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Do non-performing loans matter for bank lending and the business cycle in euro area countries?

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05/15/2020 | 05:15am EDT

Ivan Huljak, Reiner Martin, Diego Moccero, Cosimo Pancaro

Working Paper Series

Do non-performing loans matter for bank lending and the business cycle in euro area countries?

No 2411 / May 2020

Disclaimer: This paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.

Abstract

We contribute to the empirical literature on the impact of non-performing loan (NPL) ratios on aggregate banking sector variables and the macroeconomy by estimating a panel Bayesian VAR model for twelve euro area countries. The model is estimated assuming a hierarchical prior that allows for country-specic coecients. The VAR includes a large set of variables and is identied via Choleski factorisation. We estimate the impact of exogenous shocks to the change in NPL ratios across countries. The main ndings of the paper are as follows: i) An impulse response analysis shows that an exogenous increase in the change in NPL ratios tends to depress bank lending volumes, widens bank lending spreads and leads to a fall in real GDP growth and residential real estate prices; ii) A forecast error variance decomposition shows that shocks to the change in NPL ratios explain a relatively large share of the variance of the variables in the VAR, particularly for countries that experienced a large increase in NPL ratios during the recent crises; and iii) A three-year structural out- of-sample scenario analysis provides quantitative evidence that reducing banks’ NPL ratios can produce signicant benets in euro area countries in terms of improved macroeconomic and nancial conditions.

Keywords: Euro area countries, non-performing loans, panel Bayesian VAR, hierarchical priors

JEL Classification: G21, C32, C11

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Non-Technical Summary

The non-performing loan (NPL) ratio in the euro area increased from around 3% at the onset of the global nancial crisis in late 2008 to a peak of around 8% in 2014. A key driver of the substantial increase in NPL ratios was the severe and protracted recession in large parts of the euro area, which signi cantly reduced borrowers’ capacity to service their debt. At the same time, the fast increase in NPL ratios was also signi cantly inuenced by other factors, such as banks’ lending and monitoring policies and limited capacity to work-out defaulted loans. More recently, the recovery of economic activity in the euro area and the development and implementation of policies to tackle non-performing loans by the Single Supervisory Mechanism (SSM) have led to a decline in the euro area NPL ratio, which reached around 6% at the end of 2017. The evolution of the NPL ratios has been rather heterogeneous across euro area countries reecting the di erent macroeconomic conditions and diverse structural features (the eciency of legal and judicial systems, insolvency frameworks, payment culture and the level of development of distressed debt markets, among others). At the end of 2017, the NPL ratio still remained above 10% in those euro area countries most a ected by the recent economic and nancial crisis, namely Cyprus, Greece, Ireland, Italy and Portugal, while it was below 5% in countries such as Austria, Belgium, Estonia, France, Lithuania and the Netherlands.

High NPL ratios in banks’ balance sheets can adversely a ect the soundness of the banking system and its ability to lend to the real economy through three main channels. First, high non- performing loans reduce bank pro ts. They do so because they require higher provisions, they lead to lower interest income, generate higher expenses associated with their monitoring and management and lead to an increase in funding costs, as risk adverse investors are less willing to lend to institutions with a low credit quality. Second, non-performing loans feature higher risk weights, leading to higher capital needs. To maintain or boost capital adequacy, banks may thus deleverage, leading to a contraction in credit supply. Finally, the management of large NPL stocks can divert important managerial resources away from core and more pro table activities. Considering the importance of bank lending for the functioning of the euro area economy, there is a clear need to study the feedback loop between non-performing loans, bank credit and the real economy.

We contribute to the literature on the feedback loops between NPLs and the economy by estimating a panel Bayesian VAR model with hierarchical priors for twelve euro area countries for the period between the rst quarter of 2006 and the third quarter of 2017. More speci cally, the aim of our analysis is to estimate the impact of exogenous shocks to the change in NPL ratios on bank lending and the macroeconomy. Changes in NPL ratios which are unrelated to changes in the repayment capacity of borrowers (i.e., exogenous changes in NPL ratios) include, inter alia, sales of defaulted loans to investors, changes in banks’ own attitudes towards risk, write-o s, supervisory actions that incentivise banks to work out these loans (by o ering restructuring solutions to clients) and other policy initiatives which deal with NPLs’ work- outs and defaults associated with poor enforcement mechanisms. We use a Bayesian model

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because of the relatively short time-span of the available data series of our main variable of interest (NPL ratios), and because of the large number of parameters to be estimated. The adopted model allows for country-speci c coecients. This feature is especially relevant in this context as the dynamics of NPL ratios were particularly heterogeneous across countries. At the same time, the model assumes that the parameters of the VAR for individual countries share a common component across the euro area, hence ensuring an ecient use of the data. The variables included in the panel VAR are economic activity (which is a proxy for the repayment capacity of borrowers), ination, the monetary policy rate, real estate prices, bank lending volumes both to non- nancial corporations and to households for house purchase, bank lending spreads to these two sectors, the ratio of capital and reserves over total assets and the change in NPL ratios. We use the Choleski factorisation, a recursive technique commonly adopted in the literature, to identify the shock to NPLs. In particular, we use as an identi cation strategy the fact that NPLs generally do not respond within a quarter to endogenous shocks, as banks are allowed to classify a loan as non-performing only a quarter after default.

