Master Thesis Finance. Bank diversification and systemic risk

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1 Master Thesis Finance Bank diversification and systemic risk Exploring and explaining cross-country heterogeneity Name: M. Diepstraten ANR: Supervisor: dr. O.G. de Jonghe Date: Tilburg School of Economics and Management

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3 Acknowledgements I would like to show my gratitude to my supervisor dr. O.G. de Jonghe for his enthusiasm and commitment. As he could always find the time to respond and meet soon, I could spend my time as efficient as possible, which enabled me to make my thesis as big as it is today. I found our meetings truly inspiring and they always gave me an energy boost to continue working and further improving my thesis. Moreover, I would like to thank my family and friends for their endless support and faith in me. Especially in times when I had to relieve my feelings, they were always there for me to listen and cheer me up. Maaike Diepstraten November

4 Abstract This paper examines the relationship between diversification and systemic risk. First, the homogenous relationship is investigated by using a worldwide sample. Hereafter the relationship is examined across countries as banking environments differ across countries. Since large heterogeneity is observed, the ultimate goal of this paper is twofold. Namely, I aim to conclude under which circumstances activity restrictions are favorable, as well as I aim to explain observed heterogeneity. The main contributions of this paper are as follows: using Marginal Expected Shortfall (MES) as dependent variable, activity restrictions are favorable in countries with low and mid values of investor protection, mid and high values of stock market turnover, mid and high values of capital stringency, and mid and high values of deposit insurance. Based on the level of public enforcement, private credit to GDP, stock market capitalization, and credit information sharing activity restrictions are always sound. Furthermore, only investor protection explains some of the observed heterogeneity in the relationship between diversification and MES. Using CoVaR as dependent variable, activity restrictions are sound for low and mid values of stock market turnover, and for low, mid, and high values of the other country traits. Moreover, having an English or German legal origin, the level of public enforcement, the degree of overall development of private banking markets, the size and activeness of stock markets, the level of credit information sharing, the absence or presence of multiple supervisors, the degree of capital stringency, as well as the level deposit insurance explain cross-country heterogeneity 4

5 Contents Acknowledgements... 3 Abstract Introduction Literature Data Data sources Indicators of diversification and systemic risk Diversification: Herfindahl-Hirschman index Systemic risk: Marginal Expected Shortfall Systemic risk: CoVaR Results Homogenous relationship between diversification and systemic risk Relationship between diversification and systemic risk across countries Country specific characteristics Legal framework Financial development Informational infrastructure Bank regulation and financial safety Relationship between diversification and systemic risk: explaining cross-country heterogeneity MES CoVaR Conclusion References Appendix

6 1. Introduction At times of financial crises, losses tend to disperse across financial institutions and thereby threatening the stability of the entire financial system. The recent financial crisis has highlighted the importance of stability of the financial system as externalities can be imposed on the rest of the economy. As a result, regulators are devoted to monitor and regulate the industry to avoid costly governmental interventions. Long time, regulators were focused on the risk of institutions in isolation by only taking into account systematic risk and forgoing systemic risk (for example Basel II). Only recently attention has shifted towards the risk of the collapse of the entire financial system. As a result, regulators are now tasked with proposing new regulation to enhance stability of the entire system rather than focusing on banks in isolation. A major concern is the shift into noninterest generating activities and reforms on activity restrictions are examined. In the US, this has led to signing the Dodd-Frank Wall Street Reform & Consumer Protection Act (2010), which greatly restructured financial regulation. Part of this act is regulation on risky derivatives to identify excessive risk-taking. Furthermore, section reports the prohibition of proprietary trading and relationships with hedge funds and private equity funds. In the UK, the Independent Commission on Banking led by Sir John Vickers (2011) demands ring-fencing in order to improve stability and competition in the banking sector. It holds that retail banking businesses are separated from investment banking in separate legal subsidiaries within a banking group. This ensures that essential banking activities are protected and can continue without bailouts. Finally, the High-level Expert Group chaired by Erkki Liikanen (2012) proposes reforms on the structure of the banking sector in the EU. The report states that, amongst others, banks with a significant share of trading activities should place these activities in a distinct legal entity to make the vital parts of banks safer and less connected with risky trading activities. As the separation is carried out within a banking group, the universal banking model is maintained and banks are still able to offer a wide range of financial services to clients. According to modern portfolio theory, diversification is favorable since it lowers the risk of the portfolio. In addition, it is believed that diversification leads to economies of scope and a stronger competitive position. As a result, firms following a diversification strategy should have a 1 Dodd-Frank, Dodd-Frank Wall Street Reform and Consumer Protection Act ( Dodd-Frank Act ), Pub. L. No , 124 Stat (2010). 6

