not been included in the regression. Since both of them can bias the results significantly (Beck, 2008), correcting for these errors is of crucial importance for the credibility of the inference. 4 before4. This Thesis aims to contribute to this topic by (i) calculating and assessing the proxy measure of SR for the Euro area and (ii) investigating the influence of SR on the stability of the

EMU’s Member States.

The remainder of the Thesis is constructed as follows. Chapter 2 brings forward the topic of

SR. It discusses the intuition behind SR and investigates its measure proposed by Acharya et al.

(2010). Then SR is estimated for the Euro area and it is compared to other EMU’s ﬁnancial fragility indicators. Chapter 3 assesses the relationship between SR and the stability of the Euro Countries.

Chapter 4 concludes.

4As far as the Social Science Research Network Database shows, there has been no research that would question this topic from the Eurozone’s perspective so far. The methodology proposed by Acharya et al.(2010) is also relatively new what additionally makes this Thesis a novelty. Chapter 2

Systemic Risk and its consequences

2.1 Systemic Risk

Following the Oxford Dictionary of English, the adjective systemic means ”[b]elonging to, sup- plying, or affecting the system or body as a whole; [o]f or pertaining to a system” (ODE, 2010).

Therefore, Systemic Risk (SR) reflects the risk of the entire financial system and not only a part of it. Due to recent developments, such as globalisation and advanced technological progress, financial intermediation became more complex and hard to supervise. Because different parts of the

ﬁnancial sector, i.e. banking, insurance and investments, are gradually becoming more integrated and intertwined, looking at SR requires taking into account the most recent perspective.

Having pointed that out, it is also necessary to properly define which types of risk are truly systemic. Scholars struggle to give one definition of SR. Some of them claim that SR is "the probability that cumulative losses will occur from an event that ignites a series of successive losses along a chain of [financial] institutions" (Kaufman, 1996). Others define it as a ”substantial volatility in asset prices, reductions in corporate liquidity, potential bankruptcies and efficiency losses” (Kupiec and Nickerson, 2004). Some also consider SR as ”the risk that a default by one market participant

5 2.1 Systemic Risk 6

will have repercussions on other participants [...]” (Chan et al., 2005). Following Schwarcz(2008),

the only common factor in all the deﬁnitions is that ”a trigger event causes a chain of bad economic

consequences”. Those consequences could be harmful for the entire system especially when ﬁnan-

cial companies are heavily integrated.

On the one hand, the high level of the ﬁnancial markets’ integration improves their efﬁciency by

(i) producing information ex ante about possible investments and allocating capital, (ii) monitoring

investments and exerting corporate governance after providing ﬁnance, (iii) facilitating the trading,

diversiﬁcation, and management of risk, (iv) mobilising and pooling savings and (v) easing the

exchange of goods and services (Levine, 2005).

On the other hand, the increased level of integration may cause difﬁculties in the ﬁnancial

sector. Each bank1 tries to diversify the risk of its portfolio by investing in products with a different risk exposure. This, however, does not guarantee ex ante that the bank will be safe and sound. One

example which fully illustrates this phenomenon is a bank run (Schwarcz, 2008).

In the literature scholars distinguish between informational and non-informational bank runs

(Carletti, 2008). The difference between them lies in the nature of the news that depositors are

being supplied with. An informational run occurs when they have details on the bank’s poor

ﬁnancial situation so that they expect that their future payoff will be lower than assumed. A

non-informational bank run occurs when agents have a feeling that the bank might be in trou-

bles because of unveriﬁed market news. In both cases, a possible threat of loosing savings triggers

depositors to start withdrawing their money from the accounts massively. Eventually, the bank

becomes illiquid and can even be put into insolvency (Carletti, 2008).

Since ﬁnancial intermediaries do not act alone in the market but are rather involved into inter-

bank transactions, the problems of one institution may easily translate into the problem of others.

1For simplicity and transparency, by banks I mean the ﬁnancial intermediaries in general and by agents I refer to market investors. 2.2 Evolution of Systemic Risk 7

Imagine that a bank, which was a net lender to agents2, faces a run. Since it does not posses enough money to finance its liabilities, it cannot repay the loan on the interbank market either. Its creditors on the interbank market start facing the problem of limited capital. It becomes more likely that they will not be able to meet their financial obligations. This is a classical example of a domino effect - the fall of one institution triggers the fall of others (Carletti, 2008). Since these failures could cause even a solvent bank to default, many companies, which would be otherwise financially secured, might be forced into bankruptcy.

2.2 Evolution of Systemic Risk

Although bank runs are an important example of SR, governments introduced protection against them by deposit insurance, increased transparency standards, full-reserve banking and lender of last resort entities (Brusco and Castiglionesi, 2007). Moreover, the ongoing trend to reduce the bank-to-customer intermediation in raising capital made bank less vulnerable to runs than they were in the past (Schwarcz, 2008). The origins of SR can be found more often in capital markets’ complexity rather than in the ﬁnancial intermediation itself as it used to be before.

The best example of this phenomenon is the broad spectrum of the complex risk-hedging ﬁ- nancial instruments (Schwarcz, 2008). In accordance with portfolio theory, market participants can diversify the risk of the portfolio up to a certain level which is described as Market Risk (MR)3

(Bodie et al., 2004). Empirical analyses fail though to capture this level correctly as there is not enough evidence in the ﬁnancial time-series4. Hedging instruments that rely on those analyses

2The level of lending to the market exceeded the level of its deposits. This means that the bank had to be a net borrower on the interbank market. 3In theory if the correlation between assets is −1 the whole risk could be diversiﬁed away. However, because this is not common in practice, MR is always greater than zero. 4The MR’s events are often called ”black swans” as they are unpredicted under normal circumstances (Taleb, 2007). 2.2 Evolution of Systemic Risk 8 cannot price that risk correctly then. As a consequence, MR is often underpriced yielding an additional threat to the market. For example, if a bank hedges its investment position on the derivatives market with underpriced hedging products, its assumed and actual risk exposures do not match.

When a ”black swan” happens, the bank will generate higher losses than it assumed as it was not sufﬁciently insured. Since one bank is put into troubles, it may easily spill over to other ﬁnancial intermediaries through the domino effect.

Schwarcz(2008) refers to Long-Term Capital Management (LTCM) as an example of SR that originated in capital markets. LTCM was a hedge fund which invested mostly in government bonds of the United States of America (for simplicity US), Japan and Europe. Its main strategy was based on ﬁxed income arbitrage. In short, theory predicts that the difference in price of the long- term obligations of the same credit risk but with slightly different maturities should be minimal

(Dunbar, 2000). However, due to liquidity differences those discrepancies were large enough to generate arbitrage proﬁts (by taking the long position on cheaper (shorter maturity) bond and shorting the more expensive one (longer maturity)). The pricing models of LTCM assumed normal circumstances and they were very susceptible to ”black swans” (Dunbar, 2000). When in 1998

Russia defaulted on its bonds, panic in the market triggered investors to sell their Japanese and

European bonds and rush to the relatively safer US Treasury Bills. As a consequence, LTCM had to close its positions at a highly unfavourable moment. The losses incurred led to its bankruptcy eventually. To avoid a potential collapse of the ﬁnancial sector the Federal Reserve Bank of New

York organised a bailout.

Although there are similarities between bank-run-originated and capital-market-originated SR5, the transmission channels are different. In the bank run example, the failures are transmitted through the bank borrowing/lending channel and interbank system. In the latter the hedging strategy acts as a transmission channel. Therefore, the literature distinguishes between Institutional

5In both examples there was a one trigger event which caused or could have caused a chain of failures. 2.3 Modelling Systemic Risk 9

Systemic Risk and Market Systemic Risk (Schwarcz, 2008).

For the purpose of this study, the deﬁnition of SR has been chosen in accordance with the Bank

for International Settlements (BIS). Following its statement, SR ”can be deﬁned as the risk of

disruption to ﬁnancial services that results from an impairment of the ﬁnancial system [...]” (BIS,

2011).

2.3 Modelling Systemic Risk

Acharya et al.(2010) distinguish between two types of SR measurement. The ﬁrst type are the

structural models which use ”contingent claims analysis of the ﬁnancial institutions’s assets”. The

second type are reduced form models which concentrate on the tail-behaviour of asset returns,

taking into account observable variables. The common factor in those two approaches is that they

treat SR from the perspective of a portfolio of ﬁnancial institutions’ assets. The proxy for SR

in those models is the comovement of returns in the presence of market distress6. In fact, this explanation is in line with the definition of SR stated in the previous section. Since the returns of financial companies represent their performance (Bodie et al., 2004), they seem to be a good variable to approximate the general situation in financial markets. If the returns of many financial

firms behave in the same manner (comovement) when the market is under distress, it can signal the systemic nature of the event. SR is a probability that market distress will result in the disruption of the entire financial sector. 6One may question whether there is a difference in comovement between returns in normal times and market distress. In fact, if the market distress does not alter the correlation between returns significantly, it is difficult to distinguish the systemic nature of an event. Revealing slightly the results from the next section, the correlation between the returns from the dataset in the 95% highest returns days was on average 7.51%, whereas during 5% worst days it was 11.75%. The difference between those values is always significant at the conventional significance levels. This underpins the motivation under the definition of SR. 2.3 Modelling Systemic Risk 10

The following research treats SR in accordance with the methodology proposed by Acharya

et al.(2010). The biggest advantage of it is that it combines the two approaches mentioned above,

i.e. it begins with a simple structural model which is then transformed7. Eventually, SR becomes

a function of observable variables ipso facto allowing for the analysis using market data.

The model contains financial institutions which have to decide how much capital to raise to maximise their risk-adjusted returns. A regulator supervises the market in a sense that she is aware of SR and tries to make banks internalize its costs. If there was no prudential supervision, financial institutions, due to their limited liability, would not consider the risk of the aggregate financial failure (i.e. the costs that they would impose on the society during the systemic crisis). The model gives an approximation of a tax on a bank8, taking into consideration both SR and the bank’s individual situation.

To give a better overview of the model proposed by Acharya et al.(2010), it is ﬁrst crucial to understand how banks manage risk. This perspective will be then applied to the system as a whole to capture the essence of SR.

2.3.1 Marginal Expected Shortfall

The standard techniques of measuring risk from the ﬁnancial ﬁrm’s perspective are Value-at-Risk

(VaR) and Expected Shortfall (ES) (Acharya et al., 2010). VaR could be viewed as the maximal loss of the returns in a given time horizon and with a given conﬁdence interval (1 − α), where α is the signiﬁcance level. For instance, if a bank has a one-day 95% VaR of $1 mln there is a 5% probability that the returns of the portfolio will fall by more than $1 mln over one day. It may be written as VaR = −qα , where

qα = sup{z|Pr[R < z] ≤ α}, (2.1)

7This transformation is often called ”form reduction”. 8This tax could be considered as a minimum capital requirement or a mandatory insurance plan. 2.3 Modelling Systemic Risk 11

and R is the return of the portfolio. ES is the expected loss of the portfolio conditional on an event. For instance, we may consider ES as a loss when the returns of a portfolio fall below a given threshold level (γ quantile):

ESq = −E[R|R ≤ qγ ]. (2.2)

In words, ES is the average returns calculated when a portfolio exceeded its VaR limits (Acharya

et al., 2010). An advantage of using ES over VaR is that the former does not suffer from the rareness

of the tail-events9. ES captures all the returns that go below a given threshold. Additionally, ES

is a coherent measure meaning that the sum of two portfolios cannot generate ES that is higher

than the sum of their individual ES (Artzner et al., 1999). Coherency of ES allows for applying

standard linear transformations and aggregation without a bias. Therefore, the following analysis

will use ES as the ﬁnancial institution’s risk measure.

