Unconventional Monetary Policy and Systemic Risk in European Banking

UNCONVENTIONAL MONETARY POLICY AND SYSTEMIC RISK IN EUROPEAN BANKING

Word count: 14.839

Evi Verhelst Student number : 01202540

Supervisor: Prof. dr. Rudi Vander Vennet Co-supervisor: Elien Meuleman

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Engineering

Academic year: 2016 - 2017

UNCONVENTIONAL MONETARY POLICY AND SYSTEMIC RISK IN EUROPEAN BANKING

Word count: 14.839

Evi Verhelst Student number : 01202540

Supervisor: Prof. dr. Rudi Vander Vennet Co-supervisor: Elien Meuleman

Master’s Dissertation submitted to obtain the degree of:

Master of Science in Business Engineering

Academic year: 2016 - 2017

PERMISSION

I declare that the content of this Master’s Dissertation may be consulted and/or reproduced, provided that the source is referenced.

Evi Verhelst

NEDERLANDSTALIGE SAMENVATTING

Toen na de uitbraak van de crisis in 2008 de conventionele maatregelen niet genoeg bleken, introduceerde de Europese Centrale Bank nieuwe, onconventionele maatregelen. Deze waren vooral gericht op het uitbreiden van de bankbalansen. Hoewel deze onconventionele maatregelen er zijn gekomen in de strijd voor de heropleving van het financiële systeem, wordt er in de literatuur toch ook veel bewijs aangereikt dat deze maatregelen bijdragen tot een verhoogd risico. Vandaar doet deze masterproef onderzoek naar het effect van de aankondigingen van deze onconventionele maatregelen op het systeemrisico. Systeemrisico is het risico op een ineenstorting van het financiële systeem als gevolg van de grote complexiteit en de wederzijdse afhankelijkheid tussen banken. In de literatuurstudie worden drie verschillende transmissiekanalen van onconventioneel monetair beleid naar risico en meer bepaald naar het systeemrisico beschreven, namelijk de transmissie door interestvoeten, activaprijzen en liquiditeit. Verder wordt ook de heterogeniteit tussen banken beschreven.

Voor het onderzoek naar het verband tussen het systeemrisico en de maatregelaankondigingen, werd een panelanalyse met 67 beursgenoteerde Europese banken uitgevoerd. De data waartussen de effecten van de aankondigingen werden onderzocht liepen van oktober 2008 tot december 2015 en de hier gebruikte indicator voor het systeemrisico is de ‘Marginal Expected Shortfall’ of kort gezegd de MES. In een eerste stap werden de belangrijkste onconventionele aankondigingen gefilterd, waarop we de effecten van de monetaire beleidsschok gemeten aan de hand van de spread van Italiaanse en Duitse overheidsobligaties op 10 jaar op de MES onderzoeken. Deze regressie werd herhaald voor verschillende bankkarakteristieken om de heterogeniteit tussen banken met veel en weinig kapitaal, banken uit kernlanden en de periferie, banken met een hoog en laag systeemrisico, banken met veel en weinig leningen, niet-uitvoerige leningen, stortingen en niet-interest inkomen te ontdekken. Aangezien deze resultaten weinig zeggend waren werden de regressies nog eens overgedaan, maar dan met andere monetaire schokken. Uit dit onderzoek is gebleken dat het systeemrisico steeg nadat onconventionele programma’s werden aangekondigd. Deze vaststelling bleef ook gelden voor bijna alle programma’s (CBPP, SMP en OMT, QE) afzonderlijk. De enige uitzondering zijn de OMT aankondigingen die, wanneer de SMP aankondigingen buiten beschouwing worden gelaten, het systeemrisico verlagen. Wanneer gekeken wordt naar FRFA en LTRO aankondigen is er geen eenduidig effect waarneembaar.

ACKNOWLEDGEMENTS

After the choice of the subject for my master’s dissertation in October 2015, the moment of finishing it is finally there. Writing this foreword is the last thing to make this work complete. During these last two years of my master in business engineering, I acquired a lot of new theoretical knowledge where writing this master’s dissertation especially contributed to. To fulfil this big task to good end, I had of course help and support from a lot of different people who I therefore want to thank.

First I want to thank my supervisor Rudi Vander Vennet, who gave me the opportunity to work on a very interesting topic and showed me new captivating insights. Secondly, a heartfelt word of thank is addressed to Elien Meuleman, who was always there to answer my questions, gave good advice, provided me the necessary data and offered support during the whole period. At last, I want to thank my parents, sister and friends for listening when things didn’t go as they should and for the support and believe in me to finish successfully my academic study.

TABLE OF CONTENTS

NEDERLANDSTALIGE SAMENVATTING ...... i ACKNOWLEDGEMENTS ...... ii LIST OF USED ABBREVIATIONS ...... iv LIST OF TABLES AND FIGURES ...... v 1. INTRODUCTION ...... 1 2. MONETARY POLICY AND SYSTEMIC RISK...... 4 2.1 Unconventional programmes ...... 4 2.2 Transmission channels from monetary policy to systemic risk ...... 5 3. LINK TO BANK HETEROGENEITY ...... 14 3.1 Loans to Total Assets and Non-Performing Loans ...... 14 3.2 Deposits to total liabilities ...... 15 3.3 Capital to total assets ...... 16 3.4 Non-interest income to total income ...... 17 4. METHODOLOGY ...... 18 4.1 Measuring systemic risk ...... 19 4.2 Measuring monetary shock ...... 20 4.3 Model ...... 20 5. DATA ...... 23 6. RESULTS ...... 24 6.1 Results regression analysis ...... 28 6.2 Discussion ...... 33 7. CONCLUSION ...... 36 8. REFERENCES ...... i 9. APPENDIX ...... vi

iii

LIST OF USED ABBREVIATIONS

ABSPP: Asset-Backed Securities Purchase Programme CAP: Capital to total assets CBPP: Covered Bond Purchase Programme CDS: Credit Default Swap CoVaR: Conditional Value at Risk CSPP: Corporate Sector Purchase Programme DIP: Distress Insurance Premium DIV: Non-interest income to total income DTL: Deposits to total liabilities ECB: European Central Bank EDF: Expected Default Frequency FRFA: Fixed-Rate Full Allotment GovC: Governing Council I-G: Italy-Germany IID: Independently Identically Distributed LTA: Loans to total assets ratio LTRO: Long-Term Refinancing Operations MES: Marginal Expected Shortfall MRO: Main Refinancing Operations NIM: Net Interest Margin NPL: Non-Performing Loans OIS: Overnight Indexed Swap OLS: Ordinary Least Squares OMT: Outright Monetary Transactions PSPP: Public Sector Purchase Programme SMP: Securities Market Programme TLTRO: Targeted Longer-Term Refinancing Operations UMP: Unconventional Monetary Policy QE: Quantitative Easing VaR: Value at Risk

LIST OF TABLES AND FIGURES

LIST OF TABLES

Table 1: Clarification on Figure 1...... 24 Table 2: Clarification on Figure 2 ...... 26 Table 3: t statistics ...... 35

LIST OF FIGURES

Figure 1: Delta MES in a one day window ...... 24 Figure 2: Delta MES plotted (high versus low capitalised banks)...... 26 APPENDIX: LIST OF TABLES AND FIGURES

LIST OF TABLES

Table A.1: List of announcement days ...... vi Table A.2: List of banks ...... ix Table A.3: Clarification on Figure A.2 ...... x Table A.4: Regression results with spread I-G gov. bond as yield...... x Table A.5: Regression results with different yields ...... xi Table A.6: Regression results with different BM ...... xii Table A.7: Regression results with different yields on particular announcement days ...... xiii Table A.8: Regression results with different yields on particular announcement days, BM: CAP ...... xiv Table A.9: Regression results with different yields on particular announcement days, Core/Per...... xv Table A.10: Regression results with different yields on particular announcement days, (Non-)Syst ... xvi Table A.11: Regression results with different yields on particular announcement days, BM: LTA ..... xvii Table A.12: Regression results with different yields on particular announcement days, BM: NPL .... xviii Table A.13: Regression results with different yields on particular announcement days, BM: DTL ...... xix Table A.14: Regression results with different yields on particular announcement days, BM: DIV ...... xx Table A.15: Regression results with distinction between FRFA and LTRO announcements...... xxi Table A.16: Regression results with distinction between SMP and OMT announcements ...... xxi

LIST OF FIGURES

Figure A.1: Evolution of the yields ...... xxii Figure A.2: Delta MES in a two day window ...... xxiii Figure A.3: Delta OIS 3 months plotted on announcement days ...... xxiii Figure A.4: Delta EURIBOR 3 months plotted on announcement days ...... xxiv Figure A.5: Delta OIS 1 year plotted on announcement days ...... xxiv Figure A.6: Delta EURIBOR 1 year plotted on announcement days ...... xxv Figure A.7: Delta spread EURIBOR-OIS 3 months plotted on announcement days ...... xxv Figure A.8: Delta log (CDS banks 5 year) plotted on announcement days...... xxvi Figure A.9: Delta covered bond yield plotted on announcement days ...... xxvi Figure A.10: Delta spread I-G plotted on announcement days ...... xxvii Figure A.11: Delta log (CDS Italy 5 year) plotted on announcement days ...... xxvii Figure A.12: Delta OIS 10 year plotted on announcement days ...... xxvii Figure A.12: Delta Germany 10 year plotted on announcement days ...... xxviii

1. INTRODUCTION

The economic meltdown of 2008 has caused a lot of financial problems in the euro area. These called for unprecedented policy responses worldwide, because there was a need to restore stability, maintain liquidity and reduce systemic risk. Systemic risk is an important term that came up during the crisis. This is the risk of collapse of an entire financial system imposed by interdependencies between banks, bank size or bank complexity. The failure of a single entity causes problems of trust which could result in bank runs and finally lead to a cascading failure, which could bring down the whole system. This is what happened to for example Lehman Brothers in 2008. Therefore, the crisis has made it clear that bank regulators have to take into account systemic risk. Since 2008, the European Central Bank (ECB) has set up several measures of unconventional monetary policy. The aim of these measures was to create price stability. However they could have a negative impact on the potential of systemic risk of banks. This is exactly what is going to be examined in this master’s dissertation. This paper will research the impact of different unconventional programmes on the systemic risk potential of 67 European banks. The paper gives an answer to the following important question: “Do unconventional policies significantly, positively or negatively, contribute to systemic risk?” Furthermore, this thesis will determine which of the programmes has had the biggest impact.

This paper will further describe the different transmission channels of the unconventional monetary policies to systemic risk. The effects through interest rates, asset prices and liquidity will be studied. The transmission could be positive, in the sense of decreasing the risk and helping the economy to recover, but it could also be negative, as these measures could also contribute to increasing the potential of systemic risk. Huang, Zhou and Zhu (2010) found in their paper that there is a linear relationship between systemic risk and a bank’s default probability and that bank size is the most important determinant, which is nonlinearly related to systemic risk. The bank’s default probability is one of the factors that can determine the risk perception. Risk-taking became an important concern during the execution of the unconventional measures. Borio and Zhu (2008) especially make notion of a “risk-taking channel”. In the paper, they describe three ways of how this channel could work. In the literature, a lot of reference to this risk-taking channel exist (see, among others, Adrian and Shin, 2009; Gambacorta, 2009; Angeloni, Faia and Lo Duca, 2010; Jimenez, Ongena, Peydro and Saurina, 2014). In the papers of Gambacorta (2009) and Altunbas, Gambacorta and Marques-Ibanez (2014), the link between interest rates and risk-taking is researched. Evidence is found for increased risk- taking in times of low interest rates prior to the crisis. This finding is supported by Jimenez et al. (2014) for Spain and Ioannidou, Ongena and Peydro (2007) for Bolivia. The risk-taking channel

primarily works through interest rates, but it could also work through other communications of the central banks (Borio and Zhu, 2008). This is linked to the signalling channel. The different links between unconventional monetary policy and risk-taking will be discussed in more detail in section 2.

Whereas a lot of papers focus on how effective the introduced unconventional monetary policies were in easing conditions in the economy and what the responses were in stock markets (Briciu and Lisi, 2015; Rogers, Scotti and Wright, 2014; Swanson, 2015; among many others), this paper contributes to the existing literature on unconventional monetary policy and the risk-taking behaviour of banks. The effect of unconventional policies on systemic risk is analysed, where heterogeneity between banks is included. To analyse the effects of the unconventional policies taken by the ECB on systemic risk, a panel analysis of 67 listed eurozone banks is performed. Our sample contains data from October 2008 until December 2015 and regressions are performed on ECB’s announcement days. Kuttner (2001) and Kohn and Sack (2003) found that announcements of the central bank have an impact on interest rates. Especially for unexpected or unanticipated policy actions (Kuttner, 2001). Following Rosa and Verga (2008), central bank announcements affect asset prices, and again, the impact is the biggest for the unexpected ones. To measure the monetary shock in this paper, we will follow the method suggested by Rogers et al. (2014) in first instance, which defines the monetary policy surprise as using the yield spread between German and Italian 10 year government bonds on the day of an ECB policy announcement. Afterwards, monetary policy shocks on announcement days will be measured with other yields, like the OIS 3 months rate, the OIS 1 year rate, the EURIBOR 3 months rate, the EURIBOR 1 year rate, the covered bond yield, the log CDS 5 year of banks rate, the log CDS 5 year of Italy rate, the OIS 10 year rate and the Germany 10 year government bond rate. The impact on systemic risk is measured by the marginal expected shortfall (MES).

In this master’s dissertation, evidence is found for increased systemic risk due to the introduction of unconventional policies. The policies that are analysed here are Fixed Rate Full Allotment and the Longer Term Refinancing Operations (FRFA and LTRO), the Covered Bond Purchase Programme (CBPP), the Securities Market Programme and the Outright Monetary Transactions (SMP and OMT) and Quantitative Easing (QE). A positive impact of the monetary policy shock on the MES, which is associated with an increase of the MES, was found for the CBPP, SMP and OMT, as well as the QE announcements. An exception of these results are OMT announcement days, when they are observed without SMP announcements. For FRFA and LTRO announcement days, ambiguous effects on systemic risk are found, which is also the case when looking at different bank characteristics in order to identify heterogeneity.

The remainder of the paper is organized as follows. Section 2 treats the transmission of monetary policy to systemic risk. Section 3 identifies bank heterogeneity. Section 4 outlines the methodology. Section 5 describes the dataset. Section 6 presents the results and section 7 concludes.

2. MONETARY POLICY AND SYSTEMIC RISK

2.1 Unconventional programmes

At the time when the crisis started and conventional measures turned out to be insufficient, central banks came up with unconventional policies to restore confidence in the interbank markets and government bond markets. The choice of which programme to use, depends on institutional characteristics, the situation of the banking sector and the types of shock hitting (Smaghi, 2009). The essence of the different unconventional policies is to influence expectations in a reliable manner. In this section, the different measures implemented by the ECB will be briefly treated.

The first unconventional programme the ECB unrolled, was the long term refinancing operations. The existence of long term refinancing operations goes back to the foundation of the economic and monetary union, but during the crisis the ECB enormously increased their maturity, up to 36 months. In 2010, after the Greek sovereign debt crisis, the ECB came up with a new facility, the Securities Market Programme (SMP) to put downward pressure on the long term interest rate and to stabilize the euro by buying assets. In July 2012, Mario Draghi (President of the ECB) gave a speech in which he announced that the ECB would do whatever it takes, followed in September 2012, by the introduction of the Outright Monetary Transactions (OMT) programme, which replaced the SMP. There is evidence that both programmes were very successful. Evidence is given by Falagiarda and Reitz (2013), Fratzscher and Rieth (2015), Altavilla, Giannone and Lenza (2014) and Eser and Schwaab (2013). However, Acharya, Eisert, Eufinger and Hirsch (2016a) acknowledge that there is also a negative side of OMT. They found that OMT indirectly recapitalised periphery country banks or banks with a big exposure to the periphery. Therefore some of these banks allowed to renew loans at low interest rates to non-viable companies. This was done in order to avoid realising losses on outstanding loans, but this contains a major risk and banks would not be able to repair their balance sheets. This phenomenon is called evergreening. Next, in September 2014, the targeted long term refinancing operations (TLTRO) took off. This programme encouraged financial institutions to provide more credit to businesses and households, which was done by giving banks the ability to borrow up to 7% of the amount of outstanding loans to the euro area non-financial private sector. Lastly, in January 2015, the ECB launched its expanded asset purchase programme, also known as Quantitative Easing (QE). The QE programme consists of the third covered bond programme (CBPP3), the asset- backed securities purchase programme (ABSPP), the public sector purchase programme (PSPP) and the corporate sector purchase programme (CSPP). Following Rogers et al. (2014) and Wright (2011),

asset purchases are effective in lowering the prices of the assets being purchased and it tries to lower interest rates that households and firms have to pay in order to boost consumption and investment spending. QE also affects agents’ expectations of monetary policy for the future (Wright, 2011).

