The time-spatial dimension of eurozone banking systemic risk

Foglia, Matteo; Angelini, Eliana Article The time-spatial dimension of eurozone banking systemic risk Risks Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Foglia, Matteo; Angelini, Eliana (2019) : The time-spatial dimension of eurozone banking systemic risk, Risks, ISSN 2227-9091, MDPI, Basel, Vol. 7, Iss. 3, pp. 1-25, https://doi.org/10.3390/risks7030075 This Version is available at: https://hdl.handle.net/10419/257913 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ risks Article The Time-Spatial Dimension of Eurozone Banking Systemic Risk Matteo Foglia * and Eliana Angelini Department of Economics, “G.d’Annunzio” University of Chieti-Pescara, Viale Pindaro n. 42, 65127 Pescara, Italy *Correspondence: [email protected] Received: 24 May 2019; Accepted: 3 July 2019; Published: 6 July 2019 Abstract: In this paper, we measure the systemic risk with a novel methodology, based on a “spatial-temporal” approach. We propose a new bank systemic risk measure to consider the two components of systemic risk: cross-sectional and time dimension. The aim is to highlight the “time-space dynamics” of contagion, i.e., if the CDS spread of bank i depends on the CDS spread of other banks. To do this, we use an advanced spatial econometrics design with a time-varying spatial dependence that can be interpreted as an index of the degree of cross-sectional spillovers. The findings highlight that the Eurozone banks have strong spatial dependence in the evolution of CDS spread, namely the contagion effect is present and persistent. Moreover, we analyse the role of the European Central Bank in managing contagion risk. We find that monetary policy has been effective in reducing systemic risk. However, the results show that systemic risk does not imply a policy intervention, highlighting how financial stability policy is not yet an objective. Keywords: spatial contagion; systemic risk measure; bank risk-taking; macroprudential policy JEL Classification: G21; E52; C23 1. Introduction In the last decade, the systemic risk concept is back in the limelight. The crisis has highlighted how a shock, originating in one country or sector of activity, can spread rapidly to other markets, given the close interconnection of capital links between institutions and financial markets. Systemic risk is generally manifested as a series of related defaults that trigger the withdrawal of liquidity and the loss of belief in the financial system as a whole ( Benoit et al. (2017) ). Therefore, to properly assess systemic risk, it is essential to identify not only the largest financial institutions—“too big to fail” (TBTF)—but also consider the interconnections between them: in this case, we are talking about “too interconnected to fail” (TITF). An increasing number of theoretical and empirical research has sought to address the problem of correct estimation of systemic risk ( see Silva et al. (2017) ). Many methodologies have recently been implemented to quantify the contribution of individual financial institutions to systemic risk, for example, the CoVaR (Conditional Value-at-Risk) proposed by Adrian and Brunnermeier (2016) , MES (Marginal Expected Shortfall) proposed by Acharya et al. (2012) and SRISK (Conditional Capital Shortfall Index) proposed by Brownlees and Engle (2016). These measures, based on market prices, estimate the probability of bank default by studying quantile distribution of the probability function. However, according to Giudici and Parisi (2018), these approaches, which are useful in establishing risk thresholds, do not identify the interconnections between systemic institutions. As it is a bivariate method, it only makes it possible to determine the risk of one financial institution depends on another financial firm. To this end, recent studies have proposed network correlation models. In particular, Billio et al. (2012) used the quarterly returns of hedge funds, banks, and insurance companies to develop various Risks 2019,7, 75; doi:10.3390/risks7030075 www.mdpi.com/journal/risks Risks 2019,7, 75 2 of 25 interconnection measures based on the Granger causality test. The results show that banks play a significant function in the transmission of shocks compared to other financial institutions. In the same vein, Diebold and Yilmaz (2014) estimated daily time-varying connectivity through the application of an autoregressive vector model (VAR) on stock return, among the major financial firms for the United States. More recently, Giudici and Parisi (2018) proposed a systemic risk measure (CoRisk) introducing the partial correlation and correlation network into a VAR model. In this work, we use an innovative approach to take into account the network (interaction) structure of the financial system, with a methodology based on advanced spatial econometrics design. Our aim is to analyse the co-movements across CDS spread using a spatial dynamic panel model (Elhorst (2014)), highlighting the “time-space dynamics” of financial contagion, i.e., the evolution of credit risk. In particular, applying spatial structure, we can explore if CDS spread of BANKi depends on the CDS spread of other BANKSj. These econometric models are a particular branch of statistic that “allow us to account for dependence between observations, which often arises when observations are collected from points or regions located in space” (LeSage (2008)). In recent years, there has been a substantial growth of spatial econometric models in finance, especially concerns the study of spillover effects, for example, to study the co-movements of the stock return (Arnold et al. (2013); Asgharian et al. (2013) ; Milcheva and Zhu (2016) ;Catania and Billé (2017)), the premium risk spreads among firms (S-CAPM; Fernandez (2011)), the sovereign credit-risk propagation (Dell’Erba et al. (2013); Blasques et al. (2016); Debarsy et al. (2018); Mili (2018)) or the financial firms credit-risk propagation ( Eder and Keiler (2015) ; Calabrese et al. (2017)). Most closely related to our article is the recent work by Blasques et al. (2016) . The authors developed a time-varying parameter version of the Spatial Autoregressive model (SAR), using a Generalise Autoregressive Score (GAS) framework. They proposed a useful method to incorporate daily credit risk dependencies between countries. High level of spatial dependency corresponds to the high level of European countries interconnection, namely a high probability of systemic risk and vice versa. Their results show that the CDS spreads have a strong time-varying degree of spatial dependence. Moreover, the study evidences how the cross-border debt linkage is a key channel of transmission for credit risk. In the same line, we study the behaviour of CDS spread with time-varying spatial spillover. Different from them, we analyse the evolution of contagion for the Eurozone financial institutions (banks). The paper has three different goals. First, we propose a new bank interconnected (systemic risk 1 ) measure following the model of Blasques et al. (2016). This indicator aims to capture the