We nd that an exogenous increase in the change in NPL ratios tends to depress bank lending, widens lending spreads and leads to a fall in real GDP growth and residential real estate prices and an easing of the monetary policy rate. While the responses of the capital and reserves- to-asset ratio vary across countries, a material increase is recorded in Cyprus, Spain, Ireland, Italy, Lithuania and Portugal, due to the increase in provisions for impairments during the crisis. The results also show that the decline in bank lending to non- nancial corporations is generally more marked than the one in mortgage loans. The forecast error variance decomposition shows that shocks to the change in NPL ratios explain a relatively large share of the variance of the variables in the VAR, particularly for countries that exhibited a large increase in NPL ratios during the crisis. Finally, a three-year structural out-of-sample scenario analysis quanti es the impact of a decline in NPL ratios for Cyprus, Ireland, Spain, Italy, Greece and Portugal (the countries that exhibited the most sizable increase in NPL ratios during the crisis). The exercise provides quantitative evidence that reducing NPL ratios can produce non-negligible bene ts in terms of improved macroeconomic and nancial conditions. These results are robust to a change in the ordering of the variables in the Choleski factorisation (whereby bank loans and the NPL ratio are a ected contemporaneously by macroeconomic variables) and also to the inclusion in the VAR of the annual rate of growth in NPL volumes (instead of the NPL ratio) as rst variable.

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The non-performing loan (NPL) ratio in the euro area increased from around 3% at the onset of the global nancial crisis in late 2008 to a peak of around 8% in 2014. A key driver of the substantial growth in NPL ratios was the severe and protracted recession in large parts of the euro area, which signi cantly reduced borrowers’ capacity to service their debt. At the same time, the fast increase in NPL ratios was also signi cantly inuenced by other factors, such as banks’ lending and monitoring policies and limited capacity to work-out defaulted loans. More recently, the recovery of economic activity in the euro area and the development and implementation of policies to tackle non-performing loans by the Single Supervisory Mechanism (SSM) have led to a decline in the euro area NPL ratio, which reached around 6% at the end of 2017. The evolution of the NPL ratios has been rather heterogeneous across euro area countries reecting the di erent macroeconomic conditions and diverse structural features (e.g. the eciency of legal and judicial systems, insolvency frameworks, payment culture and the level of development of distressed debt markets, among others). At the end of 2017, the NPL ratio still remained above 10% in those euro area countries most a ected by the recent economic and nancial crisis, namely Cyprus, Greece, Ireland, Italy and Portugal, while it was below 5% in countries such as Austria, Belgium, Estonia, France, Lithuania and the Netherlands.

High NPL ratios in banks’ balance sheets can adversely a ect the soundness of the banking system and its ability to lend to the real economy through three main channels. First, high non- performing loans reduce bank pro ts. They do so because they require higher provisions, they lead to lower interest income, generate higher expenses associated with their monitoring and management and lead to an increase in funding costs, as risk adverse investors are less willing to lend to institutions with a low credit quality.1 Second, non-performing loans feature higher risk weights, leading to higher capital needs. To maintain or boost capital adequacy, banks may thus deleverage, leading to a contraction in credit supply. Finally, the management of large NPL stocks can divert important managerial resources away from core and more pro table activities.2 Considering the importance of bank lending for the functioning of the euro area economy, there is a clear need to study the feedback loop between non-performing loans, bank credit and the real economy.

The empirical literature on NPLs features three main strands which investigates the determinants of NPLs, the impact of NPLs on the real economy and the feedback loops between NPLs and the macroeconomy, respectively.

The rst strand of literature has identi ed three main groups of determinants of NPLs, namely bank level, industry-speci c and macroeconomic. The rst group includes: i) Exogenous

  • For example, Pancaro, Zochowski_ and Arnould (2020) nd that lower credit quality seems to be associated
    with higher banks’ senior bond yields.
    2Grodzicki, Laliotis, Leber, Martin, O’Brien and Zboromirski (2015), Fell, Grodzicki, Martin and O’Brien (2016a), Fell, Grodzicki, Krusec, Martin and O’Brien (2017) extensively elaborate on the challenges for the banking system stemming from the accumulation of non-performing exposures. Additionally, they illustrate macroeconomic and microeconomic policies which could be adopted to resolve this legacy issue.

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factors, such as a sudden drop in economic activity (bad luck hypothesis); ii) Poor management, which can lead to bad credit risk decisions (bad management hypothesis); iii) Low capitalisation, which can make banks prone to risk taking (moral hazard hypothesis); and iv) Scarcity of resources allocated to underwriting and monitoring loans to boost short-term eciency (skimping hypothesis). While the literature has found support for all these hypotheses, the bad management hypothesis is the most prominent one. Industry speci c drivers point mainly to the impact of competition on risk taking. On the one hand, stronger market power may drive lending rates higher, increasing the debt burden for borrowers and, thus, their bankruptcy risk (competition- stability hypothesis). On the other hand, banks with more market power have higher franchise value and, therefore, more at stake in the event of defaults, making their underwriting more prudent (competition-fragilityhypothesis). Overall, there seems to be no consensus in the literature on whether bank competition increases or decreases stability in the banking system(Beck, De Jonghe and Schepens 2013; Goetz 2018). Finally, regarding macroeconomic drivers, the literature has focused on various measures of economic activity, ination, interest rates and the exchange rate as the most relevant drivers of NPLs (Anastasiou and Tsionas 2016; Jimenez and Saurina 2006; Louzis, Vouldis and Metaxas 2012). Improved economic conditions, higher ination and lower interest rates are found to strengthen the repayment capacity of borrowers, while exchange rate depreciations are shown to increase the debt burden of foreign-exchange denominated loans for unhedged borrowers3.

The second main strand of the literature studies the impact of non-performing loans on bank lending and economic activity. This literature relied both on bank-level and country level data. For example, Balgova and Plekhanov (2016), using data for a global sample of 100 countries, quanti ed the (positive) e ects of policy-induced declines in NPLs on the real economy. The authors nd that the foregone growth due to the overhang of NPLs can be large. Accornero, Alessandri, Carpinelli and Sorrentino (2017), coupling bank level data for Italy with borrower- based information for non- nancial corporations, study the inuence of NPLs on the supply of bank credit. They nd that bank lending is impaired by the exogenous accumulation of new NPLs and the associated increase in provisions, but it is not causally a ected by the level of NPL ratios.