2.2 Transmission channels from monetary policy to systemic risk

Systemic risk, the malfunctioning of a financial institution with the accumulation of losses could contribute to the collapse of the whole financial system as a consequence of interdependencies between different institutions. The funding of banks is strongly dependent on the willingness of banks to lend to each other on the interbank market. The danger for systemic risk occurs when the financial sector is undercapitalised, so that they could not and would not help each other out anymore. Following Black, Correa, Huang and Zhou (2013), who investigated which European banks and which bank characteristics were the biggest contributors to systemic risk, the systemic importance of a bank is a joint effect of size, the probability of default and the correlation with other banks. To decrease the risk of financial institutions, new bank regulations were formulated by the Basel-committee. The Basel 1 and 2 rules incorporate provisions for the risk-weighted assets, for the capital requirements and rules regarding providing information. However, as mentioned by Acharya, Pedersen, Philippon and Richardson (2010), these regulations are not enough to get rid of systemic risk. The imposed rules are partly effective in reducing the risk for individual institutions, but not for the whole system. The external costs associated with systemic risk have to be embedded in the internal costs of the institutions, otherwise they will have the tendency to increase their risk-taking (Acharya et al., 2010). Therefore they renewed the regulations, called Basel 3. With these rules, the liquidity of banks will be checked and the quality of capital has to improve (banks have to hold a minimum of TIER 1 capital). In addition, the “too-big-to-fail” banks will be obliged to hold additional capital buffers. “Too-big-to-fail” banks are banks with high importance, because of their size or complexity. When they still fail, they are convinced that governments will help them out. So that kind of banks are not afraid of increasing their risks. Therefore the obligation of additional capital buffers is legitimated. Black et al. (2013) state that banks with more government support have higher incentives to take risks and could increase the systemic risk potential.

As said before, the danger for systemic risk occurs when the financial sector is undercapitalised. During the financial crisis, liquidity dried up and the ECB came up with unconventional measures as described in the paragraph above. In the following part, the different transmission channels of these unconventional monetary policies to systemic risk will be described. This transmission could be

positive, in the sense of decreasing the risk and helping the economy to recover, but it could also be negative, as these measures could also contribute to increase the potential of systemic risk.

Systemic risk is a consequence of increased risk-taking, which is in turn a consequence of unconventional policies. However, Claeys and Darvas (2015) explain in their paper about the financial stability risks of unconventional measures that increased risk-taking should not always be negative. When risk-taking is less than socially desirable, it can be seen as a good thing. Originally, encouraging the economy to increase risks was an aim of the unconventional monetary policies, however, during the execution of the measures, risk-taking became a concern as it became excessive. The paper of Altunbas et al. (2014) states that the expansionary monetary policy increased bank risk and led to an increased expected default frequency. The risk-taking is also a consequence of asymmetric information and the fact that banks cannot price the risk-taking correctly (De Nicolo, Dell’ Ariccia, Laeven and Valencia, 2010). Now, the effect of the transmission of the monetary policy applied by the ECB, more in particular the introduction of the different unconventional measures, through interest rates, asset prices and liquidity on systemic risk will be studied.

2.2.1 Interest rates

With the start of the crisis in 2008, banks were reluctant to allow new loans to the real economy. As a result, during the crisis, the ECB lowered its key interest rates. In March 2016, the interest rate on the Main Refinancing Operations (MRO) reached the zero percent. At that same time, the interest on deposits was even negative, which means that banks had to pay to deposit their money at the ECB. With these measures, the ECB aims to increase the inflation and strengthen credit supply to the real economy. The effects of the low interest rates are different in the short- and long-term.

In the short-term, credit supply is enhanced, due to the fact that banks are encouraged to set their own lending and deposit rates very low. Because of these low deposit rates, saving will no longer be attractive, but it will encourage lending. Investment spending and consumption of households and firms are supported in this way. This mechanism is also presented as the lending channel in, for instance, the paper of Brissimis and Delis (2010) and Falagiarda and Reitz (2013), whereas evidence for this channel is provided in Boeckx, De Sola Perea and Peersman (2016). Brissimis and Delis (2010) state that the bank lending channel could only exist when there is a decline in the availability of bank loans and that there has to be a change in the reserves of banks. By boosting consumption and investments, low interest rates contribute to an economic recovery due to improved macroeconomic conditions. Altavilla et al. (2014) acknowledge this positive macroeconomic impact of the unconventional measures. These positive macroeconomic effects are also confirmed by Peersman

(2011) and Claeys and Darvas (2015). However, the level of inflation and economic growth is still on the low side nowadays. Nevertheless, we could say, based on earlier research (Peersman, 2011), that without the introduction of the unconventional measures, the current state of our economy would be much worse. The low interest rates could also lead to positive valuation effects (Lambert and Ueda, 2014).

Due to low interest rates, the liabilities of banks will increase. Banks will increase their short-term funding (Adrian and Liang, 2014) and get more claims from the private sector. This last reason is based on the equality principle, where an asset increase (§2.2.2) will lead to an increase of liabilities (Joyce, Miles, Scott and Vayanos, 2012). This increase in liabilities due to low interest rates, could reduce risk-taking by banks. Declining costs for holding liabilities leads to increased profits. In order to catch these gains, a bank will lower its risky activities (Dell’ Ariccia, Laeven and Marquez, 2013). Another positive effect from low short-term interest rates, in the short run, is the fact that banks could borrow at the short-term interest rate and lend the money out at higher long-term interest rates or invest this money in assets with high returns (Lambert and Ueda, 2014).

In the medium to long-term however, negative effects could dominate. Due to the low interest rates, yields and risk premia will reduce which will result in banks taking on more leverage and they will search for yield by investing money in higher yielding assets (§2.2.2), which increases risk (Lambert and Ueda, 2014). Also the profitability of banks decreases, due to decreasing revenues from long- term loans. Claeys and Darvas (2015) made a distinction between the risk incentives of long- and short-term rates. They state that lower long-term interest rates increase risk-taking, whereas low short-term interest rates and low spreads between long- and short-term interest rates might reduce risk-taking. Adrian and Liang (2014) do not make a distinction between the short-term and longer terms, but they describe the risk-return trade-off as a consequence of monetary easing. Monetary easing reduces risk premia and therefore creates incentives for high returns, which increases risk- taking. This could raise the potential of systemic risk. As mentioned above, due to monetary easing, yields on safe assets decrease, riskier assets therefore become more attractive and both borrowers, investors and banks will take on more risk and get the incentive to search for higher yields. This could be dangerous, because higher asset values lead them to underestimate risk. Another reason for a search for yield is because banks need to match the yield of their assets and liabilities (De Nicolo et al., 2010). Therefore lower interest rates lead also to investments in riskier assets. The statement that effects of low interest rates are positive in the short-term, but negative in the longer term is also acknowledged by Jimenez et al. (2014) and De Nicolo et al. (2010).

Another effect could be found on the net interest margin. Due to decreasing interest rates, one would expect the net interest margin to decline. The net interest margin is the difference between interest received on loans and interest paid on deposits and borrowings by banks. However, when the short-term interest rate falls, or following Peersman (2011) after a conventional interest rate fall, an increase in the margin could be observed. This is because the interest rate decline is passed on to bank lending rates to a lesser extent than the decline of interest rates that banks receive from the ECB. This increase in the net interest margin will lead to increased risk-taking, due to the bank’s higher equity value and rising balance sheet capacity (Peersman, 2011). However, due to that higher equity value of banks, they will increase monitoring, which decreases risk-taking (e.g. Lambert and Ueda, 2014). In contrast, the net interest margin of banks will start to decline as a consequence of the introduction of some unconventional measures, when these measures approach the long-term interest rates. As a consequence, the profitability of banks will be affected. The net interest margin (NIM) could also be seen as a proxy of the charter value (Schenck, 2014). The charter value is the value that a bank would have if it can keep continuing its business. That value is lost when it goes bankrupt. Therefore the charter value gives an indication about how profitable and stable a bank is (Hendriks and Mosk, n.d.). With regard to the NIM we could say the lower the NIM, the higher the risk-taking, the more chance a bank could go bankrupt, the more chance on a systemic crisis (Schenck, 2014). This is opposite to the intentions of the ECB of lowering interest rates to help recover the economy.

Increasing risk due to low interest rates is also acknowledged by Altunbas, Gambacorta and Marques-Ibanez (2009a,b), who state that when rates are low for a long period, there will be a sharper rise in expected default probabilities, which is consistent with greater risk-taking. This is because banks are granting loans more easily, even when the counterparty has insufficient collateral. However, the ECB states that the risk of borrowers being unable to pay back their loans is bigger when interest rates are high. Therefore when interest rates are high, banks may highly reduce the amount of funds they lend to households and firms. The statement of Altunbas et al. (2009a,b) gets a lot of approval in the literature, as for example from Lambert and Ueda (2014), Wright (2011), Brissismis and Delis (2010), Jimenez et al. (2014), Manganelli and Wolswijk (2007), etc… Black et al. (2013) found CDS spreads originally be relatively low, but they increased dramatically during the sovereign debt crisis. As the CDS spread is a measure for credit risk, the increase in the CDS spread led to increased risk of losses due to a borrower’s failure to repay. Dell’ Ariccia et al. (2013) found that a drop in interest rates leads to lower monitoring, which could also increase credit risk.

As mentioned before, lower interest rates also lead to an increase in the leverage of banks, due to the lower leverage costs. Adrian and Liang (2014) state that more leverage increases risk-taking and Adrian and Boyarchenko (2013) state that higher leverage is further associated with an increase in financial vulnerability in the form of systemic risk. However, a side remark has to be made, following Claeys and Darvas (2015), leverage has been declined during the last years, due to stricter regulation and supervision. Low interest rates also lead to increasing asset prices (§2.2.2). Due to the fact that investors want to protect themselves against declining yields, they will replace short-term assets with more risky, longer term assets. This leads to increasing asset prices and decreasing term premia (Adrian and Liang, 2014).

Despite the fact that low interest rates help increase the credit supply, low interest rates will also lead people to keep their money instead of depositing it on the bank and as a consequence increase the incentive for banks to keep reserves (Peersman, 2011). This will lead to a lower liquidity, which calls for other measures than solely reduce interest rates (§2.2.3).

2.2.2 Asset prices and risk premia

Unconventional measures led to an expansion of central banks’ balance sheets, due to the introduction of many programmes of asset purchases by the ECB. Asset purchases were found to be the most effective in lowering refinancing costs, compared to exceptional liquidity measures (Szcerbowicz, 2015). Due to the asset purchases of the ECB, the supply of assets of commercial banks will decrease. Following the law of demand and supply, this will lead to increasing prices, which in turn has lower yields as a consequence. This mechanism, in which asset prices increase, lead to higher wealth and is therefore called the wealth-effect channel. This wealth-channel is described for households in Mishkin (2001). Both the positive and negative effects of the unconventional measures on asset prices will be discussed in this paragraph.

In the literature, a lot of possible transmission channels of monetary policy through asset prices are discussed. Most frequently mentioned is the portfolio rebalancing channel. Central banks buy securities from private agents or banks, who get risk-free reserves instead. This decreases the supply for that security, which leads to an increase in asset prices and a reduction in risk premia and consequently also in yields. As a result, better economic conditions are created, due to increasing asset prices and decreasing interest rates. This could contribute to economic recovery. However, due to the reduction in yields, banks will convert cash in higher yielding loans or securities, which are more risky. The portfolio rebalancing channel is discussed by Falagiarda and Reitz (2013), Joyce et al.

(2012), Szcerbowicz (2015), Fratzscher, Lo Duca, and Straub (2014), Darvas and Claeys (2015), etc., and could only work if the condition of imperfect substitutability is satisfied.

Another popular channel in the literature is the signalling or confidence channel. The confidence channel is described by Fratzscher et al. (2014) and is the most important channel for affecting global markets through the unconventional policies. This channel might affect asset prices by driving expectations of investors through forward guidance of regulators. Another possibility is that the asset purchases of the ECB create incentives for investors to buy assets themselves again, because of the restored trust. They know now that they could sell their assets to the ECB when needed. It gives the signal that assets could be traded whenever wanted. Knowing that central banks will buy assets when necessary make investors reassured and improves market functioning. The SMP and OMT programmes are primarily driven by the effect of the signalling channel.

That asset prices increase due to monetary easing is stated by most of the previously mentioned authors. However, in some papers, there is some doubt about the impact of unconventional measures on stock markets. Haitsma, Unalmis and de Haan (2015) believe in the theory of Hosono and Isobe (2014) and Lambert and Ueda (2014) that before the crisis, monetary easing resulted in increasing stock prices, while during the crisis stock prices decreased. This would be due to the fact that declined interest rates had as a consequence that investors became suspicious about the economic state. On the other hand, Rogers et al. (2014), Wright (2011), Bohl, Siklos and Sondermann (2007) and a lot of other writers, claim the contrary to be true, that monetary easing during the crisis led to increasing stock prices. This last statement is confirmed, against their expectations, by Haitsma et al. (2015) after executing regressions.

The fact that term premia decline is another frequently mentioned consequence of the unconventional policies. These declined term premia led to a reduction in the perceived risk. In the beginning of the crisis, the long-term government bond spreads relative to the Germany long-term bond yield increased in many euro area countries (Falagiarda and Reitz, 2013). Especially in the periphery these increases were the biggest, more specifically mostly in Italy, the country on which Falagiarda and Reitz (2013) focused in their paper. This reflects the distrust of investors who demand a significant risk premium. However, Falagiarda and Reitz (2013) showed in their paper that communications about unconventional measures had a positive outcome on these concerns in financial markets by substantially decreasing the perceived sovereign risk of Italy, particularly the events that occurred during the period 2010-2012. They also found the SMP and OMT programmes to be the most effective in reducing spreads. This statement about the SMP and OMT programmes

gets approval from respectively Eser and Schwaab (2013) and Altavilla et al. (2014). Eser and Schwaab (2013) found that SMP succeeded in lowering yields when the unconventional measures were introduced and that the programme helped improving market functioning. Altavilla et al. (2014) found that an OMT announcement led to a decrease in the 2-year government bond rates in Italy and Spain. However, German and French bond yields were largely unaffected. Fratzscher and Rieth (2015) also found the OMT programme to be the most effective in reducing risk, more especially in reducing credit risk.

Darvas and Claeys (2015) find that increases in asset prices benefit their holders. However, the danger for bubbles might arise when the increases are excessive. This danger has not yet become reality in the euro zone, but it is important to keep the possibility of bubbles in mind.

2.2.3 Liquidity improvement

As mentioned in the paragraph about the transmission through interest rates, banks decreased their lending activity to the private sector and charged higher loan spreads when the financial crisis unfolded. Therefore a lot of firms, and especially firms with a high dependency on banks affected most by the sovereign debt crisis, namely banks from the periphery (Greece, Ireland, Italy, Portugal and Spain), became financially constrained (Acharya, Eisert, Eufinger and Hirsch, 2016b). The fact that these firms got funding problems was a contributor to the crisis, because some banks caused a credit crunch and because these firms faced decreasing investments and a lower growth (Acharya et al., 2016b). Other European firms were also affected due to spillover effects from the crisis in the periphery countries and the high loan spreads, which is a result of the lending relationships between banks.

An important term is liquidity risk. This is the risk that instruments could not be traded on the moment wanted and was a significant contributor to systemic risk during the financial crisis (Black et al., 2013). Therefore, the ECB started asset purchases to increase the risk-free reserves of banks and to give the signal that assets could be traded whenever wanted. The introduction of the (T)LTROs has also led to an improved liquidity by encouraging banks to allow loans to the real economy. This was needed, because before the introduction of these measures, there was a limited lending to the economy, which contributed to a reinforced recession and financial imbalances (Arghyrou and Kontonikas, 2012). Also due to the measure of the ECB of lowering the interest rates in order to improve economic conditions, people did not deposit their money anymore. This could be seen as a good thing from a macroeconomic perspective, however, few liquidity will be left in the banking

system. As a consequence, less loans to the economy were allowed. To protect the economy for a new recession or vulnerabilities, governments want a continuation of unconventional measures, but this is in contrast with the goals of central banks. Moreover, the unconventional measures contribute to a lot of heightened risks, as mentioned in earlier paragraphs. Therefore we have to make a trade- off between the risks and advantages (economic recovery) of the unconventional policies.