contagion effect and its potential to become a systemic risk. We want to show how the risk is related to the concept of “spatial” as well as temporal dependency. In our study, interlocking the banks through the financial claim, we obtain a spatial time-varying dependence, which is easy to interpret as a systemic risk, hence the shock spillover that affects the Eurozone banks. The contagion emerges from a change in a single probability default (CDS spread) that spread in the cross-sectional dimension (banks). We refer to contagion as the increase in spillover effect across the CDS market and its magnitude depends on bank interconnectedness measured by banks’ claim. Therefore, our measure derives from the state of two components: (1) the credit risk status of each individual bank (time dimension); and (ii) the structure of the banking market, i.e., the financial (lending/borrow) relationships between the banks (spatial dimension). The second goal is to verify if changes in our measure of contagion predict future movements in real economy variables such as GDP and unemployment rate, by using Granger 1 We are well aware that the parameter is not the “right definition” measure of systemic risk because we do not consider important variables such as balance sheet data. Nevertheless, in our framework, the systemic risk arises from the systematic risk component and the contagion risk component that is modelled. As in the work by Blasques et al. (2016), we interpret the spatial parameter as a measure of a change in systemic risk that associates to the interconnectedness of the system as the unconditional correlation measures in the spirit of Forbes and Rigobon (2002). In the remainder of this study, for simplicity, we use the terms contagion and systemic risk as synonymous, although the two definitions are quite different. Risks 2019,7, 75 3 of 25 causality analysis. Finally, the last aim is to evaluate the capability of monetary policy to decrease the contagion. Our idea is that bank risk is related to ECB policy and vice versa. An increase of risk should involve intervention on ECB, while a change in ECB policy should affect the risk. To test these relations, we apply the classical cointegration analysis and the Granger causality following the approach of Colletaz et al. (2018). The main results can be summarised as follows. The contagion effects depend on the bank’s “proximity”. High level of proximity provides a high level of systemic risk. This implies that the Eurozone banks have strong spatial dependence in the evolution of CDS spread, namely the contagion effect is present. In fact, from 2009 to 2017, our measure is high, suggesting that systemic risk is persistent in the Euro area banks. The results of the spatial dynamics model show how the Eurozone banking system is a “too interconnected to fail” system. Moreover, the Granger test supports the results of Brownlees and Engle (2012), who found that a shock in systemic risk has “an indirect impact on Unemployment through the Industrial Production channel” (or GDP in our case). This result suggests how the financial stability of the system is a prerequisite condition for achieving sustainable growth. Finally, referring to the policy monetary action, we find that monetary stance has been effective in reducing risk 2 . However, the results of long-run causality show that systemic risk does not imply policy intervention, highlighting how financial stability policy is not yet a goal. The contribution of our study is fourfold. First, by GAS spatial-dynamics model, we obtain a systemic risk measure where both time and cross-sectional dimension are considered simultaneously. Introducing cross-sectional correlations, we are also able to incorporate shock-induced effects on regressors, such as stock market collapses. By contrast, the works of Samaniego-Medina et al. (2016) and Annaert et al. (2013) examining the CDS spread determinants for European banks use a classic version of panel models. Second, we estimate the time-varying dynamic of contagion with respect to the papers of Eder and Keiler (2015) and Calabrese et al. (2017), who used a static version of the SAR model and binary spatial autoregressive model, respectively. The distinction is important since it is unrealistic to assume that the contagion effect (spatial coefficient) is constant over the entire period. This characteristic is particularly germane for monetary policy analysis, hence to understand the different systemic risk periods. Third, the paper extends the analysis of Colletaz et al. (2018) on the impact of the European Central Bank’s monetary policy on systemic risk. Investigating whether the channel of risk-taking, for example, is influenced by the volatility of the financial markets or whether monetary policy contributed to the expansion of risks, is relevant (Angeloni et al. (2015)). Therefore, the work contributes to the debate between intervention and non-intervention of monetary policy, in particular, between the “leaning against the wind” approach, which believes that central banks should use monetary stance also to management financial imbalances, and the “modified Jackson Hole consensus”, which argues that the central banks have to focus only on price stability (Smets (2014)). Fourth, our work contributes to different branches of literature: (i) the researches on contagion and risk spillovers (Giglio (2016); De Bruyckere et al. (2013); Battiston et al. (2012) ) 3 ; (ii) the application spatial econometrics models in the financial contest (Catania and Billé (2017)); (iii) the study of the determinants of CDS spread 4 (Annaert et al. (2013); Samaniego-Medina et al. (2016)); and (iv) the study of the bank risk-taking channel (Buch et al. (2014); Angeloni et al. (2015)). 2. The Econometrics Spatial Model The aim is to analyse the co-movements across CDS spread using a spatial dynamic panel model (see Elhorst (2014)). Thanks to this branch of econometrics, we can highlight the “time-space dynamics” 2 In addition, we use an event study approach to examine the impact of significant monetary policy events as reflected in a change in CDS spreads (see Appendix A). 3See Kireyev and Leonidov (2015) for a review of the financial network. 