The literature on the determinants of non-performing loans and on the impact of non- performing loans on bank lending and the real economy has traditionally relied on single equation estimation techniques, where either NPLs or macroeconomic variables are regressed against each other and other control variables. By modelling the dynamics of each variable separately, these studies neglect the dynamic interaction and feedbacks between the changes in non-performing loans, banking and macroeconomic variables. This is a major drawback, because an exogenous increase in NPLs is likely to impair economic activity, leading to a decline in the repayment capacity of borrowers and a further increase in NPLs. As a result, a third

3The literature nds that ination generally reduces the loan servicing burden. However, it also shows that, if wages are sticky, higher ination might induce a fall in real income and, thus, cause an increase in the debt servicing burden.

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strand of literature has estimated the impact of shocks to NPLs using structural time series models where aggregate NPL ratios and economic activity are included in a VAR together with a broader set of banking and macroeconomic variables. For example, Espinoza and Prasad (2010), Nkusu (2011), De Bock and Demyanets (2012) and Klein (2013) estimate panel VAR models for various groups of countries and use country level data to investigate the feedback interactions between NPLs and macroeconomic performance.4 In addition to the expected coun- tercyclical behaviour of NPLs, all these studies nd signi cant feedback e ects from NPLs to the real economy.

We contribute to the empirical literature on the feedback e ects between NPLs, the banking sector and the macroeconomy by estimating a panel Bayesian VAR model with hierarchical priors (Jarocinski 2010). The aim of our analysis is to estimate the impact of exogenous shocks to the change in NPL ratios on bank lending and the macroeconomy. Changes in NPL ratios which are unrelated to changes in the repayment capacity of borrowers (i.e., exogenous changes in NPL ratios) include, inter alia, sales of defaulted loans to investors, changes in banks’ own attitudes towards risk, write-o s, supervisory actions that incentivise banks to work out these loans (by o ering restructuring solutions to clients) and other policy initiatives which deal with NPLs’ work-outs and defaults associated with poor enforcement mechanisms. Estimations are performed over the period from the rst quarter of 2006 to the third quarter of 2017 for twelve euro area countries, namely Austria, Belgium, Cyprus, Estonia, France, Greece, Ireland, Italy, Lithuania, Netherlands, Portugal and Spain.

The variables included in the panel VAR are economic activity (which is a proxy for the repayment capacity of borrowers), ination, the monetary policy rate, real estate prices, bank lending volumes both to non- nancial corporations and to households for house purchase, bank lending spreads to these two sectors, the ratio of capital and reserves over total assets and the change in NPL ratios. In order to disentangle the exogenous shocks to the changes in the NPL ratio, we use the Choleski factorisation, a recursive technique largely disseminated by Christiano, Eichenbaum and Evans (1999) and commonly adopted in the literature. In particular, we use as an identi cation strategy the fact that NPLs generally do not respond within a quarter to endogenous shocks, as banks are allowed to classify a loan as non-performing only a quarter after default.

We nd that an exogenous increase in the change in NPL ratios tends to depress bank lending, widens lending spreads and leads to a fall in real GDP growth and residential real estate prices. As a consequence, monetary policy rate is eased. While the responses of the capital and reserves-to-asset ratio vary across countries, a material increase is recorded in Cyprus, Spain, Ireland, Italy, Lithuania and Portugal, due to the increase in provisions for impairments recorded during the crisis. Interestingly, the results show that the decline in bank lending to non- nancial corporations is generally more marked than that in mortgage loans. These results

  • These groups of countries include the Gulf Cooperative Council (GCC) countries, a group of 26 advanced economies, a large sample of emerging markets and Central, Eastern and South-Eastern Europe (CESEE), respectively.

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are robust to a change in the ordering of the variables in the Choleski factorisation and also when including in the VAR the annual rate of growth in NPL volumes rather annual changes in NPL ratios. The forecast error variance decomposition also shows that exogenous shocks to the change in NPL ratios explain a relatively large share of the variance of the variables in the VAR, particularly for countries that exhibited a large increase in NPL ratios during the crisis. Finally, a three-year structural out-of-sample scenario analysis assesses the impact of a decline in NPL ratios for Cyprus, Ireland, Spain, Italy, Greece and Portugal, i.e. the countries that exhibited the most sizable increase in NPL ratios during the crisis. More specically, it quanties the dierential impact of a scenario where NPL ratios remain constant versus one where they are assumed to decline in line with observed recent developments. The exercise shows that reducing NPL ratios can produce non-negligible benets in terms of improved macroeconomic and nancial conditions.

Against this background, the contribution of this work to the literature is threefold. To our knowledge, this paper is the rst which studies the impact of a shock to the change in NPL ratios using a panel Bayesian VAR model which allows for country specic coecients hence capturing country specic dynamics for a large group of euro area countries with a consistent approach. This is an important contribution, given that euro area countries experienced rather heterogeneous dynamics of the NPL ratios in the considered time period due to dierent economic developments as well as key structural and institutional features. At the same time, the model assumes that the parameters of the VAR for individual countries share a common component which is compatible with the fact that euro area countries are part of a common market and share a common monetary policy. This assumption ensures an ecient use of the data. Second, this empirical analysis, thanks to the use of a Bayesian approach which allows to estimate a large number of parameters despite the relatively short time-span of the available data series for NPL ratios, benets from the inclusion in the VAR of a larger set of variables than those typically used in the literature. The use of a richer VAR allows to better characterize the feedback loop between non-performing loans, the real economy and the banking sector. In particular, the inclusion in the VAR of the capital and reserves to total asset ratio and lending spreads and the distinction between lending and spreads to non-nancial corporations and to households for house purchases are a novelty. Including the capital and reserves to total assets ratio is important because shocks to NPL ratios aect capital and provisions for impairments. Also having bank lending spreads among the endogenous variables is valuable because the exogenous shocks might lead to a re-pricing of bank loans, hence aecting the quantity of loans provided to the economy and, thus, macroeconomic conditions. Finally, this paper is the rst study which constructs and relies on a balanced panel of quarterly time series of NPL ratios for almost thirteen years and for a large number of euro area countries.