Because of the improved liquidity, banks could build up higher buffers. This could lead to a decrease in risk-taking, because of protection considerations. It could also lead to a decrease in funding risk, because banks will now more easily obtain the necessary funds. Brunnermeier, Dong and Palia (2012) found that banks who have enough liquidity at their disposal rather tend to engage in traditional banking operations instead of more risky, trading activities. This leads to lower financial losses and therefore less risk. Another merit of improved liquidity is provided by Szcerbowicz (2015). She states that when banks have enough liquidity, banks’ uncertainty will be diminished, counterparty risk premiums decrease and money market tensions will reduce. She also found that in general, improving bank creditworthiness diminishes the chance of a sovereign crisis due to reduced bank bailouts. Moreover, banks with liquidity could restore the financial system themselves by buying sovereign bonds to increase the prices. Large, liquid and well capitalised banks are also more shock resistant, less risky, more profitable and could easily provide loans even when monetary policy is tightening (Altunbas et al., 2014).

Due to the increased quantity of credit, as a consequence of monetary easing, there is an influence on the quality of credit. Adrian and Liang (2014) state that banks with higher levels of capital have lower incentives to reduce the quality of credit. This is confirmed by Brissimis and Delis (2010), who state in their paper that the higher the liquidity, the higher the risk averseness. Because of a sufficient liquidity level, banks do not feel the need to search for yield and want to protect their built up assets. Peersman (2011) also state that through an increase in collateral a higher quality and value of outstanding loans is obtained. However, opposite to the finding of especially Adrian and Liang (2014), due to this increase in collateral, bank’s risk-taking will increase, which results in a greater loan supply (Peersman, 2011). Also Jimenez et al. (2014) state that in times of monetary easing, the lending activity to risky firms increases. This statement is confirmed by Acharya et al. (2016a). They state that after the OMT announcement, more loans were granted to risky firms. Lambert and Ueda (2014) go further and emphasize the principle of evergreening, which was mentioned earlier when the OMT measure was discussed. Evergreening is the phenomenon where banks keep on renewing loans to creditors who actually cannot pay back their loans. This is very risky and when doing this, banks delay their balance sheet repair (Lambert &Ueda, 2014).

Despite the evidence for increased credit risk, Rogers et al. (2014) declared that central bank purchases are enough to make default unlikely. This is converted into practice through forward guidance by the ECB, which signals that it will buy as much as needed to restore financial stability. In that way, the ECB reduces term and risk premia (§2.2.2) (Rogers et al., 2014).

3. LINK TO BANK HETEROGENEITY

In the literature, there is a lot of observed heterogeneity at bank level. This especially manifests itself in the response of banks to monetary policy. In this paper, a distinction is made between differences in the amount of bank loans, deposits, capital and the kind of income. This is according to the theory of Brissimis and Delis (2010), who state in their paper that the heterogeneity between banks originates from their different balance sheet characteristics. In particular, they state that banks with more liquidity, capital and market power will be less influenced by changes in monetary policy. They also made claims with regard to risk-taking, among which that banks with higher liquidity or banks which are big in size are more risk averse and that banks with more capital will suffer less from risks, due to higher buffers. In his paper, Ricci (2014) also found evidence for heterogeneity in the impact of monetary policy announcements on stock prices. This was assessed by executing event studies and to achieve more detailed results, regressions were performed. One of the assumptions made in his research is that banks which are perceived as riskier will experience a bigger impact of changes in monetary policy. The ratio between risk-weighted assets and total assets is used by Ricci (2014) to measure the individual risk of banks. A systemic measure is included as well, namely the rating for the country banking system where the institution is located. When the changes in monetary policy are expansive, the assumption is only partially supported. When the monetary policy measures are restrictive, evidence could be found in favour of the assumption.

3.1 Loans to Total Assets and Non-Performing Loans

A first distinction can be made based on the loans to total assets (LTA) ratio and non-performing loans (NPL). Granting loans is dependent on the level of a bank’s risk tolerance. This principle is stated by Paligorova and Santos (2013). They found in their paper that banks with a high risk tolerance undercut the loan spread for more risky firms in times of monetary easing. Therefore, the loan spread between risky and less risky borrowers decreases. Moreover, they state that the longer the easing regime is, the higher the interest rate discount for risky borrowers is. They also found that in times of monetary easing, banks allow bigger loans to riskier borrowers relative to safer ones.

Altunbas et al. (2009a,b) state that banks which are more liquid and well capitalised will grant relatively more loans. These are banks with a low expected default frequency (EDF). Banks with a low EDF could more easily grant loans, due to their high capital buffers, which makes them more resistant to shocks. Banks with a high EDF on the other hand, will be restricted in their lending behaviour. Banks with a high EDF are, for example, smaller banks or banks with higher loan-loss

provisions. These smaller banks are perceived as more risky, due to their limited capitalisation. When especially looking for evidence after the introduction of the unconventional monetary policies, Acharya et al. (2016b) and Gambacorta and Shin (2016) agree with the statement that well capitalised banks grant more loans. In the paper of Acharya et al. (2016b), they distinguish between weakly and well capitalised banks from the periphery. They state that especially weakly capitalised peripheral banks reduce their lending to the real economy and increase their loan spreads. Considering lower capitalised banks, Boeckx et al. (2016) found evidence for a higher response to credit support measures, on the condition of having a sufficient amount of capital. In general, Mamatzakis and Bermpei (2016) found that banks with a higher LTA ratio have a better performance.

In general, banks with a high LTA ratio will also have more non-performing loans. Following the paper of Acharya et al. (2016a), banks with high levels of capital more easily declare loans non-performing, while lower capitalised banks rather tend to the evergreening of loans. Nevertheless, despite the fact that high capitalised banks more easily declare a loan non-performing, lower capitalised banks will still have a higher stake of NPLs, due to the higher risks they are willing to take by evergreening loans (Klein, 2013). When we look at the LTA ratio, Klein (2013) found that banks with a high LTA ratio are willing to take more risks and that they will have a higher amount of NPLs. This is also confirmed by Gambacorta and Mistrulli (2004). Banks facing a higher level of NPLs will have to deal with a growing uncertainty regarding their capital position, as described by Klein (2013). Therefore, they will have more financing difficulties on the interbank market which will in turn increase their interest rates.

3.2 Deposits to total liabilities

Another way of bank heterogeneity is the distinction between the division of bank liabilities, measured here through the deposits to total liabilities (DTL) ratio. The liquidity measures of the ECB were especially helpful for countries in distress. When the central bank provides liquidity to these countries, the liability side of their balance sheets grows. Ricci (2014) made the assumption that banks with lower liquidity ratios are influenced more, as could be seen from the stock prices, by monetary policy interventions. This assumption is confirmed for an expansionary monetary policy measured by the ratio of customer deposits in total short-term funding.

Demirgüç-Kunt and Huizinga (2010) found that larger banks obtain more non-deposit funding. They also found that banks that rely more on deposit funding face lower funding costs and therefore lower risks, than banks that rely on wholesale capital market. Altunbas et al. (2014) found some similar conclusions. In their paper, they state that banks with lower DTL ratios face higher costs and that

banks with higher DTL ratios are more stable and contain less risks, due to the existence of deposit insurance. However, Mamatzakis and Bermpei (2016) examine in their paper the relationship between unconventional monetary policy in the US and the performance of banks of different funding structure. They found a negative relationship between unconventional measures and the performance of banks with a high amount of deposits. Demirgüç-Kunt and Huizinga (1999) found in an earlier paper that banks with more deposit funding (than non-deposit funding) are less profitable.

3.3 Capital to total assets

Most of the time, a distinction is made between low- and high capitalised banks. The level of capital can be determined by means of the capital to total assets (CAP) ratio. As mentioned before, Adrian and Liang (2014) state that banks with higher levels of capital have lower incentives to reduce the quality of credit. In turn, banks with low levels of capital will grant more and bigger loans often with fewer collateral to risky firms when the overnight interest rate is low (Jimenez et al., 2014). This incredibly increases their default risk. This statement is partly confirmed by Acharya et al. (2016a). They state that after the OMT announcement, more loans were granted to risky firms. They also find that weakly capitalised firms have high incentives for evergreening loans, which increases risk, whereas highly capitalised banks declare loans from insolvent borrowers non-performing (§4.1). Moreover, capital constrained banks are more exposed to liquidity risk (Pierret, 2015). Capital constrained banks could also start fire sales of assets, which will negatively affect their prices (Szcerbowicz, 2015). Besides, it could be possible that you could not find a buyer to buy these assets in a systemic crisis, because they could not get the funding needed due to the contagion effects of capital restrictions by constrained banks (Pierret, 2015). Brunnermeier et al. (2012) found that banks with a higher amount of leverage are bigger contributors to systemic risk.

However, De Nicolo et al. (2010) claim the contrary. They state that when there are a lot of banks with low capital, a positive relationship between the policy rate and bank risk taking can be found, which implies that risk-taking will be reduced thanks to the presence of low capitalised banks in times of monetary easing. Acharya et al. (2016b) agree with this finding. They distinguish between banks from the core and banks from the periphery. They found weakly capitalised periphery country banks to take less risks regarding granting loans with respect to the higher capitalised periphery country banks (§3.1). Ricci (2014) assumes that higher capitalised banks experience a lower impact of monetary policy interventions than lower capitalised banks. This assumption is partially supported, but only for an expansionary monetary policy measured with the TIER 1 ratio.

3.4 Non-interest income to total income

Brissimis and Delis (2010) found that profitability of banks is dependent on a change in the interest rates. Banks could take deposits available at short term at lower rates and use these borrowings to invest in high-yield investments or lend it out at higher rates. This also contains a risk. Moreover, banks with a high profitability are very reassured and could relax lending standards. By easily giving away loans, they increase their credit risk, which could in turn also decrease the profitability. In the paper of Ricci (2014), event studies were performed, which indicate that abnormal returns of stocks seem to be higher for more profitable banks, as well as for banks with a higher ratio between cost and income.

When really looking at the non-interest income to total income (DIV) ratio, mixed results are found. Brunnermeier et al. (2012) found that banks with a higher DIV ratio are bigger contributors to systemic risk. They also decomposed the non-interest income in two components and the effects of both of the components can be found in their paper. In the past, the volume of these non-interest income of banks was still bigger than it is nowadays, thanks to the regulations that were set up since the crisis started. However, the paper of Saunders, Schmid and Walter (2016) showed that banks with higher non-interest income have a higher profitability and no evidence is found that banks with a higher DIV ratio increased bank risk exposure nor that they would contribute more to systemic risk. They even found that a high DIV ratio lowered failure probabilities when looking at individual banks. De Jonghe, Diepstraten and Schepens (2015) found that the effect of the DIV ratio on systemic risk is dependent on the size of the bank. Stiroh (2004) found no clear and unambiguous relation between the DIV ratio and profitability and risk of banks.

4. METHODOLOGY

In the literature, a lot of evidence was given for obtaining lower risk after the introduction of the unconventional measures, however, there is also found a lot of theoretical and empirical evidence that the unconventional measures contributed to increased risk. Therefore, in this master’s dissertation, the extent to which the unconventional programmes had and have an influence on the systemic risk of European banks will be determined.

When the ECB announces an intervention of unconventional monetary policy, it is very likely that there are changes in bond and stock market returns. To determine if the unconventional programmes had an influence on these bond and stock markets, it is necessary that the announcement is unexpected. Otherwise the information regarding the programme would already be priced in. Measuring the effects of unexpected announcements (monetary policy surprise) is done in very different ways by different authors. Some use survey data, others newspaper articles, but mostly used are asset prices (Haitsma et al., 2015; among many others). In this master’s dissertation, I will make use of the method of Rogers et al. (2014), which defines the monetary policy surprise by using the yield spread between Italian and German 10 year government bonds at the day of an ECB policy announcement. The methodology that will be applied is the event study approach. The surprise (the event) will be investigated in different windows around the announcement date. The choice of the window size must be considered carefully, because when the window is too wide, other shocks might play a role and when it is too small, the effect of the news might not yet got through. Due to the fact that the spread between Italian and German 10 year government bonds will especially capture SMP and OMT announcements, other yields (money market rates, CDS rates, government bond rates,…) will also be used to capture the effects of (particular) unconventional measures on systemic risk.

To explore the influence of the unconventional programmes on systemic risk, we also need data on a systemic risk indicator, in this case the marginal expected shortfall (MES) of European banks. Because of the absence of intraday data, daily data will be used. As defined by Acharya et al. (2010), the MES is “the average of return of each bank during the 5% worst days for the market”. They found in their paper that the MES and leverage predict each bank’s contribution to a crisis. In a last step, it is investigated if the impact of the unconventional programmes is substantial and which of the programmes had the biggest impact (positive or negative) on systemic risk, with heterogeneity between banks taken into account. Therefore the sign and the magnitude of different

unconventional policies at announcement dates will be analysed. These policies will be outlined further. 4.1 Measuring systemic risk

As mentioned above, to capture and measure systemic risk, the marginal expected shortfall (MES) will be used. There are a lot of other measures for risk however, such as value at risk (VaR), but the expected loss or volatility are found to have almost no explanatory power for specific events, as described in Acharya et al. (2010). Therefore it is decided to use the MES here. The VaR measure is used to measure risk of an individual institution in isolation (Adrian and Brunnermeier, 2011). Therefore it is not suited to measure systemic risk, which needs to capture the risk of the whole system and not only the risk of one institution in isolation. To overcome this problem, Adrian and Brunnermeier (2011) propose a new method: the CoVaR. However, this measure still has the disadvantage that it only measures the system’s loss conditional on the individual institutions, so it can only identify systemically important institutions and cannot appropriately aggregate the systemic risk contributions of individual institutions (Adrian and Brunnermeier, 2011). Another disadvantage of this risk measure is that CoVaR treat firms that have the same correlation with the market, but that have different volatilities in the same manner (Acharya, Engle and Richardson, 2012). The MES however does take this limitation of the CoVaR into account. Other important measures of risk are the distress insurance premium (DIP) (Black et al., 2013) and SRISK (Acharya et al., 2012). An advantage of these two indicators is that they take the size of a financial institution explicitly into account, whereas neither MES nor CoVaR do this (Black et al., 2013). A concern of SRISK is that it computes only partly the systemic risk of a firm, namely it computes only the expected capital shortfall. Following Huang, Zhou and Zhu (2010), the MES and DIP measures are very similar. The difference is in the fact that the MES is calculated based on equity returns, while the DIP is based on CDS data. CDS data capture the default risk, whereas equity returns are important for having information about the market capitalisation. It could be concluded that each measure has its advantages and disadvantages. The paper of Idier, Lamé and Mésonnier (2013) investigated how useful the MES is as a systemic risk indicator. They found that the MES is not that good of an indicator for predicting the risk before a crisis. Nevertheless, in this paper, the MES is used for measuring the risk of past events. Moreover, given the fact that the MES is an objective distribution- based statistical measure (Black et al., 2013) and that it is, together with the leverage, effective in predicting each bank’s contribution to a crisis (Acharya et al., 2010), this research will capture systemic risk by making use of the MES.

4.2 Measuring monetary shock

In first instance, the monetary shock will be measured by using a sovereign bond spread. Therefore I will make use of the method of Rogers et al. (2014), which defines the monetary policy surprise by using the yield spread between Italian and German 10 year government bonds at the day of an ECB policy announcement. This approach is different from measuring a monetary shock in the United States or Japan. This is due to the fact that the circumstances were different in the euro zone. In the euro zone, the intra-euro area spreads are very important in the transmission mechanism of monetary policy (Rogers et al., 2014). People would reach wrong conclusions when only German yields would be measured. This is because actions that succeeded in lowering sovereign spreads tended to drive German yields up (Rogers et al., 2014). Therefore the unconventional measures would wrongly seem to be part of a contractionary policy. Despite this finding, other yields will be included in the analysis to measure the impact of particular unconventional programmes on systemic risk. However, it will be kept in mind that yields could go up instead of go down when actions for lowering sovereign spreads are undertaken.

4.3 Model

In a first step, an event study is performed in which the difference in the MES (ΔMES) in a one day window is plotted on announcement days. This is done for 67 European banks (§9. Appendix: Table A.2) as the mean of the cross-sections. This visual representation will also be showed for banks with high versus low capital and for a two day window.