An important feature of ES is that it can be decomposed into the factors that contribute to

risk. Financial institution’s overall returns are the weighted sum of each individual investment

(or groups of investments) R = ∑i yiri, where yi is the weight of the investment i in the aggregate

portfolio and ri are its returns. The formula (2.2) can be rewritten as:

ESq = −E[∑yiri|R ≤ qα ] = −∑yiE[ri|R ≤ qα ]. (2.3) i i The sensitivity of ES can be calculated by taking the ﬁrst order partial derivatives with respect to

the individual risk exposure of investment i:

∂ESq i = −E[ri|R ≤ qα ] = MESα . (2.4) ∂yi

i Marginal Expected Shortfall of the investment i [MESα ] reﬂects its contribution to the ﬁnancial institution’s overall risk. Indeed, if the risk exposure of asset i increases, its contribution to overall risk will be proportional to the weight of that asset in the general portfolio.

9A very risky yet very rare event may not produce sufﬁciently large VaR. ES does not suffer from this shortcoming as it captures all the events that go below a given threshold level (Acharya et al., 2010). 2.3 Modelling Systemic Risk 12

From a broader perspective, one may treat R as returns of the whole ﬁnancial sector and i

may represent a single financial institution. ESi approximates the total risk exposure and yi the relative size of company i in the financial market.The expected MES of that institution is then its contribution to the global SR. Therefore this methodology allows to capture both aggregate and company-specific SR.

2.3.2 Economic model

Acharya et al.(2010) build the model upon the above mentioned techniques of managing risk in the banking sector.

The optimisation problem of the regulator is to maximise a social utility function by choosing the best taxation scheme. One should think of this as if the regulator knows ex ante what the costs of the debt insurance program will be in the future and charges banks appropriately. What is important, the optimisation is done ex ante in period 0 so that no redistribution is allowed for in period 1. This makes sense as the planner is mostly interested in aligning the incentives of the institutions before the crisis begins.

i Let w1 reflect the income of an individual company in period 1, W1 the income of the entire financial sector in period 1 and A financial sector’s total assets. Additionally, assume that xi is the value of assets of an individual company, g is the administrative cost of collecting taxes, c is the opportunity cost of equity capital, e is the external cost of a crisis and z is the minimum capital requirement for a financial institution. The solution to the problem is given by the tax policy that satisfies:

xig e τi = · P (wi < 0) · [−E[wi |wi < 0]] + · P (W < zA) · E[zai − wi |W < zA] + τ . (2.5) c 0 1 1 1 c 0 1 1 1 0

Equation (2.5) consists of two basic parts. The ﬁrst part proves that the tax function should depend

i on the probability of default of institution i [P0(w1 < 0)] times the expected loss in a case of default 2.3 Modelling Systemic Risk 13

i i i [[−E[w1|w1 < 0]]]. It should also be proportional to the fraction of insured debt [x ] in the sense that the more insurance an institution wants to have from the government, the higher the tax rate

should be. The tax rate is also increased by the costs of collecting taxes [g] which one may view

as the administrative costs of a deposit insurance program. The tax rate decreases with the costs of

raising equity capital [c] which will be described in detail later.

The second part of equation (2.5) is devoted to systemic events. The tax contribution increases

with the probability of a systemic crisis [P0(W1 < zA)], the costs born by an individual institution i i if the system in undercapitalised [E[za − w1|W1 < zA] ] and the costs of externalities [e]. At the same time, it decreases with the costs of equity capital [c]. This solution is intuitive. The more

expensive ﬁnancial capital, the less incentives banks have to take excessive risk so that the crisis

is less likely to occur. Besides, if capital is more costly, the regulator does not want to burden the

ﬁnancial sector too much.

The last term of equation [τ0] (2.5) satisﬁes the liquidity constraint. The main focus of the research is the second part of equation (2.5) which reﬂects the costs of

SR. Denoted by Systemic Expected Shortfall (SES), it is:

e SESi = · P (W < zA) · E[zai − wi |W < zA]. (2.6) c 0 1 1 1

Equation (2.6) states that SR in terms of SES depends on the Market Systemic Risk (the ﬁrst

term) and on the Individual Systemic Risk (the second term) which is consistent with the theory

described in the previous section.

If we denote the leverage of company i in period 0 by its debt-to-assets ratio, i.e. how much i i i a −w0 N i assets are financed by debt, we get l = ai and the aggregate leverage is equal to L = ∑i=1 l = i i A−W0 i w1−w0 A . Additionally, the returns of company i equal R = i and the aggregate returns of the w0 financial sector are then R = W1−W0 . Combining those formulas with the definition of systemic W0 2.4 Empirical analysis 14

crisis, i.e. W1 < zA, we get:

zW z W < zA ≡ (R + 1)W < 0 ≡ R < − 1, (2.7) 1 0 1 − L 1 − L

and the formula for SESi can be rewritten as:

e z zwi z SES = P (R < − 1) · E[ 0 − (Ri + 1)wi |R < − 1] i c 0 1 − L 1 − li 0 1 − L e z z z = wi P (R < − 1) · E[−Ri + − 1|R < − 1] c 0 0 1 − L 1 − li 1 − L e z z = wi P (R < − 1) · (MES (Ri) + ( − 1)), (2.8) c 0 0 1 − L q 1 − li

z where q is a 1−L −1 percentile of the probability distribution. This formula is also intuitively clear. The higher the aggregate leverage in the economy is, the more severe the consequences of the tail

events are. Moreover, the higher the leverage of an individual company, the more risk exposure it

has on other counter-parties and the more likely that its default will have contagion effects. Scaling

the (2.8) by the initial equity we get the ﬁnal SR measure, i.e.:

e z z SESi = P (R < − 1) · (MES (Ri) + ( − 1)). (2.9) % c 0 1 − L α 1 − li

2.4 Empirical analysis

The aim of this section in to calculate the SES% for the Eurozone using the formula proposed in the previous section over the given timespan. Due to data availability, the period chosen for the analysis are Q1 2006 - Q3 2010. This timespan gives the opportunity to observe market behaviour one year prior to the recent ﬁnancial crisis and one year after it.

2.4.1 Calculation methods

The formula (2.9) can be divided into four main components. The ﬁrst one is the externality cost of the systemic crisis [e]. As claimed by Acharya et al.(2010), it is common to assume that it 2.4 Empirical analysis 15

is constant across countries. Moreover, it is always conditional on the probability of a systemic

event. Without losing explanatory power, we may assume that the externality cost [e] is constant over time as it shows the magnitude of damage of the systemic crisis only when the latter occurs.

The time variability is then captured by the probability of a crisis.

The second component is the opportunity cost of capital [c]. Although Acharya et al.(2010) also prove that it is constant in a cross section, it is hard to believe that it will be time-invariant. The economies within the Eurozone are dynamic entities. Therefore, market characteristics influence the cost of equity and debt. We may understand the opportunity costs of equity as the cost of foregoing using debt as a source of capital. In words, the higher the cost of debt is, the less likely it is that a company will raise funds by issuing bonds, ceteris paribus, and the more likely that it will raise equity. We may also think of it in terms of net profits. Assume that at the beginning the costs of equity and debt financing are equal. Net profits are then calculated by subtracting the costs of funding from the returns generated in the next period. If the cost of debt has slightly increased, ceteris paribus, the net profits are higher for equity financing so we are more likely to use it as a source of financing. Therefore, the opportunity cost of using debt instead of equity has decreased.

There is an inverse relation between the opportunity cost of equity and the cost of debt capital, as increased interest on debt will make it more likely to forego it as a source of funds. It can be written as: 1 ct = , (2.10) βit

where it is the interest rate on debt and β is just a normalising constant and it reﬂects the magnitude to which changes in interest rates determine the changes in the cost of equity capital.

The third part of the equation is the probability that the system as a whole is undercapitalised.

Although it should also be equal in a cross section (Acharya et al., 2010), it is hard to believe that

it would be time-invariant10. To capture the time variability of it, one could rewrite it in terms of

10For the same reasons as it was with the opportunity cost of equity. 2.4 Empirical analysis 16

the probability distribution, i.e.:

z z Z 1−L −1 P0(R < − 1) = f (R)dR. (2.11) 1 − L −∞ For simplicity, the returns in the following analysis have been assumed to be jointly normally distributed. According to Sewell(2011), the normal probability distribution does not capture the stylised facts of the individual ﬁnancial time series11. However, at the aggregate level it is more likely that ﬁnancial time series follow a normal distribution (Bodie et al., 2004). The possible individual anomalies are corrected for in the MES factor which is described in detail below.

The fourth and last part of the equation (2.9) consists of two components. The ﬁrst one is the Marginal Expected Shortfall evaluated at a given quantile q. Assuming that q = 5%, we may formulate: T Ri i ∑t=1 t1[Rt ≤5%quan.] MESq = T . (2.12) ∑t=1 1[Rt ≤5%quan.] In words, the MES are the average returns obtained during 5% lowest returns days. To capture

the tail behaviour of the individual time series, the distribution of the returns is scaled. Following

Acharya et al.(2010), the scaling factor has been set up at the level of 60 . 60 corresponds to the dt

crisis drop in the equity during the crisis, what is also in line with Chakrabarti et al.(2011). dt reﬂects the average drop in equity prices in period t.

The second part includes the leverage of a ﬁnancial company [li] relative to the capital require-

ments [z].

This set of equations offers simple tools to calculate time series for SR in the Eurozone.

2.4.2 Data and motivation

The data to calculate the SES for the Euro Countries come from two databases. The information on

equity returns and leverage of ﬁnancial companies comes from Thomson Reuters Datasream. The

11Sewell(2011) points out that ﬁnancial time series are characterised by: (i) autocorrelation of returns with positive kurtosis, (ii) the mean and the variance of returns are not linear and (iii) complicated scaling properties. 2.4 Empirical analysis 17

leverage has been calculated as total debt to total assets ratio which is consistent with Bodie et al.

(2004). The total returns have been gathered on a daily basis from January 2nd 2006 to Septem-

ber 31st 2010. Leverage is measured on a quarterly basis and covers the same time span. The

ﬁnancial companies used in the research include banks, life and non-life insurers and investment

companies that are listed on Eurozone Stock Exchanges12. Together, the analysis includes 237 Eu-

ropean ﬁnancial companies: 84 banks, 8 life insurance, 23 non-life insurance and 122 investment

companies. All the returns have been translated into the Euro using the daily closing exchange

rate.

The proxy for equity opportunity cost was taken as the 3 months Euro Interbank Offer Rate

(EURIBOR). The interbank market has been considered as efﬁcient and self-regulating, providing

advantages of quick and ﬂexible reallocation of resources initially given by the Central Bank (Vento

and La Ganga, 2009). Therefore, the ﬁrst source of debt capital for a ﬁnancial institution should be

the interbank market which is exactly what is needed to determine the opportunity cost for equity

capital.

The parameter z has been set at the level of 6% in accordance with Tier-1 Basel II Capital Ac-

cord. Since external cost of ﬁnancial crisis [e] is constant in panel approach, it has been normalised

to 1 for simplicity, the same as the scaling factor [β] from equation (2.10). SES is calculated only in countries which were within the Eurozone in a given year. In the time span of this analysis, the Eurozone experienced three extensions: in 2007, 2008 and 200913. Table

2.1 presents the countries taken into account to calculate SES.