The rest of the paper is structured as follows. Section 2 presents the empirical methodology, including the econometric model and the priors adopted. The variables included in the panel VAR and the identication scheme are presented in Section 3 and 4, respectively. Section 5

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presents three sets of results: the impulse response analysis, the forecast error variance decom- position, the robustness analysis and the out of sample structural counterfactual analysis. The last section concludes.

We estimate a panel VAR model for twelve euro area countries and ten variables for the period between the rst quarter of 2006 and the third quarter of 2017.5 The twelve countries included in the analysis are Austria, Belgium, Cyprus, Estonia, France, Greece, Ireland, Italy, Lithuania, Spain, the Netherlands and Portugal. The model allows for cross-subsectional heterogeneity, hence capturing country specic dynamics. The following subsections describe in detail the methodology used to estimate the panel VAR, the variables included in the model and the adopted identication scheme.

2.1 Econometric model

We estimate the impact of shocks to real GDP growth and to the change in NPL ratios on bank lending and the economy based on the following panel V AR(p) model:

yi;t = Ci + Ai1yi;t1 + : : : + Aipyi;tp + “i;t

(1)

Where i is an individual country (i = 1; ; N), t is time (t = 1; ; T ), yi;t is a column vector of n endogenous variables (ten endogenous variables at time t are included in the model), Ci is a vector of n constants which are country specic and A1i ; : : : ; Api are matrices of coe- cients for dierent order of lags until lag p. The model we estimate allows for country-specic coecients, allowing us to capture the dierent impacts of the shocks across countries. Finally, it is assumed that the error term is normally distributed, as follows:

i;t N(0; i)

Transposing Equation (1) and expressing it in compact form, one obtains:

0 1

(A1i )0

yi;t0 = yi;t0

1 : : : yi;tp0

...

C

+ “i;t0

B

B

C

@

A

(APi )0

Stacking Equation (3) over the T periods it follows:

5The estimations in this paper were implemented relying on the BEAR toolbox and MATLAB codes developed by Dieppe, van Roye and Legrand (2016).

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0yi;0

2 1

0 yi;0

1

yi;0

1

C

yi;0

0

B ...

=

B ...

B

C

B

B

C

B

Byi;T0

C

Byi;T0

1

@

A

@

Which can be expressed as:

yi;0

1p

1 0

(Ai1)0

1

i;0

1

1

yi;0

2p

(Ai2)0

+

0i;0

2

...

C B

...

C

B ...

C

C B

C

B

C

yi;Tp0

C B

P

C

B

C

C B(Ai )0C

Bi;T0

C

A @

A

@

A

Y i = XiBi + Ei

In turn, equation (5) can be vectorised as follows:

vec(Y i) = (In Xi)vec(Bi) + vec(Ei)

Calling “i = vec(Ei) and from Equation (2) it follows that:

i N(0; i); with i = i IT

(7)

Moreover, calling i = vec(Bi), the random coecient model estimated here assumes that i can be expressed as:

With b a n2p 1 vector of parameters and assuming that bi N(0; b), it follows that:

Equations (8) and (9) imply that the coecients in the VAR will dier across countries while being drawn from the same distribution, centered around a common mean for the euro area (hence capturing similarity across country’s coecients). This model is particularly appealing because it captures a common component across countries while allowing for cross-country heterogeneity in the response to shocks. This feature is useful in our context because of the dierent dynamics exhibited by the NPL ratios in the countries included in our sample. The next sub-section describes the priors used in the paper.

2.2 The priors

The hierarchical prior adopted in this paper follows Jarocinski (2010).6 The advantage of this prior is that it treats the set of vectors i (i = 1; ; N), the residual covariance matrices i (i = 1; ; N), and the common mean and variance of the VAR coecients b and b as random variables. In particular, the hyperparameters b and b will have a hyper-prior distribution.

6The author compares impulse responses to monetary policy shocks in ve euro area countries before the EMU and in four of the newer European Union member states from central{eastern Europe.

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The hyper-prior distribution proposed by Jarocinski (2010) is diuse (improper) for b, whereas for b the functional form relies on a diagonal matrix b of dimension q q, q = Nn2p, which is inspired by the specication of the variance matrix of the Minnesota prior. In partic- ular, for parameters relating endongenous variables to their own lags, the variance is given

2

1

2

by aii

=

l3

, whereas for parameters related to cross-lag coecients the variance is dened

2

i2

2

2

as aij

=

. Because some coecients are large, while others are small, it is neces-

j2

l3

sary to scale each coecient’s variance by a factor which adjusts the size of the coecients of variables i and j. The values for i2 and j2 are obtained by tting autoregressive models by OLS for the n endogenous variables of the model (after pooling the data for all units) and then their standard deviation is computed. These standard errors capture the scale of unexpected movements in the variables. The full covariance matrix is then dened as b = (1 Iq) b. The parameter 1 captures the overall tightness of the prior for b. Note that when 1 = 0 the prior variance is null and all the coecients in i will take the value b (full pooling of the data across countries). By contrast, when 1 grows larger, coecients dier more and more across the countries in the sample and become similar to the respective single country estimates. When 1! 1 the coecients for each country are their own individual estimates and there is no sharing of information across countries. Because the number of estimated coecients in the dynamic equations diers substantially in the two cases (n2p in the pooled panel and Nn2p in the hierarchical model), it is desirable to assume an intermediate value for 1 to ensure a reasonable balance between tting individual countries’ data, on the one hand, and constraining the specication to make the estimates tighter, on the other. Hence, in order for the model to allow some degree of information sharing, Jarocinski (2010) proposes as prior for 1 an inverse Gamma distribution with very small values for the shape and scale parameters. Small parameters for the inverse Gamma distribution make the prior weakly informative, letting the data talk about the posterior common mean and variance.7 The values considered for the other two hyperparameters are those typically assumed in the literature, namelly 2 = 0:5 and 3 = 1. Finally, the prior distribution for i is simply a diuse prior. Combining the likelihood function with the priors mentioned in this sub-section one can obtain the full posterior distribution. However, this distribution does not allow for analytical derivation of the marginal posteriors, hence requiring the use of numerical methods.