In a next step, different regressions are performed. Again, the panel of the 67 European banks is used with daily figures (5 days/week) due to the absence of intradaily data. To interpret the regression results correctly, the delta yield is multiplied by minus one and due to the really small obtained coefficients, delta MES is also multiplied by 100, so that more clearness can be created about the differences and the magnitude of the coefficients. The parameters are estimated by the ordinary least squares (OLS) method with a fixed effects estimator. To adjust for heteroscedasticity, all of the regressions are performed with a robust standard error, namely white cross-section. To execute the first type of regression, the following formula is used,

Δ, = + β∗ Δ spread I − G + ∗ dummy +δ ∗ Δ spread I − G ∗ dummy + ε,, ε, ~ iid (0, ) (1)

in which delta spread I-G is the difference of the spread between Italian and German 10 year government bonds on two subsequent days and the dummy has a value of one at announcement days and zero for other days. Announcement days are days on which statements about the monetary

2 policy are made. Lastly, the assumption is made that ε ~ iid (0,σε ). This assumes that observations are serially uncorrelated and that they are homoscedastic (Verbeek, 2004). Due to the fact that we want to perform an event study, the previous regression formula is fixed on announcement days. Therefore we set the dummy at one and the formula becomes simpler,

Δ, = + β∗ Δ spread I − G + ε,, ε, ~ iid (0, ) (2) In the first place, this formula is executed for a list of 67 European banks in a one day window. These regressions were also performed with different business model characteristics, such as banks with high versus low capital, banks from peripheral countries versus core countries, systemic banks versus non-systemic banks, banks with a high versus low loan to total assets ratio, banks with a high versus low non-performing loans ratio, banks with a high versus low deposits to total liabilities ratio and banks with a high versus low non-earnings to total earnings ratio. Thereafter, the same is done for a two day window. In a next step, other yields are regressed on the MES to check their impact for the set of unconventional monetary policy announcement days, this regression formula becomes,

Δ, = + β ∗ Δ OIS 3M + β ∗ Δ OIS 1Y + β ∗ Δ Log(CDS banks 5Y) + β ∗

Δ Covered Bond yield + β ∗ Δ Spread I − G + β ∗ Δ OIS 10Y + β ∗ Δ Germany 10Y + ε,, ε, ~ iid (0, ) (3) This regression is also performed with the dummy set at one and heterogeneity is included by performing the regressions for the different business model characteristics.

In the following step, regressions were performed with a mix of business models in the regression formula and the dummy is again set at one,

Δ, = + β ∗ Δ measure + ∑ ∗ ,, + ∑ δ ∗ Δ measure ∗ ,, + ε,, ε, ~ iid (0, ) (4) in which delta measure is the difference of the yields on two subsequent days. Eleven different yields are included in this analysis, namely the Overnight Indexed Swap (OIS) 3 months and 1 year, EURIBOR 3 months and 1 year, EURIBOR-OIS 3 months spread, log 5 years CDS rate of banks, covered bond yield, spread between the Italian and German 10 year government bond, log CDS Italy 5 year rate, Germany 10 year government bond and the OIS 10 year rate. The mix of business models (BM)

contains a good mix of balance sheet ratios, namely the capital ratio (CAP), the loans to total assets ratio (LTA), the non-performing loans (NPL) ratio, the deposits to total liabilities ratio (DTL) and the non-interest income to total income ratio (DIV) for these European banks. In a last step, the formula is adapted to,

Δ, = + β ∗ Δ measure + ε,, ε, ~ iid (0, ) (5) In this type of regressions, the yield is dependent on which type of unconventional monetary policy is announced. The sample is therefore restricted to particular announcement days. A distinction is made between four groups of announcements: (i) liquidity measures that impact a relatively short- term funding period, like FRFA and (T)LTRO, (ii) CBPP announcements, (iii) SMP and OMT announcements and (iv) long-term measures like QE. For the first group, different money market rates, like the OIS 3 months, the EURIBOR 3 months, the OIS 1 year, the EURIBOR 1 year and the EURIBOR-OIS 3 months spread, are the measures used to capture their effects. The effects of the second group are captured by the log 5 year CDS rate of banks and the covered bond yield, those of the third group by the spread between the Italian and German 10 year government bonds and the log CDS Italy 5 year rate and those of the last group by the Germany 10 year government bond and the OIS 10 year rate. In the paper of Briciu and Lisi (2015), in which they perform an event study of different balance sheet policies, a comparable distinction is made between different unconventional programmes and which banks’ funding indicators or interest rates they specifically target.

5. DATA

The panel data used in this research contain daily data (Monday-Friday) of 67 European banks, starting at the 1st of October 2008 until the 30th of December 2015. The dataset contains data of the MES and rates of the different measures (OIS 3 months, EURIBOR 3 months, OIS 1 year, EURIBOR 1 year, EURIBOR-OIS 3 months spread, log 5 year CDS Banks, covered bond yield, spread of the Italy- Germany 10 year government bond, log 5 year CDS Italy, OIS 10 year and the Germany 10 year government bond rate) and is obtained from Datastream. The business model characteristics, generated from bank balance sheet data, are obtained from Bankscope.

To identify the monetary policy shock, a list of all monetary policy announcements by the ECB is needed. The list contains 124 announcements. Most of the announcements contain decisions to actively steer monetary policy, however also decisions of leaving the interest rate unchanged are included. All the announcements with regard to monetary policy can be found on the website of the ECB under ‘media’ (press conferences and press releases). An overview of the 67 investigated European Banks is given in the Appendix (§9) Table A.2. In Table A.1 (§9. Appendix), a list can be found of the days on which the ECB announced changes in monetary policy from October 2008 to December 2015.

6. RESULTS

To find the effects of the unconventional programmes on the systemic risk of banks, we first start by looking at the graphical representation of the evolution of the MES. The results of this event study are shown in Figure 1. In the graph, the mean of 67 cross-sections of the difference in the MES (ΔMES) in a one day window is plotted on announcement days. The biggest value for delta MES is found on the 10th of May 2010. This was an important date due to the decision of the Governing Council (GovC) to proceed with the SMP. Other important announcement days, on which a high delta MES is observed and therefore high systemic risks, concern announcements regarding OMT and FRFA. The highest bars are indicated with dates, which are further elaborated on in Table 1. The bars indicated in light-blue are the most important unconventional programmes introduced by the ECB.

Figure 1: Mean of cross-sections of delta MES plotted for 67 European banks. The most important announcements are coloured light-blue, the announcement days on which the reactions were the biggest are marked with a date.

Table 1: Clarification on the content of the different announcements that are marked in Figure 1. Dates and information are retrieved from the ECB website.

DATE ANNOUNCEMENT 13/10/2008 The GovC decided to conduct US dollar liquidity-providing operations at FRFA 5/03/2009 The GovC decided to continue the fixed rate tender procedure with full allotment for all main refinancing operations, special-term refinancing operations and supplementary and regular longer-term refinancing operations for as long as needed + MRO rate decreased to 1.50% 2/04/2009 MRO rate decreased to 1.25% 4/02/2010 Interest rates remain unchanged 6/05/2010 Interest rates remain unchanged

DATE ANNOUNCEMENT 10/05/2010 The GovC decided to proceed with the SMP, to reactivate the temporary liquidity swap lines with the Fed, to adopt a fixed-rate tender procedure with full allotment in the regular 3-month longer-term refinancing operations, and to conduct new special longer-term refinancing operations 2/08/2012 Interest rates remain unchanged 6/09/2012 The GovC announced the technical details of OMT (no ex-ante size limit) and decided on additional measures to preserve collateral availability (no interest changes) 10/10/2013 ECB and the People’s Bank of China establish a bilateral currency swap agreement 4/9/2014 ECB modifies loan-level reporting requirements for some asset-backed securities/MRO rate decreases to 0,05%

In a next step, the mean of the 67 cross-sections of delta MES is presented with a distinction made between high- versus low capitalised banks (see Figure 2). To make this distinction, the capital-to- total assets ratio was divided in quartiles. The orange bars show the mean of the 16 cross-sections of the first quartile, namely the low capitalised banks, and the blue bars show the mean of the cross- sections of the high capitalised banks (16 banks of the last quartile). From the graph, we found that the biggest value of delta MES, for banks with a low amount of capital, is again on the 10th of May 2010, the date on which the proceeding of SMP was announced. Other announcements regarding OMT, FRFA, and LTRO also had a substantial importance. When we look at the reactions for banks with a high amount of capital, we can see that in most of the cases the effects on announcement days are greater. The announcement with the biggest value for the systemic risk parameter was the one on the 13th of October 2008, when the Governing council decided to conduct US dollar liquidity- providing operations at FRFA. This announcement was of much less importance for low capitalised banks. The announcements that were mentioned when looking at the biggest effects for low capitalised banks and additionally, announcements regarding MRO rates, are also of substantial importance for the high capitalised banks. Again, the bars with the highest values are indicated with dates, which are further elaborated on in Table 2. The bars indicated in light-blue and light-orange are the most important unconventional programmes introduced by the ECB.

Figure 2: Mean of cross-sections of delta MES plotted for high versus low capitalised banks (represented with blue and orange bars respectively). The most important announcements are coloured light-blue/light-orange, the announcement days on which the reactions were the biggest are marked with a date.

Table 2: Clarification on the content of the different announcements that are marked in Figure 2. (H/L indicates on which type of banks (high versus low capitalised banks) the announcement had the biggest influence). Dates and information are retrieved from the ECB website.

DATE H/L ANNOUNCEMENT 13/10/2008 H The GovC decided to conduct US dollar liquidity-providing operations at FRFA 5/03/2009 H The GovC decided to continue the fixed rate tender procedure with full allotment for all main refinancing operations, special-term refinancing operations and supplementary and regular longer-term refinancing operations for as long as needed + MRO rate decreased to 1.50% 2/04/2009 H MRO rate decreased to 1.25% 4/02/2010 L Interest rates remain unchanged 8/04/2010 H Interest rates remain unchanged 6/05/2010 H & L Interest rates remain unchanged 10/05/2010 H & L The GovC decided to proceed with the SMP, to reactivate the temporary liquidity swap lines with the Fed, to adopt a fixed-rate tender procedure with full allotment in the regular 3-month longer-term refinancing operations, and to conduct new special longer-term refinancing operations 9/06/2011 H The GovC decided to continue to conduct its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, and to conduct 3- month longer-term refinancing operations as fixed rate tender procedures with full allotment (no interest changes) 8/08/2011 H The GovC decided to actively implement its Securities Markets Programme for Italy and Spain 2/08/2012 L Interest rates remain unchanged 6/09/2012 L The GovC announced the technical details of OMT (no ex-ante size limit) and decided on additional measures to preserve collateral availability (no interest changes) 10/10/2013 H ECB and the People’s Bank of China establish a bilateral currency swap agreement 6/02/2014 H Interest rates remain unchanged 4/9/2014 H ECB modifies loan-level reporting requirements for some asset-backed securities/MRO rate decreases to 0,05%

The analysis of the height of delta MES on the announcement days for the 67 European banks is also plotted for a two day window. This figure and associated table can be found in the appendix (Figure A.2, Table A.3). Based on this figure, it is found that the event on January 2013 is the most important for increasing the MES, which is the date on which the ECB extended an assistance programme to Serbia. However, this seems rather unimportant, therefore it is concluded to only focus on the one day window in the further research.

Also in the appendix, in Figure A.1 ((a)-(k)) and Figure A.3-A.13, figures of the different yields1 and delta yields respectively can be found. In these figures, the eleven different measures and the delta measures are multiplied with minus one and plotted respectively for the whole sample and on announcement days. This is done in order to see the effect of an expansive shock on these particular days. As explained before, dates on which announcements about FRFA and (T)LTRO were made are captured by the OIS 3 months, EURIBOR 3 months, OIS 1 year, EURIBOR 1 year rates and EURIBOR- OIS 3 months spread, CBPP announcements are captured by the log 5 years CDS rate of banks and the covered bond yield, SMP and OMT announcements by spread between the Italian and German 10 year government bond and the log CDS Italy 5 year rate and QE announcements by the Germany 10 year government bond and the OIS 10 year rate. The announcement days captured by the specific yields are indicated with bars coloured dark-blue. The most important announcements are again marked and coloured light-blue.

From the line graphs2 in Figure A.1 ((a)-(k)), we observe an increasing trend. This trend can be associated with an expansive policy, as the degree of expansiveness is plotted on the Y-axis against the time on the X-axis. For most of the yields, a drop in the curve can be observed between April and October 2011. A possible explanation is the increase in the MRO rate between that time. Another remarkable fact is that the two logarithmic rates, the log CDS banks rate and the log CDS Italy 5 year rate, are flatter and more monotone. A last observation that catches the eye is the curve of the spread between Italy and Germany, which has a downward slope in the first half of the curve (until 2012). This increase in the sovereign bond spread is due to the growing uncertainty of the public at the start of the crisis and is acknowledged by Falagiarda and Reitz (2013). Also, two big drops can be observed around November 2011-January 2012 and around July-August 2012. This second drop

1 OIS 3 month, EURIBOR 3 month, OIS 1 year, EURIBOR 1 year, EURIBOR-OIS 3 months spread, log 5 year CDS Banks, covered bond yield, spread Italy-Germany 10 year government bond, log 5 year CDS Italy, OIS 10 year and the Germany 10 year government bond rate 2 Attention should be paid to the scale of the Y-axis, which is different in every graph.

could possibly be explained by the following increase due to the speech Draghi gave concerning the OMT programme.

Next, a deeper look is taken into the graphs with only a focus on the announcement days. From the graphs3 in Figure A.3-A.13, we can see that for delta OIS 3 months, the difference of two subsequent days in the yield was the biggest for an FRFA announcement on the 8th of October 2008. For the delta EURIBOR 3 months and 1 year, there are only some peaks in 2008 and at the beginning of 2009, which is explicable due to the persisting conventional measures at that time and those are captured well by money market rates. Afterwards, the yield changes are very small. Also, for the delta EURIBOR-OIS 3 months spread, which captures the stress in the interbank market, the peaks can be found at the beginning of the sample. When looking at the delta covered bond yield, we can see that the yield change is negative on the CBPP1 and CBPP2 announcement days, which is opposite to the delta log 5 year CDS banks rate. Therefore, we can say that the announcements regarding the CBPP programmes were not effective in easing the covered bond yield. However, as was concluded from the ECB monthly Bulletin of August 2010, especially the first CBPP programme has improved the functioning of the covered bond market. Concerning the delta spread of Italy-Germany 10 year government bonds, enormous yield changes can be observed on SMP and OMT announcement days. This is also true, but to a lesser extent for OMT announcement days, for the delta log 5 year CDS of Italy rate. Regarding the OIS 10 year rate and the Germany 10 year government bond yield, we cannot see big changes on QE announcement days. The only remarkable yield change is a negative one on the 3rd of December 2015, the day on which the ECB announced the extension of QE until end March 2017. A possible explanation for this fact is that banks lost their faith, because the state of the financial system kept being in a relatively bad place. Therefore they didn’t react as much on the new announcement, which led to the measure to be ineffective to ease conditions.

6.1 Results regression analysis

To check whether there is a relation between the introduced unconventional programmes and systemic risk, a regression analysis was performed. The results of the regression analysis are explained in this section. Here, only a one day window is considered, because of the effects observed above in the MES two day window graph. Therefore, the possibility of an overlap on two consecutive days is excluded. However, it is kept in mind that there could be more than one monetary policy

3 Again, attention should be paid to the scale of the Y-axis.

announcement on the same day4. Positive effects found are associated with an increase in the MES and therefore a higher systemic risk, the opposite is true for negative effects. A. Regression analysis with delta spread of Italy-Germany

In a first part, analogous to the method of Rogers et al. (2014), the delta spread of Italy-Germany 10 year government bonds is regressed on delta MES, by making use of equation (2). At first, the regressions were performed with 124 periods included (all announcement days of Table A.1 in the appendix) for all 67 banks. To check for heterogeneity between banks, the regressions were also performed with different business model characteristics5. However, a very low explanation power (R²) of the regressions was found and none of the parameters were significant. Therefore, the regressions were performed once more, but this time only the most important announcement days were filtered, so that only 41 periods were included anymore. These are the days concerning announcements regarding FRFA, (T)LTRO, CBPP, SMP, OMT and QE6 and marked in Table A.1. The results of the regressions are shown in Table A.4. The explanation power is still quite low. But now, there are some significant effects observed, namely for banks in the core countries, for banks with a low NPL ratio and for banks with a high DTL ratio. For these three categories, an adjustment of monetary policy through the delta spread of Italy-Germany 10 year government bonds has a significant positive relation with the MES.

B. Regression analysis with different yields

From the previous part, we could conclude that delta spread Italy-Germany is not a good measure to capture all the effects. This is due to the fact that all kind of announcements are included in one model and the spread I-G will especially capture announcements regarding SMP and OMT. Therefore, the analysis will be repeated, but with (some of) the ten other measures included this time.