4 For an exhaustive and complete exposition of the CDS market see Angelini (2012) and its determinants Ericsson et al. (2009). Risks 2019,7, 75 4 of 25 (Milcheva and Zhu (2016)) of contagion, i.e., the dynamic of credit risk. In particular, applying spatial structure, we can explore if CDS spread of BANKidepends on the CDS spread of other BANKSj. The use of this model design means that shocks on explanatory variables are transmitted to all other “neighbours” within the spatial network. These dependencies can result from spatial spillovers deriving to contagion effects (see LeSage and Pace (2010)). This model permits us to separate the contagion measure into two parts: the direct and indirect effects. Since financial variables such as CDS spreads show a high level of co-movement, it is likely that development of CDS spreads in one bank are affected by developments in CDS spreads in other banks depending on the degree of interconnectedness. We can interpret this as financial contagion. The Spatial Autoregressive model (SAR), which is also known as “spatial lag” model is given by the following formula: yt=ρWyt+Xtβ+εt(1) εt∼p(εt,Σ;λ) where yt denotes a vector of observations on a dependent variable ( ∆ CDS spread) at time t , X is n×k matrix of observations on exogenous regressors (e.g., company’s financial fundamental), β is the vector of coefficients, ρ is the spatial dependency coefficient 5 that captures the effect spread in neighbouring “banks”, ε denotes the disturbance (error) vector with multivariate density pe(εt , Σ ; λ)6 , and W is the spatial weights matrix allowing to measure the interaction between banks, where each component seizes the bilateral cross-bank closeness. It captures the relationship between banks, therefore how the credit risk of bank i is affected (spillover from) by the credit risk of bank j . Wyt captures the contemporaneous interactions co-movement across the N bank. This impact is seized by a spatial dependence coefficient ρ. The principal intuition of this model is that the CDS premium for BANKi is directly affected by the values of CDS spread in neighbouring BANKS (Dell’Erba et al. (2013)). Specially, the CDS spread of any bank yi depends on all other CDS spreads. In this case, the parameter ρ specifies the degree of shock infection in the system. In this study, we follow the model of Blasques et al. (2016) that applies the spatial lag model with a time-varying parameter ρ to estimate a measure of daily interconnections. This is a novel kind of dynamic spatial models, which are based on the score driven framework, namely Generalised Autoregressive Score (GAS) models (see Harvey (2013) and Creal et al. (2013))7. The main advantage of this method is that we can assume ρ as a degree of the depth of cross-sectional spillovers. A high level of ρ represents a high level of the closeness, namely a high probability of systemic risk, while low level indicates a small degree of contagion. The time-varying (ρt) SAR model is: yt=ρtWyt+Xtβ+εt(2) εt∼pe(εt,Σ;λ) where ρt=h(ft)|ρ∈(− 1, 1 ) is a monotonic transformation of a time-varying parameter ft . The score is driven on the scaled score of the conditional density pe to derive the time-varying in ft . Its dynamic (updating mechanism) is given by: ft+1=ω+ p−1 ∑ i=0 aist−1+ q−1 ∑ j=0 bift−1(3) 5The spatial autocorrelation coefficient is bound to ρ<1 for standardised weighting matrices. 6perepresents the Student’s tdistribution where λis the degrees of freedom parameter. 7 For details, visit www.gasmodel.com, which provides a general framework for modelling time variation in parametric models. Risks 2019,7, 75 5 of 25 where ω is a vector of constant (scalar coefficient), ai and bi are fixed scalar parameters, while st−1=St∇t is the scaled score function. This is defined as the first derivative of the log-likelihood function at time twith respect to ft, formally ∇t=δlt δρt;St=h(ft) δft Let ρt=h(ft), lt=log pe(yt−ρtWyt−Xtβ,Σ;λ) + log(In−ρtW)(4) Finally 8 , the static parameters Θ= (ω , a , b , β , λ)0 , are estimated via the numerical maximisation of the likelihood function: `T= T ∑ t=1 lt(5) “Spatial” Distance in Finance To build the spatial weighted (interaction) matrix is a crucial step of the spatial framework model. Usually, most of the works assume the space (network) as a pure geographical distance. However, in finance, the neighbourhood is an immaterial concept (Catania and Billé (2017)). According to Hellwig (2014), there are several distinct channels of propagation (contagion) of shock, for example: (i) via physical exposures, i.e. banks are interconnected via claim and liabilities; and (ii) via market and price, i.e. the spiral of co-sell assets when one bank is distressed. In our estimation, we consider these channels of propagation by using two different interaction matrices (bank proximity). First, we fashion a financial interaction matrix (an estimation of the interbank matrix), using the Financial Claim matrix, provided by BIS, to incorporate the Physical exposures, following Calabrese et al. (2017) . This is the most direct channel by contractual relations. A default of one financial institution implies a high level of bankruptcy probability for all institutions with its the counterparty. This matrix is an aggregate claim of the entire banking sector in one country to the total banking sector in another. We define CAi as the claims from the banking sector in country A of bank i , and CBj as the claims from the banking sector in country B of bank j . This implies the equal weight for banks in the same country (CAi =CAj). To check for the robustness of our results, we try a different weighting matrix based on financial distances. We use a stock correlation weighting matrix, following Fernandez (2011), to capture the market and price exposure (the spiral of co-sell assets). In particular, we build an empirical Spearman correlation matrix estimated from daily equity returns over the period. Each element within the matrix W(i , j) is given by the Euclidean distance ( di,j ) between a daily stock return associated with two banks iand j: d(i,j)=q2(1−ρi,j)(6) where ρi,j is the Spearman’s correlation coefficient between returns i and j . According to the spatial literature (LeSage and Pace (2010)), we standardise the weighting matrix by classical rule (row-standardisation), such that for each i , ⇒∑iwi,j= 1. Since these weights are likely to be endogenous, we lag the correlation coefficient used as a weight Wi,j by one year, in order to obtain exogeneity of the weighting matrix. 8 Following Blasques et al. (2016), we adopt unit scaling, i.e. St= 1 such that st=∇t . In addition, we assume that the inverse matrix Z= (In−ρW)−1 exists with In as the n×n identity matrix. We consider the multivariate Student’s t distribution as pertinent to assign the disturbance density pe. Risks 2019,7, 75 6 of 25 3. Data We measure contagion effects using CDS spread; specifically, we select the five-year CDS spread 9 , from 1 December 2008 to 24 February 2017 (2110 daily obs.). Our sample consists of 22 listed Eurozone banks from Austria (2), Belgium (1), France (3), Germany (2), Greece (3), Ireland (2), Italy (4), Netherlands (1), Portugal (1), and Spain (3) (see Table A1 in Appendix Afor details). The range is made based on the data availability of CDS and stock prices in the Datastream database. Figure 1shows the median Euro bank CDS spread across the period. The impacts of the subprime crisis, the market turbulence and the sovereign debt crisis are clear. 