12A detailed description of companies can be found in Appendix A. 13In 2011 Estonia joined the EMU, however, since it is out of the range of the dataset it has not been reported here. 2.4 Empirical analysis 18

Table 2.1 The list of countries in Eurozone in each year of the dataset for SES calculation. Source: European Commission, 2011.

2006 2007 2008 2009 2010

Basis Countries ∗ Basis Countries Basis Countries Basis Countries Basis Countries + Slovenia + Slovenia + Slovenia + Slovenia + Cyprus + Cyprus + Cyprus + Malta + Malta + Malta + Slovakia + Slovakia

∗ Basis Countries are: Austria, Belgium, Finland, France, Germany, Ireland, Italy, Luxem- bourg, Netherlands, Portugal and Spain, which joined EMU in 1999 and Greece which joined in 2001.

2.4.3 Results

The general results for the SES within the Eurozone can be summarised in Figure (2.1)14. To capture the cross sectional differences in ﬁnancial market structure, SR has been averaged over the entire ﬁnancial sector in each country15. SES has been calculated on the quarterly basis. Because of the fact that the data on the market capitalisation was not available for 34 companies over the whole period, two SES indices are provided: the equally weighted index [SESEW ] and the market weighted index [SESMW ]. Both indices are weighted by the GDP to calculate the index for the entire Eurozone16. Positive values of the SES mean that the system as a whole was undercapitalised

14Detailed results can be found in Appendix B. 15Another solution would be to sum individual SR. This, however, brings the problem that countries with many stable ﬁnancial institutions may look relatively more risky than countries with smaller number of very unstable institutions. The question of how one should aggregate SR has not been resolved yet as different methods could lead to different conclusions. For robustness, the analysis has been done also using the sum of individual SR across countries. The ﬁnal conclusions are the same, therefore, for transparency they are not reported here. 16GDP was taken from the Eurostat Database in constant prices 2000 and seasonally adjusted. 2.4 Empirical analysis 19

Figure 2.1 The Systemic Expected Shortfall (scaled) in the Eurozone in years 2006-2010. The solid line is the equally weighted index and the dotted line is the market weighted index. Source: Thomson Reuters Datastream.

in the given period (under the assumptions on the parameters stated in the previous section). In words, the positive SES increases the tax rate [τ] which the regulator imposes on the ﬁnancial institutions to provide liquidity during the distress.

Looking at Figure (2.1), we can divide the whole timespan into three sub-periods. The ﬁrst one is from the beginning of 2006 to the second quarter of 2007 and could be named the uncertainty period. This period can be characterised by: (i) medium volatility of the SES (σEW = 0.52 and

σMW = 0.71), (ii) two peaks i.e., in the second quarter of 2006 and in the ﬁrst quarter of 2007 (during the remaining time the SES was at the level of around 0.1817) and (iii) the differences between the SESEW and the SESMW were small (on average 0.18). The second period was from the second quarter 2007 to the second quarter 2009 and it could

17 For transparency, the calculations are reported from now on for the market weighted index [SESMW ], however, they have been conﬁrmed with the equally weighted index. The results are similar unless stated otherwise. 2.4 Empirical analysis 20

be described as the crisis period. In this time, (i) the SES was the highest (over six times higher

than in the previous period), (ii) there was a high volatility (σMW = 2.7) and (iii) the differences between the SESEW and the SESMW were greatest (on average 1.36). The last period is between the second quarter 2009 and the third quarter 2010 and it could be called the calm period. It was characterised by (i) lowest absolute values of SES (on average it was

0.33) (ii) lowest variance of SES (σMW = 0.2) and (iii) the difference between the SESEW and the

SESMW was relatively small (on average 0.14).

The market weighted index was dominating over the whole period in the sense that SESMW >

SESMW in every quarter. This important difference could be explained in two ways: (i) the com-

panies that were excluded from the calculations of the SESMW absorbed the effects of the crisis quicker on average or/and (ii) the companies with higher market capitalisation suffered more than

average during the crisis.

In general, the SES during the crisis period was almost 6.02 higher than in uncertainty period

and almost 11 times higher than it was in the calm period.

This classiﬁcation is familiar with the literature. The breakout of the crisis period is in line with

Getter et al.(2007), who claim that the recent ﬁnancial crisis began in mid-August, when several

ﬁnancial institutions faced liquidity problems. In the second quarter of 2009 the effects of the

ﬁnancial crises seem to have been absorbed by the markets and the situation stabilised (NCCFEC,

2011).

The proposed methodology offers also an advantage on looking at the SR from two perspec-

tives, i.e., (i) institution specific and (ii) country specific. Let us first focus on the former.

Figures (2.2) and (2.3) suggest that the two indices yield different results on the sector speciﬁc

SR18. According to the equally weighted index, the biggest contributors to the overall SR were the

banking and the investment companies (on average 46% and 37% respectively). Under the market

18Detailed SR values are reported in the Appendix C. 2.4 Empirical analysis 21 weighted index, the biggest contributor to the overall SR is the banking sector (on average 61%).

The impact of the investment sector is reduced to 10 %. The non-life insurance contributes 21% and life insurance sector 9%.

The discrepancies between two indices are caused by the fact that the investment sector has many companies but on average they are relatively small. There are only a few insurance companies in our sample but together they posses great amount of capital which makes them a very signiﬁcant source of SR. The equally weighted index exacerbates the relative role of the investment sector and decreases the role of the insurance sector. Therefore, I expect the market weighted index to give more credible results.

Figure 2.2 The Equally Weighted Systemic Expected Shortfall (scaled) in different ﬁnan- cial sectors in the Eurozone in years 2006-2010. The fat solid line is the banking sector, the fat dotted line is the investments sector, the slim solid line is the non-life insurance sector and the slim dotted line is the life-insurance sector. Source: Thomson Reuters Datastream.

The last part of the analysis consists of measuring the contribution of each individual Member

State to the European SR19.

The equally weighted index shows that the biggest contributors to the Eurozone’s SR are:

19Detailed SR values are reported in the Appendix D. 2.4 Empirical analysis 22

Figure 2.3 The Market Weighted Systemic Expected Shortfall (scaled) in different ﬁnan- cial sectors in the Eurozone in years 2006-2010. The fat solid line is the banking sector, the fat dotted line is the investments sector, the slim solid line is the non-life insurance sector and the slim dotted line is the life-insurance sector. Source: Thomson Reuters Datastream.

Ireland, Spain, Belgium and Portugal. The market weighted index conﬁrms those results but in the second and fourth quarter of 2009 the Netherlands is the biggest contributor to the overall SR. The discrepancies are again the result of different market structure. In the crisis period both indices show that Ireland, Spain and Portugal have the highest SR. The problems in most risky countries mainly originated in the banking sector.

Luxembourg, Slovenia and Malta have the lowest SR.

2.4.4 Benchmarking

Having calculated the SES variables for Eurozone countries it is of crucial importance to analyse how plausible those results are. In other words, is the ”story” told by the SES is in line with other

ﬁnancial distress indicators.

To check the robustness of the results, two Financial Stress Indices (FSI) have been selected, i.e. the International Monetary Fund Financial Stress Index (IMF FSI) and the Slingenberg/de 2.4 Empirical analysis 23

Table 2.2 The correlation between the Systemic Expected Shortfall Index and IMF FSI (country speciﬁc comparison) in period Q1 2006 - Q2 2009. Source: IMF

Austria Belgium Spain Finland France Germany Italy Netherlands

SESEW 0.85 0.82 0.77 0.65 0.70 0.71 0.78 0.8

SESMW 0.82 0.84 0.81 0.46 0.74 0.77 0.82 0.87

Haan Financial Stress Index (SH FSI) (Slingenberg and De Haan, 2011). The FSI is a measure that

captures stability of the ﬁnancial system. Financial distress is then understood as a time when the

ﬁnancial system is under strain (Balakrishnan et al., 2009). Therefore, FSI also captures SR and

can be used as a benchmark.

When it comes to measuring ﬁnancial stress, the indices mentioned above use two methodolo-

gies. The ﬁrst, applied by the IMF, is a weighted average of ﬁnancial indicators20. Those include

banking variables, spread on treasury bills and the slope of the yield curve21. The SH FSI additionally includes corporate characteristics and exchange rate volatility (Slingenberg and De Haan,

2011).

The country speciﬁc SES is compared to the country IMF and SH FSI22. The pairwise correlation analysis is carried out for the whole period on a quarterly basis. Since both indices ﬁnish in the second quarter of 2009, the time span covers the period Q1 2006 - Q2 2009.

Tables 2.2 and 2.3. show that both indices are always positively correlated with their benchmarking equivalents. This underpins the robustness of the results. Looking at the values, the lowest correlation can be found between the IMF FSI index and the SESMW index in Finland (0.46) and

20We use here the IMF FMI for advanced economies. 21The detailed description of this index can be found in Balakrishnan et al.(2009); Cardarelli and Elekdag(2009). 22The IMF and SH FSI indices are available only for 8 EMU countries, henceforth it was not possible to check its behaviour on the whole Eurozone. However, the countries included in the analysis are the biggest in Europe when it comes to GDP and stock market capitalisation what makes this sample a good approximation of the EMU’s ﬁnancial stability. 2.4 Empirical analysis 24

Table 2.3 The correlation between the Systemic Expected Shortfall Index and Slingen- berg/de Haan Index (country speciﬁc comparison) in period Q1 2006 - Q2 2009. Source: Slindenberg/de Haan.

Austria Belgium Spain Finland France Germany Italy Netherlands

SESEW 0.69 0.66 0.59 0.37 0.54 0.53 0.60 0.65

SESMW 0.83 0.71 0.67 0.12 0.67 0.60 0.67 0.74 the highest in the Netherlands (0.87). On average both SR indices were 0.76 correlated to the IMF

FSI index.

The SH index is less correlated, as expected taking into account additional company and exchange rate specific variables. However, the correlation is still positive. On average the equally weighted index was 0.58 and market weighted index 0.63 correlated to the SH FSI, which confirms that the behaviour of the SES indices is similar to other financial distress indicators.

In the majority of countries, the weighted index shows higher correlation with the IMF/SH

FSI23. This could be explained by the fact that the IMF and SH FSI are also market-weighted indices so it captures the heterogeneity of the ﬁnancial institutions in the same manner as the weighted SES.

23The only exception here are Austria and Finland. This phenomenon can be explained by the fact that the weighted index is mostly influenced by the Erste Group Bank in Austria and SAMPO ’A’ in Finland, which have weights of 0.74 and 0.78 in the country’s sample respectively. The behaviour of one bank/insurance company does not have to reflect the situation in the whole financial system, especially if it the bank/insurance company covers almost three quarters of the whole market and is very likely to receive governmental support first during the distress. The institutions’ size in other countries is more dispersed. Chapter 3

The inﬂuence on countries’ stability

3.1 When is a country stable?

To ﬁnance government expenditures, countries often raise funds through debt issuing. This debt, commonly in the form of bonds, bills or securities1, commits government to repay the loan in a given period and with a certain interest. Investors used to treat those bonds as risk-free (Bodie et al., 2004). However, as history shows, excessive sovereign debt ﬁnancing may lead to a crisis and eventually to a country’s default (Yue, 2009). Since the end of Napoleonic Wars there were

250 defaults of 106 countries (Tomz and Wright, 2007).

There are many factors that may disturb a country’s ability to repay its obligations. We may distinguish between macroeconomic fundamentals (Hilscher and Nosbusch, 2007) and political fundamentals (Satyanath and Subramanian, 2004). The former comprises the level of indebtedness (both primary deficit and interests), terms of trade and their volatility, inflation level, growth of GDP etc. Following Satyanath and Subramanian(2004), the latter could be described as dis- tributive conflicts, quality of political institutions and political openness.

