Working paper THE INTERTWINING OF FINANCIALISATION AND FINANCIAL INSTABILITY. Jérôme CREEL OFCE-Sciences Po and ESCP-Europe - PDF

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Working paper THE INTERTWINING OF FINANCIALISATION AND FINANCIAL INSTABILITY Jérôme CREEL OFCE-Sciences Po and ESCP-Europe Paul HUBERT OFCE-Sciences Po Fabien LABONDANCE CRESE-Université de Franche-Comté

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Working paper THE INTERTWINING OF FINANCIALISATION AND FINANCIAL INSTABILITY Jérôme CREEL OFCE-Sciences Po and ESCP-Europe Paul HUBERT OFCE-Sciences Po Fabien LABONDANCE CRESE-Université de Franche-Comté and OFCE-Sciences Po May 2015 The Intertwining of Financialisation and Financial Instability * Jérôme Creel OFCE Sciences Po & ESCP Europe Paul Hubert OFCE Sciences Po Fabien Labondance CRESE Université de Franche-Comté & OFCE Sciences Po May 2015 Abstract This paper aims to quantify the link between financialisation and financial instability, controlling for the financial and macroeconomic environment. Our main identification assumption is to represent these two concepts as a system of simultaneous joint data generating processes whose error terms are correlated. Based on panel data for EU countries from 1998, we test the null hypotheses that financialisation positively affects financial instability -a vulnerability effect- and that financial instability has a negative effect on financialisation -a trauma effect-, using Seemingly Unrelated Regressions and 3SLS. We find a positive effect of credit/gdp on non-performing loans - a vulnerability effect- in the EU as a whole, in the Eurozone, in the core of the EU but not at its periphery, and a negative effect of non-performing loans on credit/gdp - a trauma effect - in all samples. Even when relaxing our identification assumption, both opposite effects hold. Keywords: Financial depth, financial instability, financial vulnerability, SUR model. JEL Classification: E44; G10. * We thank Guillaume Arnould, Cécile Bastidon-Gilles, Christophe Blot, Michael Brei, Massimo Cingolani, Salim Dehmej, Bruno Ducoudré, Marie-Sophie Gauvin, Céline Gimet, Nicolas Huchet, Catherine Refait-Alexandre, Jean-Charles Rochet and seminar participants at LEAD (Toulon), the 2014 FESSUD Annual Conference (Warsaw), and the Université of Franche-Comté s workshop on systemic risk (Besançon) for their helpful comments. Any remaining errors are ours. This research project benefited of funding from the EU Seventh Framework Program (FP7/ ) under grant agreement n (FESSUD). Contact s: (corresponding author), Postal address: OFCE Sciences Po, 69 quai d Orsay, Paris, France. 1 1. Introduction The objective of this paper is to assess the interrelationships between financialisation and financial instability. The global financial crisis has shed light on the intertwining between the growth of the banking and financial sectors (financial deepening), financial deregulation (or absence of regulation in the case of wholesale financial markets) and financial instability (see Gorton and Metrick, 2012). The former two concepts (deepening and deregulation) are usually considered as two prominent aspects of financialisation (Sawyer, 2014) which, at the macroeconomic level, is often associated to the level of bank credit to GDP. Because the European Union (EU), under the initiative of the European Commission, has adopted a banking union which gives the European Central Bank (ECB) a role of prudential supervisor for most banks in the EU, the ECB is de facto in charge of monitoring financialisation and financial stability. Assessing their intertwining precisely for EU countries is an important issue in this context. We additionally focus on the potential heterogeneity of this link within the EU between Eurozone (EZ), core EU and periphery EU countries and question the relevance of a one-size-fits-all reform of banking supervision in the EU. Although the determinants of bank credit to GDP have been largely investigated in the empirical and theoretical literature (see infra), the relationship between bank credit and financial instability has been rarely studied to our knowledge. One reason for this is the difficulty to quantitatively capture the concept of financial instability, and we assume that the most relevant candidate when analysing bank credit should be the share of nonperforming loans to gross loans (see Cihak and Schaeck, 2010). Figure 1 shows a scatter plot of the latter variable and bank credit to GDP. The relationship is unclear and the raw correlation is The contribution of this paper is to assess their conditional correlation and to single out the effect of each of these two variables on the other, for EU countries, imposing a panel structure on data and controlling for time and country fixed effects, and financial and macroeconomic environments. Our specification includes long-term real interest rates, taxes, a financial regulation index and market capitalisation, as well as inflation, real GDP and trade openness, all potential determinants of financialisation, as shown in the