0 200 400 600 800 1000 1200 1400 2009 2010 2011 2012 2013 2014 2015 2016 2017 0.25 percentile Median 0.75 percentile Figure 1. Banks CDS Spread. Plot of the median (red line) CDS spread; blue line indicates the 0.75, while the light blue line indicates 0.25 percentiles. Covariates Local bank variables. The equity value variable is represented by each bank’s stock returns. Generally speaking, an improvement of stock return implies a decrease of the probabilities of default and may thus lead to lower spreads. For that reason, a negative relationship with CDS spread is expected. Common variables. We add three variables that represent the Economic state: the term-spread, the volatility risk, and the financial sector stress. The term spread is the slope of the term structure that captures the business cycle predictor (Estrella and Mishkin (1997)), as a proxy for the drift rate (expected rate of return of the firm’s assets). Following the model of Merton (1974), we expect a negative relationship with CDS spread, as well as a high level of slope spread the economic growth. We apply the difference between 10-year bond yield and two-year bond yield following the literature (Alexander and Kaeck (2008)) for each country as a proxy of the slope of the curve (local country variables). Volatility risk is a measure of uncertainty of the future. A higher volatility represents a higher level of uncertainty about economic prospects. This implies a positive relationship with credit default spreads. We use the option implied volatility on the VStoxx, which seizes the implied volatility in the stock market. Finally, we add the difference between Euro Overnight Index (Eonia) and the three-month Euribor rate (E-E spread). This spread captures both credit risk/banking stress and market liquidity, as well as the health of the banking system (Pelizzon et al. (2016)). We expect a positive relationship with CDS spread because this difference is usually associated with economic distress, namely the spread is an indicator of the soundness of the banking system (Eder and Keiler (2015)). Therefore, the Euribor–Eonia spread is a measure of interbank funding pressure in the European Monetary Union. 9 We use relative changes (log differences multiplied by 100) of CDS spreads for each bank. We select the CDS spread contract based on five-year senior bond since these obligations are the most liquid (Meng and Gwilym (2008)). Risks 2019,7, 75 7 of 25 All data are stationary, as indicated by the Levin–Lin–Chu unit-root test 10 . In addition, to avoid endogeneity issues, we lag the covariates by one period following Blasques et al. (2016). 4. Estimation Results Table 1shows the static and dynamic (time-varying parameter) results 11 . Looking at the static model, the significance and high level of ˆ ρ coefficient (0.52), means that it is important to account for spatial linkages across banks. This implies that the Eurozone banks are strong spatial dependence in the evolution of CDS spread, namely the contagion effect is present. This means that there is a high probability of systemic risk, thus the BANKi level of CDS depends on the level of BANKj CDS and vice versa. Table 1. The spatial model results. Estimated parameters and their robust (sandwich) standard errors in parentheses, for the static spatial lag model and the time-varying spatial model, based on Student’s t distributed errors. Static Model Time-Varying ρ0.52 (0.003) ω0.0048 (0.000) a0.004 (0.000) b0.9918 (0.000) σ21.0434 (0.017) 1.046 (0.014) VStoxx −0.037 (0.004) −0.04 (0.014) E-E 0.098 (0.011) 0.01 (0.044) Local Stock Return −0.05 (0.001) −0.035 (0.000) Term structure −0.001 (0.000) −0.005 (0.000) const −0.0002 (0.0004) −0.0002 (0.0003) λ1.735 (0.016) 1.738 (0.034) logLik/T −52.00 −51.88 AICc 120 123.76 Concentrating on the dynamic spatial model, we can observe how the spatial dependence parameter is extremely persistent ( b is close to unity). Furthermore, the unconditional mean of ft equals ω/( 1 −b) = 0.59 with tanh (0.52) equal to the static model. The log-likelihood value is greater than the static model, which testifies to how the dynamic model fits better 12 . In addition, as the model suggests, the volatility clustering is high and present. The coefficients of the two models have the same and correct signs. The negative coefficient of VStoxx suggests that a high level of market volatility is correlated with a lower level of CDS spreads. On the other hand, a high level of market turmoil reduces the CDS premium. Although the result may seem misleading 10 See Appendix ATable A2. 11 Table A3 in Appendix Areports the results with a stock correlation weighted matrix. 12 To validate this result, we have applied the Vuong test following Engle (2016). The Voung test (=3.28) suggests significant improvement using time-varying ρ. The results from residual diagnostic are shown in Appendix A(see Figure A1). Risks 2019,7, 75 8 of 25 and in contrast with our hypothesis, this finding is consistent with Alexander and Kaeck (2008) and Annaert et al. (2013) who found the same sign for market volatility. This result supports the phenomenon of “flight to quality” ( Caballero and Krishnamurthy (2008) ; Beber et al. (2009) ): in turbulent times, investing in the banking sector is considered safer (Gatev and Strahan (2006)). The positive effect of stock return on the decrease of risk could be attributed to the fact that, when the performance increases (higher growth in firm value), as well as the financial market has good performance, the probability of default decreases (Zhang et al. (2009)). If we consider the stock return as a proxy of leverage (following Annaert et al. (2013)), then a positive performance will cause a decrease of leverage, leading to lower CDS premium. In our case, an increase of 1 bp of the stock return, generates a reduction of 0.03 bp around of CDS. The term structure slope carries a significant right sign. The term structure reflects the expected negative relationship with the changes in CDS spreads. This confirms our hypothesis. An increase in the slope of the term structure is an index of expected growth in economic activity (suggesting an increase in inflation): this implies a reduction in CDS spreads. On the other hand, an improvement suggests also the expectation of a tighter monetary policy (Alexander and Kaeck (2008)). E-E spread has a positive effect on risk. A lower level of this spread implies a lower propensity to borrow overnight, namely an increase of liquidity. Indeed, the overnight rate is an index of the widespread liquidity in the financial system as well as in the economy, and therefore the rate could rise during low liquidity periods. Moreover, it may increase due to a lack of confidence among banks, as observed in the 2008 liquidity crisis. Therefore, the malfunctioning of the interbank market may increase the fear of bank bail-outs (upturns of CDS) and therefore possible sovereign debt problems, due to the “diabolical loop” (Shambaugh (2012)) between the banks and the government. Figure 2shows the path of the spatial dependency parameter. The plot suggests that there are interlinkages between banks’ five-year CDS bond markets. These linkages are not equally strong over time, and the pattern seems to change in response to the business cycle, as well as policy events during the European sovereign debt crisis. The spillover effect dominants the Eurozone banks. The year 2009 showed a high level of spillover due to the financial crisis turbulence that affects the financial market 13 . The level sunk towards about 0.5, in 2010, following the creation of European Financial Stability Facility (EFSF), the “special purpose vehicle”, founded on 7 June 2010 by the member countries of the monetary union. The EFSF was created with the aim of preserving the Eurozone’s financial stability through assistance to member countries, especially to bail out troubled banks. 