7 LU CA KZ BG GB CY SG PE PT ES CH IL FR US DE PL CN SE TH AU KR ID AT TR CL LK PH DK BR GR NO IT ZA JP HK AR IN VE NL TW RU MY RO PKSI -.5 Correlation coefficient 0.5 diversification premium, i.e. firms with a diversification strategy should be valued more than firms with a focus strategy. This is in accordance with findings of, amongst others, Villalonga (2004), Nachum (2004), Yiu, Bruton, and Lu (2005), Ramaswamy, Li, and Petitt (2005), and Mursitama (2006). Applying this concept to banks implies that combining lending with other financial activities results in less risk and thus a lower chance of bankruptcy. Since the Second Banking Directive of 1989, European banks are not restricted in their activities anymore. Hence, financial conglomeration is allowed, which results in commercial banks engaging in investment banking, insurances or other financial services in Europe. Later on, in 1999, the Gramm-Leach- Bliley Act permitted US banks to pursue diversification. However, it is questionable whether diversification also leads to benefits within financial institutions. For example Laeven and Levine (2007) argue that financial conglomerates trade at a discount, implying that diversification destroys value. In addition, Wagner (2010) states that diversification within banks reduces the likelihood of individual failure, which is favorable, but at the same time increases the likelihood of a systemic crisis. Hence, it increases the probability of failure of the entire system, thus the failure of many financial institutions simultaneously. This is obviously unfavorable. Prior research has investigated the relationship between diversification and systemic risk in for example Europe (De Jonghe, 2010) and the US (Brunnermeier, Dong, & Palia, 2012). This study takes a worldwide approach. I start with examining the homogenous relationship by using a worldwide sample. Hereafter, I examine the relationship across countries as banking environments differ across countries. The graphs below show the pair wise correlations between the systemic risk measures and diversification per country. Figure 1: Graphical representation of pair wise correlations of systemic risk measures and diversification per country. MX FI BE CO KR ES CY PE SE JP LU FR AU PT DK CN NO ID PH CA SG US GB AR Correlation coefficent Pair wise correlations delta CoVaR and diversification BG FI ZA SI DE CH IT AT NL RO PL VE IL LK GR BR IN TH MY TR KZ PK RU CL HK CO TW BE MX Pair wise correlations MES and diversification 7

8 Obviously, correlations differ greatly over countries and even both positive and negative correlation coefficients are reported. As a result, the ultimate goal of this paper is twofold. First, I aim to conclude under which circumstances activity restrictions are favorable to guide regulators. Moreover, the question rises where the observed heterogeneity stems from. As a result, the second goal is to explain cross-country heterogeneity by taking country traits into account. To gauge systemic risk, two measures are used as there are two ways to view systemic risk. First, one can investigate the impact of an aggregate shock on individual banks, as examined in for example De Jonghe (2010). On the other hand, one can investigate the marginal contribution of a bank to overall systemic risk, as documented in for example Brunnermeier, Dong, and Palia (2012). For sake of completeness, this paper studies both ways. Marginal Expected Shortfall or MES (Acharya et al., 2010) is used to examine the impact of an aggregate shock on individual banks. CoVaR (Adrian & Brunnermeier, 2011) is used to examine the marginal contribution of a bank to overall systemic risk. To measure the degree of diversification, an adjusted Herfindahl-Hirschman index is constructed. Traditionally, banks earn interest income. However, due to financial liberalization, revenues of banks consist nowadays of both interest income and noninterest income, like trading income and net commission and fees. Consequently, the degree of diversification is based on the different sources of income. In addition, the shares of noninterest incomes are included to control for the riskiness of the revenue streams. To explain observed heterogeneity in the relationship between diversification and systemic risk, four categories of country specific characteristics are considered: the legal framework, financial development, informational infrastructure, and bank regulation and financial safety. Each of these categories consists of multiple variables. To enable to answering the two main questions, a regression including two interaction terms is run for each continuous country specific variable. The interaction terms are the product of a dummy variable and the measure of diversification. One dummy represents low value countries and the other dummy represents high value countries. To conclude under which circumstances activity restrictions are favorable, the coefficient of diversification for each group is investigated. Besides, the coefficients of the interaction terms show whether the respectively low and high group are different from the mid group. The betas of the interaction terms are tested against each other to show whether the relationship is different for low and high value countries 8