  • Variables included in the panel VAR

The panel VAR includes 10 variables, which is a larger set than those typically used in the literature. This allows us to better characterise the dynamic interaction and feedback loops between non-performing loans, the real economy and the banking sector.8 In particular, the

  • In particular, the values for the shape and scale parameters are given by s20 and v20 , with s0 = v0 = 0:001.
    8For example, Espinoza and Prasad (2010) includes up to four variables, De Bock and Demyanets (2012) and

Klein (2013) include ve variables, while Nkusu (2011) includes nine variables.

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variables included are economic activity (which is a proxy for the repayment capacity of bor- rowers), ination, the monetary policy rate, real estate prices, bank lending volumes both to non- nancial corporations and to households for house purchase, bank lending spreads to these two sectors, the ratio of capital and reserves over total assets and the change in NPL ratios. Table 1 and Table 2 provide information on data sources and summary statistics, respectively.

Economic activity is measured by the annual rate of growth of real GDP (adjusted for calendar and seasonal e ects). In the case of Ireland, economic growth is computed as the annual growth rate of the nominal modi ed Gross National Income (GNI*), deated using the deator of the modi ed domestic demand (MDD).9 Ination is de ned as the annual rate of growth in the Harmonised Index of Consumer Prices (HICP) (working day and seasonally adjusted). The source of these data is Eurostat and the Irish Central Statistics Oce. The average over daily observations of the three-month Euribor rate is used as a proxy for the policy interest rate. The source of the data is the ECB Statistical Data Warehouse (SDW).

Bank lending is de ned as the annual rate of growth in bank lending to non- nancial corporations and to households for house purchase. Originally, these two variables are de ned in terms of an index of notional stocks.10 The source of these series is the MFI Balance Sheet Statistics of the ECB.11

Including bank lending spreads among the endogenous variables in the VAR is important because the exogenous shocks might lead to a re-pricing of bank loans, hence a ecting the quantity of loans provided to the economy and macroeconomic conditions. In the particular case of mortgage spreads, they may a ect the business cycle via changes in house prices, housing wealth and collateral valuations (Walentin 2014). The bank lending spreads are de ned as the di erence between bank lending rates (to households for house purchase and to non- nancial corporations) and Euribor. The lending rates used to compute the spreads are the interest rates on new business loans granted in euros, all maturities combined.12 The source is the MFI Interest Rate Statistics of the ECB.

The series of residential real estate prices is included to account for the role that real estate markets play in business cycle uctuations. This sector matters because it is a sizable sector of the real economy and rms and households own real estate properties, often used as collateral. Moreover, real estate transactions usually require credit, which is often provided by leveraged

9We use the modi ed Gross National Income (GNI*) instead of GDP because changes in the latter have become increasingly disconnected from actual trends in domestic living standards due to the sizeable distortion resulting from widespread activities of multinational companies. Instead, the GNI* attempts to control for (part of) the impact of globalisation on Irish macro-economic statistics. See Department of Finance (2018) for more details.

  1. Using notional stocks to compute the annual growth rates, rather than outstanding amounts, is important because the latter reect not only the cumulative e ect of nancial transactions but also the impact of other non-transaction related changes (e.g., instrument reclassi cation, changes in exchange rates, price uctuations and loan write-o s/write-downs, etc.). Excluding such non-transaction related changes is more meaningful for economic analysis.
  2. Data for Estonia for loans to non- nancial corporations before 2008 has been compiled by the Central Bank of Estonia and kindly shared with the authors.
  3. The exception is lending rates to non- nancial corporations in Greece, where the rates based on outstanding amounts have been used due to lack of data on new businesses.

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lenders. If borrowers default, the eects can be further amplied through a deleveraging process of the latter. As a consequence, changes in real estate prices can have large real eects and welfare implications (Hartmann 2015). Other studies that have included house prices in a VAR framework similar to ours include Bjrnland and Jacobsen (2010), Iacoviello (2005) and Meeks (2017). The residential real estate prices used in this analysis refer to new and existing dwellings for the whole country and are computed as the annual growth rate of the underlying index. The source of the data is the ECB SDW.

The ratio of bank capital and reserves over total assets is also included in the VAR. As with bank lending, this variable is dened in terms of an index of notional stocks and the source is MFI Balance Sheet Statistics of the ECB. Capital and reserves (the numerator) include total equity capital, non-distributed benets or funds and specic or general provisions against loans, securities and other types of assets. The capital and reserves to assets ratio is then computed as the ratio between this series and total assets.13 Including the capital and reserves ratio is important because institutions with larger buers are better prepared to support lending.