For the purpose of this study, the stability of a country has been deﬁned as a probability of

1For transparency, I name those products simply by ”bonds”. 25 3.2 Systemic Risk and a country’s stability 26 not-repaying sovereign debt (a default).

Looking at the country’s stability from the perspective of its ability to repay its ﬁnancial obligations brings several advantages. First, it comprises both economic and political fundamentals.

In fact, since both of them are intertwined2, one should take the complete measure into account.

Second, the deﬁnition is intuitive what makes the reasoning more transparent as well as easier to understand.

Cantor and Packer(1995) suggest the Credit Default Swap as a good proxy for the investors’ uncertainty about a product. Following this reasoning, a country’s default risk exposure should be included in the Credit Default Swaps on government bonds, i.e. the Sovereign Credit Default

Swaps (SCDS).

Credit Default Swaps are a kind of insurance contracts where one party agrees to pay a compen- sation when a previously deﬁned event occurs (Packer and Suthiphongchai, 2003). In the SCDS, such an event is usually a default of a country and the price of the SCDS (in thousands) describes the price of the insurance contract that covers 10 million of a country’s debt. So, the SCDS suits the purpose of this study well as it reﬂects a country’s default risk from the investors’ perspective.

3.2 Systemic Risk and a country’s stability

During the recent global financial crisis, government support was necessary to fight the consequences of the financial downturn (Schich and Kim, 2010). In other words, the problems of the

ﬁnancial sector were eventually shouldered by governments. By strengthening already existing deposit insurance schemes or by liquidity support, countries internalised the external costs of systemic crisis. Especially, taking into account the 2007-2009 crisis, its ﬁscal costs tend to be relatively high (IMF, 2009).

2As pointed out by Hilscher and Nosbusch(2007); Satyanath and Subramanian(2004), political condition of a country may affect its economic performance and vice versa. 3.3 Data 27

Schich and Kim(2010) point out, that systemic crisis is the materialisation of SR. Therefore,

SR inﬂuences country’s performance indirectly, through the channel of rescue packages when a crisis occurs.

Additionally, since the ﬁnancial sector plays a central role in the economy, systemic crisis can lead to real output losses (Schich and Kim, 2010). The macroeconomic performance of a country is disturbed what can eventually drive the country’s stability down (Hilscher and Nosbusch, 2007).

This research assesses whether SR is sufﬁcient enough to be recognised as a potential threat to a country’s stability.

3.3 Data

As a proxy for SR the SES is used. The data for the SCDS comes from Thomson Reuters Datas- tream and covers the period Q1 2006 - Q3 2010 on a daily basis3. For robustness check, the analysis is carried out on the SCDS with maturities of 3, 5 and 7 years. The SCDS are denominated in Euro.

Following previous studies, (Hilscher and Nosbusch, 2007; Satyanath and Subramanian, 2004), the following control variables have been chosen: (i) the growth rate of GDP, (ii) general government consolidated debt and (iii) political stability. Consolidated government debt is the cumulative indebtedness of a country expressed as a percentage of GDP. Political stability is approximated by the Political Stability Index (PSI), created by the World Bank. According to Kaufmann et al.

(2010), it represents ”the aggregate views of companies, citizens and expert survey respondents”.

The PSI is in the form of a country’s percentile rank among all other countries. 0 corresponds to the lowest and 100 to the highest political stability.

The data on GDP growth and debt comes from Eurostat. The time span is 2006-2010. For countries that joined the EMU after 2006, the dataset includes the period one year prior to the

3Since the data on the SCDS is not available for Luxembourg, it has been excluded from the dataset. Slovakia has been also excluded as it joined the EMU in 2009, eventually yielding only two years of observations. 3.3 Data 28 accession. This gives additional degrees of freedom to the regressions, which should translate into higher explanatory power of the model (Woolridge, 2009). Even a small number of missing observations in such a small dataset could signiﬁcantly alter the results (Woolridge, 2009).

Table 3.1 provides an overview of the data. The data are on a yearly basis as political stability and debt are available at this frequency only.

Table 3.1 Descriptive statistics of the aggregate Eurozone’s dataset. The SCDS are expressed in 10 thousand Euro, Debt as a percentage of the GDP, GDP growth as a yearly percentage change and political stability as an average percentile rank. Source: Datas- tream, Eurostat, World Bank.

Year Statistic SCDS (3y) SCDS (5y) SCDS (7y) GDP growth Debt P. Stability

2006 Obs 11 11 11 12 12 12 Mean 5.875 7.946 13.603 3.650 60.483 75.767 Std. Dev. 5.362 7.807 12.628 1.382 28.938 13.144

2007 Obs 14 14 14 14 14 14 Mean 3.796 5.824 9.268 3.900 58.149 75.864 Std. Dev. 2.856 4.972 7.701 1.489 25.988 15.811

2008 Obs 14 14 14 14 14 14 Mean 28.976 39.288 44.216 1.186 62.500 76.793 Std. Dev. 13.242 18.358 21.218 2.185 25.617 16.613

2009 Obs 14 14 14 14 14 14 Mean 116.143 129.875 129.613 -4.314 72.674 72.107 Std. Dev. 61.046 64.650 65.270 2.203 25.978 16.992

2010 Obs 14 14 14 14 14 14 Mean 160.518 169.137 167.931 1.221 79.273 74.414 Std. Dev. 180.278 161.231 150.776 2.090 27.903 9.211

In 2006 there were 12 countries in the sample (including one prior year of Slovenia). The missing observations are generated by Finland, which did not have data on the SCDS available. 3.3 Data 29

As it could be observed, the SCDS of all maturities behaved similarly, i.e. first there was a slight fall in 2007 and then all of them were rising continuously until 2010. Worth noting is the fact that the average 3-year SCDS is always the smallest. In the 5- and 7-year SCDS there was a turnabout in 2009 so that the shorter term SCDS became more expensive than the longer-term. In fact, this phenomenon can be recognised as a transition from a normal to a humped (or bell-shaped) yield curve (Bodie et al., 2004)4. A humped yield curve is often perceived as a sign of economic slowdown (CS, 2006). Economic growth was minor after 2007 (in 2009 some countries even faced a recession) confirming the market’s expectations, observed in the yield curve. However, not all of the Euro countries faced the problem of a humped yield curve. It was mostly visible in three countries with the highest SCDS’s levels, i.e. Greece, Ireland and Spain5. Those countries were having most severe budget difficulties (Blackstone et al., 2010). In fact, since investors discount the far future more heavily (Bodie et al., 2004), this could have convinced them that the short-term bonds are more risky than the long term.

The PSI reached the pit in 2009. The debt variable was steadily increasing since 2007.

The standard deviations of the SCDS and debt were increasing with the mean since 2007. The standard deviations of other variables were steadily increasing with a slight drop in 2010. This additionally conﬁrms that the discrepancies between the Member States were rising over most of the period.

Since consolidated debt and political stability are available only at the annual basis, the whole dataset is annualised. Marcellino(1999) points out that temporal aggregation of time series often alters short-term stochastic properties that exist at the disaggregated frequency. The main purpose of this study concentrates on the long-run impact of SR on the country’s stability. The short-term anomalies do not bias the ﬁnal results eventually. However, to assure that temporal aggregation

4Normally, the short-term rates should be lower because of the maturity risk. A bell-shaped curve is characterised by the higher rates of the middle maturity bonds. 5In 2010, Greece had even the inverted yield curve. 3.4 Methodology 30 does not damage the credibility of the results, its possible long-run threats are investigated.

The long-run characteristics that may be affected by temporal aggregation are exogeneity and causality (Marcellino, 1999; Engle et al., 1983)6. Both of them seem to be a vital problem that is not only originated in data aggregation but was already mentioned in the literature on ﬁnance

(Beck, 2008). Therefore, the research technique proposed below corrects for the endogeneity and reverse causation problems.

3.4 Methodology

To determine the inﬂuence of SR on a country’s stability, the Dynamic Panel Technique (DPT) is applied. The method brings several advantages over the pure cross-country or time series models.

First, by getting both cross-sectional and time dimensions we gain additional degrees of freedom

(Beck et al., 2000). In fact, with a relatively small dataset this should contribute to the robustness and credibility of the analysis. Second, the DPT limits the country-speciﬁc omitted variable bias.

A possible correlation of unobserved country-specific characteristics [µ] with other explanatory variables may significantly exacerbate the coefficient estimates (Beck et al., 2000). The method used in this study corrects for this. Third, the DPT controls for the endogeneity, and thereafter reverse causation, of the explanatory variables7 (Beck et al., 2000; Roodman, 2006). As stated in

6As a long-run property, Marcellino(1999) investigates also the stationarity of the time-series and the cointegration relationship. He shows that both of them are invariant to temporal aggregation. 7In fact, the DPT corrects for the weak version of endogeneity. The variables are weakly endogenous when the explanatory variables can be affected by the current and past realisations of the response variable, but not by its future realisations (Beck et al., 2000). Turning to the research question, it means that the future realisations of the SCDS should not affect SR today. This does not mean, however, that the financial markets do not include expected future rates of the SCDS. It rather means that the future shocks to SCDS do not affect the current financial markets’ behaviour (Beck et al., 2000). The assumption of weak endogeneity is not very stringent then, especially taking into account that its statistical significance is assessed. 3.4 Methodology 31 the previous section, the nature of the financial data and its time aggregation could cause inappro- priate inference (Beck, 2008; Marcellino, 1999). Therefore, the DPT is of a great advantage as it can solve some of the data-originated shortcomings.

The system GMM estimator is applied to solve the model. As proved by Roodman(2006), it fully comprises the characteristics of the dataset and the DPT. For transparency reasons, the complete description of the method can be found in Appendix E.

Eventually, the moment conditions for the system GMM can be written as:

0 E[Xi,t−sK(µi + υi,t)] = 0 for s ≥ 1 and t > 1, (3.1)

E[(Xi,t−1 − Xi,t−2)(µi + υi,t)] = 0 for t > 2, (3.2) where X is the set of explanatory variables, µ is an unobserved country-speciﬁc component and υ is the time-speciﬁc component. Subscripts i and t refer to a country and a year respectively. K is the orthogonal deviations transformation matrix.

Additionally, following La Porta et al.(1997); Beck(2008); Beck et al.(2000); Beck and

Levine(2003) ,this research uses legal origins as external instruments in the level equation 8. The majority of countries obtained their legal systems by colonisation or occupation. Therefore, we may consider them exougenous (Beck, 2008). At the same time, La Porta et al.(1997) proves that legal origins signiﬁcantly contribute to the level of ﬁnancial development in a cross-section. Those two aspects make legal origins valid instruments in this research which creates additional set of moment conditions, i.e.:

E[(Zi,t)(µi + υi,t)] = 0 for t > 0, (3.3) where Z is the matrix of legal origin dummies.

To address the possible heteroscedasticity and autocorrelation of the error terms, a two-step estimator is applied (Beck et al., 2000). To correct for small sample size, the Windmeijer correc-

8The data on legal origins come from the World Bank. Because of the colinearity, three legal origin types are taken into account: British, French and German. 3.5 Results 32

tion and small-sample adjustment are exerted (Roodman, 2006). The former limits the downward

bias originated in ﬁnite samples (Windmeijer, 2005). The latter applies more accurate statistic

distribution so that the inference on a small sample is more reliable9.

The validity of the instruments is being checked by the Sargan/Hansen tests. Additionally, the autocorrelation test is being performed. Eventually, all of the assumptions of the model are being checked statistically. If they are satisﬁed, a system GMM yields consistent and efﬁcient estimates.