9 to conclude whether the trait explains heterogeneity. For binary country traits, no interaction terms are created as the variable itself is already a dummy. The coefficients of diversification are examined to conclude whether activity restrictions are favorable. Furthermore, the coefficient of the interaction term shows whether the relationship is different for countries in which a trait is present versus countries in which the trait is absent. Hence, it shows whether the country trait explains heterogeneity. The main contributions of this paper are as follows: using MES as dependent variable, activity restrictions are favorable in countries with low and mid values of investor protection, mid and high values of stock market turnover, mid and high values of capital stringency, and mid and high values of deposit insurance. Based on the level of public enforcement, private credit to GDP, stock market capitalization, and credit information sharing activity restrictions are always sound. Furthermore, only investor protection explains some of the observed heterogeneity. Using CoVaR as dependent variable, activity restrictions are sound for low and mid values of stock market turnover, and for low, mid, and high values of the other country traits. Finally, having an English or German legal origin, the level of public enforcement, the degree of overall development of private banking markets, the size and activeness of stock markets, the level credit of information sharing, the absence or presence of multiple supervisors, the degree of capital stringency, as well as the level of deposit insurance explain cross-country heterogeneity. These findings are robust to different samples periods implying that the crisis is not driving the results. The remainder of this paper is organized as follows. Section 2 provides an overview of existing literature on diversification and systemic risk. Section 3 describes the indicators of diversification and systemic risk. Section 4 shows the regression results of the homogenous relationship between diversification and systemic risk. Furthermore this section examines this relationship over countries by examining pair wise correlations. Ultimately, cross-country heterogeneity is explained by introducing 12 country traits. Lastly, section 5 concludes. 2. Literature Traditional portfolio theory favors diversification as combining assets with a low covariance improves the risk-return ratio. A low covariance between the constituent assets implies that the portfolio has less risk than the weighted average risk of its assets. Risks are spread whereas when 9

10 one asset is performing badly, the other asset is performing well. The introduction of universal banking by the Second Banking Directive of 1989 in Europe and the Gramm-Leach-Bliley Act in 1999 in the US has led to functional diversification within banks. From then on, banks were allowed to engage in a wide range of financial activities. As a result, banks started to combine commercial banking with investment banking and insurances under one umbrella. From the viewpoint of policy makers, diversification is desirable since banks are better able to cope with adverse shocks. Next to spreading risks, banks can benefit from economies of information as banks can use client specific information to offer other services as well. Thus, economies of information are realized by offering multiple services to a single client. Consequently, a bank earns both interest and noninterest income and the covariance between these revenue streams determines the extent to which a bank can benefit from diversification. On the other hand, predators argue that diversification destroys value, leading to a diversification discount. If the institution becomes too complex it can give rise to agency problems (Jensen & Meckling, 1976). Managers can pursue diversification to extract private benefits which is not in favor of the firm. Moreover, value can be destroyed by entering markets in which the bank lacks expertise and experience. Prior research has empirically investigated the link between diversification, performance, and risk within financial institutions to determine which theory dominates. DeLong (2001) considers mergers of similar banks and dissimilar banks to conclude whether the focus or diversification strategy is value creating. The results show that only mergers that have a focus strategy in terms of geography and activities are value enhancing. Demirgüç-Kunt and Huizinga (2010) examine the relationship between diversification and risk at the bank level. It is shown that only at low levels of noninterest income a bank can benefit from diversification by increasing these shares. Banks that already have a large share of noninterest income become more risky when further increasing this share. As a result, traditional banks, which rely heavily on interest income, are safer than banks that have a great share of noninterest income. Furthermore, Stiroh (2006) states that banks that rely heavily on noninterest generating activities are more risky in terms of return volatility and market betas and do not earn higher average returns. He brings out that some banks have over-extended in diversification. A study from DeYoung and Roland (2001) yields similar results. They restrict their sample to commercial banks and report that moving from interest generating activities to fee-based activities results in more volatile earnings. Stiroh (2004a) documents that noninterest income, and in particular trading income, is more 10

11 volatile than interest income in the US. At the same time, correlations between interest and noninterest have risen which results in fewer diversification benefits. Hence, the risk-adjusted performance is lower and the risk is higher for banks engaging in more noninterest generating activities. In a study of community banks, Stiroh (2004b) reports mixed results. Although diversification within broad activity classes leads to benefits, diversification between classes does not. Research in Europe provides similar results. Baele, De Jonghe, and Vander Vennet (2007) show that in Europe banks with a higher share of noninterest income have a higher franchise value, but at the same time face more systematic risk. A higher franchise value implies that the stock market expects higher future bank profits. However, this better performance is offset by higher risk due to higher covariances. Mercieca, Schaek, and Wolfe (2007) do not find diversification benefits either within or across business lines in small European banks. They find an inverse relationship between noninterest income and risk-adjusted bank performance, suggesting that the shift into noninterest income reduces the performance. Additionally, Acharya, Hasan, and Saunders (2002) use bank-level data to compare a focus versus diversification strategy in Italy. They conclude that there seem to be diseconomies of diversification for certain banks, stemming from poor monitoring incentives and/or credit risk. Finally, using a worldwide sample, De Nicolo et al. (2004) show that financial conglomerates face more risk than specialized financial firms. To summarize, prior research mostly concludes that diversification increases bank fragility as systematic risk increases with an increase in noninterest generating activities. Wagner (2010) extends the discussion of the desirability of diversification by examining systemic risk. Systemic risk is the risk that the institutions within a system fail simultaneously. The recent financial crisis highlights the importance of stability of the entire system rather than the stability of individual banks. Wagner (2010) predicts that diversification enhances the probability of failure of the entire system. In the extreme case of full diversification, all banks hold the same portfolio, namely the market portfolio in portfolio theory. As a result, banks are similar to each other and are exposed to the same risks which enhances the probability of a collapse of the entire system. Thus, similarity of banks leads to systemic risk. Other channels through which systemic risk arises are information channeling, interconnectedness between banks and the domino-effect. Information channeling can lead to systemic risk as information of one bank can spill over to other banks. For example, when one bank is in default, clients of similar banks might expect difficulties at their bank as well (Barth & Schnabel, 2012). To prevent financial trouble, they 11