Finally, we include in the VAR the change in NPL ratios which is the most relevant variable in our analysis and is dened as the yearly dierence in NPL ratios. NPL ratios are dened as non-performing loans divided by total gross loans and were computed relying on several sources. The main source was the IMF Financial Soundness Indicators (FSI) database. This database provides data on the nancial health and soundness of member countries’ nancial systems since 2001. The IMF has oered guidelines to the member countries in order to improve the cross-country comparability of the data. In particular, it recommends that loans have to be classied as non-performing especially when: i) Payments of the principal and interest are past due by one quarter (90 days) or more; or ii) The interest payments equal to one quarter (90 days) interest or more have been capitalized (reinvested into the principal amount), renanced, or rolled over (that is, payment has been delayed by agreement). This guideline is based on the observation that 90 days is the horizon that is most widely used by countries to determine whether a loan is non-performing (IMF 2006).

The eective period covered by the FSI database varies across variables and countries. For most of the countries, data on NPLs dates back to the start of the global nancial crisis (2008 and 2009). In most cases, these series were extended backwards until the rst quarter of 2006 by using bank level information extracted from Bankscope. In particular, the weighted average of bank specic NPL ratios (using banks’ assets as weights) was used to construct the system wide gure for each quarter for Austria, Belgium, Estonia, Greece, Ireland, Lithuania and Portugal.

For Cyprus, France and Spain, data provided by the national central banks were used as well. In particular, the FSI NPL ratio data for Cyprus were extended backwards relying on data kindly provided by the Central Bank of Cyprus for the period between the fourth quarter of 2005 and the third quarter of 2011. For the rst three quarters of 2005, bank level information was used instead. In the case of France, the NPL ratio has been calculated by dividing the

13Data for Estonia for capital and reserves and total assets before 2008 has been compiled by the Central Bank of Estonia and shared by the ECB.

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series of Creances douteuses brutes” (available from the website of the Banque de France) and the stock of loans to the non-nancial private sector (sourced from the ECB SDW). Finally, for Spain, data provided by the national central bank were used to extend the FSI series before the fourth quarter of 2013.

The series of NPL ratios per country are displayed in Figure 1. It can be observed that the set of countries in the sample exhibits dierent dynamics in the evolution of the NPL ratios over time. In particular, there are countries where the NPL ratio increased during the crisis and decreased thereafter, but to dierent degrees and from dierent starting levels (Austria, Belgium and the Netherlands). In some cases, the NPL ratio increased signicantly during the crisis and declined also substantially afterwards (Ireland and Spain), also to levels close to those prevalent before the crisis (Estonia and Lithuania). There are also countries where the NPL ratio increased, but did not decline so far (Greece), or did only very recently (Cyprus, Italy and Portugal). Finally, in France, the NPL ratio remained overall unchanged from the beginning to the end of the sample.

Focusing on the contemporaneous correlation among the variables in the VAR (Table 3), we can observe that the change in NPL ratios (our main variable of interest) appears to be negatively and signicantly correlated with economic activity and bank lending. Also, an increase in the change in NPL ratios is signicantly associated with a widening of bank lending spreads. The Table also reports the contemporaneous correlation between the annual percentage change in NPL volumes and the variables in the VAR. This variable is described in Sub-section 5.3 and is used to perform a robustness analysis (see the discussion in that Sub-section for more details). The annual percentage change in NPL volumes is also negatively and signicantly correlated with economic activity and real estate prices. By contrast, the contemporaneous correlation with loans is positive and the one with the spreads is insignicant. Overall, these correlations appear to be lower than in the case of the changes in NPL ratios. These simple descriptive statistics may suggest that it takes time for the increase in NPL volumes to impair lending conditions.

The simple correlations between GDP growth, changes in the NPL ratios and volumes and the remaining macroeconomic and banking sector variables reported above do not allow to disentangle the source of variation of these variables. As the relation between these variables can run both ways, it is important to structurally identify the panel VAR.

We use Choleski decomposition in order to estimate the impact of changes in NPL ratios (De Bock and Demyanets 2012; Espinoza and Prasad 2010; Klein 2013). This recursive identi- cation approach implies that variables appearing earlier in the ordering are considered more exogenous than those appearing later. As such, variables that are ordered before a particular structural shock do not react to this shock on impact. Our identifying assumptions are as

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follows. First, according to the two-pillar monetary policy strategy of the ECB, the monetary policy rate is assumed to respond to a large number of indicators (Bernanke and Boivin 2003; Ciccarelli, Maddaloni and Peydro 2013; ECB 2011). Hence, we rank the monetary policy rate last in the VAR. Second, bank lending and lending spreads a ect the capital and reserves-to- asset ratio within the same quarter. This assumption reects the impact of the pro t and loss account (P&L) on capital in the same period when the result was generated. Hence, the capital and reserves-to-asset ratio is ranked before last in the system. Third, we assume that bank lending spreads move faster than macroeconomic variables (GDP and ination). Hence, we rank spreads after macroeconomic variables but before the capital and reserves-to-asset ratio. Fourth, we follow Bjrnland and Jacobsen (2010) and assume that real estate prices react to macroeconomic developments within the same quarter. Fifth, we assume that macroeconomic variables do not simultaneously react to the policy rate, while policy reacts to the macroeconomic environment simultaneously, as mentioned before. Also, we follow the standard literature on monetary policy and assume that ination is impacted simultaneously by a shock to economic activity (Bernanke and Gertler 1995; Christiano, Eichenbaum and Evans 1996). Sixth, we assume that it takes time to obtain a loan but once the loan is granted, it a ects macroeconomic variables instantaneously. Indeed, we place the macroeconomic variables (real GDP growth and ination) after the lending variables and the change in the NPL ratio. Seventh, the change in the NPL ratio is placed after the loans because a shock to loans a ects contemporaneously this ratio (through a change in its denominator). Lastly, we assume that changes in NPL ratios move slowly, meaning that GDP growth and ination a ect NPLs only with a lag. Indeed, accounting rules allow a loan to be classi ed as non-performing one quarter after the customer defaults. Hence, the change in the NPL ratio is placed before the macroeconomic variables. This ordering is similar to the ones used by Hancock, Laing and Wilcox (1995), Klein (2013) and De Bock and Demyanets (2012).