First, by making use of equation (3), seven of the measures are included in one regression model and are regressed on the MES. Not all the measures are included, due to the high correlation between some of them. If all the yields were taken into account, the reliability of the model would therefore be reduced. The model includes the OIS 3 months and 1 year rate, the 5 year CDS banks rate, the covered bond yield, the spread I-G, the OIS 10 year rate and the 10 year German government bond

4 This is for example the case for the CBPP 1 and LTRO announcement day. 5 The division of the business models went as follows: the capital-to-total assets ratio was divided in quartiles, where the first 16 banks were low capitalised and the last 16 banks were high capitalised. For the other high- low ratio’s, the data was plotted from low to high and visually divided into two groups. 6 Both fundamental and detail announcements are included.

rate. The results can be found in Table A.5. By performing the regressions for the different business models, heterogeneity is also taken into account and in this regression and all following regressions, only the set of the 41 most important announcements are incorporated. From Table A.5 was found that all significant effects on the MES were positive, except for the effect of the OIS 1 year rate. This could be explained by the relationship between low short-term interest rates and the NIM. When the short-term interest rate decreases, the NIM will rise, which could lead to increased monitoring and therefore reduced risk-taking (Lambert and Ueda, 2014). Secondly, we see that in most of the cases of the different business models, the adjustments of monetary policy through the log 5 year CDS banks rate has a significant relationship with the MES. Moreover, these significant coefficients are rather big. As a last finding, most of the significant, positive effects on the MES working through the different yields are observed for the low capitalised banks, non-systemic banks, banks with a high DTL and low DIV ratio.

Next, the different business models7 were included in the regression model (Table A.6 – equation (4)). As a first result, when looking at the upper part of the table, both delta EURIBOR 3 months and 1 year are found to be very big and significant parameters, which have a positive relationship with the MES and therefore lead to an increase of the systemic risk. The same significant, but smaller effects can be found for the EURIBOR-OIS 3 months spread and the spread I-G. The table also shows that banks with more capital increase the MES and banks with a high amount of non-performing loans decrease the MES. However, none of the coefficients of the CAP parameter are significant. The negative effect on the MES, which decreases the systemic risk potential, from banks with a high amount of non-performing loans is significant when the monetary policy is adjusted through the EURIBOR 3 months or the log 5 year CDS banks rate. This is a remarkable result, as it goes against the statement of Klein (2013)8. The further findings in part C find evidence in favour of Klein. As a second result, from the interaction effects observed in the lower part of the table, there is a significant interaction found between the LTA ratio of banks and the OIS 3 months/1 year rate, the EURIBOR-OIS 3 months spread, the log CDS Italy 5 year rate or the Germany 10 year government bond rate. These interactions all have a negative effect on the MES, except for the EURIBOR-OIS 3 months spread and the log CDS Italy 5 year rate. A last observation concerns the influence of the EURIBOR 3 months rate and EURIBOR-OIS 3 months spread, where the interaction effects with the capital ratio and especially with the DTL ratio are negative and significant.

7 Capital ratio, LTA, NPL, DTL and DIV ratio 8 Klein (2013) states that banks who have a lot of non-performing loans increase the risk.

C. Regression analysis split up in different unconventional programmes

Next, the announcements are divided into four categories and a specific yield is applied to the days when announcements about a particular category are made (equation (5)). The kind of yield is dependent on the type of unconventional monetary policy that is announced, as was mentioned in the methodology section (§4)9. The regression results for taking the whole sample of 67 banks into account can be found in Table A.7 and contain only fundamental announcements. This means that days on which only specific details of the programmes are explained are removed from the analysis. A remarkable increase of the explanation power can be observed. From the table, we can find a positive, significant effect on the MES for CBPP announcements measured through the covered bond yield. Similar positive and significant effects on the MES are found for SMP and OMT announcement days measures through the log CDS Italy 5 year rate and for QE announcement days measured through both the OIS 10 year rate and the Germany 10 year government bond rate. To check whether these results differ for different business models, the previous research is repeated, but now bank heterogeneity is included.

In this final step, we want to check in which of the business models10, the announcements of the unconventional programmes have the biggest effects on the MES. For the 14 different business model characteristics, the conforming yield of the specific announcement days is regressed on the MES. In this analysis, again only the fundamental announcements are included (no details). In all of the 14 cases, the results are largely similar to those outlined above and can be found in Table A.8- A.14. The observed effects will be summarized here.

The regressions performed on the FRFA and (T)LTRO announcement days with the OIS 3 months/1 year and EURIBOR 3 months/1 year contain some variations in the sign of the coefficients between the different business model characteristics. Despite this finding, when looking only at significant effects, the coefficients are always positive. Declarations on FRFA and (T)LTRO announcement days, measured through the OIS 3 months rate have a positive, significant effect on the MES for banks with

9 FRFA and (T)LTRO announcements are captured by the OIS 3 months, EURIBOR 3 months, OIS 1 year, EURIBOR 1 year and the EURIBOR-OIS 3 months spread, CBPP and (T)LTRO announcements by the log 5 year CDS rate of banks and the covered bond yield, SMP and OMT announcements by the spread between the Italian and German 10 year government bond and the log CDS Italy 5 year rate and QE announcements by the Germany 10 year government bond and the OIS 10 year rate. 10 Business models: Capital (high/low), Core/Periphery, Systemic/Non-systemic, LTA (high/low), NPL (high/low), DTL (high/low), DIV (high/low)

a low CAP ratio, a high NPL ratio, a high DTL ratio, a high DIV ratio, banks located in the periphery and for systemic banks. Similar results are found when the OIS 1 year rate is used to measure effects on the MES, but here the significant effects on the MES are only found for a more limited set of cases (namely for systemic banks, for banks with a high NPL ratio and banks with a high DIV ratio). With regard to the EURIBOR 3 months and 1 year rate, higher values of the coefficients can be observed. Significant effects on the MES can be found for banks with a high capital ratio, a low LTA ratio, a high DTL ratio and banks from core countries. To summarize the most important effects that were just mentioned, the effects on the MES on FRFA and LTRO announcement days measured through the OIS rates are found to have significant effects for low capitalised banks and banks from peripheral countries, whereas when the same is measured through the EURIBOR rates, significant and much bigger effects could be observed for high capitalised banks and banks from core countries. When looking at the EURIBOR-OIS 3 months spread, both positive and negative coefficients can be found. When using this yield, significant effects are observed on the MES for all of the business models, where in each table both of the signs occur. The biggest positive effect on the MES can be found for banks with a high CAP ratio. Smaller positive effects are obtained for banks from core countries, non- systemic banks, banks with a low LTA ratio, a low NPL ratio, a low DTL ratio and banks with a low DIV ratio. In turn, negative effects are found for all of the banks in the other column of the different tables (namely banks with a low CAP ratio, banks with a high LTA/NPL/DTL/DIV ratio, banks from peripheral countries and systemic banks). As the significant coefficients of the regressions with the interbank, money market rates deliver different signs, no unambiguous conclusion can be drawn regarding the effect of FRFA and LTRO announcements on systemic risk.

A programme that has a rather big, positive and in all cases significant impact on the MES when measured by the covered bond yield is the CBPP. The coefficients of the regression are the biggest for banks with a high capital ratio. When the impact of CBPP announcement days on the MES is measured by the log 5 year CDS banks rate, negative effects are observed, however none of them is significant. Next, the effects on SMP and OMT announcement days are observed. Positive, significant effects on the MES are found for banks with a low NPL ratio, a high DIV ratio and banks from core countries when measuring effects with the spread of Italy-Germany 10 year government bonds. When using another yield, namely the log CDS Italy 5 year rate, positive, significant effects are found for al type of banks and the effect is found to be the biggest for banks with a high LTA ratio. Lastly, the effects of QE are analysed. Again, a rather big, positive and significant effect on the MES is found in all cases measured by both the OIS 10 year rate and the Germany 10 year government bond rate. The biggest effect can be found for systemic banks. Remarkable is that now, compared with the

analysis in part B, almost none of the coefficients is significantly negative. The reason for this could be the difference in dataset11.

In Table A.15, the FRFA and (T)LTRO announcements, when measured through the OIS 3 months rate, are unbundled and the effects of the separate programme announcements are analysed. This result is showed here, because an important effect of FRFA announcements can be observed, namely a significant negative relationship with the MES for high capitalised banks. Therefore, in some cases, FRFA announcements are found to possibly contribute to a lower systemic risk potential of the banks. The reason could again be found in the impact of short-term interest rates on the NIM. The table only shows the results for the distinction between high- and low capitalised banks, but similar effects can be found for the other business models. To finish this analysis, SMP and OMT announcements measured through the log CDS Italy 5 year rate were also unbundled and these results are showed in Table A.16. Again, this is only done for the distinction between high- and low capitalised banks, of which the results are similar for the other business models. Negative effects of OMT announcements on the MES are found for all business models, which also means that OMT announcements decrease the systemic risk potential of banks. However, none of the coefficients are significant, so no clear conclusions can be drawn. Fratzscher and Rieth (2014), who studied the relationship between bank risk and sovereign credit risk found in their paper the same conclusion regarding SMP and OMT announcements (§6.2).

6.2 Discussion

As outlined above, in almost all of the cases, the adaption of the monetary policy through one of the yields led to a positive effect on the MES. This is negative for the systemic risk as this increases. In several papers, evidence was given that the unconventional measures had positive effects on stock prices and improved the macroeconomic conditions. Fratzscher et al. (2014) found a positive effect on equity prices and lower bond yields for the periphery. Falagiarda and Reitz (2013) also found unconventional programmes to be effective in reducing the Italian-German long-term bond yield spread. In turn, Peersman (2011) found a significant positive effect of the unconventional programmes on inflation and on economic recovery. However, as was found here, the risk of the banks due to the introduction of these measures still increases, except for the FRFA and OMT programmes on their own, which decrease systemic risk and FRFA and LTRO announcement days measured through the EURIBOR-OIS 3 months spread for which ambiguous effects are found.

11 In part C only fundamentals are included.

Despite this finding, some authors claim the opposite to be true, for example Fratzscher et al. (2014) who found that SMP and OMT announcements led to decreasing bank and sovereign credit risk. Eser and Schwaab (2013) found evidence for improvements in liquidity risk premia and default risk. Nevertheless, a lot of other authors confirm our outcome of increased risk after introduction of unconventional policies (Lambert and Ueda, 2014; Fratzscher and Rieth, 2014; Black et al., 2013, among others). Lambert and Ueda (2014) found evidence for increasing bank credit risk. This is confirmed by Fratzscher and Rieth (2014) for the SMP programme, who found in addition that the OMT programme was effective in reducing the bank credit risk, which is similar to our findings. Black et al. (2013) found high levels of systemic risk, with a peak in 2011, to which especially Italian and Spanish banks contributed. Also in section §2 about the transmission mechanism, a lot of evidence for increased risk-taking and higher risk appetite was extended. The significant effects on CBPP, OMT and SMP and QE announcement days are all similar what concerns the magnitude. Those on FRFA and LTRO announcement days are much higher when measured through the EURIBOR 3 months rate. A side note has to be made, namely the coefficients that came out of the analysis are now a hundred times bigger than they actually are, due to the multiplication of the MES with hundred. Therefore, rather small effects are observed.

When the sample is restricted to predefined banks to discover heterogeneity of banks, no unambiguous effect of FRFA, CBPP, SMP, OMT and QE announcements can be observed. When looking at the difference of high- and low capitalised banks and banks with a high versus low NPL ratio, CBPP and QE announcements contribute more to an increase in the MES for high capitalised banks and for banks with a low NPL ratio. The opposite is true for SMP and OMT announcement days. The statements of neither Adrian and Liang (2014) nor those of De Nicolo et al. (2010) of reduced risk-taking for respectively high- and low capitalised banks are confirmed. Likewise, no evidence is found in this master’s dissertation that banks with higher NPL ratio’s face increased risks (Klein, 2013). Ambiguous effects are also found for the comparison between banks with a high versus low LTA ratio, for banks from the core versus periphery and for systemic versus non-systemic banks. However, when comparing banks with a low versus high DTL and DIV ratio, a small, unambiguous effect can be observed. Both banks with a low DTL, as banks with a low DIV ratio, have a (little bit) bigger effect on the increase in the MES. Therefore, the statement of Brunnermeier et al. (2012) that banks with a higher DIV ratio are bigger contributors to systemic risk is contradicted here. With respect to the DTL ratio, we can agree with the statements of Altunbas et al. (2014) and Demirgüç- Kunt and Huizinga (2010) in which is stated that banks with a higher DTL ratio have lower risk.

In Table 3, the t statistics of the MES are provided to check for significant differences between the business models. The t statistics are calculated with following formula:

() = (6) 2 2 1+ 2 1 2 with mean the mean of the MES of the specific sample, stdev the standard deviation of the specific sample and N the number of observations. From the table, we found that the differences in the MES only significantly differ between banks with a high versus low capital ratio for SMP and OMT announcements days and for systemic versus non-systemic banks for QE announcement days. As the difference in the MES between banks with a high versus low DTL and DIV ratio do not differ significantly from each other, the previous conclusion of the higher impact of low DTL and DIV banks on the MES is very weak.

Table 3: Comparison of t statistics per unconventional programme

High vs. Periphery Syst. vs. High vs. High vs. High vs. High vs. Low CAP vs. Core Non-syst. Low LTA Low NPL Low DTL Low DIV FRFA + LTRO 0,060 0,495 1,459 0,398 0,743 0,231 0,110 CBPP 1,083 0,228 0,584 1,033 0,801 1,143 0,019 SMP + OMT 1,945** 1,210 0,236 0,259 0,525 0,253 1,002 QE 0,116 0,286 1,874* 0,673 0,288 0,464 0,341

7. CONCLUSION

As there was a need for the unconventional programmes to restore confidence in the financial markets after the outbreak of the crisis, they clearly didn’t contribute to the reduction of systemic risk. The findings of this master’s dissertation largely agree with the findings of many other authors, for instance Lambert and Ueda (2014), Fratzscher and Rieth (2014), Black et al. (2013), among others. They also found evidence in their work for increased bank risk after the introduction of unconventional programmes. The unconventional programmes analysed in this master’s dissertation are fixed rate full allotment and the longer term refinancing operations, the covered bond purchase programme, the securities market programme and the outright monetary transactions and quantitative easing. On these announcement days, the effect of a monetary policy shock on the MES was analysed. In the case of CBPP, SMP and OMT and QE announcements, a positive impact of the monetary policy shock on the MES was found. An important exception were OMT announcement days, when these effects were observed in isolation from the SMP announcements. Those results showed that there was a decrease in the systemic risk when OMT was announced. When the distinction was made between banks with different balance sheet characteristics, to check for heterogeneity effects, these conclusions didn’t change. Differences between high versus low capitalised banks, banks from core versus periphery countries, systemic versus non-systemic banks, banks with a high versus low loans to assets ratio, banks with a high versus low non-performing loan ratio, banks with a high versus low deposits to liabilities ratio and banks with a high versus low non- interest income ratio were checked. Between these different business models, no unambiguous effects were observed, so it couldn’t be decided here which type of banks had the biggest influence on the MES. Also for FRFA and LTRO announcement days, the effects on the MES were ambiguous.

To reduce the increased vulnerabilities, attention has to be paid to macroprudential tools. As macroprudential policies can be very effective in reducing systemic risk, some drawbacks should be taken into account. However, this goes beyond the scope of this master’s dissertation, but more information can be found in the research of Adrian and Liang (2014). A lot of papers propose higher capital requirements as the key to economic recovery. However, as no clear conclusion is found in this paper regarding whereas high versus low capitalised banks contributed most to the buildup of systemic risk after the introduction of unconventional policies, the assumption can be made here that there exist an optimal capital level. There is a need for capital requirements from a protection consideration point of view, however, high levels of capital contribute to increased systemic risk. Another, more drastic, option could be the tightening of monetary policy. However, this will have a

lot of negative consequences on a macroeconomic level, which is therefore not considered as being an option. However, one day there will come an end to the implementation of the unconventional programmes. This should be managed and communicated in an appropriate manner, so that financial markets are informed and prepared (Claeys and Darvas, 2015).

The findings of this master’s dissertation possibly arouse ideas for future research. Due to the absence of intradaily data, this study was performed with daily data. Therefore, it could be interesting to compare the results found here with the results obtained from regressions executed purely in the time range of some hours before and after announcement. It could also be interesting to check for different effects per country or per year or verify which components of the MES are determining on announcement days. Finally, the existence of an optimal capital level can be investigated.