12 withdraw their money from the banks leading to a bank run. The degree of similarity between banks determines the extent to which information channeling results in systemic risk. Besides, systemic risk can arise from interconnectedness between institutions via interbank markets, causing negative risk spillover effects (Adrian & Brunnermeier, 2011). This includes counterparty risk and can lead to write-offs if the counterparty defaults. Finally, a domino effect can occur through liquidity spirals; when banks hold similar assets and one bank suffering from short liquidity has to sell assets at fire-sale price, it can cause an overall decline in prices (Barth & Schnabel, 2012). A few studies empirically investigate the relationship between diversification and systemic risk. De Jonghe (2010) examines this relationship in Europe by focusing on tail beta. That is the probability of a crash in a bank s stock conditional upon a crash in a banking index (p.388). He finds that a shift towards noninterest income increases banks tail beta and thus increases bank fragility. In particular, a shift towards trading income increases tail beta as this revenue stream reports the highest significant coefficient. Brunnermeier, Dong, and Palia (2012) show similar results for the US; noninterest income contributes more to systemic risk than interest income. As a result, shifting away from traditional banking threatens bank system stability. Lastly, Moshirian, Sahgal, and Zhang (2011) take the role of concentration into account when examining the relationship between diversification and systemic risk. They find a contrasting effect of diversification on systemic risk based on the competitive environment. While diversification increases systemic risk in a highly competitive banking environment, it decreases systemic risk in a highly concentrated banking environment. This study adds to current debate by exploring and explaining cross-country heterogeneity. 3. Data This section provides an overview of the data, the data collection process, and the variables used in this study. Section 3.1 starts with an overview of the data sources. Thereafter the indicators of diversification and systemic risk are examined in section 3.2. Hence, shows the computation of the Herfindahl-Hirschman index which is used as a proxy for diversification. Sections and report the computation of systemic risk measures, namely CoVaR and 12

13 Marginal Expected Shortfall. Appendix A provides an overview of the variables used in this study. 3.1 Data sources Since the goal of the paper is to explore and explain cross-country variation, the sample is not restricted to a specific geographical area. Consequently, listed banks from all over the world are included in the analysis. Only listed banks are considered as market capitalization data is required to compute systemic risk measures. Besides, it enhances comparability across countries. To capture different economic circumstances and because of data availability, the period of analysis is In the final dataset, 2,143 banks from 49 countries are included, resulting in 18,268 observations. Appendix B reports the countries, the according country codes, the number of banks within each country, and the share of banks per country. As one can see, more than half of the banks is from the US. A few restrictions are imposed. First, countries with less than 40 observations are excluded from the analysis. Besides, banks with missing information on basic variables are dropped. For banks which provide both consolidated and unconsolidated data, consolidated data is used in order to prevent double counting. Moreover, the analysis is limited to commercial, savings, and cooperative banks, and bank holding companies. The largest part of the observations stems from commercial banks, namely 53.60%. Bank holding companies account for 42.25%, savings banks for 2.20%, and cooperative banks for 1.96%. The average value of noninterest income is respectively 32.34%, 28.13%, 28.17%, and 40.91%. This implies that cooperative banks have on average the most noninterest income relative to interest income. All numbers which are not expressed in ratios are inflation adjusted 2007 US dollars. To collect balance sheet and income statement data Bureau van Dijk s Bankscope is used. In addition, Datastream is used to obtain market capitalization data. Although daily data is retrieved, the calculations of systemic risk measures are done using weekly data to enhance faster computation. Accounting- and market data is matched on ISIN-number. Data about the country specific characteristics is retrieved from several sources. The anti self-dealing index, the public enforcement index, and the legal origin of a country are obtained from Djankov et al. (2008). Data about financial development is extracted from the dataset of Beck et al. (2000), updated in Furthermore, data about the informational infrastructure is retrieved from the World Bank. Data about financial regulation and safety is retrieved from the bank regulation and supervision 13