3.5 Results

The inﬂuence of SR on the EMU’s Member States’ stability is investigated through the framework proposed in the previous section. Since SR is approximated by the equally and market weighted

SES, both of them are examined. Additionally, the model is being applied to the SCDS of 3-, 5- and 7-year maturities. Due to colinearity between political stability and debt, those variables enter the model separately.

Roodman(2006) points out that too many instruments may yield overidentiﬁcation problems and eventually lead to wrong inference. In fact, Sargan(1958) states clearly that the error of his test statistics increases with the number of instruments. As an arbitrary rule of thumb, Windmeijer

(2005) suggests that the number of instruments should not exceed the number of groups in the sample. However, as he also admits, this approximation is very general. Roodman(2006) suggests to check the robustness of the analysis by ”playing” with the number of instruments.

To address the problem of too many instruments, the following study uses: (i) the collapsed form of instruments10 and (ii) only one lag of the explanatory variables for the transformed equation (3.1)11. Additionally, the sensitivity of the inference is investigated by excluding the lagged

9In fact, after the adjustment, coefficients follow a t-Student distribution. 10As pointed out by Roodman(2006), this transformation offers the same expectations about the estimates with lower number of instruments. 11Because first lags of the explanatory variables in the equation (3.1) were weak instruments, second lags are used 3.5 Results 33 differences of the explanatory variable as instruments. In other words, the equation (3.2) is being deleted from the model. This lowers the number of instruments and ipso facto increases the power of overidentification tests.

The results for the 3-, 5- and 7- year SCDS are presented in Tables 3.2, 3.3 and 3.4 respectively.

Table 3.2 The results of the system GMM estimation of the inﬂuence of SES on the country’s stability approximated by the 3-year SCDS.

1 2 3 4 5 6 7 8

SCDSt−1 1.3 1.38 1.28 1.41 1.31 1.34 1.39 1.46 (0.003)*** (0.000)*** (0.023)** (0.002)*** (0.080)* (0.046)** (0.019)** (0.050)**

SESEW 13.80 - 11.01 - 12.68 - 17.41 - 0.256 0.280 0.425 0.202

SESMW - 11.40 - 8.78 - 13.78 - 18.35 0.181 0.319 0.230 0.257 GDP growth -10.56 -9.96 -8.92 -9.13 -12.78 -13.68 -9.07 -10.41 (0.001)*** (0.000)*** (0.002)*** (0.001)*** (0.010)*** (0.008)*** (0.097)* (0.024)** Debt 0.45 0.99 - - 0.62 0.96 - - 0.504 0.234 0.430 0.250 Political stability - - -1.70 -1.88 - - -0.76 -0.49 0.360 (0.068)* 0.592 0.777 Const -24.31 -65.51 144.68 152.00 -33.81 -71.32 59.17 29.94 0.611 0.301 0.336 0.073 0.612 0.304 0.615 0.830

Sargan p-value 0.104 0.090 0.152 0.239 0.124 0.086 0.134 0.286 Hansen p-value 0.637 0.612 0.351 0.516 0.235 0.237 0.168 0.323 Autocorrelation (2) 0.393 0.326 0.524 0.406 0.537 0.503 0.310 0.343 # of instruments 12 12 12 12 8 8 8 8 # of observations 53 53 53 53 53 52 53 52

P-values in parentheses. Two step estimation procedure with small sample size correction, Windmeijer correction and FOD transformation. Legal origins as external instruments. Columns (1-4) show the complete model and (5-8) the model without lagged differences as instruments. *, **, *** mean 10%, 5% and 1% signiﬁcance levels respectively. instead. 3.5 Results 34

Table 3.3 The results of the system GMM estimation of the inﬂuence of SES on the country’s stability approximated by the 5-year SCDS.

1 2 3 4 5 6 7 8

SCDSt−1 1.29 1.33 1.21 1.34 1.22 1.31 1.30 1.35 (0.000)*** (0.000)*** (0.005)*** (0.001)*** (0.032)** (0.022)** (0.011)** (0.018)**

SESEW 15.28 12.76 13.30 18.84 (0.096)* 0.109 0.291 0.135

SESMW 13.49 11.68 16.40 18.54 (0.096)* 0.112 0.153 0.144 GDP growth -10.17 -9.37 -8.63 -8.63 -11.87 -12.84 -9.57 -10.34 (0.003)*** (0.000)*** (0.002)*** (0.001)*** (0.013)** (0.016)** (0.089)* (0.026)** Debt 0.31 0.76 0.45 0.80 0.623 0.373 0.555 0.359 Political stability -1.10 -1.33 -0.39 -0.31 0.539 0.417 0.786 0.848 Const -14.98 -52.59 96.93 105.57 -19.21 -62.97 32.27 18.99 0.705 0.402 0.509 0.398 0.755 0.422 0.786 0.885

Sargan p-value 0.172 0.133 0.153 0.257 0.161 0.101 0.095 0.263 Hansen p-value 0.677 0.710 0.412 0.578 0.279 0.304 0.172 0.382 Autocorrelation (2) 0.332 0.297 0.411 0.339 0.398 0.381 0.308 0.270 # of instruments 12 12 12 12 8 8 8 8 # of observations 53 52 53 52 53 52 53 52

P-values in parentheses. Two step estimation procedure with small sample size correction, Windmeijer correction and FOD transformation. Legal origins as external instruments. Columns (1-4) show the complete model and (5-8) the model without lagged differences as instruments. *, **, *** mean 10%, 5% and 1% signiﬁcance levels respectively. 3.5 Results 35

Table 3.4 The results of the system GMM estimation of the inﬂuence of SES on the country’s stability approximated by the 7-year SCDS.

1 2 3 4 5 6 7 8

SCDSt−1 1.33 1.41 1.27 1.39 1.38 1.45 1.35 1.46 (0.000)*** (0.000)*** (0.002)*** (0.000)*** (0.022)** (0.008)*** (0.014)** (0.018)**

SESEW 17.53 15.21 16.95 20.96 (0.054)* 0.106 0.150 0.140

SESMW 15.78 13.63 18.53 22.40 (0.066)* 0.107 (0.063)* 0.122 GDP growth -9.38 -8.46 -7.43 -7.53 -10.15 -11.72 -8.54 -9.61 (0.006)*** (0.000)*** (0.013)** (0.001)*** (0.045)** (0.034)** 0.105 (0.034)** Debt 0.12 0.49 0.16 0.54 0.850 0.595 0.846 0.573 Political stability -0.62 -0.86 -0.02 0.14 0.718 0.624 0.987 0.931 Const -8.57 -44.15 52.67 62.82 -12.42 -56.15 -1.67 -28.95 0.828 0.497 0.710 0.643 0.830 0.441 0.989 0.829

Sargan p-value 0.410 0.242 0.176 0.242 0.292 0.175 0.087 0.268 Hansen p-value 0.712 0.779 0.495 0.634 0.310 0.369 0.163 0.354 Autocorrelation (2) 0.277 0.242 0.264 0.236 0.272 0.261 0.225 0.236 # of instruments 12 12 12 12 8 8 8 8 # of observations 53 52 53 52 53 52 53 52

P-values in parentheses. Two step estimation procedure with small sample size correction, Windmeijer correction and FOD transformation. Legal origins as external instruments. Columns (1-4) show the complete model and (5-8) the model without lagged differences as instruments. *, **, *** mean 10%, 5% and 1% signiﬁcance levels respectively.

In the regression for the 3-year SCDS, neither of SES enters the model significantly. However, all of the coefficients are of expected sign. A higher GDP growth, the same as political stability, should convince financial markets about the stability of a country (Hilscher and Nosbusch, 2007;

Satyanath and Subramanian, 2004). In contrast, high level of indebtedness should be a sign of a possible stability’s threat (Hilscher and Nosbusch, 2007).

The influential variables are lagged SCDS and GDP growth. In the regression number (4) political stability enters the model at the 10% significance level. The overidentification is found 3.5 Results 36

to be not a problem at a 8% signiﬁcance level, according to the Sargan test and at conventional

signiﬁcance levels according to the Hansen test12. Standard errors do not show autocorrelation.

The change in the number of instruments does not alter the results much.

Looking only at the 3-year SCDS narrows the analysis. To get a broader view of the topic, we should also investigate 5- and 7-year SCDS. In fact, the influence of SR on the SCDS proves to be positive and significant at the 10% significance level. Changing the number of instruments increases the p-values only slightly above the 10% significance level. Taking into account the small sample size with some missing observations, one may consider that the results prove the actual causal relationship between SR and long-term sovereign stability. The models pass the overidentification tests and prove no second order autocorrelation problems. All the remaining explanatory variables are almost always of expected sign13. The statistical importance of debt and political stability variable has significantly decreased. The influence of lagged SCDS and GDP growth is still significant at the conventional significance levels.

Worth noting is the fact that the regressions with the market weighted index and debt pass the

Sargan overidentiﬁcation test at the lowest signiﬁcance levels, compared to the equally weighted index regressions. The explanation for that might be twofold. First, the companies that are excluded from the market weighted index, could actually cause the moment condition (3.1) to hold.

By not taking them into account, we limit the explanatory power of the model. Second, the market structure itself may be correlated with the unobserved country speciﬁc effect [µ] what additionally violates the condition (3.1).

As pointed out by Roodman(2006), one should rather not believe small Sargan p-values, especially with the relatively large number of instruments. However, the majority of regressions presented in Tables 3.2, 3.3 and 3.4 pass the overidentiﬁcation tests even at higher than 10% sig-

12The null hypothesis for the Sargan/Hansen tests assumes the validity of instruments. 13The exception here is the political stability variable in the regression (8) in the 7-year CDS analysis. At the same time, however, it is statistically not different than zero so that we may not consider it as a threat. 3.5 Results 37 niﬁcance levels. Additionally, decreasing number of instruments does not alter the estimates much.

Although this does not prove explicitly that SR may bring a country down, it could be a signal that

SR may inﬂuence long-term country’s stability.

Quantifying the inﬂuence, a one standard deviation increase in SESEW increases the 5-year

14 SCDS from 19.24 to 28.4 thousands Euro and in SESMW from 27.53 to 43.69 thousands Euro, ceteris paribus. The inﬂuence in 7-year SCDS is higher. In the equally weighted index ﬁts in range from 22.94 to 31.6 and in the market weighted index from 32.13 to 52.8 thousands Euro.

The pattern from the analysis proves that the inﬂuence of SR increases and becomes more signiﬁcant with longer maturity of SCDS. The reason for that can be threefold. First, short term

SCDS could be heavily inﬂuenced by shocks (Bodie et al., 2004). The pure relationship between

SR and SCDS could be blurred by other factors which effects evaporates in the long run. Second, as proved by the recent global financial crisis, governments are ready to bail out systemic risky companies (Blackstone et al., 2010). In words, the influence of the short term SR can be absorbed by the government, which additionally can alter the short term results. Third, the inference can be affected by the ”humped” yield curve that was observed in the data. In words, the dataset could be influenced by other macroeconomic factors that affected the difference in patterns between 3-, 5- and 7-year SCDS.

Small sample size and temporal aggregation can alter the statistical characteristics of the dataset

(Marcellino, 1999). The Windmeijer’s transformation can correct for the former, however, the latter still remains a problem. In other words, it is possible that there are some patterns on a quarterly basis that evaporate when the time series are aggregated to years.