literature, and also possible determinants of financial instability. Figure 1 Financialisation and Financial Instability (Source: GFDD) While estimating the link between financialisation and financial instability, we are confronted to two types of endogenous phenomenon. The first type is directly related to the 2 joint determination of these two left-hand-side variables. As price and quantity on a given market, financialisation and financial instability can be considered as the opposite sides of the same coin. To correct for their simultaneity, we use a Seemingly Unrelated Regressions (SUR) model which takes into account the correlation of error terms and provides more efficient estimates than OLS. The second type of endogeneity refers to the right-hand-side variables and to the estimation of their causal effect and to a potential omitted variable bias or reverse causality that would make these variables and the error term correlated. This second type of endogeneity is handled with instrumental variables. The first and main identification assumption of this paper is to represent financialisation and financial instability as a system of simultaneous joint data generating processes (estimated with SUR) whose contemporaneous error terms are correlated. We test the following two null hypotheses: (i) there is a positive effect of financialisation on financial instability labelled a vulnerability effect and (ii) there is a negative effect of financial instability on financialisation that we label a trauma effect. The first hypothesis would stem from the increasing fragility and risks of marginal loans, whereas the second would result from the potential deleveraging and reduced risk-taking of banks following a period of financial instability. In a second step and because our different variables of interest on the right-handside of our model might potentially be endogenous, we perform three-stage least squares (3SLS) estimation which enable us to combine the system estimation of SUR to the instrumental-variables method of 2SLS. We limit our empirical investigation to the period for which we have access to macroeconomic, banking and market data for most of the EU countries. On this period, we can split the sample to characterize the interrelationships in the EZ, in the EU core and periphery. Despite the raw negative correlation between financialisation and financial instability, we find a positive causal effect of the level of bank credit to GDP on the share of non-performing loans, and a negative causal effect of financial instability on financialisation. These results are robust to alternative financial instability variables, to the introduction of government debts, 1 to some EU subsamples, to non-linear specifications and to a 3-equation SUR model in which long-term interest rates are also considered endogenous. More precisely, we find the existence of a vulnerability effect in the EU as a whole, in the Eurozone, in the core of the EU but not at its periphery, and of a trauma effect in all samples. We also find some evidence of non-linearities between the two main variables. Whereas non-performing loans have the same linear effect on credit to GDP whatever the specification, the effect of credit to GDP on non-performing loans the vulnerability effect appears state and time contingent. It depends on and is reinforced by the level of credit to GDP and on the level of nonperforming loans, and appears to kick in during crisis times rather than during good times. We also investigate a market view of financialisation which draws on different characteristics than the credit view. The market view confirms the vulnerability effect in most cases; meanwhile, it gives rise to a distinction between the EU core and periphery countries as regards the trauma effect: the vulnerability effect remains whereas the trauma effect disappears in all samples, except the EU core countries. In the EU periphery, the vulnerability effect is concomitant with a reverse trauma effect. 1 The introduction of European government debts in the analysis is an indirect test of their link with banking risk. The contagion between sovereign default risk and bank vulnerability has been investigated by Bolton and Jeanne (2011), Caruana and Avdjiev (2012), Acharya and Steffen (2015) and Acharya et al. (forthcoming). 3 Finally, even when relaxing our main identification assumption and performing individual panel estimations (pooled OLS, fixed- and random-effects) rather than joint ones over the entire sample of countries, both opposite effects hold. The rest of this paper is organized as follows. Section 2 presents the literature. Section 3 describes the model, our empirical strategy and our hypotheses. Section 4 presents the data. Section 5 discusses the results. Section 6 concludes. 