Overall, for our identi cation strategy, we use the following ordering: annual rate of growth in bank lending volumes to non- nancial corporations, annual rate of growth in bank lending volumes to households for house purchase, annual change in the NPL ratio, real GDP growth, ination rate, real estate prices, bank spreads on lending to non- nancial corporations, bank spreads on lending to households for house purchase, bank capital and reserves to assets ratio and monetary policy interest rate.

4.1 Exogenous changes in NPL ratios

As mentioned before, we are interested in estimating the impact of exogenous changes in NPL ratios on bank lending and the macroeconomy. Against this background, while the accounting rule mentioned above serves to justify the ordering of the NPL ratio among the variables in the VAR, it is useful to clarify what an exogenous change in NPL ratios can be. Overall, there are several sources of exogenous variations in NPL ratios which are unrelated to changes in the repayment capacity of borrowers.

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An exogenous change in NPL ratios can be related to the application of a new deni- tion of non-performing exposures. In particular, while the IMF makes an eort to ensure the cross-country comparability of NPL ratios, it recognises that reporting practices dier among countries (IMF 2006). For the countries in our sample, such reporting practices are aected not only by changes in the national denitions but also by the application of common reporting standards at the euro area level since 2014. In particular, the application of the Final draft technical standards on NPLs and Forbearance” by the EBA (2013) generally resulted in increases of recognised NPLs, which required banks to record additional provisions and in some cases also aected their capital positions.

Another possible exogenous change in NPL ratios is related to the transfer of non-performing loans from banks to Asset Management Companies (AMCs), which are dedicated entities that manage and workout distressed assets. The aim of these transfers is generally to cleanse banks’ balance sheets of bad loans, enabling banks to resume normal lending activities and to support a recovery in the economy. Various AMCs have been established in Europe after the nancial crisis. The rst was the National Asset Management Agency (NAMA) which was established in Ireland in 2009. The transfer of impaired assets to the National Asset Management Agency (NAMA) took place between 2009 and 2011. It took several months after it was announced by the authorities due to the necessary administrative arrangements and to the time needed to assess the value of the assets to be transferred. In November 2012, the Management Company for Assets Arising from the Banking Sector Reorganisation (SAREB) was created in Spain. In this case, the authorities had favoured alternative solutions before moving to the establishment of a system-wide AMC.14 Such AMCs resulted in signicant reductions in the level of arrears in the banking system of these two countries, although borrower behaviour or repayment ability did not change as a result of the creation of these entities.

Furthermore, supervisory actions can also have an impact on non-performing loan ratios. For example, at a national level, the supervisory authorities in Cyprus and Ireland set NPL restructuring targets to incentivise banks to accelerate the resolution of bad loans and to encourage sustainable solutions, rather than forbearance.15 More specically, the targets in Ireland referred to mortgage loans while in Cyprus they applied to the whole lending book. Also, the targets were not public in Ireland while banking system targets were published in Cyprus.

Actions by the supervisors at the euro area level also had an impact on the level of non- performing loan ratios in recent years. For example, the Comprehensive Assessment” carried out by the ECB in 2014, which consisted of an Asset Quality Review (AQR) and a Stress Test (ST), resulted in a signicant increase in the reported amount of NPLs in the euro area banking system and in the associated level of provisions for impairments (ECB 2014). Furthermore, the NPL guidance provided by the ECB to banks in 2017 and 2018 strengthened banks’ incentives to reduce their NPLs by means of write-os, restructurings or sales in secondary-markets

14For more details about AMCs in the euro area see Fell, Grodzicki, Martin and O’Brien (2016b).

15The targets involved dierent steps in the restructuring process, for example, the number of proposed sustainable restructurings, the number of concluded restructurings, etc.

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(SSM 2017, 2018). For example, it recommended the timely write-o of uncollectable loans and set up expectations regarding the assessment of bank’s levels of prudential provisions for nonperforming loans. It also recommended the setup of dedicated management units to deal with high risk clients and bad debt, which are separated from the banks’ sales units. The speed of implementation of such changes has varied from bank to bank and country to country and some countries had started to implement such recommendations beforehand. Importantly, sales of non-performing loans required the creation of secondary markets in the rst place, which usually required the adoption of legal and judicial reforms. Taken together, it is estimated that transactions in secondary markets (sales) reduced the euro area NPL ratio by 1.7 p.p. while write-o s and restructurings led to a decline of 3.2 p.p. over the period between the fourth quarter of 2016 and 2018 (ECB 2019).

Finally, another source of exogenous variation in non-performing loan ratios are the so called strategic defaults”. These are deliberate defaults which occur when solvent borrowers stop making repayments on a loan as a result of a rational nancial strategy. Strategic defaulters are unwilling, rather than unable to pay back their loan. Strategic defaults tend to occur when borrowers see other borrowers defaulting on their obligations without any immediate implication for them. They are more frequent for household mortgages and commercial real estate loans. Such behaviour is usually the reection of inecient legal systems, weak enforcement rules and bankruptcy laws, the presence of borrower protection schemes and permissive bank’s attitudes towards risk, among others. While there is wide recognition that the number of strategic defaulters” might be large in some countries, the size of the problem is dicult to quantify. However, an analysis conducted using data for corporate loans in Greece for the period 2008 to 2015 showed that one in six rms with a nonperforming loan were strategic defaulters (Asimakopoulos, Avramidis, Malliaropulos and Travlos 2016). As a result, the authors highlight the importance of distinguishing the latter from nancially distressed defaulters.