Figures (3.1) and (3.2) present correlation plots between SES and SCDS for the Eurozone. The period Q1 2009 has been excluded from the analysis as it was more than 3 standard deviations

14The calculations are based on the standard deviation of variables for the whole Eurozone. The parameters are taken as the minimal and maximal signiﬁcant SES coefﬁcients from Tables 3.3 and 3.4. 3.5 Results 38 from the trend and it was treated as an outlier.

Figure 3.1 Scatterplot between the equally weighted SES and SCDS in the Eurozone in period Q1 2006 - Q3 2010. Green (up peak) triangles represent 3-year, yellow (right peak) 5-year and blue (left peak) represent 7-year average SCDS. Red (dotted) line is a ﬁtted regression line.

Figures (3.1) and (3.2) reveal a possible explanation why the significance of the SES coefficients from the regressions dropped below the 10% level. We may observe that there is a change in the correlation pattern between SES and SCDS after a certain threshold level for SES. First, the correlation coefficient between SES and SCDS is high but not significant15. After the threshold level is reached, the correlation coefficient decreases (but remains positive) but becomes significant

(at standard signiﬁcance levels). Looking at the dataset as a whole, one would get no signiﬁcant correlation results.

This brings forward an important question of the nature of the relationship between SR and a country’s stability. GMM regressions show that there is a weak linear relationship. Examining the data in detail, however, proves that this connection might be non-linear. This means that SR

15Statistical properties were examined using Ordinary Least Squares (OLS). 3.5 Results 39

Figure 3.2 Scatterplot between the market weighted SES and SCDS in the Eurozone in period Q1 2006 - Q3 2010. Green (up peak) triangles represent 3-year, yellow (right peak) 5-year and blue (left peak) represent 7-year average SCDS. Red (dotted) line is a ﬁtted regression line.

posses no threat to a country’s stability when it is at relatively low levels. However, after it crosses a certain threshold, it significantly affects sovereign stability. One may consider this situation as an effect of ”black swans”. In normal times, financial markets do not consider SR as being a serious threat. However, its importance starts increasing during financial distress, when it is relatively higher.

To approximate the threshold level, the relationship between SR and SCDS in different quan- tiles of SES is examined. The estimation window has been assumed to be 20 percentiles long. It is then ”moved” by 1 percentile up, until the whole sample is covered16. The relationship is measured using OLS. The estimation is carried out between equally weighted SES, market weighted

SES and SCDS of all maturities. The results are presented in Figures (3.1) and (3.2).

16In other words, the ﬁrst regression checks the relationship in the 20th percentile of SES, the second between 1st and 21st percentile etc. 600 600 600 200 200 200 0 0 0 -200 -200 -200 -600 -600 -600 Equally Weighted SES on 3-year SCDS SES 3-year on Weighted Equally SCDS SES 5-year on Weighted Equally SCDS SES 7-year on Weighted Equally 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80

Starting Percentile Starting Percentile Starting Percentile 300 300 300 200 200 100 100 100 0 0 0 -100 -200 -200 Market Weighted SES on 3-year SCDS SES 3-year on Market Weighted SCDS SES 5-year on Market Weighted SCDS SES 7-year on Market Weighted 0 20 40 60 80 0 20 40 60 80 0 20 40 60 80

Starting Percentile Starting Percentile Starting Percentile

Figure 3.3 The inﬂuence of SES on SCDS in different percentiles of SES. The whole sample. Estimation window of 20 percentiles. The shaded area is the 5% conﬁdence interval. 20 20 20 10 10 10 0 0 0 -10 -10 -10 -20 -20 -20 Equally Weighted SES on 3-year SCDS SES 3-year on Weighted Equally SCDS SES 5-year on Weighted Equally SCDS SES 7-year on Weighted Equally 60 65 70 75 80 60 65 70 75 80 60 65 70 75 80

Starting Percentile Starting Percentile Starting Percentile 20 20 20 10 10 10 0 0 0 -10 -10 -10 -20 -20 -20 Market Weighted SES on 3-year SCDS SES 3-year on Market Weighted SCDS SES 5-year on Market Weighted SCDS SES 7-year on Market Weighted 60 65 70 75 80 60 65 70 75 80 60 65 70 75 80

Starting Percentile Starting Percentile Starting Percentile

Figure 3.4 The inﬂuence of SES on SCDS in different percentiles of SES. The top percentiles of the SES. Estimation window of 20 percentiles. The shaded area is the 5% conﬁdence interval. 3.5 Results 42

The results of the regressions conﬁrm the pattern from Figures (3.1) and (3.2). In lower per-

centiles of SES, the inﬂuence is highly volatile and not signiﬁcantly different from 0. For higher

percentiles the volatility decreases and the inﬂuence becomes signiﬁcant and positive17. Figure

(3.4) zooms in the top percentiles of SES. The threshold level for the equally weighted SES is around 79th percentile of the distribution. For the market weighted index the threshold line can be draw already around 70th percentile. The difference in those quantities is again driven by market characteristics. Because the market weighted index includes market structure, it is more accurate.

In other words, when a market distress comes to the economy it is captured by the market weighted index first. This translates into the lower threshold level of SESMW . Both indices show, that the influence of SR on SCDS varies in different percentiles of the distribution of SR. For low SR this influence is not significant. However, going to the right tail of the distribution, the impact becomes more positive and significant.

Although, the shortcomings of the dataset could alter the statistical properties of the results, robustness checks proves that SR positively inﬂuenced Euro countries’ stability in years 2006-

2010, especially in the long run. There is evidence that this relationship is non-linear.

Looking at the Eurozone a year after, we may observe even more severe consequences of SR and the crisis. The threat of bankruptcy of some of the Member States has been brought forward

(Blackstone et al., 2010). The importance of SR has been recognised by Euro countries. At the end of 2010 they created the European Systematic Risk Board (ESRB) to deal with the macro prudential supervision and detect SR in the Euro area (Eijfﬁnger, 2011).

This research pioneers the quantiﬁcation of the inﬂuence of SR on a country’s stability using the system GMM estimator. The framework proposed in this Thesis could be applied to analyse other regions as well as it could be easily extended to measure other factors that affect country’s

17The coefﬁcients between 40th and 65th percentile are negative and signiﬁcant. This is the result of the transition from the below-threshold to the above-threshold subsample. 3.5 Results 43 stability. Chapter 4

Conclusions

This Thesis describes two exercises that have been made to measure the inﬂuence of Systemic Risk

(SR) on sovereign stability in the Eurozone. First, SR has been calculated using the methodology

proposed by Acharya et al.(2010) over the years 2006-2010. Second, the system GMM estimator

is applied to quantify its direct impact on the country’s stability, approximated by the Sovereign

Credit Default Swaps (SCDS). The dataset comprises banks, investment and insurance companies

that are listed on the Eurozone market. Together 237 ﬁrms have been analysed. Individual SR

has been averaged over the countries and sectors using two indices: the equally weighted and the

market weighted.

The evolution of SR allowed to distinguish three sub-periods: Q1 2006 - Q2 2007 (uncertainty period), Q2 2007 - Q2 2009 (crisis period) and Q2 2009 - Q3 2010 (calm period). This classiﬁca- tion is in line with the literature (Getter et al., 2007). The biggest contributor to the overall SR was the banking sector, followed by investment and insurance sectors. Countries that faced highest SR levels were Ireland, Spain, Belgium and Portugal, in contrast to Luxembourg, Slovenia and Malta which were least systemic risky.

The influence of SR on SCDS have been measured using the estimator proposed by Arellano and Bond(1991). SCDS of 3-, 5-, and 7-year maturities have been examined. The results prove 44 45 that SR positively and significantly affects country’s stability, especially in the long run. Moreover, the analysis of the correlation coefficient between SR and SCDS shows that this relationship could be non-linear. After a certain threshold level, SR affects SCDS more significantly. The threshold levels have been approximated at the 79th (for the equally weighted index) and 70th percentile (for the market weighted index) of the distribution of SR.

SR could influence sovereign stability because (i) governments internalise the external costs of financial distress and/or (ii) financial sector is heavily linked to the economy so that a crisis can lead to real output losses. Short term stability proves to be not significantly influenced by

SR, although the coefficient remains still positive. The explanation of this phenomenon can be threefold. First, short term SCDS could be heavily influenced by shocks (Bodie et al., 2004). The pure relationship between SR and SCDS could be blurred by other factors which effects evaporates in the long run. Second, as proved by the recent global financial crisis, governments are ready to bail out systemic risky companies (Blackstone et al., 2010). In words, the influence of the short term SR can be absorbed by the government, which additionally can alter the short term results.

Third, the inference can be affected by the ”humped” yield curve that was observed in the data. In words, the dataset could be inﬂuenced by other macroeconomic factors that affected the difference in patterns between 3-, 5- and 7-year SCDS.

Although Acharya et al.(2010) proposes ex ante method of quantifying SR, empirically its consequences are measured ex post. The model should not be used to forecast SCDS for the future periods, especially that (i) it is inﬂuenced by other economic and political factors (as stated above) and (ii) the methodology used in this Thesis aims to measure the importance of SR for the SCDS and not forecasting SCDS itself.

This Thesis offers a simple framework to analyse the impact of SR on country’s stability. Since the sample used in this research is relatively small (Roodman, 2006; Beck et al., 2000), its possible extensions should include improvements in the size of the dataset, both when it comes to the 46 amount of countries and periods. Given the complexity of the topic, a continuation of this study should investigate also the impact of other factors on sovereign stability, like global imbalances, terms of trade etc. Moreover, Eijfﬁnger(2011) points out that to get a better understanding of the

SR’s behaviour, one should apply its different measurement techniques.

Having a better comprehension of the relationship between SR and sovereign stability, prudential supervisors and governments would be able to deal with ﬁnancial distress more effectively.

When we compare SR to the rainfall that allows the seeds of crisis to grow (Eijfﬁnger, 2011), the understanding of this topic would serve as an umbrella. Appendix A

Table 1 The detailed list of the companies used for calculations of the Systemic Expected Shortfall (SES).