0.3 0.4 0.5 0.6 0.7 0.8 2009 2010 2011 2012 2013 2014 2015 2016 2017 Rho Figure 2. The figure exhibits the time-varying spatial spillover, ˆ ρt. 13 In addition, in 2009, Greece reached its highest (negative) deficit level. In addition, in 2011, Greece self-proclaimed a much larger than expected fiscal deficit. This event spread to a series of downgrades, financial market turbulence, affecting the other countries members, as well as other financial systems. Risks 2019,7, 75 15 of 25 C(x−→ y|F) = ln det[J∗ 0Σ∗ 0(h)J0∗ 0] det[J∗ 1Σ(h)J0∗ 1](11) where F is the information set, Σ(h) stands for the residual of covariance matrix from the unconstrained model, h is the horizon, Σ0(h) ( Σ∗ 0(h) ) is the covariance matrix from constrained model unless y , J0= [ 1 0 ] ( J∗ 0= [ 1 0 ] ) and J1= [ 1 0 0 ] ( J∗ 0= [ 0 1 0 ] ) identify the block corresponding to the variable x ( y ) in the covariance matrix. If the numerator is higher than the denominator, then y causes x (and vice versa). The causality measure is express as a per cent of total relationship within xand yfor any h. 6.2.2. Building a (One) Monetary Policy Stance We use a unique index of monetary policy, namely a measure able to take into account the conventional and unconventional strategy. To do this, we make use of “shadow rate” ( sr — Lombardi and Zhu (2018) ;Pattipeilohy et al. (2017)) to measure monetary policy stance (x) . sr is a measure adequate to summarise information from both policies and market expectations ( Krippner (2015) ), at the Zero Lower Bound (ZLB) era. The sr shows the behaviour of short interest rate ( it ) not constrained by ZLB. The short interest rate is the maximum between 0 and shadow rate sr . rt=max(r,sr) If the short interest rate is positive, then it is equal to the value of sr ; if it should have negative values, the nominal interest rate is constrained by the ZLB level. Comparative to this, the shadow rate can assume negative values and therefore it is unconfined (Lombardi and Zhu (2018)). We use a factor analysis to extract the two components from yield curve, following the methodology of Pattipeilohy et al. (2017) . In the words of Avery (1979), we can interpret the monetary policy as “a single dimensioned unobserved variable”. The latter consists of two unobservable components: the term premium and the expectations component. In formula: ZM t=ˆ aM 1F1,t+ˆ aM 2F2,t+εt(12) where ZM t is the yield for maturity bucket M , ˆ aM 1 stands for the loading on factor i , Fit represents the score and εt is the mean zero error term. To build this measure, we use the weekly yield curve data, over the period from September 2004 to 24 February 2017, provided by ECB Data Warehouse. Figure A2, shows the dynamics of term premium (Factor 1) and the expectations component (Factor 2). A higher value of sr with respect to zero stands for a restrictive policy, while a lower value corresponds to an expansive monetary policy. From 2012 to 2017, the corresponding monetary stance is negative, highlighting the monetary stimulus implemented by unconventional strategies. 6.2.3. Auxiliary Variables We include different auxiliary variables (z) related to global financial risk and specific bank risk. For the first, we use a Global Risk Aversion indicator (GRAI) that captures the global risk perception. For the second set of variables, we look upon the cost of equity for banks (COE) , the return of equity (ROE) , and liquidity to asset ratio (LIQ) , which capture the financing conditions. All data are taken from the ECB Data Warehouse database at monthly frequency. We standardise the variables as mean equal to zero and unit variance and we estimate the VAR model, with trend using AIC criteria to choose the appropriate lag. 6.2.4. Results Figure 6shows the causality results between the monetary policy ( y ) and the contagion risk ( ˆ ρt ), through the globally financial risk (GRAI) as an auxiliary ( z ) variable. The plot suggests that the monetary stance seems to cause the systemic risk, after 14 periods when the measure becomes Risks 2019,7, 75 16 of 25 significant. This confirms that the implementation of ECB policy has important effects on reduction of contagion. Figure 6. Dashed lines represent the 90% confidence intervals; the values of our measures are between 0 (not significant) and 1. z = GRAI. Figures 7–9report the causality measure with bank variables as auxiliaries. Figure 7shows the causality measures when the auxiliary variable is the Cost of Equity (COE) . We can see how the policy stance, via COE , implies the contagion. For Periods 2–7, the measure is significant and explains about 60% of total causality. Figure 7. Dashed lines represent the 90% confidence intervals; the values of our measures are between 0 (not significant) and 1. z = COE. In Figure 8, we present the Granger causality via ROE . In this circumstance, the monetary policy is a predictor of contagion. Starting with Period 4, the causality measure Policy → Risk becomes significant and it remains so throughout the time horizon. As specified by Lambert and Ueda (2014), the monetary policy “could have a positive and negative effect on banks’ profitability”. A quantitative easing policy could cause an increase in the bank asset price, as well as an interest rate close (equal) to zero, could have a positive effect on banks balance-sheet, reducing their funding cost. This implies a possible increase of ROE . However, low rates could tighten the interest margin of banks, due to the reduction of revenues from loans with variable rates. This implies a possible decrease of ROE . Therefore, our results suggest that the monetary policy causes systemic risk via the return on equity of banks. Considering as the auxiliary variable the banks’ liquidity to asset ratio (Figure 9), we see how policy Granger causes the risk (from Period 6 to all horizons), further confirming our above results. An injection of capital by ECB can increase the supply of money, which in turn drives down interest rates and causes a decrease in the cost banking debt, therefore a dwindling in systemic risk. Looking at the right-panel plot, we can recognise—always—the non-significant relations between Risk and Policy, suggesting that the risk does not Granger cause the policy of ECB. The results seem to highlight that the systemic risk does not cause the ECB policy during the sovereign debt crisis. These findings are qualitatively similar to the classical above cointegration analysis and they are consistent with the findings of Colletaz et al. (2018) who provided the same evidence from 2001M1 to 2008M4. For the authors, the not relationship from Risk to Policy can be attributed to the fact that Risks 2019,7, 75 17 of 25 financial stability “was not an objective per se for the ECB before the GFC”. We find the same results during and after the