2. Related literature This analysis refers to two strands of the existing literature. The first relates to bank credit and its determinants. Bernanke and Blinder (1988) revived the empirical literature on bank credit and its determinants after they extended the IS/LM model to include a banking sector, drawing on the assumption made by Tobin (1970) that bonds and credits are not perfect substitutes. They conclude that periods of money-demand and credit-demand shocks alternated between 1974 and The following literature expanded on the analysis of monetary policy channels of transmission, whereas the empirical determinants of bank credits were usually limited to economic activity and financing costs (e.g. Fase, 1995). In the 2000s, the bulk of empirical papers about bank credit devoted attention to its impact on economic growth (see Ang, 2008, for a survey) whereas only a few papers investigated bank credit determinants. After Goodhart (1995), and drawing on a cointegrating VAR model of real credit to the private non-financial sector, real GDP, real interest rate and real property prices, Hofmann (2004) shows that shocks to property prices could explain the persistence in financial cycles. Cottarelli, Dell Ariccia and Vladkova-Hollar (2005) study the bank credit growth in Central and Eastern European countries (CEECs) and test whether it could be attributed to a structural change of financial deepening. Their list of bank credit determinants includes public debt to GDP ratio, GDP per capita, an indicator of high inflation, an indicator of financial liberalization, and different institutional characteristics like accounting standards, legal origins and bank entry requirements. Except for the latter, all variables have the significant expected sign. Aisen and Franken (2010) explain real credit growth in 83 countries, with a distinction between, first, variables of economic performance, external shocks and policy stance; second, local characteristics of the credit market (like size, integration, and openness); and, third, bank characteristics per se (like share of public ownership, bank leverage, and bank return on equity). Despite this long list of variables, only a few are significant: namely, GDP growth and changes in money market rate. After having taken into consideration possible interactions between regions, trading partners GDP growth rate of emerging Asia can be added to the list of significant determinants. A recent survey of determinants of domestic bank credit in emerging economies can be found in Gozgor (2014) who focuses his empirical study on the role of external factors. Chinn and Ito (2006) also discuss this role, relating it to capital controls and institutions, thus questioning the relationship between financial openness and financial development. Aiyar, Calomiris and Wieladek (2014) investigate the supply of credit and its linkages with (and leakages towards) credit substitution channels via foreign affiliates and branches to comply with macro-prudential measures. The literature on financial instability and its determinants has developed more or less along two different lines of reasoning. The first one assumes that capitalism is intrinsically unstable (Minsky, 1995) and leads to leverage and credit booms and busts. The second one sticks to a general equilibrium approach and assumes that financial instability is caused by financial frictions (due to asymmetric information), hence by financial shocks and their propagation to 4 the rest of the economy (Calomiris, 1995; Mishkin, 1999). In contrast with the literature on bank credit determinants, empirical papers dedicated to financial stability determinants have been scarcer, to our knowledge. The reason can certainly be related to the difficulty of defining and quantifying this concept. Different measures have emerged in the literature. Loayza and Ranciere (2006) measure financial instability as the standard deviation of the growth rate of the private credit/gdp ratio over non-overlapping 5-year averages. The ECB has developed a Composite Indicator of Systemic Stress (CISS) for the euro area as a whole, available since The International Monetary Fund (IMF) has developed a financial stress index for 13 industrialized countries. At the micro level, several authors capture financial stability in the banking sector through the Z-score (Uhde and Heimeshoff, 2009; Fink et al., 2009), which measures the probability of default for a bank or a banking system. The share of non-performing loans in bank balance sheets is also used as a proxy of financial instability (Cihak and Schaeck, 2010), as it can trigger the onset of a banking crisis (Reinhart and Rogoff, 2011). Louzis, Vouldis and Metaxas (2012) study the macroeconomic and bank-specific determinants of non-performing loans in Greece, and find that they mostly respond to GDP, unemployment, interest rates and public debt. Our contribution to the literature is to estimate simultaneously the interrelationships of bank credit and financial instability. We introduce financial instability as an explanatory variable of bank credit to GDP and the opposite, controlling for the main determinants put forward by the existing literature. 