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9. APPENDIX

TABLES

Table A.1: List of announcement days (date + details) retrieved from the ECB website (press conferences and press releases). The most important announcements (announcements regarding FRFA, (T)LTRO, CBPP, SMP, OMT and QE) are marked.

Date Details of announcement 2/10/2008 Interest rates remain unchanged 7/10/2008 The GovC decided to enhance a longer-term refinancing operation and expand US dollar liquidity-providing operations 8/10/2008 MRO rate decreased to 3.75% + The GovC decided to adopt a fixed rate tender procedure with full allotment 13/10/2008 The GovC decided to conduct US dollar liquidity-providing operations at FRFA 15/10/2008 The GovC decided to expand the list of assets eligible as collateral, enhance the provision of longer-term refinancing operations, and provide US dollar liquidity through foreign exchange swaps 6/11/2008 MRO rate decreased to 3.25% 4/12/2008 MRO rate decreased to 2.50% 18/12/2008 The GovC decided that the main refinancing operations will continue to be carried out through a fixed rate tender procedure with full allotment for as long as needed 19/12/2008 The GovC decided to continue conducting US dollar liquidity-providing operations 15/01/2009 MRO rate decreased to 2.00% 3/02/2009 The GovC decided to extend the liquidity swap arrangements with the Fed 5/02/2009 Interest rates remain unchanged 5/03/2009 The GpvC decided to continue the fixed rate tender procedure with full allotment for all main refinancing operations, special- term refinancing operations and supplementary and regular longer-term refinancing operations for as long as needed + MRO rate decreased to 1.50% 19/03/2009 The GovC decided to continue conducting US dollar liquidity-providing operations 2/04/2009 MRO rate decreased to 1.25% 6/04/2009 The GovC decided to establish a temporary reciprocal currency arrangement (swap line) with the Fed 7/05/2009 The GovC decided to proceed with the ECS. In particular, the GovC decided to purchase euro-denominated covered bonds issued in the euro area, and to conduct liquidity-providing longer-term refinancing operations with a maturity of one year, MRO rate decreased to 1% 4/06/2009 The GovC decided upon the technical modalities of the CBPP1 25/06/2009 The GovC decided to extend the liquidity swap arrangements with the Fed 2/07/2009 Interest rates remain unchanged 6/08/2009 Interest rates remain unchanged 3/09/2009 Interest rates remain unchanged 24/09/2009 The GovC decided to continue conducting US dollar liquidity-providing operations 8/10/2009 Interest rates remain unchanged 5/11/2009 Interest rates remain unchanged 3/12/2009 The GovC decided to continue conducting its main refinancing operations as fixed rate tender procedures with full allotment for as long as is needed, and to enhance the provision of longer-term refinancing operations (no interest changes) 14/01/2010 Interest rates remain unchanged 4/02/2010 Interest rates remain unchanged 4/03/2010 The GovC decided to continue conducting its main refinancing operations as fixed rate tender procedures with full allotment for as long as is needed, and to return to variable rate tender procedures in the regular 3-month longer-term refinancing operations 8/04/2010 Interest rates remain unchanged 6/05/2010 Interest rates remain unchanged 10/05/2010 The GovC decided to proceed with the SMP, to reactivate the temporary liquidity swap lines with the Fed, to adopt a fixed-rate tender procedure with full allotment in the regular 3-month longer-term refinancing operations, and to conduct new special longer-term refinancing operations 10/06/2010 The GovC decided to adopt a fixed rate tender procedure with full allotment in the regular 3-month longer-term refinancing operations 8/07/2010 Interest rates remain unchanged 28/07/2010 Collateral rules tightened, revised haircuts

Date Details of announcement 5/08/2010 Interest rates remain unchanged 2/09/2010 The GovC decided to continue to conduct its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, and to conduct 3-month longer-term refinancing operations as fixed rate tender procedures with full allotment (no interest changes) 7/10/2010 Interest rates remain unchanged 4/11/2010 Interest rates remain unchanged 2/12/2010 The GovC decided to continue to conduct its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, and to conduct 3-month longer-term refinancing operations as fixed rate tender procedures with full allotment (no interest changes) 17/12/2010 The ECB announced a temporary swap facility with the Bank of England 21/12/2010 The GovC decided to extend the liquidity swap arrangements with the Fed 13/01/2011 Interest rates remain unchanged 3/02/2011 Interest rates remain unchanged 3/03/2011 The GovC decided to continue to conduct its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, and to conduct 3-month longer-term refinancing operations as fixed rate tender procedures with full allotment (no interest changes) 7/04/2011 MRO rate increased to 1.25% 5/05/2011 Interest rates remain unchanged 9/06/2011 The GovC decided to continue to conduct its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, and to conduct 3-month longer-term refinancing operations as fixed rate tender procedures with full allotment (no interest changes) 29/06/2011 The GovC decided to extend the liquidity swap arrangements with the Fed 7/07/2011 MRO rate increased to 1.50% 4/08/2011 The GovC decided to continue conducting its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, to conduct 3-month longer-term refinancing operations as fixed rate tender procedures with full allotment, and to conduct a liquidity-providing supplementary longer-term refinancing operation with a maturity of six months as a fixed rate tender procedure with full allotment,SMP covers Spain and Italy (no interest changes) 8/08/2011 The GovC decided to actively implement its Securities Markets Programme for Italy and Spain 25/08/2011 The GovC decided to extend the liquidity swap arrangement with the Bank of England 8/09/2011 Interest rates remain unchanged 15/09/2011 The GovC decided to conduct three US dollar liquidity-providing operations in coordination with other central banks 6/10/2011 The GovC decided to continue conducting its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, to conduct 3-month longer-term refinancing operations as fixed rate tender procedures with full allotment, to conduct two liquidity-providing supplementary longer-term refinancing operation with a maturity of twelve and thirteen months as a fixed rate tender procedure with full allotment, and to launch a new covered bond purchase program (CBPP2) 3/11/2011 The GovC decided upon the technical modalities of CBPP2, MRO rate decreased 1.25% (marginal lending facility:2%, deposit facility: 0.5%) 30/11/2011 The GovC decided in cooperation with other central banks the establishment of a temporary network of reciprocal swap lines 8/12/2011 The GovC decided to conduct two longer-term refinancing operations with a maturity of three years and to increase collateral availability,reserve ratio to 1%, MRO rate to 1% 12/01/2012 Interest rates remain unchanged 9/02/2012 The GovC approved specific national eligibility criteria and risk control measures for the temporary acceptance in a number of countries of additional credit claims as collateral in Eurosystem credit operations (no interest changes) 28/02/2012 The Governing Council of the European Central Bank (ECB) has decided to temporarily suspend the eligibility of marketable debt instruments issued or fully guaranteed by the Hellenic Republic for use as collateral in Eurosystem monetary policy operations. 8/03/2012 Interest rates remain unchanged 4/04/2012 Interest rates remain unchanged 3/05/2012 Interest rates remain unchanged 6/06/2012 The GovC decided to continue to conduct its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, and to conduct 3-month longer-term refinancing operations as fixed rate tender procedures with full allotment 22/06/2012 The GovC took further measures to increase collateral availability for counterparties 5/07/2012 MRO rate decreased to 0.75%, deposit facility rate to 0 26/07/2012 Draghi's London speech (“…the ECB is ready to do whatever it takes to preserve the euro.”) 2/08/2012 Interest rates remain unchanged 27/08/2012 Asmussen's Hamburg speech supporting the new bond purchase program 4/09/2012 Maria Draghi leaks comments indicating the bank will buy two and three-year bonds from Spain and Italy 6/09/2012 The GovC announced the technical details of OMT (no ex-ante size limit) and decided on additional measures to preserve collateral availability (no interest changes) 12/09/2012 The GovC decided to extend the liquidity swap arrangement with the Bank of England

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Date Details of announcement 4/10/2012 Interest rates remain unchanged 8/11/2012 Interest rates remain unchanged 6/12/2012 The GovC decided to continue conducting its main refinancing operations as fixed rate tender procedures with full allotment for as long as necessary, and to conduct 3-month longer-term refinancing operations as fixed rate tender procedures with full allotment 13/12/2012 The GovC decided to extend the liquidity swap arrangements with the Fed 10/01/2013 Interest rates remain unchanged 31/01/2013 ECB extends an EU-funded assistance programme with the National Bank of Serbia 7/02/2013 Interest rates remain unchanged 7/03/2013 Interest rates remain unchanged 22/03/2013 ECB announces changes to the use as collateral of certain uncovered government-guaranteed bank bonds 4/04/2013 Interest rates remain unchanged 2/05/2013 ECB announces change in eligibility of marketable debt instruments issued or guaranteed by the Cypriot government ,MRO rate to 0.5%, FRFA extended to July 2014 6/06/2013 Interest rates remain unchanged 28/06/2013 Eligibility of marketable debt instruments issued or guaranteed by the Republic of Cyprus 4/07/2013 ECB announces change in the eligibility of marketable debt instruments issued or guaranteed by the Republic of Cyprus 1/08/2013 Interest rates remain unchanged 5/09/2013 Interest rates remain unchanged 2/10/2013 Interest rates remain unchanged 10/10/2013 ECB and the People’s Bank of China establish a bilateral currency swap agreement 31/10/2013 ECB establishes standing swap arrangements with other central banks 7/11/2013 MRO rate decreases to 0,25% 22/11/2013 ECB suspends early repayments of the three-year LTROs during the year-end period 5/12/2013 Interest rates remain unchanged 9/01/2014 Interest rates remain unchanged 6/02/2014 Interest rates remain unchanged 6/03/2014 Interest rates remain unchanged 3/04/2014 Interest rates remain unchanged 8/05/2014 Interest rates remain unchanged 5/06/2014 ECB decides to conduct a series of targeted longer-term refinancing operations (TLTROs) aimed at improving bank lending and to intensify preparatory work related to outright purchases of asset-backed securities (ABS). , ECB introduces a negative deposit facility interest rate , MRO rate decreases to 0,15% 17/06/2014 ECB extends US dollar liquidity-providing operations beyond 31 July 2014 3/07/2014 ECB announces further details of the targeted longer-term refinancing operations 29/07/2014 ECB publishes legal act relating to targeted longer-term refinancing operations 7/08/2014 Interest rates remain unchanged 22/08/2014 Draghi hints on QE (Jackson Hole speech) 4/09/2014 ECB modifies loan-level reporting requirements for some asset-backed securities/MRO rate decreases to 0,05% 18/09/2014 ECB allots €82.6 billion in first targeted longer-term refinancing operation 2/10/2014 ECB announces operational details of asset-backed securities and covered bond purchase programmes (no interest changes) 6/11/2014 Interest rates remain unchanged 7/11/2014 ECB suspends early repayments of the three-year LTROs during the year-end period 4/12/2014 Interest rates remain unchanged, CBPP and ABSPP has started 22/01/2015 ECB announces a modification to the interest rate applicable to future targeted longer-term refinancing operations, ECB announces expanded asset purchase programme 4/02/2015 Eligibility of Greek bonds used as collateral in Eurosystem monetary policy operations 5/03/2015 Interest rates remain unchanged 15/04/2015 Interest rates remain unchanged 3/06/2015 Interest rates remain unchanged 18/06/2015 ECB Governing Council takes note of ruling on OMT 16/07/2015 ECB confirmed that the central bank was increasing emergency liquidity assistance (ELA) to Greek banks. (no interest changes) 3/09/2015 Interest rates remain unchanged 23/09/2015 Eurosystem adjusts purchase process in ABS programme 22/10/2015 Interest rates remain unchanged 3/12/2015 deposit facility rate decreases to -0,3% + extension of QE "until end March 2017"

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Table A.2: List of the 67 European listed banks of which data is used for the analysis.

BANKS BANKS Crédit Industriel et Commercial SA - CIC Banco de Sabadell SA BNP Paribas Aktia Bank Plc Société Générale SA Nova Kreditna Banka Maribor d.d. Crédit Agricole S.A. Cyprus Popular Bank Public Co Ltd Caixabank, S.A. Hellenic Bank Public Company Limited Commerzbank AG OP Corporate Bank plc Deutsche Bank AG Alandsbanken Abp-Bank of Aland Plc Aareal Bank AG Raiffeisen Bank International AG IKB Deutsche Industriebank AG Alpha Bank AE Deutsche Postbank AG Banca Piccolo Credito Valtellinese-Credito Landesbank Berlin Holding AG-LBB Holding AG Valtellinese Soc Coop MLP Ag Banco di Desio e della Brianza SpA-Banco Desio Banca Finnat Euramerica SpA Banca popolare dell'Etruria e del Lazio Soc. coop. Unione di Banche Italiane Scpa-UBI Banca Banca popolare dell'Emilia Romagna Hypo Real Estate Holding AG National Bank of Greece SA Banca Ifis SpA Piraeus Bank SA Wüstenrot & Württembergische Mediobanca SpA-MEDIOBANCA - Banca di Credito Banca Generali SpA-Generbanca Finanziario Società per Azioni Attica Bank SA-Bank of Attica SA Natixis SA Allied Irish Banks plc SRH NV Bank of Ireland-Governor and Company of the Bank Dexia SA of Ireland Banco BPI SA Banca Popolare di Spoleto SpA Credito Emiliano SpA-CREDEM Banca Monte dei Paschi di Siena SpA-Gruppo Monte Banca Popolare di Sondrio Societa Cooperativa per dei Paschi di Siena Azioni Banca Popolare di Milano SCaRL Banca Profilo SpA Banca Carige SpA Erste Group Bank AG Banco Popolare - Società Cooperativa-Banco Banca Intermobiliare di Investimenti e Gestioni Popolare UmweltBank AG Van Lanschot NV Intesa Sanpaolo ING Groep NV UniCredit SpA Banco Espirito Santo SA Banco Santander SA Banco Comercial Português, SA-Millennium bcp Baader Bank AG Banco Bilbao Vizcaya Argentaria SA KBC Groep NV/ KBC Groupe SA-KBC Group Bankia, SA Eurobank Ergasias SA Bankinter SA Permanent Tsb Group Holdings P.L.C Banco Popular Espanol SA

Table A.3: Clarification on the content of the different announcements that are marked in Figure A.2. Dates and information are retrieved from the ECB website.