14 database (Barth et al., 2008) and the deposit insurance around the world database of Demirgüç- Kunt et al. (2005). Lastly, the Federal Reserve Board H.15 release is used to obtain US interest rates. 3.2 Indicators of diversification and systemic risk This section documents the computation of the indicators of diversification and systemic risk. Table I shows descriptive statistics of the variables Diversification: Herfindahl-Hirschman index The explanatory variable in this study is diversification. In the past, banks had a single source of income, namely interest income. Since the rise of diversification within the banking industry, banks have multiple sources of income. Consequently, banks earn both interest and noninterest income. Noninterest income can be split up in three categories, namely: commission and fees, trading income, and others. Hence, the degree of diversification can be measured with an adjusted Herfindahl-Hirschman index as in Elsas, Hackethal and Holzh user (2010): = 1 - (1) In which, = Diversification INT = Gross interest revenue COM = Net commission and fees TRAD = Net trading revenue OTH = Other net revenue TOR = Total operating revenue ( = INT + COM + TRAD + OTH) As Bankscope does not report gross noninterest revenues, the net revenues are used for these revenue streams. Elsas, Hackethal & Holzh user (2010) conclude that this approach is not materially different as the expenses are between 5-15% of gross revenues of each noninterest stream. To circumvent problems due to a highly negative value for a single source of income absolute values are used. Highly negative value can reduce the amount of total operating revenue in such a way that it is lower than a single sources of income, resulting in a revenue share over 14

15 100%. Since interest goes to the magnitude of the revenue streams instead of the profitability, this does not harm the analysis. The value for diversification lies, by construction, between 0 and A higher number indicates more diversification. In the extreme case where diversification takes the value of zero, no diversification is present which means that the bank receives income from only one source. In the other extreme case, diversification is 0.75 which indicates that the bank is fully diversified. A drawback of this method is that is does not provide information about the sources of income. Since not all revenue sources face the same amount of risk, same values for diversification do not necessarily imply same values for risk. Hence, the shares of noninterest income are included as well in the regression model. Gross interest revenue is excluded to be able to interpret the betas. The obtained coefficients show the impact of a shift from interest revenues to the noninterest revenue stream. Figure 2 shows the evolution of the share of interest income versus the share of noninterest income over time based on all countries. These shares are measured as respectively interest income/total operating income and noninterest income/total operating income. A higher share of noninterest revenues implies more diversification. Figure 2 shows that the shares are relatively stable. Approximately 70% of the revenues stems from interest generating activities. Zooming in at the country level yields different results as shown by the graphs of figure 3. Different countries show different developments over time. Some countries start with a high share of interest revenue which diminishes over time, for example Canada (CA), and Germany (DE). Others start with a high share of noninterest revenues but end up with a high share of interest revenues, like Colombia (CO). Furthermore, some countries show very volatile pictures, for example Cyprus (CY), the Netherlands (NL), and Sweden (SE). In general, most countries show a higher share of interest income than noninterest income. To gain more insight in the revenue streams, noninterest income is further split up in net commission and fees, trading income, and other operating income. Table I shows descriptive statistics of these shares for all countries, next to the share of traditional interest income. The denominator for these shares is total operating income. The table shows that the mean of the share of interest income is the highest, indicating that interest income makes up the largest part of the revenue streams. The mean value is almost 70% which is in accordance with figure 2. In 15

16 addition, table I shows descriptive statistics of the adjusted Herfindahl-Hirschman index. The mean value is As the maximum value is by construction 0.75, the mean value indicates that banks are on average quite diversified Systemic risk: Marginal Expected Shortfall Systemic risk is defined as the risk that the intermediation capacity of the entire financial system is impaired (Adrian & Brunnermeier p. 1). The first way to look at systemic risk is to examine the extent to which an individual bank is affected by a systemic crisis. To this end, Marginal Expected Shortfall (MES) as introduced by Acharya et al. (2010) is considered. It is the average return of an individual bank during the 5% worst days on the market: = E (2) In which, = Marginal Expected Shortfall during the 5% worst trading days on the market = Return of bank i = The 5% worst outcomes at the market Weekly data of equity returns are used to estimate MES: = (3) In which t denotes the 5% worst observations on the market during a. This equals the three worst trading days as weekly data is used. Hence, MES measures the loss of an individual firm when the entire market is doing poorly. A more negative value for MES indicates more systemic risk. From now on, a higher value of MES is interpreted as a more negative value for MES. Thus a higher value for MES implies more systemic risk. To determine the 5% worst trading days on the market, bank indices created by Datastream are used. To mitigate the impact of outliers, MES is winsorized at the 1% level. 16