3. Model and Empirical Strategy When assessing the link between financialisation and financial instability, we face the issue of potential endogeneity between our two variables of interest. One solution, and this is the main identification assumption of this paper, consists in thinking the problem not in a singleequation space, but as a system of simultaneous equations that jointly determine both dependent variables. The two equations are therefore mechanically related as the contemporaneous errors associated with each dependent variable are correlated, which seems a reasonable assumption for the two data processes. Estimating the system provides estimates that are more efficient, because it takes into account the correlation between the error terms and therefore add information on the error structure. The most basic form of joint-system estimation is Seemingly Unrelated Regressions (SUR), also called Zellner (1962)-efficient regressions, using feasible generalised least-squares (FGLS). When the two equations do not have the same set of explanatory variables and are not nested, it leads to more efficient estimates than estimating each individual equation separately with OLS. Generally, the coefficients are only slightly different, but the standard errors are uniformly larger. We estimate simultaneously the cross-effects of financialisation and financial instability using the following model, in which we assess the contribution of our variables of interest beyond financial and macro controls and the information captured by the lagged value of our dependent variables:,,,,,,,,,,,, (1) where F i,t is the financialisation variable for a country i, S i,t is the financial instability variable, X i,t is the vector of financial controls, namely long-term real interest rates, the stock market 5 capitalisation, taxes and a financial regulation variable, and Z i,t is the vector capturing the macroeconomic environment, namely real GDP, inflation, trade openness together with country and time fixed effects. Using this model, we test two hypotheses: Hypothesis n 1: there is a positive effect of financialisation on financial instability labelled a vulnerability effect, as suggested by Gorton and Metrick (2012) or Gourinchas and Obstfeld (2012), where the latter assert that financial vulnerabilities stem from high credit to GDP ratio. These vulnerabilities would stem from the increasing fragility and risks of marginal loans. This effect may also arise from the dependence of loan-loss provisioning to the evolution of bank lending. Pool et al. (forthcoming) show that banks reduce their loan-loss provisioning as a percentage of their total assets when bank lending increases, and therefore take on more risks. Hypothesis n 2: there is a negative effect of financial instability on financialisation that we label a trauma effect, and which would result from the potential deleveraging and reduced risk-taking of banks following a period of financial instability. We include financial variables in the regression that could impinge on the relationships between financialisation and financial instability. 2 We expect a negative effect of long-term real interest rates measuring financing costs on financialisation assuming that credit demand decreases and credit supply increases with interest rates and that the equilibrium on the credit market is driven by its short side. Fase (1995) reports results on financialisation for the Netherlands using nominal long-term interest rates. Alternatively, we focus on real longterm interest rates. We expect a positive correlation between the long-term real interest rate and financial instability: the latter materializes after real interest rates go up, hence weakening debtors positions. A negative link between stock market capitalisation and financialisation would capture a substitution effect between banking intermediation and direct financing through financial market operations inducing a negative correlation between stock market capitalisation and financial instability as substitution should act as an insurance mechanism. We expect a positive link between taxes and financialisation and between taxes and financial instability. As regards the former link, the argument would come from the development of financial innovation for tax optimization and/or because of the deduction of interest payments from profits. The second link would proceed along the following logic: the higher the corporate tax, the higher the incentive to borrow (to grasp the full benefit of interest payments deduction), the lower equity, the weaker banks, and the more unstable the banking and financial system. Stated differently, and following Keen and De Mooj (2012) and De Mooj, Keen and Orihara (2013), the corporate tax would violate the Modigliani-Miller theorem in the case of banking institutions: the high corporate tax induces recourse to borrowing (debt) at the expense of equity. Finally, we control for the existence of a positive link between financial deregulation and financialisation and a positive link between financial deregulation and financial instability as deregulation may increase risk-taking. Chinn and Ito (2006) report a positive relationship between financial openness a
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