Global Financial Crisis periods, highlighting how the central bank policy is still too a “price-stability focused”. Nevertheless, although macro- and micro-prudential policy (MMP) measures have been implemented in recent years, the analysis suggests that systemic risk is not yet a central focus of the new monetary policy19. However, the ECB could only decrease the market and bond turmoil, as well as the bank stock return interconnections, particularly with an announcement effect policy. The credibility of ECB played a major role in the management of the contagion (see Event Studies analysis in Appendix A). Figure 8. Dashed lines represent the 90% confidence intervals; the values of our measures are between 0 (not significant) and 1. z = ROE. Figure 9. Dashed lines represent the 90% confidence intervals; the values of our measures are between 0 (not significant) and 1. z = LIQ. 7. Conclusions In this paper, we develop a new measure of banking systemic risk. Using the spatial-temporal econometrics model of Blasques et al. (2016), we estimate a time-varying spatial ˆ ρt , namely a time-spatial dependency across Euro area banks. We provide evidence that the Euro banking system is spatially dependent on the spread of systemic risk. To test the predictive power of this measure, we engage an out-of-sample evaluation exercise. We carry out a Granger Causality analysis to verify if changes in our measure predict troublesome changing in macroeconomic variables such as GDP and unemployment. The findings show that a shock in systemic risk has “an indirect impact on Unemployment through the Industrial Production channel”. 19 To ensure the robustness of the analysis, we applied the classic Granger test between the shadow measure of monetary policy ( sr ) and ˆ ρt . The results (Table A5 in Appendix A) show how monetary policy has an effect on systemic risk and not the other way around, supporting the results from the model of Dufour and Taamouti (2010). Risks 2019,7, 75 18 of 25 Finally, we asked the following questions: What is the effect of monetary policies on systemic risk? Is there a shadow channel which influences the effect of monetary policy on systemic risk? To answer, we applied two cointegration analyses: the “classical” and the method of Dufour and Taamouti (2010). The results of our two cointegration exercises can be summarised as follows: We found that a restrictive monetary policy increases the level of contagion. The climb in systemic risk due to high-interest rates is persistent during the whole studied period. On the contrary, monetary expansion decreases the value of systemic risk. The second cointegration exhibits that policies by ECB have at least been partly effective in breaking the contagion (see also the event studies), but that such actions are not yet part of a specific and common objective. That is, financial stability, as the Granger’s causality shows, is not yet a monetary policy goal. Beyond our approach, a further development might be to consider the weighted matrix as a time-varying diagonal covariance matrix to take into account the updated status dependency at each observation. This model could highlight the dynamics of contagion by distinguishing potential spillovers between different financial system networks. Author Contributions: The current paper is a combined effort of M.F., and E.A.: conceptualisation, M.F. and E.A.; data curation, M.F.; formal analysis, M.F.; methodology, M.F.; resources, M.F. and E.A.; software, M.F.; validation, M.F.; visualisation, M.F.; writing—original draft preparation, M.F.; writing—review and editing, M.F and E.A.; and supervision, E.A. Funding: This research received no external funding. Acknowledgments: We wish to thank participants of the “12th South-Eastern European Economic Research Workshop” at Bank of Albania (BoA, Tirana), 6–7 December 2018, and the “XXVII International Rome Conference on Money, Banking and Finance”, at LUISS University (Rome), 10–11 December 2018, for very helpful comments and remarks. The usual disclaimers apply. Conflicts of Interest: The authors declare no conflict of interest. Abbreviations The following abbreviations are used in this manuscript: ABS Asset-Backed Securities CDS Credit Default Swap ECB European Central Bank EFSF European Financial Stability Facility ESM European Stability Mechanism ESRB European Systemic Risk Board LTROs Long-Term Refinancing Operations OMT Outright Monetary Transactions SMP Securities Markets Programme SSM Single Supervisory Mechanism TBTF Too Big To Fail TITF Too Interconnected To Fail TLTRO Targeted Longer-Term Refinancing Operations Appendix A. Event Study Approach In this section, we analyse the impact of diverse events on our measure of systemic risk, i.e., the power of episodes to increase/reduce the bank contagion. To estimate these effects, we apply the event study methodology, specially the constant-mean model: ACρt=Cˆ ρt−E[Cˆ ρt|Xt](A1) Risks 2019,7, 75 19 of 25 where AC ˆ ρt , Cˆ ρt and E[Cˆ ρt|Xt] are the abnormal actual and normal change of contagion, respectively, while Xtis the information for normal contagion, and E[Cˆ ρt|Xt] = E[Cˆ ρt] = µi(A2) then, Cˆ ρt=µi+εt with E[εt] = 0 and Var[εt] = σ2. We aggregate the cumulating abnormal change in systemic risk by: CAC ˆ ρt= t ∑ i=1 AC ˆ ρt(A3) Finally, to verify the statistical significance of an abnormal contagion response to the events, we employed the usual version of the t-test. The event period is, in event time, ( − 10, +10) days around the event, which refers to the cumulative return between the values of the index 10 days after the event and 10 days before the event, such that the event is centred. The estimation window is, in event time, ( − 100, +100), following the classic event study approach (Park (2004)). In the period from December 2008 to February 2017, we identified 13 important events. Several episodes had a negative effect (i.e., growth of risk), while others had a positive impact (i.e., decrease of risk) on contagion. Table A6 shows the estimation results, while Figure A4 plots the cumulative abnormal CDS “return”. Most of the events are significant, as shown by p-values, highlighting the importance of expectation and the “announcement effect” on financial stability in the Eurozone. Correlation matrix RBI KN BIRG ING UCG NBGIG KBC ISP EBS EGFEY DBK ACA CBK CABK BNP BPM BBVA SAB BCP BMPS ALPHA AIBG AIBG ALPHA BMPS BCP SAB BBVA BPM BNP CABK CBK ACA DBK EGFEY EBS ISP KBC NBGIG UCG ING BIRG KN RBI -1 -0.5 0 0.5 1 Correlation matrix RBI KN BIRG ING UCG NBGIG KBC ISP EBS EGFEY DBK ACA CBK CABK BNP BPM BBVA SAB BCP BMPS ALPHA AIBG AIBG ALPHA BMPS BCP SAB BBVA BPM BNP CABK CBK ACA DBK EGFEY EBS ISP KBC NBGIG UCG ING BIRG KN RBI -1 -0.5 0 0.5 1 Figure A1. Cross-Correlation Matrix: ( top ) the cross-correlation matrix for raw data; and ( bottom ) for the full model residuals. Breusch–Pagan LM test of independence: F-statistic: 0.61, p-value: 0.4348. This implies there is no cross-sectional dependence among residuals. -4 -3 -2 -1 0 1 2 3 4 5 2006 2008 2010 2012 2014 2016 2018 Factor1 Factor2 Figure A2. The Shadow Rate. Factor 1 explains 