17 Table I shows the descriptive statistics for MES. The average return of a bank is % during the 5% worst days on the market Systemic risk: CoVaR The second way to view systemic risk is to measure the contribution of an individual bank to the overall systemic risk of the system. To this end, CoVaR is used as proposed in Adrian and Brunnermeier (2011). CoVaR is the conditional Value at Risk, in which Co stands for conditional, contagion, or co-movement. The condition can either be bank i being in distress or bank i operating at its median state. In addition, CoVaR is the difference between these conditions. Thus, the difference between the CoVaR of the financial system conditional on bank i being in distress and the CoVaR of the financial system conditional on bank i operating in its median state (Brunnermeier, Dong, and Palia, 2012). Hence, it captures the marginal contribution of bank i to the overall systemic risk. = - (4) Denote as the contribution of bank i to the systemic risk of the entire system at time t. Denote as the Value at Risk of the entire system conditional on bank i being in distress at time t. It is the q%-value at Risk of the entire system conditional on bank i operating at its VaR level. Denote as the Value at Risk of the entire system conditional on bank i operating at its median state at time t. As one can conclude from subscript t, the terms are time varying, which implies that the model is a dynamic conditional model instead of a stable unconditional model. Value at Risk measures the worst expected loss over a specific time interval at a given confidence level. In this study, is the percentage of asset value that one, i, might lose with a probability of q% over a given horizon, T: Pr ( ) = q, (5) 17

18 As a result, the value for VaR is typically negative. In this study, q is set at the 1% level and therefore the 1% quantile is considered. Thus, bank i is in distress when it is at its 1% VaR level. To estimate CoVaR, both CoVaR conditional on bank i being in distress and bank i operating at its median state need to be estimated. The first step is to regress the growth rate of market valued assets on the state variables. Since it is about tail events, a 1% quantile regression is run to estimate and : = + * + (6) In which, = Weekly growth rate of market-valued assets of bank i at time t = Vector of state variables (lagged variables) The growth rate of the firm is defined as the growth rate of market valued assets. This growth rate is calculated as: = - 1 (7) The state variables are conditional variables that affect the conditional mean and volatility rather than that they are systematic risk factors. Since these factors are time dependent they capture the evolution of tail risk dependence over time. The state variables include the return on the market, volatility of the market, interest rate risk, term structure, and default risk. Datastream indices are used as proxies for the market. Volatility is computed as the standard deviation of the returns of the market. The change in three month T-bill rate is used as a proxy for interest rate risk. The term structure is the change in yield curve, which is the difference between the 10- T-bond rate and the three month T-bill rate. Lastly, default risk is measured as the credit spread between the rate of 10- BAA corporate bonds and the 10- T-bond rate. Due to limited data availability US rates are used for all countries 2. 2 For the US, the pair wise correlation coefficient of CoVaR with and without interest variables is As this coefficient is positive and relatively high, the measures tend to move together. Without interest variables CoVaR gets more negative, versus , which means that there is more systemic risk, which is due to more 18

19 In addition, a 1% quantile regression is run in which the growth rate of the entire system is regressed on the state variables and the growth rate of bank i: = + * + * + (8) In which, = Weekly growth rate of the market-valued total assets of all banks in the financial system at time t = Vector of state variables (lagged) = Weekly growth rate of the market valued-assets of bank i at time t-1 To calculate the growth rate of the entire system, a value-weighted approach is used: = (9) Furthermore, to reflect the median state, the following 50% quantile regression is run to estimate and : = + * + (10) In which, = Weekly growth rate of market-valued assets of bank i at time t = Vector of state variables (lagged variables) reliance on the market. Furthermore, table III documents average values of winsorized absolute t-statistics. It shows that many of them are smaller than 2 and thus not significant. Lastly, table IV shows the amount of significant coefficients out of 1,142 coefficients, leading to the same conclusion. Hence, although the measures are related, without taking into account interest variables CoVaR is a little too negative. As a result, I will not exclude the interest variables at all, but will use a proxy for this, namely US data. 19

20 The just obtained coefficients,,, and are now used to predict an individual bank s VaR and the median asset return: = = + * (11) = = + * (12) As a result, the systemic risk conditional on bank i being in distress can now be predicted: = = + * + * (13) In which, = Value at Risk of the system conditional on bank i being in distress = Vector of state variables (lagged variables) = Value at Risk of bank i in the q% quantile at time t In addition, the systemic risk conditional on bank i operating at its median state can be calculated in a similar way: = + * + * (14) In which, = Value at Risk of the system when bank i operates at its median state = Vector of state variables (lagged) = The asset return of bank i in its median state Hence, can now be calculated as: = - (15) 20