DATE ANNOUNCEMENT DATE ANNOUNCEMENT 7/10/2008 The GovC decided to enhance a longer-term refinancing operation and 2/12/2010 The GovC decided to continue to conduct its main refinancing operations expand US dollar liquidity-providing operations as fixed rate tender procedures with full allotment for as long as 13/10/2008 The GovC decided to conduct US dollar liquidity-providing operations at necessary, and to conduct 3-month longer-term refinancing operations as FRFA fixed rate tender procedures with full allotment (no interest changes) 5/03/2009 The GpvC decided to continue the fixed rate tender procedure with full 4/04/2012 Interest rates remain unchanged allotment for all main refinancing operations, special-term refinancing 2/08/2012 Interest rates remain unchanged operations and supplementary and regular longer-term refinancing 6/09/2012 The GovC announced the technical details of OMT (no ex-ante size limit) operations for as long as needed + MRO rate decreased to 1.50% and decided on additional measures to preserve collateral availability (no 4/02/2010 Interest rates remain unchanged interest changes) 6/5/2010 Interest rates remain unchanged 31/01/2013 ECB extends an EU-funded assistance programme with the National Bank 10/05/2010 The GovC decided to proceed with the SMP, to reactivate the temporary of Serbia liquidity swap lines with the Fed, to adopt a fixed-rate tender procedure 7/08/2014 Interest rates remain unchanged with full allotment in the regular 3-month longer-term refinancing operations, and to conduct new special longer-term refinancing operations

Table A.4: Panel regression results by using the fixed effects model. The difference in the spread I-G of 10 year government bonds on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta spread I-G is multiplied by -1. The regression is executed only on announcement days that contain the most important announcements regarding FRFA, (T)LTRO, CBPP, SMP, OMT and QE (see the marked announcements in table A.1). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

41 ann. days All High Low CAP Periphery Core Systemic Non- High Low High Low High Low High Low DIV banks CAP systemic LTA LTA NPL NPL DTL DTL DIV Constant -0,009 0,007 -0,020 0,005 -0,026 0,016 -0,015 -0,015 -0,003 0,002 -0,017 -0,034 -0,001 -0,008 -0,009 (0,028) (0,067) (0,023) (0,028) (0,038) (0,043) (0,027) (0,024) (0,039) (0,026) (0,036) (0,026) (0,034) (0,021) (0,032) Delta spread 0,304 0,453 0,393 0,208 0,422*** 0,230 0,321 0,391 0,224 0,176 0,395* 0,339* 0,292 0,265 0,315 I-G (0,264) (0,303) (0,285) (0,400) (0,142) (0,228) (0,279) (0,375) (0,181) (0,371) (0,202) (0,198) (0,293) (0,168) (0,295) R² 0,015 0,010 0,051 0,025 0,012 0,020 0,015 0,038 0,009 0,023 0,014 0,043 0,013 0,041 0,014 F-stat 0,613 0,424 2,158*** 1,021 0,496 0,800 0,595 1,587** 0,361 0,930 0,569 1,795** 0,516 1,692** 0,556 Observations 2747 656 656 1517 1230 533 2214 1312 1435 1148 1599 656 2091 615 2132 Banks 67 16 16 37 30 13 54 32 35 28 39 16 51 15 52

Table A.5: Panel regression results by using the fixed effects model. The difference in multiple yields (OIS 3M/OIS 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/OIS 10Y/Germany 10Y) on two consecutive days are regressed on delta MES. Delta MES is multiplied by 100 and the delta yields are multiplied by -1. The regression is executed only on announcement days that contain the most important announcements regarding FRFA, (T)LTRO, CBPP, SMP, OMT and QE (see the marked announcements in table A.1). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

41 ann. days All High Low CAP Periphery Core Systemic Non- High LTA Low High NPL Low High DTL Low High DIV Low DIV banks CAP systemic LTA NPL DTL Constant -0,003 0,052 -0,028 -0,007 0,003 0,026 -0,010 -0,031 0,022 -0,015 0,006 -0,055** 0,012 -0,011 -0,001 (0,026) (0,053) (0,022) (0,028) (0,029) (0,038) (0,025) (0,025) (0,031) (0,024) (0,031) (0,024) (0,031) (0,019) (0,030) Delta OIS 3M 1,243 0,640 1,282** 1,347 1,135 1,769 1,111 1,249 1,237 1,652* 0,948 2,148*** 0,966 0,537 1,451 (1,208) (3,081) (0,633) (1,014) (1,732) (1,309) (1,212) (0,918) (1,702) (0,891) (1,584) (0,595) (1,436) (0,607) (1,413) Delta OIS 1Y -2,064 -3,639 -1,509** -1,783 -2,460 -1,542 -2,194* -1,747 -2,352 -1,677 -2,368 -1,229* -2,310 -0,367 -2,569* (1,256) (3,299) (0,706) (1,217) (1,948) (1,411) (1,259) (1,136) (1,882) (1,118) (1,709) (0,696) (1,497) (0,588) (1,491) Delta Log 5Y 6,090** -2,256 8,906*** 9,744*** 1,349 7,693* 5,691** 9,840*** 2,669 9,025*** 3,897 6,732** 5,897* 6,439*** 5,990* CDS BANKS (2,975) (5,662) (2,573) (3,132) (4,000) (4,345) (2,763) (3,006) (3,906) (3,358) (3,395) (2,979) (3,134) (2,352) (3,215) Delta COV. 1,669* 5,776** 0,465 1,312 2,147 0,051 2,074** 1,187 2,109 1,041 2,150* -0,036 2,178** -0,428 2,295** BOND YIELD (0,977) (2,363) (0,953) (1,328) (1,592) (1,485) (0,947) (1,341) (1,371) (1,393) (1,285) (0,953) (1,061) (0,756) (1,073) Delta SPREAD 0,285* 0,524 0,361** 0,097 0,533*** 0,285 0,285* 0,230 0,336* 0,054 0,461** 0,285** 0,285 0,252* 0,295 I-G (0,172) (0,351) (0,149) (0,220) (0,191) (0,253) (0,161) (0,184) (0,198) (0,204) (0,183) (0,128) (0,199) (0,131) (0,191) Delta OIS 10Y -0,015 1,374 -0,912 -0,711 0,893 0,025 -0,026 -0,192 0,146 -0,417 0,283 -0,137 0,023 -0,645 0,175 (1,305) (3,000) (0,759) (1,181) (1,691) (1,587) (1,269) (1,046) (1,722) (1,052) (1,624) (0,761) (1,538) (0,649) (1,533) Delta 0,554 -2,317 1,624 -0,977 0,017 2,387 0,096 0,323 0,765 0,732 0,428 1,018 0,414 1,767 0,190 GERMANY 10Y (1,435) (2,230) (1,222) (1,426) (1,566) (2,276) (1,258) (1,337) (1,628) (1,486) (1,471) (1,402) (1,482) (1,165) (1,538) R² 0,035 0,034 0,175 0,099 0,028 0,105 0,033 0,108 0,024 0,085 0,030 0,119 0,032 0,086 0,035 F-stat 1,284* 1,026 5,741*** 3,748*** 0,881 3,182*** 1,182 3,928*** 0,820 3,048*** 1,013 3,650*** 1,168 2,673*** 1,244 Observations 2747 656 656 1517 1230 533 2214 1312 1435 1148 1599 656 2091 615 2132 Banks 67 16 16 37 30 13 54 32 35 28 39 16 51 15 52

Table A.6: Panel regression results of 67 banks by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Spread EUR-OIS 3M/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days and different business models (CAP/LTA/NPL/DTL/DIV) are regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed only on announcement days that contain the most important announcements regarding FRFA, (T)LTRO, CBPP, SMP, OMT and QE (see the marked announcements in table A.1). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

41 ann. days OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y Spread EUR- Log 5Y CDS Cov. bond Spread I-G Log CDS Italy OIS 10Y Germany OIS 3M banks yield 5Y 10Y Constant 0,108 0,115 0,142 0,078 0,121 0,183 0,175 0,136 0,208 0,203 0,193 (0,160) (0,156) (0,158) (0,175) (0,160) (0,174) (0,169) (0,163) (0,182) (0,177) (0,167) Delta measure -2,252 26,303*** -1,780 18,906* 3,849*** -12,481 1,524 0,596* -1,989 2,054 1,198 (2,021) (8,481) (1,510) (11,296) (1,158) (12,135) (2,037) (0,309) (3,054) (1,854) (2,135) BM= CAP 0,006 0,007 0,005 0,007 0,005 0,005 0,006 0,006 0,004 0,006 0,008 (0,019) (0,018) (0,019) (0,019) (0,019) (0,019) (0,019) (0,019) (0,019) (0,019) (0,019) BM= LTA 0,121 -0,283 0,010 -0,234 0,161 -0,127 -0,129 -0,156 -0,208 -0,148 -0,185 (0,406) (0,330) (0,345) (0,382) (0,373) (0,364) (0,384) (0,380) (0,380) (0,368) (0,397) BM= NPL -0,520 -0,795* -0,640 -0,734 -0,496 -0,694* -0,720 -0,621 -0,675 -0,701 -0,636 (0,418) (0,435) (0,420) (0,479) (0,387) (0,422) (0,474) (0,480) (0,461) (0,433) (0,436) BM= DTL -0,403 0,060 -0,294 0,041 -0,452 -0,212 -0,183 -0,145 -0,153 -0,230 -0,210 (0,306) (0,290) (0,244) (0,244) (0,302) (0,215) (0,233) (0,200) (0,206) (0,239) (0,235) BM= DIV 0,001 -0,009 -0,017 0,000 -0,005 -0,077 -0,046 -0,029 -0,085 -0,051 -0,059 (0,093) (0,098) (0,089) (0,103) (0,091) (0,089) (0,091) (0,097) (0,091) (0,083) (0,086) Delta measure* 0,083 -0,506** 0,084 -0,353 -0,112** 0,298 0,057 -0,012 0,057 0,017 0,060 CAP (0,056) (0,318) (0,051) (0,397) (0,056) (0,211) (0,060) (0,009) (0,054) (0,047) (0,050) Delta measure* -2,239*** 3,285 -1,919* 1,321 2,456*** 6,644 -0,569 0,004 2,641* -1,016 -2,413* LTA (0,703) (7,636) (0,990) (6,699) (0,629) (5,635) (1,420) (0,462) (1,503) (1,112) (1,338) Delta measure* 4,716 -4,771 2,784 -4,855 -5,064 10,196 1,138 -3,015 -3,017 0,858 4,714 NPL (5,270) (35,470) (4,724) (32,300) (4,947) (20,490) (3,433) (1,835) (7,859) (4,917) (3,021) Delta measure* 5,382 -36,434* 3,630 -22,397 -7,682*** 18,644 -1,832 0,418 2,907 -2,839 -1,639 DTL (3,688) (20,320) (2,793) (13,996) (2,453) (15,152) (3,222) (0,577) (2,310) (3,124) (2,900) Delta measure* 0,126 -7,439* -0,249 -5,913 -0,450 3,581 -0,958 -0,288 1,157 -0,922 -0,417 DIV (1,186) (4,462) (0,893) (6,313) (1,139) (8,354) (0,973) (0,284) (2,643) (0,911) (1,373) R² 0,022 0,039 0,019 0,020 0,031 0,029 0,017 0,020 0,026 0,017 0,017 F-stat 0,742 1,326** 0,637 0,654 1,033 0,988 0,577 0,680 0,866 0,561 0,570 Observations 2560 2560 2560 2560 2560 2560 2560 2560 2560 2560 2560

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Table A.7: Panel regression results of 67 banks by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

FRFA + (T)LTRO CBPP SMP + OMT QE OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y EUR-OIS 3M Log 5Y CDS Cov. Bond Spread I-G Log CDS Italy OIS 10Y Germany BANKS Yield 5Y 10Y Constant 0,018 0,009 0,019 0,007 0,021 0,128 0,154*** -0,112 -0,162* -0,053 -0,083*** (0,031) (0,025) (0,031) (0,028) (0,032) (0,129) (0,005) (0,078) (0,087) (0,054) (0,004) Delta measure 0,099 3,238 -0,006 2,700 0,120 -3,644 2,099*** 0,494 3,777*** 2,961*** 2,263*** (0,188) (2,212) (0,232) (1,853) (0,137) (4,348) (0,119) (0,421) (0,678) (0,334) (0,022) R² 0,010 0,014 0,010 0,011 0,010 0,288 0,317 0,148 0,281 0,487 0,497 F-stat 0,204 0,304 0,203 0,243 0,205 0,801 0,922 0,863 1,950*** 1,883*** 1,962*** Observations 1474 1474 1474 1474 1474 201 201 402 402 201 201 Ann. days 22 22 22 22 22 3 3 6 6 3 3

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Table A.8: Comparison of panel regression results of high versus low capitalized banks. Regressions are performed by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

FRFA + (T)LTRO OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y EUR-OIS 3M HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP Constant 0,037 0,002 -0,027 0,013 0,027 0,008 -0,022 0,012 0,045 0,003 (0,101) (0,016) (0,082) (0,020) (0,099) (0,017) (0,100) (0,019) (0,101) (0,016) Delta measure -1,393 0,570** 13,654* -0,774 -1,321 0,269 8,727 -0,204 2,409*** -0,653*** (1,312) (0,254) (7,926) (0,687) (1,480) (0,298) (5,950) (0,570) (0,795) (0,177) R² 0,010 0,058 0,029 0,038 0,009 0,040 0,012 0,035 0,016 0,064 F-stat 0,212 1,288 0,615 0,823 0,199 0,864 0,248 0,762 0,350 1,422 Observations 352 352 352 352 352 352 352 352 352 352 Ann. days 22 22 22 22 22 22 22 22 22 22 Banks 16 16 16 16 16 16 16 16 16 16

CBPP SMP + OMT QE Log 5Y CDS BANKS Cov. Bond Yield Spread I-G Log CDS Italy 5Y OIS 10Y Germany 10Y HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP Constant 0,263 0,065 0,358*** 0,073*** 0,109 -0,179* 0,006 -0,210* -0,017 -0,058 -0,053*** -0,083*** (0,268) (0,048) (0,028) (0,001) (0,101) (0,103) (0,107) (0,116) (0,088) (0,042) (0,012) (0,006) Delta measure -3,287 -1,439 3,836*** 0,777*** 0,319 0,629 3,882*** 4,134*** 3,682*** 2,439*** 2,856*** 1,857*** (9,007) (1,605) (0,662) (0,033) (0,419) (0,491) (1,148) (0,749) (0,541) (0,260) (0,059) (0,032) R² 0,280 0,266 0,313 0,295 0,069 0,200 0,168 0,362 0,602 0,463 0,623 0,474 F-stat 0,754 0,701 0,883 0,811 0,364 1,232 0,998 2,807*** 2,925*** 1,672 3,201*** 1,743* Observations 48 48 48 48 96 96 96 96 48 48 48 48 Ann. days 3 3 3 3 6 6 6 6 3 3 3 3 Banks 16 16 16 16 16 16 16 16 16 16 16 16

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Table A.9: Comparison of panel regression results of banks from peripheral versus core countries. Regressions are performed by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

FRFA + (T)LTRO OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y EUR-OIS 3M PERIPHERY CORE PERIPHERY CORE PERIPHERY CORE PERIPHERY CORE PERIPHERY CORE Constant 0,023 0,012 0,033 -0,020 0,027 0,011 0,031 -0,022 0,023 0,018 (0,023) (0,053) (0,023) (0,040) (0,023) (0,052) (0,023) (0,051) (0,023) (0,053) Delta measure 0,505* -0,401 -0,814 8,234** 0,450 -0,569 -0,188 6,262* -0,586*** 0,992*** (0,260) (0,663) (1,131) (3,985) (0,274) (0,698) (1,287) (3,373) (0,157) (0,342) R² 0,034 0,008 0,027 0,023 0,032 0,008 0,026 0,012 0,037 0,011 F-stat 0,748 0,167 0,588 0,486 0,694 0,177 0,556 0,264 0,804 0,224 Observations 814 660 814 660 814 660 814 660 814 660 Ann. days 22 22 22 22 22 22 22 22 22 22 Banks 37 30 37 30 37 30 37 30 37 30

CBPP SMP + OMT QE Log 5Y CDS BANKS Cov. Bond Yield Spread I-G Log CDS Italy 5Y OIS 10Y Germany 10Y PERIPHERY CORE PERIPHERY CORE PERIPHERY CORE PERIPHERY CORE PERIPHERY CORE PERIPHERY CORE Constant 0,188 0,053 0,198*** 0,101*** -0,128 -0,092 -0,234* -0,073 -0,062 -0,042 -0,086*** -0,079*** (0,171) (0,079) (0,004) (0,017) (0,110) (0,071) (0,131) (0,065) (0,060) (0,047) (0,008) (0,020) Delta measure -7,363 0,942 3,043*** 0,933** 0,434 0,568*** 4,581*** 2,787*** 2,528*** 3,495*** 1,963*** 2,633*** (5,739) (2,667) (0,096) (0,386) (0,671) (0,164) (1,155) (0,268) (0,372) (0,290) (0,043) (0,103) R² 0,268 0,311 0,341 0,316 0,102 0,278 0,277 0,328 0,446 0,590 0,457 0,599 F-stat 0,723 0,887 1,023 0,907 0,565 1,191*** 1,904*** 2,420*** 1,591** 2,835*** 1,658** 2,937*** Observations 111 90 111 90 222 180 222 180 111 90 111 90 Ann. days 3 3 3 3 6 6 6 6 3 3 3 3 Banks 37 30 37 30 37 30 37 30 37 30 37 30

Table A.10: Comparison of panel regression results of systemic versus non-systemic banks. Regressions are performed by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

FRFA + (T)LTRO OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y EUR-OIS 3M SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. Constant 0,051 0,010 0,052 -0,001 0,056 0,011 0,045 -0,002 0,055 0,012 (0,038) (0,032) (0,034) (0,027) (0,038) (0,031) (0,032) (0,031) (0,040) (0,032) Delta measure 0,742*** -0,056 3,281 3,227 0,768*** -0,192 3,814 2,432 -0,552*** 0,282** (0,231) (0,203) (2,126) (2,634) (0,237) (0,306) (3,104) (2,043) (0,168) (0,143) R² 0,046 0,008 0,051 0,012 0,046 0,008 0,046 0,009 0,039 0,008 F-stat 1,020 0,170 1,132 0,254 1,011 0,174 1,020 0,197 0,841 0,179 Observations 286 1188 286 1188 286 1188 286 1188 286 1188 Ann. days 22 22 22 22 22 22 22 22 22 22 Banks 13 54 13 54 13 54 13 54 13 54