0.93 of variance, while Factor 2 explains 0.07. Risks 2019,7, 75 20 of 25 0.3 0.4 0.5 0.6 0.7 0.8 2009 2010 2011 2012 2013 2014 2015 2016 2017 Rho CBPP I Support to Greece SMP CBPP II LTRO LTRO II 'Whatever it takes' OMT CBPP III ABSPP PSPP TLTRO II and APP New TLTRO Figure A3. The ˆ ρttogether with key policy in Table A6. Figure A4. Each figure refers to CAC ˆ ρt10 days before and after the event estimate. Risks 2019,7, 75 21 of 25 Table A1. Sample Banks. The table lists the larger banks in the Eurozone in 2012. Source: Bankscope. Country Bank Name Ticker Size of Bank (USD bn, 2012) GDP (USD bn, 2012) %GDP Austria Erste Group Bank AG EBS 282 334 84 Austria Raiffeisen Zentralbank RBI 179 334 54 Belgium KBC Group KBC 339 406 83 France BNP Paribas BNP 2516 2189 115 France Crédit Agricole ACA 2134 2189 98 France Natixis KN 697 2189 32 Germany Deutsche Bank AG DBK 2668 2894 92 Germany Commerzbank AG CBK 839 2894 29 Greece National Bank of Greece NBGIF 138 201 69 Greece EFG Eurobank Ergasias EGFEY 89 201 45 Greece Alpha Bank ALPHA 76 201 38 Ireland Allied Irish Banks Plc AIBG 161 184 88 Ireland Bank of Ireland Plc BIRG 148 184 80 Italy Unicredit UCG 1223 1692 72 Italy Intesa Sanpaolo ISP 888 1692 53 Italy BPM BPM 616 1692 36 Italy Monte dei paschi di Siena BMPS 288 1692 17 Netherlands ING Group NV ING 1538 676 227 Portugal Banco Commercial Portugues BCP 118 177 67 Spain Banco Santander SAN 1675 1090 154 Spain Banco Bilbao Vizcaya Argentaria BBVA 841 1090 77 Spain CaixaBank CABK 459 1090 42 Table A2. Panel unit-root test: Levin–Lin–Chu. T-statistics are reported; *** stands for statistical significance at 1%. Panel Unit-Root Test: Levin–Lin–Chu Statistics CDS Spread Unadjusted t −1.3 ×102 Adjusted t* −1.8 ×102*** VStoxx Unadjusted t −1.2 ×102 Adjusted t* −1.5 ×102*** Eonia-Euribor Unadjusted t −18.91 Adjusted t* −9.203 *** Stock Return Unadjusted t −99.66 Adjusted t* −44.61 *** Term Structure Unadjusted t −29.36 Adjusted t* −18.63 *** Table A3. The spatial model results. Estimated parameters and their robust (sandwich) standard errors in parentheses, for the static spatial lag model and the time-varying spatial model, based on Student’s t distributed errors. Wmatrix = Spearman correlation matrix of stock return. Static Model Time-Varying ρ0.7129 (0.000) ω0.030 (0.009) a0.029 (0.107) b0.966 (0.021) log σ21.036 (0.000) 1.037 (0.000) logLik −51.99 −51.98 Risks 2019,7, 75 22 of 25 Table A4. Augmented Dickey–Fuller (ADF) test. T-statistics are reported; *** stands for statistical significance at 1%; the appropriate lag length () for ADF test is selected using Schwarz Bayesian criterion (SC). Variables Level Differences ˆ ρt−2.71 (0) −12.4 (0) *** M2 −1.98 (0) −7.33 (0) *** MRO −1.37 (0) −8.69 (0) *** HICP −1.13 (0) −9.16 (0) *** Table A5. The results of the pairwise Granger causality. Null Hypothesis F-Statistic Prob. srdoes not Granger Cause ˆ ρt7.626 0.000 ˆ ρtdoes not Granger Cause sr0.686 0.506 Table A6. Significant Event: ( − 10, +10) days around the event, in which the event is centred. Significance is assessed with a two-sided t-test where the observed changes on announcements days are compared with the corresponding means on non-announcements days; * denotes statistical significance at 10%, ** denotes statistical significance at 5%, *** denotes statistical significance at 1%. Events t-test 5 July 2009 CBPP I 6.94 *** 25 March 2010 Support to Greece −3.58 *** 10 May 2010 SMP 6.89 *** 6 October 2011 CBPP II −0.37 22 December 2011 LTRO I 7.41 *** 1 March 2012 LTRO II −7.11 *** 27 July 2012 “Whatever it takes” 6.98 *** 2 August 2012 OMT −1.67 15 October 2014 CBPP III −5.43 *** 19 October 2014 ABSPP −2.27 ** 9 March 2015 PSPP −2.11 10 March 2016 TLTRO II and APP −4.18 *** 2 June 2016 New-TLTRO −0.73 TOTAL Effect 4.76 *** References Acharya, Viral, Robert Engle, and Matthew Richardson. 2012. Capital shortfall: A new approach to ranking and regulating systemic risks. The American Economic Review 102: 59–64. [CrossRef] Tobias, Adrian, and Markus K. Brunnermeier. 2016. CoVaR. The American Economic Review 106: 1705–41. Alexander, Carol, and Andreas Kaeck. 2008. Regime dependent determinants of credit default swap spreads. Journal of Banking & Finance 32: 1008–21. Angelini, Eliana. 2012. Credit Default Swaps and their role in the credit risk market. International Journal of Academic Research in Business and Social Sciences 2: 584. Angeloni, Ignazio, Ester Faia, and Marco Lo Duca. 2015. Monetary policy and risk taking. Journal of Economic Dynamics and Control 52: 285–307. [CrossRef] Annaert, Jan, Marc De Ceuster, Patrick Van Roy, and Cristina Vespro. 2013. What determines euro area bank CDS spreads?. Journal of International Money and Finance 32: 444–61. [CrossRef] Arnold, Matthias, Sebastian Stahlberg, and Dominik Wied. 2013. Modeling different kinds of spatial dependence in stock returns. Empirical Economics 44: 761–74. [CrossRef] Asgharian, Hossein, Wolfgang Hess, and Lu Liu. 2013. A spatial analysis of international stock market linkages. Journal of Banking and Finance 37: 4738–54. [CrossRef] Avery, Robert B. 1979. Modeling monetary policy as an unobserved variable. Journal of Econometrics 10: 291–311. [CrossRef] Risks 2019,7, 75 23 of 25 Battiston, Stefano, Domenico Delli Gatti, Mauro Gallegati, Bruce Greenwald, and Joseph E. Stiglitz. 2012. Liaisons dangereuses: Increasing connectivity, risk sharing, and systemic risk. Journal of Economic Dynamics and Control 36: 1121–41. [CrossRef] Beber, Alessandro, Michael W. Brandt, and Kenneth A. Kavajecz. 2009. Flight-to-quality or Flight-to-liquidity? Evidence from the Euro-area bond market. Review of Financial Studies 22: 925–57. [CrossRef] Bekaert, Geert, Marie Hoerova, and Marco Lo Duca. 2013. Risk, uncertainty and monetary policy. Journal of Monetary Economics 60: 771–88. [CrossRef] Benoit, Sylvain, Jean-Edouard Colliard, Christophe Hurlin, and Christophe Pérignon. 2017. Where the risks lie: A survey on systemic risk. Review of Finance 21: 109–52. [CrossRef] Beyer, Andreas, Giulio Nicoletti, Niki Papadopoulou, Patrick Papsdorf, Gerhard Rünstler, Claudia Schwarz, Joao Sousa, and Oliver Vergote. 2017. The Transmission Channels of Monetary, Macro-and Microprudential Policies and Their Interrelations. (No. 191). Frankfurt: European Central Bank. Billio, Monica, Mila Getmansky, Andrew W. Lo, and Loriana Pelizzon. 2012. Econometric measures of connectedness and systemic risk in the finance and insurance sector. Journal of Financial Economics 104: 535–59. [CrossRef] Blasques, Francisco, Siem Jan Koopman, André Lucas, and Julia Schaumburg. 2016. Spillover dynamics for systemic risk measurement using spatial financial time series models. Journal of Econometrics 195: 211–23. [CrossRef] Brownlees, Christian T., and Robert Engle. 2012. Volatility, correlation and tails for systemic risk measurement. Social Science Research Network. Available online: http://econ.au.dk/fileadmin/site_files/filer_oekonomi/ subsites/creates/Seminar_Papers/2012/mes.pdf (accessed on 24 May 2019). Brownlees, Christian T., and Robert Engle. 