21 A more negative value for implies more systemic risk. From this point, a more negative value of CoVaR is reported as a higher value of CoVaR. Thus a higher value for CoVaR means more systemic risk. From the regressions, a panel of weekly measures is obtained. To arrive at a ly measure of CoVaR, the risk measures within a are cumulated. Since not all banks have observations for 52 weeks in a, it is multiplied with a ratio of 52/amount to correct for the number of weeks. Ultimately, cumulative CoVaR is matched with Bankscope data. For this reason, the last value of cumulative CoVaR is retained and used to merge with accounting data 3. To enhance comparability with other papers, CoVaR is first calculated for the US. Table II shows descriptive statistics on the variables needed to construct this measure. The value for the 3- month treasury change is of the order of the variable documented in Adrian and Brunnermeier (2011). In contrast, the mean value of equity return is somewhat higher in Adrian and Brunnermeier. This might be caused by a difference in time span. Since they calculate it from 1986 onwards, the recent crisis has a less severe impact on their analysis. Therefore, the average return on the market is higher. Besides, Adrian and Brunnermeier show a positive value for term spread change and credit spread change, while these are negative in this analysis. The mean of CoVaR is close to the mean documented by Adrian and Brunnermeier (2011), namely versus The standard deviation is somewhat higher; versus In addition, the mean and standard deviation of CoVaR approximate the values presented in Barth and Schnabel (2012). Table II shows that the average value of cumulative CoVaR is which is about 4 times as much as the quarterly CoVaR reported by Adrian and Brunnermeier. Table I reports CoVaR for the entire sample. The average value of CoVaR is more negative with respect to the average value of CoVaR of the US-sample. This implies that systemic risk is higher in non- US countries than in the US. The value for cumulative CoVaR ranges from very negative to very positive, although not reported here. As a result, CoVaR is winsorized at the 1% level to 3 85% Of the banks reports in December of the. This implies that 15% of the banks reports in other months. To match cumulative CoVaR with Bankscope data correctly, three ways of matching cumulative CoVaR are considered. First, the average cumulative CoVaR of each for each bank is calculated. Besides, the last observation of each bank of each is considered. Furthermore, the last observation of each month of each bank is used. This results in the correlation matrix presented in table V. Since the correlations are extremely close to 1, the methods give similar results. Therefore, the last value of the is used as cumulative CoVaR and will be used to match with Bankscope data. 21

22 mitigate the impact of extreme outliers. As one can see, the average value of the winsorized cumulative CoVaR is somewhat less negative than the original value. From now on, winsorized cumulative CoVaR will be abbreviated to CoVaR. 4. Results This chapter starts with examining the homogenous relationship between diversification and systemic risk. Subsequently, the relationship between diversification and systemic risk across countries is examined. As the results show large variation over countries, country specific characteristics are used to explain these differences. Consequently, the country traits are first discussed as well as the according hypothesis per country trait. Finally it is investigated whether the country traits explain the documented cross-country heterogeneity. 4.1 Homogenous relationship between diversification and systemic risk Up till now, measures of diversification and systemic risk are considered in isolation. As interest goes to the link between the variables, the relationship between the variables is examined in this paragraph. Table VI documents pair wise correlations of risk measures and lagged values of diversification. From this table no conclusions can be drawn about the causal linkage of the variables, but it does provide information about the relationship between the variables. Although MES and CoVaR are both measures of systemic risk, their pair wise correlation is only 0.373, which is due to differences in definitions. The coefficients of the correlations between total volatility and MES and CoVaR are negative. This means that when total volatility increases, systemic risk increases as a lower value for systemic risk indicates more risk. The coefficient between total volatility and MES is larger than the coefficient between total volatility and CoVaR. Hence, the first two variables tend to move more together than the last two variables. The negative values for the correlation between the Herfindahl-Hirschman index and the systemic risk measures indicate that banks that are more diversified face more systemic risk. In addition, the coefficients between noninterest income and systemic risk are negative, implying that banks with more noninterest revenues have more systemic risk. The breakdown of noninterest revenues in net commission and fees, net trading income, and other operating income shows that net commission and fees move most together with systemic risk. However, the coefficient between net commission and fees and total volatility is negative. This indicates that 22

23 bank with more commission and fees have a lower total volatility. Thus, banks with more commission and fees have more systemic risk and at the same time less total volatility. Furthermore, it can be concluded that net commission and fees move more with CoVaR than with MES while this is the reverse for net trading income. Ultimately, MES slightly increases with other operating income, whereas CoVaR slightly decreases with other operating income. Additionally, it is shown how the average values of CoVaR, MES, total volatility, and bank size change when there is more noninterest income. To accomplish this, deciles are created based on noninterest income in which decile 1 includes banks with the 10% lowest share of noninterest income. For each decile the average values are reported in table VII. As one can see, CoVaR gets, in general, more negative with more noninterest income. Only the last decile faces less systemic risk than the decile before. In addition, MES gets more negative with more noninterest income. This implies that banks with more noninterest income face more systemic risk. Furthermore, total volatility shows a turning point at the 3 th decile. With a low share of noninterest income, an increase in noninterest income lowers total volatility. This is in accordance with findings of Demirgüç-Kunt and Huizinga (2010) as they state that only at low levels of noninterest income increasing the share results in less risk. After the 3 th decile, an increase in noninterest revenues increases total volatility. In addition, table VII shows that bigger firms have, in general, more noninterest income. To gain insight in the causal linkage between diversification and systemic risk, more systematic testing through the use of regressions is conducted. Tables VIII and IX show the results of regressing MES and CoVaR on diversification using the entire sample. In all regressions country-time fixed effects are included to eliminate the effect of a third variable that correlates with both diversification and systemic risk. In addition, it solves the omitted variable bias. The lagged control variables include bank specific characteristics and specialization dummies. The bank specific characteristics consist of the ratio of equity to total assets, net loans to total assets, residuals of log (total assets), return on average equity and cost-to-income ratio. The ratio of equity to total assets is included to control for capitalization as more capitalized banks might be less vulnerable to market-wide shocks. The ratio of net loans to total assets is used as a proxy for a bank s liquidity position as it measures what part of the assets is tied up in loans. As previous research proved the influence of bank size on systemic risk (Adrian & Brunnermeier, 2011), a proxy for size is included as well. As size is correlated with diversification, the residuals of size 23