CBPP SMP + OMT QE Log 5Y CDS BANKS Cov. Bond Yield Spread I-G Log CDS Italy 5Y OIS 10Y Germany 10Y SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. SYSTEMIC NON-SYST. Constant 0,103 0,134 0,104*** 0,166*** -0,091 -0,117 -0,100 -0,177** -0,095 -0,043 -0,154*** -0,066*** (0,094) (0,138) (0,004) (0,007) (0,126) (0,079) (0,129) (0,083) (0,058) (0,054) (0,046) (0,005) Delta measure -4,350 -3,474 1,690*** 2,197*** 0,466 0,500 2,790*** 4,015*** 5,398*** 2,374*** 4,033*** 1,837*** (3,154) (4,652) (0,088) (0,169) (0,328) (0,454) (0,687) (0,700) (0,356) (0,331) (0,233) (0,027) R² 0,300 0,286 0,346 0,315 0,163 0,145 0,237 0,292 0,792 0,448 0,795 0,458 F-stat 0,825 0,794 1,015 0,911 0,957 0,845 1,532 2,052*** 7,326*** 1,605** 7,451*** 1,672** Observations 39 162 39 162 78 324 78 324 39 162 39 162 Ann. days 3 3 3 3 6 6 6 6 3 3 3 3 Banks 13 54 13 54 13 54 13 54 13 54 13 54

xvi

Table A.11: Comparison of panel regression results of banks with a high versus low LTA ratio. Regressions are performed by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

FRFA + (T)LTRO OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y EUR-OIS 3M HIGH LTA LOW LTA HIGH LTA LOW LTA HIGH LTA LOW LTA HIGH LTA LOW LTA HIGH LTA LOW LTA Constant 0,006 0,029 0,015 0,004 0,008 0,029 0,015 0,001 0,005 0,035 (0,019) (0,052) (0,019) (0,039) (0,019) (0,051) (0,020) (0,047 (0,019) (0,052) Delta measure 0,321 -0,104 -1,392 7,471** 0,234 -0,225 -1,021 6,103* -0,434*** 0,627** (0,237) (0,509) (0,904) (3,701) (0,252) (0,551) (1,343) (3,444) (0,110) (0,270) R² 0,029 0,008 0,030 0,022 0,027 0,008 0,026 0,013 0,031 0,009 F-stat 0,619 0,169 0,639 0,478 0,576 0,172 0,568 0,286 0,680 0,199 Observations 704 770 704 770 704 770 704 770 704 770 Ann. days 22 22 22 22 22 22 22 22 22 22 Banks 32 35 32 35 32 35 32 35 32 35

CBPP SMP + OMT QE Log 5Y CDS BANKS Cov. Bond Yield Spread I-G Log CDS Italy 5Y OIS 10Y Germany 10Y HIGH LTA LOW LTA HIGH LTA LOW LTA HIGH LTA LOW LTA HIGH LTA LOW LTA HIGH LTA LOW LTA HIGH LTA LOW LTA Constant 0,126 0,129 0,121*** 0,184*** -0,134 -0,092 -0,224** -0,106 -0,068 -0,040 -0,086*** -0,081*** (0,125) (0,134) (0,008) (0,017) (0,105) (0,075) (0,111) (0,077) (0,055) (0,053) (0,014) (0,021) Delta measure -6,633 -0,912 2,366*** 1,854*** 0,605 0,392 5,218*** 2,460*** 1,983*** 3,856*** 1,557*** 2,909*** (4,208) (4,507) (0,193) (0,405) (0,632) (0,243) (0,960) (0,449) (0,342) (0,330) (0,070) (0,107) R² 0,197 0,300 0,302 0,316 0,138 0,176 0,333 0,250 0,464 0,553 0,473 0,562 F-stat 0,482 0,844 0,852 0,911 0,796 1,062 3,480*** 1,655** 1,705** 2,439*** 1,768** 2,530*** Observations 96 105 96 105 192 210 192 210 96 105 96 105 Ann. days 3 3 3 3 6 6 6 6 3 3 3 3 Banks 32 35 32 35 32 35 32 35 32 35 32 35

xvii

Table A.12: Comparison of panel regression results of banks with a high versus low NPL ratio. Regressions are performed by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

FRFA + (T)LTRO OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y EUR-OIS 3M HIGH NPL LOW NPL HIGH NPL LOW NPL HIGH NPL LOW NPL HIGH NPL LOW NPL HIGH NPL LOW NPL Constant 0,024 0,013 0,037 -0,011 0,031 0,011 0,032 -0,010 0,027 0,017 (0,020) (0,046) (0,024) (0,037) (0,021) (0,045) (0,023) (0,044) (0,020) (0,046) Delta measure 0,803*** -0,407 0,021 5,547 0,704*** -0,516 1,067 3,873 -0,842*** 0,811*** (0,143) (0,401) (1,053) (3,811) (0,266) (0,525) (0,881) (2,930) (0,085) (0,237) R² 0,041 0,009 0,022 0,017 0,035 0,009 0,024 0,010 0,042 0,011 F-stat 0,896 0,181 0,482 0,357 0,769 0,188 0,509 0,219 0,916 0,225 Observations 616 858 616 858 616 858 616 858 616 858 Ann. days 22 22 22 22 22 22 22 22 22 22 Banks 28 39 28 39 28 39 28 39 28 39

CBPP SMP + OMT QE Log 5Y CDS BANKS Cov. Bond Yield Spread I-G Log CDS Italy 5Y OIS 10Y Germany 10Y HIGH NPL LOW NPL HIGH NPL LOW NPL HIGH NPL LOW NPL HIGH NPL LOW NPL HIGH NPL LOW NPL HIGH NPL LOW NPL Constant 0,100 0,148 0,104*** 0,190*** -0,103 -0,119 -0,214* -0,125 -0,050 -0,056 -0,077*** -0,088*** (0,099) (0,152) (0,003) (0,011) (0,101) (0,078) (0,116) (0,080) (0,055) (0,054) (0,000) (0,008) Delta measure -4,379 -3,117 1,770*** 2,335*** 0,385 0,572** 4,413*** 3,321*** 2,720*** 3,134*** 2,089*** 2,388*** (3,316) (5,115) (0,069) (0,255) (0,634) (0,280) (1,097) (0,423) (0,340) (0,333) (0,002) (0,040) R² 0,216 0,293 0,293 0,318 0,105 0,212 0,266 0,311 0,469 0,511 0,477 0,525 F-stat 0,543 0,817 0,813 0,921 0,580 1,336 1,800** 2,242*** 1,733** 2,067*** 1,789** 2,180*** Observations 84 117 84 117 168 234 168 234 84 117 84 117 Ann. days 3 3 3 3 6 6 6 6 3 3 3 3 Banks 28 39 28 39 28 39 28 39 28 39 28 39

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Table A.13: Comparison of panel regression results of banks with a high versus low DTL ratio. Regressions are performed by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

FRFA + (T)LTRO OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y EUR-OIS 3M HIGH DTL LOW DTL HIGH DTL LOW DTL HIGH DTL LOW DTL HIGH DTL LOW DTL HIGH DTL LOW DTL Constant -0,005 0,025 0,008 0,009 0,007 0,023 -0,001 0,010 -0,001 0,028 (0,018) (0,040) (0,027) (0,032) (0,022) (0,039) (0,022) (0,036) (0,019) (0,041) Delta measure 1,231*** -0,256 1,643 3,738 0,785 -0,254 3,410*** 2,478 -1,179*** 0,528** (0,263) (0,301) (1,472) (3,126) (0,552) (0,412) (1,176) (2,415) (0,242) (0,229) R² 0,118 0,009 0,042 0,014 0,064 0,009 0,057 0,010 0,108 0,010 F-stat 2,814*** 0,196 0,919 0,294 1,428 0,195 1,265 0,215 2,522*** 0,219 Observations 352 1122 352 1122 352 1122 352 1122 352 1122 Ann. days 22 22 22 22 22 22 22 22 22 22 Banks 16 51 16 51 16 51 16 51 16 51

CBPP SMP + OMT QE Log 5Y CDS BANKS Cov. Bond Yield Spread I-G Log CDS Italy 5Y OIS 10Y Germany 10Y HIGH DTL LOW DTL HIGH DTL LOW DTL HIGH DTL LOW DTL HIGH DTL LOW DTL HIGH DTL LOW DTL HIGH DTL LOW DTL Constant 0,029 0,159 0,060*** 0,184*** -0,119* -0,110 -0,159*** -0,163 -0,077*** -0,046 -0,110*** -0,075*** (0,074) (0,147) (0,010) (0,004) (0,061) (0,085) (0,056) (0,100) (0,026) (0,063) (0,030) (0,004) Delta measure -0,436 -4,651 1,003*** 2,442*** 0,475 0,500 3,471*** 3,874*** 2,978*** 2,956*** 2,218*** 2,279*** (2,477) (4,950) (0,229) (0,086) (0,306) (0,461) (0,468) (0,755) (0,161) (0,390) (0,153) (0,018) R² 0,203 0,290 0,256 0,321 0,209 0,138 0,400 0,263 0,740 0,460 0,737 0,471 F-stat 0,492 0,809 0,668 0,934 1,306 0,796 3,296*** 1,777*** 5,524*** 1,684** 5,422*** 1,766*** Observations 48 153 48 153 96 306 96 306 48 153 48 153 Ann. days 3 3 3 3 6 6 6 6 3 3 3 3 Banks 16 51 16 51 16 51 16 51 16 51 16 51

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Table A.14: Comparison of panel regression results of banks with a high versus low DIV ratio. Regressions are performed by using the fixed effects model. The difference in a particular yield (OIS 3M/EURIBOR 3M/OIS 1Y/EURIBOR 1Y/Log 5Y CDS Banks/Covered bond yield/Spread I-G/Log CDS Italy 5Y/ OIS 10Y/Germany 10Y) on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

FRFA + (T)LTRO OIS 3M EURIBOR 3M OIS 1Y EURIBOR 1Y EUR-OIS 3M HIGH DIV LOW DIV HIGH DIV LOW DIV HIGH DIV LOW DIV HIGH DIV LOW DIV HIGH DIV LOW DIV Constant 0,018 0,018 0,020* 0,006 0,020* 0,019 0,017 0,005 0,019 0,021 (0,012) (0,039) (0,012) (0,031) (0,011) (0,038) (0,012) (0,035) (0,012) (0,040) Delta measure 0,268*** 0,050 0,531 4,018 0,265*** -0,084 1,059*** 3,173 -0,244*** 0,226** (0,051) (0,238) (0,381) (2,844) (0,093) (0,308) (0,361) (2,345) (0,049) (0,170) R² 0,028 0,009 0,024 0,015 0,027 0,009 0,026 0,011 0,027 0,010 F-stat 0,599 0,197 0,520 0,320 0,587 0,197 0,555 0,240 0,577 0,202 Observations 330 1144 330 1144 330 1144 330 1144 330 1144 Ann. days 22 22 22 22 22 22 22 22 22 22 Banks 15 52 15 52 15 52 15 52 15 52

CBPP SMP + OMT QE Log 5Y CDS BANKS Cov. Bond Yield Spread I-G Log CDS Italy 5Y OIS 10Y Germany 10Y HIGH DIV LOW DIV HIGH DIV LOW DIV HIGH DIV LOW DIV HIGH DIV LOW DIV HIGH DIV LOW DIV HIGH DIV LOW DIV Constant 0,121 0,130 0,149*** 0,156*** -0,133** -0,106 -0,143** -0,168* -0,034 -0,059 -0,064*** -0,089*** (0,126) (0,131) (0,006) (0,005) (0,063) (0,083) (0,065) (0,095) (0,043) (0,058) (0,013) (0,002) Delta measure -3,252 -3,757 1,984*** 2,131*** 0,413* 0,517 2,519*** 4,141*** 2,872*** 2,987*** 2,173*** 2,289*** (4,221) (4,405) (0,140) (0,114) (0,242) (0,474) (0,407) (0,771) (0,267) (0,356) (0,067) (0,010) R² 0,292 0,287 0,341 0,314 0,225 0,140 0,358 0,281 0,466 0,491 0,473 0,502 F-stat 0,797 0,797 0,999 0,906 1,432 0,814 2,750*** 1,942*** 1,685 1,912*** 1,734* 1,998*** Observations 45 156 45 156 90 312 90 312 45 156 45 156 Ann. days 3 3 3 3 6 6 6 6 3 3 3 3 Banks 15 52 15 52 15 52 15 52 15 52 15 52

Table A.15: Comparison of panel regression results of banks with a high versus low CAP ratio (further details on FRFA+(T)LTRO announcements of Table A.7). Regressions are performed by using the fixed effects model. The difference in the OIS 3M rate on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

OIS 3M FRFA+(T)LTRO FRFA (T)LTRO HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP Constant 0,037 0,002 0,153 0,020 0,033 -0,014 (0,101) (0,016) (0,147) (0,021) (0,187) (0,022) Delta -1,393 0,570** -2,442* 0,668*** 3,000* -0,010 measure (1,312) (0,254) (1,300) (0,158) (1,610) (0,266) R² 0,010 0,058 0,030 0,098 0,019 0,085 F-stat 0,212 1,288 0,372 1,290 0,189 0,926 Observations 352 352 208 208 176 176 Ann. days 22 22 13 13 11 11

Table A.16: Comparison of panel regression results of banks with a high versus low CAP ratio (further details on SMP+OMT announcements of Table A.7). Regressions are performed by using the fixed effects model. The difference in the Log CDS Italy 5Y rate on two consecutive days is regressed on delta MES. Delta MES is multiplied by 100 and delta yield is multiplied by -1. The regression is executed on particular announcement days, which are presented in the tables. These announcement days contain only fundamental announcements (no details). Robust standard errors are presented in parenthesis and stars indicate significance levels: *,**,*** represent significance at the 10 percent, 5 percent and 1 percent threshold respectively.

Log CDS Italy 5Y SMP+OMT SMP OMT HIGH CAP LOW CAP HIGH CAP LOW CAP HIGH CAP LOW CAP Constant 0,006 -0,210* 0,101 -0,393*** 0,013 -0,009 (0,107) (0,116) (0,204) (0,102) (0,027) (0,065) Delta 3,882*** 4,134*** 3,555*** 5,136*** -0,374 -1,296 measure (1,148) (0,749) (1,228) (0,614) (2,237) (5,419) R² 0,168 0,362 0,178 0,543 0,238 0,233 F-stat 0,998 2,807*** 0,420 2,301** 0,605 0,588 Observations 96 96 48 48 48 48 Ann. days 6 6 3 3 3 3

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FIGURES

Figure A.1 ((a)-(k)): evolution of the yields between 1/10/2008 and 30/12/2015, all yields are multiplied by minus one.

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DELTA MES (2 DAY WINDOW)

Figure A.2: Mean of cross-sections of delta MES plotted for 67 European banks in a two day window. The most important announcements are coloured light-blue, the announcement days on which the reactions were the biggest are marked with a date.

DELTA OIS 3 months

Figure A.3: Bar graph representing delta OIS 3 months on announcement days. Dates on which announcements about FRFA and LTRO were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

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DELTA EURIBOR 3 months

Figure A.4: Bar graph representing delta EURIBOR 3 months on announcement days. Dates on which announcements about FRFA and LTRO were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

DELTA OIS 1 year

Figure A.5: Bar graph representing delta OIS 1 year on announcement days. Dates on which announcements about FRFA and LTRO were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

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DELTA EURIBOR 1 year

Figure A.6: Bar graph representing delta EURIBOR 1 year on announcement days. Dates on which announcements about FRFA and LTRO were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

DELTA EURIBOR-OIS 3 months spread

Figure A.7: Bar graph representing delta EURIBOR-OIS 3 months spread on announcement days. Dates on which announcements about FRFA and LTRO were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

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DELTA LOG (CDS BANKS 5 year)

Figure A.8: Bar graph representing delta log 5-year CDS of banks on announcement days. Dates on which announcements about CBPP were made are indicated with dark blue, other important announcements are marked and coloured light-blue. DELTA COVERED BOND YIELD

Figure A.9: Bar graph representing delta covered bond yield on announcement days. Dates on which announcements about CBPP were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

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DELTA SPREAD ITALY-GERMANY 10 year gov. bond

Figure A.10: Bar graph representing delta spread Italy-Germany 10-year government bond on announcement days. Dates on which announcements about SMP and OMT were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

DELTA LOG (CDS ITALY 5 year)

Figure A.11: Bar graph representing delta log CDS Italy 5-year on announcement days. Dates on which announcements about SMP and OMT were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

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DELTA OIS 10 YEAR

Figure A.12: Bar graph representing delta OIS 10-year on announcement days. Dates on which announcements about QE were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

DELTA GERMANY 10 year gov. bond

Figure A.13: Bar graph representing delta Germany 10-year government bond on announcement days. Dates on which announcements about QE were made are indicated with dark blue, other important announcements are marked and coloured light-blue.

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