2016. SRISK: A conditional capital shortfall measure of systemic risk. The Review of Financial Studies 30: 48–79. [CrossRef] Buch, Claudia M., Sandra Eickmeier, and Esteban Prieto. 2014. In search for yield? Survey-based evidence on bank risk taking. Journal of Economic Dynamics and Control 43: 12–30. [CrossRef] Caballero, Ricardo J., and Arvind Krishnamurthy. 2008. Collective risk management in a flight to quality episode. The Journal of Finance 63: 2195–230. [CrossRef] Calabrese, Raffaella, Johan A. Elkink, and Paolo S. Giudici. 2017. Measuring bank contagion in Europe using binary spatial regression models. Journal of the Operational Research Society 68: 1503–11. [CrossRef] Cao, Jin, and Gerhard Illing. 2015. “Interest rate trap”, or why does the central bank keep the policy rate too low for too long? The Scandinavian Journal of Economics 117: 1256–80. [CrossRef] Catania, Leopoldo, and Anna Gloria Billé. 2017. Dynamic spatial autoregressive models with autoregressive and heteroskedastic disturbances. Journal of Applied Econometrics 32: 1178–96. [CrossRef] Claeys, Peter, and Borek Vasicek. 2015. Systemic risk and the sovereign-bank default nexus: A network vector autoregression approach. Journal of Network Theory in Finance 1: 27–72. [CrossRef] Colletaz, Gilbert, Grégory Levieuge, and Alexandra Popescu. 2018. Monetary policy and long-run systemic risk-taking. Journal of Economic Dynamics and Control 86: 165–84. [CrossRef] Creal, Drew, Siem Jan Koopman, and André Lucas. 2013. Generalized autoregressive score models with applications. Journal of Applied Econometrics 28: 777–95. [CrossRef] De Bruyckere, Valerie, Maria Gerhardt, Glenn Schepens, and Rudi Vander Vennet. 2013. Bank/sovereign risk spillovers in the European debt crisis. Journal of Banking & Finance 37: 4793–809. De Grauwe, Paul, and Yuemei Ji. 2013. Self-fulfilling crises in the Eurozone: An empirical test. Journal of International Money and Finance 34: 15–36. [CrossRef] Debarsy, Nicolas, Cyrille Dossougoin, Cem Ertur, and Jean-Yves Gnabo. 2018. Measuring sovereign risk spillovers and assessing the role of transmission channels: A spatial econometrics approach. Journal of Economic Dynamics and Control 87: 21–45. [CrossRef] Dell’Erba, Salvatore, Emanuele Baldacci, and Tigran Poghosyan. 2013. Spatial spillovers in emerging market spreads. Empirical Economics 45: 735–56. [CrossRef] Dell’Ariccia, Giovanni, Luc Laeven, and Robert Marquez. 2014. Real interest rates, leverage, and bank risk-taking. Journal of Economic Theory 149: 65–99. [CrossRef] Diamond, Douglas W., and Raghuram G. Rajan. 2012. Illiquid banks, financial stability, and interest rate policy. Journal of Political Economy 120: 552–91. [CrossRef] Risks 2019,7, 75 24 of 25 Diebold, Francis X., and Kamil Yılmaz. 2014. On the network topology of variance decompositions: Measuring the connectedness of financial firms. Journal of Econometrics 182: 119–34. [CrossRef] Dufour, Jean-Marie, and Abderrahim Taamouti. 2010. Short and long run causality measures: Theory and inference. Journal of Econometrics 154: 42–58. [CrossRef] ECB. 2017. MFI lending Rates: Pass-Through in the Time of Non-Standard Monetary Policy. Issue 1, Article 1. Economic Bulletin. Frankfurt: European Central Bank. Eder, Armin, and Sebastian Keiler. 2015. CDS Spreads and Contagion Amongst Systemically Important Financial Institutions—A Spatial Econometric Approach. International Journal of Finance & Economics 20: 291–309. Elhorst, J. Paul. 2014. Dynamic spatial panels: Models, methods and inferences. In Spatial Econometrics. Berlin and Heidelberg: Springer, pp. 95–119. Engle, Robert F., and Clive WJ Granger. 1987. Co-integration and error correction: Representation, estimation, and testing. Econometrica: Journal of the Econometric Society 55: 251–76. [CrossRef] Engle, Robert F. 2016. Dynamic conditional beta. Journal of Financial Econometrics 14: 643–67. [CrossRef] Ericsson, Jan, Kris Jacobs, and Rodolfo Oviedo. 2009. The determinants of credit default swap premia. Journal of Financial and Quantitative Analysis 44: 109–32. [CrossRef] ESRB. 2018. Adverse Macro-Financial Scenario for the 2018 EU-Wide Banking Sector Stress Test. Frankfurt: European Systemic Risk Board. Estrella, Arturo, and Frederic S. Mishkin. 1997. The predictive power of the term structure of interest rates in Europe and the United States: Implications for the European Central Bank. European Economic Review 41: 1375–401. [CrossRef] Fernandez, Viviana. 2011. Spatial linkages in international financial markets. Quantitative Finance 11: 237–45. [CrossRef] Forbes, Kristin J., and Roberto Rigobon. 2002. No contagion, only interdependence: Measuring stock market comovements. The Journal of Finance 57: 2223–61. [CrossRef] Gatev, Evan, and Philip E. Strahan. 2006. Banks’ advantage in hedging liquidity risk: Theory and evidence from the commercial paper market. The Journal of Finance 61: 867–92. [CrossRef] Giglio, Stefano. 2016. Credit default swap spreads and systemic financial risk (No. 15). ESRB Working Paper Series. Frankfurt: European Systemic Risk Board. Giudici, Paolo, and Laura Parisi. 2018. Corisk: Credit risk contagion with correlation network models. Risks 6: 95. [CrossRef] Harvey, Andrew C. 2013. Dynamic Models for Volatility and Heavy Tails: With Applications to Financial and Economic Time Series. Cambridge: Cambridge University Press. Hellwig, Martin. 2014. Systemic Risks and Macro-prudential Policy. In Putting Macroprudential Policy to Work. DNB Occasional Studies 1207: 42–77. Amsterdam: Netherlands Central Bank, Research Department. Johansen, Søren, and Katarina Juselius. 1990. Maximum likelihood estimation and inference on cointegration—With applications to the demand for money. Oxford Bulletin of Economics and Statistics 52: 169–210. [CrossRef] Kireyev, Alexei, and Andrei Leonidov. 2015. Network Effects of International Shocks and Spillovers. No. 15-149. Washington: International Monetary Fund. Gross, Marco, and Christoffer Kok. 2013. Measuring Contagion Potential among Sovereigns and Banks Using a Mixed-Cross-Section GVAR. No. 1570. Frankfurt: European Central Bank. Kremer, Manfred. 2016. Macroeconomic effects of financial stress and the role of monetary policy: A VAR analysis for the euro area. International Economics and Economic Policy 13: 105–38. [CrossRef] Krippner, Leo. 2015. Zero Lower Bound Term Structure Modeling: A Practitioner’s Guide. New York: Springer. Lambert, Frederic, and Kenichi Ueda. 2014. The Effects of Unconventional Monetary Policies on Bank Soundness. No. 14-152. Washington: International Monetary Fund. LeSage, James P. 2008. An introduction to spatial econometrics. Revue D’économie Industrielle 3: 19–44. [CrossRef] LeSage, James P., and R. Kelley Pace. 2010. Spatial econometric models. In Handbook of Applied Spatial Analysis. Berlin and Heidelberg: Springer, pp. 355–76. Litterman, Robert B., and Laurence Weiss. 1983. Money, Real Interest Rates, and Output: A Reinterpretation of Postwar US Data. No. 1077. Cambridge: National Bureau of Economic Research, Inc. Lombardi, Marco, and Feng Zhu. 2018. A Shadow Policy Rate to Calibrate US Monetary Policy at the Zero Lower Bound. International Journal of Central Banking 14: 305–346.