24 are used to extract the pure size effect 4. Moreover, the return on average equity is included to capture profitability as more profitable banks may be more exposed to market-wide shocks. Lastly, the cost-to-income ratio is incorporated to control for efficiency. To alleviate the effect of outliers, the control variables are winsorized at the 1% level. Descriptive statistics of the control variables are reported in table I. Furthermore, standard errors are clustered on the country time level. This implies that there is zero correlation across the residuals of the clusters while the correlation within a cluster can be anything at all. Starting with MES as dependent variable, table VIII shows the regression results 5. The first regression only includes diversification as explanatory variable. The coefficient is negative and statistically significant at the 1% level, which is accordance with the idea of diversification increasing systemic risk. The economic impact is calculated as the marginal effect of diversification multiplied by the standard deviation of diversification, divided by the average value of MES. The economic impact of diversification in the first regression is 18.23%, indicating that a one standard deviation increase in diversification results in a 18.23% increase of MES 6. As a result, diversification is both statistically and economically significant. Regression 2 includes the revenue shares as explanatory variables. The coefficients are all negative, indicating that a shift away from interest income increases systemic risk. Moreover, regression 3 combines the first two regressions. As before, the coefficient of diversification is negative and statistically significant. The economic impact is 9.82%, thus smaller than before, but still economically significant. Furthermore, the revenue shares are all negative and significant, which is in accordance with findings of De Jonghe (2010), which uses tail beta as dependent variable. As MES and tail beta both measure the impact on a single bank when the market is performing badly, these measures are related which enables comparison of the results. The last line shows values for. As the last column reports the highest value, the complete model explains most of the variation. Table IX reports the results when systemic risk is measured as CoVaR. The first regression shows a negative coefficient for diversification as expected. The economic impact is 4 The natural logarithm of total assets is regressed on all independent variables to decompose size in a organic and a fixed component. This method is in line with previous research (for example: De Jonghe, 2010). 5 When a bank does not have observations for all weeks within a, three days do not correspond to the 5% worst trading days. I checked for this by imposing the restriction that the amount of observations in a should be at least 50. However, as this restriction does not influence the results, the restriction is dropped. 6 The economic impact should be interpreted with caution as a positive number means an increase in systemic risk here. 24

25 17.8%. Thus a one standard deviation increase in diversification leads to an increase of 17.8% of CoVaR. The next regression includes only the noninterest revenue shares as independent variables. Except the share of net trading income, all revenue shares are negative. In addition, they are significant at the 5% level. This implies that a shift from interest revenues to these revenue streams increases systemic risk. It is remarkable that the coefficient for trading income is positive as this revenue stream is known to be the most volatile revenue stream. As a result, I would expect that a shift from interest revenues to trading income increases systemic risk. The last regression shows the results of including both the Herfindahl-Hirschman index and the revenue shares as explanatory variables. With respect to regression 1, the coefficient of diversification keeps its significance and sign, while getting more negative. Thus, while controlling for the revenue shares, an increase in diversification leads to more systemic risk. The economic impact of diversification is 22.6%, implying that diversification is both statistically and economically significant. Net commission and fees and other operating income now switch sign with respect to regression 2. Hence, the coefficients for all noninterest revenue streams are positive. As the coefficients for trading income and other operating income are both statistically significant, it means that a shift from interest revenues to one of these streams lowers systemic risk. This result is counterintuitive. A possible explanation is the noisy nature of CoVaR. As before, the complete model explains most of the variation of the dependent variable. Consequently this model is used to explain cross- country heterogeneity. 4.2 Relationship between diversification and systemic risk across countries To serve the goals of this study, the relationship between diversification and systemic risk over countries is considered here. Pair wise correlations between diversification and systemic risk are examined as it is inappropriate to run regressions due to limited data availability for some countries. Figure 4 shows pair wise correlations for both systemic risk measures and diversification. 25

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