Banks Banks (continued) Investment Companies (continued)

Name Country 81 BANCO ESPANOL DE CREDITO Spain 42 SC.FONFNC.ET DE PARTS. France 1 BKS BANK Austria 82 BANCO SANTANDER Spain 43 SOFRAGI France 2 ERSTE GROUP BANK Austria 83 BANKINTER ’R’ Spain 44 VERNEUIL PARTICIPATIONS France 3 OBERBANK Austria Life Insurance 45 VIEL ET CIE France 4 OEST.VOLKSBANKEN PC. Austria Name Country 46 WENDEL France 5 BANQUE NALE.DE BELGIQUE Belgium 1 AGEAS (EX-FORTIS) Belgium 47 AAREAL BANK Germany 6 DEXIA Belgium 2 LIBERTY LIFE INSURANCE Cyprus 48 ADCAPITAL Germany 7 KBC GROUP Belgium 3 CNP ASSURANCES France 49 ALBIS LEASING Germany 8 HELLENIC BANK Cyprus 4 CASH LIFE Germany 50 ALLERTHAL Germany 9 MARFIN POPULAR BANK Cyprus 5 WURTTEMBERGISCHE LEB. Germany 51 BAADER BANK Germany 10 USB BANK Cyprus 6 MEDIOLANUM Italy 52 BERLINER EFFTG. Germany 11 AKTIA ’A’ Finland 7 AEGON Netherlands 53 BMP Germany 12 ALANDSBANKEN ’A’ Finland 8 ING GROEP Netherlands 54 CAM Germany 13 POHJOLA PANKKI A Finland Non-Life Insurance 55 CAMBODGE (CIE DU) Germany 14 BANQUE REUNION France Name Country 56 CAPITAL STAGE Germany 15 BANQUE TARNEAUD France 1 UNIQA VERSICHERUNGEN Austria 57 CARTHAGO CAPITAL Germany 16 BNP PARIBAS France 2 COSMOS INSURANCE Cyprus 58 COMDIRECT BANK Germany 17 CIC ’A’ France 3 MINERVA INSURANCE CO. Cyprus 59 CONCORD INVESTBK. Germany 18 CR.AGR.ALPES PROVENCES France 4 SAMPO ’A’ Finland 60 DAB BANK Germany 19 CR.AGR.SUD RHONE ALPES France 5 APRIL GROUP France 61 DEUTSCHE BALATON Germany 20 CR.AGRICOLE MORBIHAN France 6 AXA France 62 DEUTSCHE BOERSE Germany 21 CRCAM ATLANTIQUE France 7 EULER HERMES France 63 DVB BANK Germany 22 CRCAM ILLE France 8 ALLIANZ Germany 64 EUWAX Germany 23 CRCAM NORMANDIE SEINE France 9 GENERALI DTL.HLDG. Germany 65 FALKENSTEIN NEBENWERTE Germany

Continued on the next page

47 48

Table 1 – continued from the previous page

Banks (continued) Non-Life Insurance (continued) Investment Companies (continued)

24 CREDIT AGR.ILE DE FRANCE France 10 HANNOVER RUCK. Germany 66 FEEDBACK Germany 25 CREDIT AGR.LOIRE France 11 MANNHEIMER HOLDING Germany 67 FONDIARIA Germany 26 CREDIT AGR.TOULOUSE France 12 MUENCHENER RUCK. Germany 68 FORIS Germany 27 CREDIT AGR.TOURAINE France 13 NUERNBERGER BETS. Germany 69 GIGASET Germany 28 CREDIT AGRICOLE France 14 RHEINLAND HOLDING Germany 70 GREENWICH BETEILIGUNGEN Germany 29 CREDIT FONCIER DE MONACO France 15 EUROPEAN REL.GEN.INS.CR Greece 71 HEIDELB.BETS.HLDG. Germany 30 SOCIETE GENERALE France 16 FBD HOLDINGS Ireland 72 HSBC TRINKAUS & BURKHD. Germany 31 BANKVEREIN WERTHER Germany 17 GENERALI Italy 73 IMPERA TOTAL RETURN Germany 32 COMMERZBANK Germany 18 MILANO ASSICURAZIONI Italy 74 IPG INV.PTNS.GROUP Germany 33 DEUTSCHE BANK Germany 19 UNIPOL Italy 75 KONSORTIUM Germany 34 IKB DEUTSCHE INDSTRBK. Germany 20 VITTORIA ASSICURAZIONI Italy 76 KST BETEILIGUNGS Germany 35 LANDESBANK BL.HLDG. Germany 21 VOLKSBANK VBG.PC. Italy 77 MISTRAL MEDIA Germany 36 MERKUR BANK Germany 22 FOYER Luxembourg 78 MLP Germany 37 OLDENBURGISCHE LB. Germany 23 PREMAFIN Slovenia 79 MPC MUENCHMEYER CAP. Germany 38 UMWELTBANK Germany Investment Companies 80 MWB FAIRTRADE WERTPAH. Germany 39 AGRI.BANK OF GREECE Greece Name Country 81 PEH WERTPAPIER Germany 40 ALPHA BANK Greece 1 AB EFFECTENBETEILIGUNGEN Austria 82 POMM.PRVZ.ZUCKSIE. Germany 41 ATTICA BANK Greece 2 QINO FLAGSHIP Austria 83 RM RHEINER MANAGEMENT Germany 42 BANK OF GREECE Greece 3 UNTERNEHMENS INVEST Austria 84 SCHERZER & COMPANY Germany 43 BANK OF PIRAEUS Greece 4 WIENER PRIVBK.IM.INVT. Austria 85 SHAREHOLDERS VALUE BET. Germany 44 EFG EUROBANK ERGASIAS Greece 5 ACKERMANS & VAN HAAREN Belgium 86 SM WIRTSCHAFTSBERATUNGS Germany 45 EMPORIKI BK.OF GREECE Greece 6 BELUGA Belgium 87 U C A Germany 46 GENERAL BANK OF GREECE Greece 7 BREDERODE Belgium 88 VALUE Germany 47 MARFIN EGNATIA BANK Greece 8 CIE.DU BOIS SAUVAGE Belgium 89 VENTEGIS CAPITAL Germany 48 NATIONAL BK.OF GREECE Greece 9 GBL NEW Belgium 90 VESTCORP Germany 49 T BANK Greece 10 GIMV Belgium 91 WUESTENROT & WUERTT. Germany 50 ALLIED IRISH BANKS Ireland 11 SOFINA Belgium 92 KOUMBAS HOLDINGS CR Greece 51 BANK OF IRELAND Ireland 12 A L PROCHOICE GROUP Cyprus 93 PARNASSOS ENTERPRISES Greece 52 BANCA CARIGE Italy 13 CCC HOLDINGS&INVESTMENTS Cyprus 94 SCIENS INTL.INVS.&HDG. Greece 53 BANCA FINNAT Italy 14 CPI HOLDINGS PUBLIC Cyprus 95 IFG GROUP Ireland 54 BANCA MONTE DEI PASCHI Italy 15 CYVENTURE CAPITAL Cyprus 96 BANCA IFIS Italy 55 BANCA POPOLARE DI MILANO Italy 16 DEMETRA INVESTMENT Cyprus 97 BANCA INTERMOBILIARE Italy 56 BANCA POPOLARE ETRURIA Italy 17 ELLINAS FINANCE Cyprus 98 BANCA PROFILO Italy 57 BANCA PPO.DI SONDRIO Italy 18 LI MARFIN CLR Cyprus 99 DEA CAPITAL Italy 58 BANCA PPO.DI SPOLETO Italy 19 SAFS HOLDINGS Cyprus 100 EXOR PRV Italy 59 BANCA PPO.EMILIA ROMAGNA Italy 20 SUPHIRE HOLDINGS PUBLIC Cyprus 101 INTEK Italy 60 BANCO DI SARDEGNA RSP Italy 21 CAPMAN ’B’ Finland 102 INVEST E SVILUPPO Italy 61 BNC.DI DESIO E DELB. Italy 22 PANOSTAJA Finland 103 MITTEL Italy 62 CREDITO ARTIGIANO Italy 23 ABC ARBITRAGE France 104 BRAIT Luxembourg

Continued on the next page 49

Table 1 – continued from the previous page

Banks (continued) Investment Companies (continued) Investment Companies (continued)

63 CREDITO BERGAMASCO Italy 24 ARTOIS INDFIN.DE L’ARTO. France 105 GEFINOR Luxembourg 64 CREDITO EMILIANO Italy 25 ASSYA CIE.FINANCIERE France 106 IDB HOLDINGS Luxembourg 65 CREDITO VALTELLINES Italy 26 AVENIR FINANCE France 107 MIDILUX HOLDINGS Luxembourg 66 INTESA SANPAOLO Italy 27 BOURSE DIRECT France 108 QUILVEST Luxembourg 67 MEDIOBANCA Italy 28 BOURSORAMA (EX FIMATEX) France 109 VENTOS Luxembourg 68 UBI BANCA Italy 29 COURBET LIMITED DATA France 110 BINCKBANK Netherlands 69 UNICREDIT Italy 30 ELECTRICITE MADAGASCAR France 111 HAL TRUST Netherlands 70 ESPIRITO SANTO FINL.GP. Luxembourg 31 EURAZEO France 112 HAMBURGER GETR. Netherlands 71 BANK OF VALLETTA Malta 32 FIMALAC France 113 KAS BANK Netherlands 72 FIMBANK Malta 33 FINANC MART MAUREL France 114 DT.EFF.UD.WCH. Slovakia 73 VAN LANSCHOT Netherlands 34 FORESTIERE EQUATORIALE France 115 MAJETKOVY HOLDING Slovakia 74 BANCO BPI Portugal 35 IDSUD France 116 VIPO Slovakia 75 BANCO COMR.PORTUGUES ’R’ Portugal 36 IRDNORDPASDECALAIS France 117 MP NALOZBE Slovenia 76 BANCO ESPIRITO SANTO Portugal 37 LEBON France 118 NFD HOLDING Slovenia 77 DEXIA BANKA SLOVENSKO Slovakia 38 MONCEY FINANCIERE France 119 CARTERA INDUSTRIAL REA Spain 78 OTP BANKA SLOVENSKO Slovakia 39 ODET (FINC DE L’) France 120 CORPORACION FINCA.ALBA Spain 79 TATRA BANKA Slovakia 40 PARIS ORLEANS France 121 DINAMIA CAPITAL PRIVADO Spain 80 VSEOBECNA UVEROVA BANKA Slovakia 41 SALVEPAR France 122 UNION EUROPEA DE INVERS Spain Appendix B

Table 2 The Systemic Expected Shortfall indices for the Eurozone in years 2006-2010. Source: Thomson Reuters Datasream, 2011.

Q1 2006 Q2 2006 Q3 2006 Q4 2006 Q1 2007 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008 Q1 2009 Q2 2009 Q3 2009 Q4 2009 Q1 2010 Q2 2010 Q3 2010 50 SESEW 0.05 1.34 0.18 0.09 0.76 0.12 1.73 1.31 2.88 1.11 3.46 5.89 1.36 0.41 0.10 0.25 0.12 0.40 0.11

SESMW 0.08 1.87 0.27 0.16 1.03 0.21 2.54 1.94 4.20 1.75 5.75 9.12 2.98 0.76 0.16 0.40 0.24 0.66 0.20 Appendix C

Table 3 The equally weighted Systemic Expected Shortfall [SESEW ] indices in different ﬁnancial sectors in the Euro- zone in years 2006-2010. Source: Thomson Reuters Datasream, 2011.

Q1 2006 Q2 2006 Q3 2006 Q4 2006 Q1 2007 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008 Q1 2009 Q2 2009 Q3 2009 Q4 2009 Q1 2010 Q2 2010 Q3 2010 51 Banks 0.02 0.48 0.07 0.04 0.32 0.05 0.69 0.58 1.32 0.51 1.47 2.6 0.73 0.2 0.05 0.12 0.07 0.21 0.06

Nonlife Insurance 0 0.06 0.01 0.01 0.04 0.01 0.08 0.06 0.14 0.07 0.27 0.37 0.12 0.03 0.01 0.02 0.01 0.02 0.01

Life Insurance 0.01 0.13 0.02 0.01 0.07 0.02 0.16 0.14 0.24 0.11 0.34 0.51 0.16 0.04 0.01 0.03 0.01 0.04 0.01

Investments 0.02 0.66 0.08 0.03 0.34 0.04 0.79 0.51 1.13 0.41 1.38 2.38 0.32 0.13 0.03 0.07 0.03 0.12 0.03

sum 0.05 1.34 0.18 0.09 0.76 0.12 1.73 1.3 2.84 1.1 3.46 5.86 1.33 0.41 0.09 0.24 0.12 0.38 0.11

Table 4 The market weighted Systemic Expected Shortfall [SESMW ] indices in different ﬁnancial sectors in the Euro- zone in years 2006-2010. Source: Thomson Reuters Datasream, 2011.

Q1 2006 Q2 2006 Q3 2006 Q4 2006 Q1 2007 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008 Q1 2009 Q2 2009 Q3 2009 Q4 2009 Q1 2010 Q2 2010 Q3 2010

Banks 0.05 0.94 0.16 0.10 0.57 0.12 1.41 1.09 2.38 1.04 3.16 5.26 1.93 0.50 0.10 0.23 0.18 0.48 0.13

Nonlife Insurance 0.02 0.40 0.06 0.04 0.26 0.05 0.47 0.39 0.97 0.39 1.49 2.05 0.56 0.13 0.03 0.06 0.03 0.09 0.03

Life Insurance 0.01 0.14 0.03 0.01 0.07 0.02 0.19 0.15 0.29 0.14 0.64 0.81 0.33 0.08 0.02 0.06 0.02 0.05 0.01

Investments 0.00 0.38 0.02 0.01 0.12 0.03 0.47 0.30 0.56 0.19 0.46 0.99 0.15 0.05 0.01 0.04 0.02 0.03 0.02

sum 0.08 1.87 0.27 0.16 1.03 0.21 2.54 1.94 4.20 1.75 5.75 9.12 2.98 0.76 0.16 0.40 0.24 0.66 0.20 Appendix D

Table 5 The equally weighted Systemic Expected Shortfall [SESEW ] indices in Euro Countries in years 2006-2010. Source: Thomson Reuters Datasream, 2011.

Q1 2006 Q2 2006 Q3 2006 Q4 2006 Q1 2007 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008 Q1 2009 Q2 2009 Q3 2009 Q4 2009 Q1 2010 Q2 2010 Q3 2010 52 Austria 0.04 1.01 0.30 0.03 0.44 0.00 0.99 1.09 2.18 0.68 1.53 3.87 0.81 0.28 0.05 0.02 0.08 0.30 0.01

Belgium 0.07 2.14 0.32 0.10 1.06 0.17 2.53 1.77 3.65 1.28 6.10 10.53 2.20 0.61 0.21 0.35 0.19 0.54 0.14

Cyprus ------1.72 0.24 1.32 1.67 0.39 0.00 0.02 0.14 0.04 0.06 0.05

Finland 0.00 0.96 0.16 0.11 0.57 0.08 1.21 1.01 2.42 0.83 2.56 3.76 0.76 0.32 0.10 0.18 0.03 0.44 0.10

France 0.01 1.03 0.11 0.05 0.51 0.07 1.34 1.02 3.35 0.81 2.86 4.61 0.90 0.25 0.07 0.23 0.07 0.33 0.07

Germany 0.02 1.25 0.14 0.05 0.58 0.07 1.25 0.85 1.84 0.70 2.76 4.24 0.68 0.26 0.03 0.15 0.04 0.13 0.06

Greece 0.10 1.19 0.23 0.04 0.78 0.04 1.14 1.14 2.25 0.64 3.04 5.39 0.95 0.42 0.06 0.17 0.16 0.49 0.13

Ireland 0.08 1.50 0.27 0.12 1.56 0.37 3.36 2.46 2.51 3.13 8.06 8.46 6.77 0.90 0.20 0.67 0.32 0.85 0.18

Italy 0.07 1.48 0.19 0.10 0.93 0.18 1.67 1.79 2.72 1.26 3.44 6.71 1.58 0.56 0.13 0.30 0.13 0.48 0.14

Luxembourg 0.02 0.07 0.00 0.00 0.05 0.00 0.09 0.15 0.48 0.00 0.04 1.15 -0.12 0.06 0.02 0.00 0.00 -0.03 0.00

Malta ------1.42 -0.17 0.33 0.92 -0.09 -0.08 -0.01 -0.02 0.07 0.02 -0.01

Netherlands 0.06 1.98 0.26 0.10 0.75 0.14 1.85 1.17 3.28 1.23 5.61 6.75 2.18 0.67 0.16 0.45 0.18 0.53 0.14

Portugal 0.03 0.62 0.06 0.02 0.78 0.06 2.68 1.31 6.23 2.13 4.62 6.67 1.54 0.72 0.15 0.48 0.28 0.94 0.28

Slovakia ------0.26 0.03 0.01 0.03 0.04 0.07 0.01

Slovenia - - - - 0.41 0.08 0.73 0.55 0.98 0.25 1.71 2.66 0.42 0.03 0.02 0.03 0.02 0.09 0.04

Spain 0.11 1.84 0.28 0.25 1.51 0.27 3.85 2.46 4.88 2.44 4.73 10.92 2.59 0.71 0.21 0.33 0.35 0.94 0.27

sum 0.05 1.34 0.18 0.09 0.76 0.12 1.73 1.31 2.88 1.11 3.46 5.89 1.36 0.41 0.10 0.25 0.12 0.40 0.11 Table 6 The market weighted Systemic Expected Shortfall [SESEW ] indices in Euro Countries in years 2006-2010. Source: Thomson Reuters Datasream, 2011.

Q1 2006 Q2 2006 Q3 2006 Q4 2006 Q1 2007 Q2 2007 Q3 2007 Q4 2007 Q1 2008 Q2 2008 Q3 2008 Q4 2008 Q1 2009 Q2 2009 Q3 2009 Q4 2009 Q1 2010 Q2 2010 Q3 2010

Austria 0.12 2.48 0.26 0.09 0.54 0.03 2.69 2.88 4.17 1.32 4.08 9.62 3.95 1.18 0.19 0.40 0.23 0.78 0.16

Belgium 0.13 2.54 0.33 0.15 1.33 0.23 3.01 2.50 5.43 2.32 9.53 11.15 4.30 0.97 0.31 0.61 0.22 0.70 0.21

Cyprus ------2.14 0.93 3.26 6.23 1.13 0.10 0.05 0.27 0.18 0.35 0.18

Finland 0.00 1.69 0.36 0.21 0.81 0.14 1.72 2.11 4.70 1.58 4.00 2.37 1.69 0.52 0.18 0.25 0.06 0.63 0.13

France 0.10 1.91 0.35 0.20 1.06 0.24 2.79 2.28 4.80 2.03 6.36 9.71 3.35 0.89 0.16 0.46 0.27 0.81 0.22

Germany 0.07 2.26 0.23 0.14 0.92 0.20 2.47 1.74 3.51 1.45 5.27 8.42 2.08 0.56 0.08 0.28 0.14 0.27 0.16

Greece 0.09 1.09 0.26 0.05 0.70 0.02 1.19 1.25 3.21 1.21 3.76 5.94 1.34 0.54 0.10 0.27 0.23 0.65 0.18

Ireland 0.08 2.01 0.44 0.16 1.66 0.29 3.36 3.18 4.75 4.56 13.35 13.21 7.72 1.23 0.25 0.70 0.41 1.27 0.32

Italy 0.04 1.07 0.20 0.10 0.87 0.21 1.72 1.41 2.88 1.19 3.65 6.19 2.24 0.52 0.13 0.27 0.16 0.54 0.15

Luxembourg 0.01 0.05 -0.01 0.00 0.08 0.00 0.13 0.21 0.61 0.01 0.21 1.49 -0.14 0.05 0.01 0.00 0.01 -0.04 -0.01

Malta ------2.91 -0.33 0.65 1.12 0.06 -0.16 -0.03 -0.03 0.10 0.05 0.02

Netherlands 0.08 2.08 0.37 0.17 0.94 0.19 2.69 2.12 4.52 2.03 10.02 12.06 5.05 1.31 0.29 0.95 0.32 0.82 0.22

Portugal 0.03 0.75 0.03 0.02 0.84 0.05 3.04 1.18 6.73 2.29 4.79 6.90 1.47 0.75 0.17 0.48 0.31 1.05 0.29

Slovakia ------0.15 -0.02 0.04 0.03 0.15 -0.01 -0.01

53 Slovenia - - - - 0.00 0.00 -0.03 -0.02 0.00 0.00 0.00 0.08 0.00 0.00 0.00 0.00 0.00 0.00 0.00

Spain 0.11 2.02 0.33 0.37 1.76 0.34 3.83 2.37 6.60 2.48 6.09 14.73 4.29 1.01 0.33 0.42 0.60 1.44 0.36

sum 0,08 1,87 0,27 0,16 1,03 0,21 2,54 1,94 4,20 1,75 5,75 9,12 2,98 0,76 0,16 0,40 0,24 0,66 0,20 Appendix E

The basic DPT regression equation can be written in the following form:

0 yi,t = αyi,t−1 + β Xi,t + µi + υi,t, (1)

where y describes the dependent variable, X is the set of explanatory variables, µ is an unobserved

country-specific component and υ is the time-specific component. Subscripts i and t refer to a country and a year respectively. Additionally, it is assumed that the country and time effects are orthogonal and that the time-specific component is not autocorrelated between individuals.

The estimator proposed by Arellano and Bond(1991) (and further expended by Arellano and

Bover(1995); Blundell and Bond(1998)) is applied to solve the model (3.1). The method is founded on the Generalized Method of Moments (GMM). In fact, this estimator fully comprises the scope of this dataset. It was designed for ”small T, large N” panels, in a sense that they should have few time periods and many individuals (Roodman, 2006). Looking at the dataset (with 5 periods and 14 individuals) this criterium is satisﬁed.

The method is often called ”system GMM” as it encompasses two types of moment conditions.

The first type is described by the difference equation. To solve the problem of non-observed country-specific effects, the first difference of the equation (3.1) is taken. This yields:

0 yi,t − yi,t−1 = α(yi,t−1 − yi,t−2) + β (Xi,t − Xi,t−1) + (υi,t − υi,t−1). (2)

Although this transformation eliminates the country-speciﬁc error [µ], it introduces a correlation

between the lagged response variable [yi,t−1 − yi,t−2] and the error term [υi,t − υi,t−1](Beck et al., 54 55

2000). In fact, this violates the initial assumption on the distribution of the error terms. Addition-

ally, it exacerbates the gaps in unbalanced panels (Roodman, 2006). To correct for those short-

comings, Arellano and Bover(1995) propose using the Forward Orthogonal Deviations (FOD)

transformation. Compared to the difference transformation, instead of subtracting the lagged ob-

servation from the current one, FOD subtracts the average future realisations of the variable. This

transformation can be described by the matrix K which is of the form: q  T−1 √ 1 √ 1 √ 1 T − − ··· ··· −  T(T−1) T(T−1) T(T−1)   q   0 T−2 −√ 1 ··· ··· −√ 1   T−1   (T−1)(T−2) (T−1)(T−2)   . q .  K =  . 0 T−3 .  , (3)  T−2   . . . q   . . .. − 1 2   . . 2 3   q  0 0 ··· 0 1 − 1 2 2 T−1×T where T is the number of periods (Arellano and Bover, 1995; Roodman, 2006). Since all of the columns are orthogonal, the lags of the variable might be used as instruments. Moreover, the possible gaps in the unbalanced panels are substituted by the FOD.

Applying this estimator, may trigger several problems, however. First, this transformation weakens the pure cross-country relationship in the dataset (Beck et al., 2000). Second, it decreases the signal-to-noise ratio which exacerbates the error bias (Griliches and Hausman, 1986). Finally, when the lagged dependent or explanatory variables are relatively stable over time, their lagged levels might be weak instruments (Alonso-Borrego and Arellano, 1999).

To address those problems, Arellano and Bover(1995) introduced a second set of equations in levels. It limits the potential bias in ﬁnite samples as well as reduces the asymptotic impreci- sion originated in the transformed estimator (Arellano and Bover, 1995; Beck et al., 2000). The required assumption to introduce the second set of moment conditions is that the correlation between country-speciﬁc effects [µ] and the levels of the independent variables are constant over time. Under this assumption Arellano and Bover(1995) prove that the lagged differences of the 56 explanatory variables are valid instruments for equations in levels. Bibliography

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