Having presented the methodology to estimate the model and the assumptions regarding the identi cation strategy, we illustrate the impact of shocks to the change in NPL ratios in twelve euro area countries relying on three sets of results. First, we present the impulse response functions for this shock. We are especially interested in estimating the size and shape of the responses of the endogenous variables. Second, we report the share of the forecast error variance for each variable and country to assess the degree by which a variable is driven by this shock. Third, we perform additional estimations to assess the robustness of the results. Finally, we implement an out of sample structural conditional forecast analysis to assess and quantify the macroeconomic and nancial bene ts stemming from a decline in NPL ratios.

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5.1 Impulse response analysis

Based on the estimated VAR model described in Equation (1), we generate the impulse responses of the endogenous variables to the structural shock for each individual country. Figures 2 and 3 report the impulse responses to a one standard deviation shock to the change in the NPL ratio for two groups of countries, namely Austria, Belgium, Cyprus, Estonia, Spain and France in the rst chart, and Greece, Ireland, Italy, Lithuania, the Netherlands and Portugal in the second. The countries are reported in the columns, while the variables are displayed in the rows. The impulse responses are plotted over a four-year horizon (16 quarters) after the shock, which is assumed to take place at time 0. The median of the accepted draws is shown together with the 16% and 84% Bayesian credibility bands.

Figure 2 and 3 show that the size of the instantaneous shock to the change in NPL ratios is stronger for those countries where the NPL ratio increased the most over the sample period, namely Cyprus, Ireland, Lithuania, Portugal, Greece, Spain and Italy (third row). The impact for these countries ranges between 0.3 and 4.3 percentage points.

The shock to the change in NPL ratios leads to a decline in bank lending which is stronger for non- nancial corporations than for households. Indeed, the annual growth of lending declines by up to 1.7 percentage points for non- nancial corporations, while it decreases by up to 1 percentage point for households.16 The relative size of these responses suggests that following a shock to the change in the NPL ratio, banks materially deleverage their balance sheets. At the same time, the impulse responses show that there is more heterogeneity in the timing of the peak response for mortgages (between four and twelve quarters after the shock) rather than for non- nancial corporations (between six and ten quarters after the shock). The shock also leads to a slight widening in both bank lending spreads (of up to about 0.3 percentage points) and to a decline in residential property prices (of up to 3.4 percentage points). For all these variables, the maximum impact is recorded for Cyprus, but strong e ects can be seen also in Ireland, Lithuania and Estonia. While the responses of the capital and reserves to asset ratio vary across countries, a material increase is recorded in Cyprus, Spain, Ireland, Italy, Lithuania and Portugal, due to the recorded increase in provisions for impairments during the crisis.

The shock to the change in the NPL ratio also leads to a decline in real GDP growth in most of the countries (by between 0.07 and 1 percentage point), between two and seven quarters after the shock. As a result of the deterioration in economic activity, monetary policy is relaxed. The response of the ination rate is rather heterogeneous across countries. These ndings are in line with those of Klein (2013) and Espinoza and Prasad (2010). These authors estimate the impact of much larger shocks, but their relative impact are comparable to ours.17 Theoretical

  1. This result is consistent with Fell, Grodzicki, Metzler and O’Brien (2018). Using bank level data, they nd that there is a negative signicant relationship between the ratio of NPLs over tier 1 capital and loan origination. This relationship appears to be stronger for lending to non-nancial corporations than for mortgages.
  2. Klein (2013) estimate that a 3 percentage point instantaneous shock to the change in the NPL ratio leads to a decline in real GDP growth of about 2 percentage point after one year. Espinoza and Prasad (2010) nd a relatively stronger impact. Indeed, they nd that a 2.3 percentage point increase in the change in the NPL ratio leads to a decline in GDP growth of about 2 percentage points.

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models also support our ndings. For example, Curdia and Woodford (2010) develop a dynamic stochastic general equilibrium model with credit frictions and nd that an increase in the loss rate of loans (i.e., the equivalent to non-performing loans in our empirical model) leads to a widening in credit spreads, a contraction in credit and also to a substantial fall in real activity. All in all, it can be observed that the shape of the responses tends to vary widely across the countries in the sample, both in terms of size and shape. This nding may be attributed to the fact that the various banking sectors experienced dierent degrees of variations in NPL ratios and at dierent times, hence generating heterogeneous responses.

5.2 Forecast error variance decomposition

In this section, we present a forecast error variance decomposition to uncover further details on the relationship among the variables included in the model. The analysis shows the share of the forecast error variance of individual variables explained by exogenous shocks to other variables. In general, we expect that shocks to the change in NPL ratios are relatively more relevant drivers of the variables in the countries where NPL ratios increased the most because these countries have been impacted by stronger and more frequent shocks to NPLs. The results of this analysis are presented in Table 4. In the Table, we report the share of the variance for each variable and each country in the panel VAR up to a 16-quarter horizon.

The shock to the change in the NPL ratio explains a non-negligible share of the variance of the variables included in the VAR. In particular, these shocks are sizable drivers of real GDP growth, explaining between 10 and 33% of the variance in Lithuania, Estonia, Ireland and Cyprus. For the remaining countries, the NPL shock still explains between 2% and 5% of the variance of real GDP growth. Regarding bank lending, the share explained is larger for corporate lending than for mortgages. For NFC lending, the NPL shock explains between 5% and 17% of the variance for Cyprus, Ireland, Lithuania, Italy and Portugal. For mortgage lending, the share is large only for Cyprus, but smaller than 3% for the remaining countries.18 For spreads (both corporate and mortgage), the share is above 10% for Cyprus, Ireland and Lithuania, and for Italy is larger than 10% only for spreads on corporate lending. For residential real estate prices, the share is large for Cyprus, Ireland and Estonia (between 12% and 56%). Importantly, we observe that shocks to the change in the NPL ratio explain more than 50% of the variance in the same variable at the end of the horizon in countries like Cyprus, Greece, Ireland, Italy, Lithuania, Portugal and Spain. This nding suggests that exogenous factors have been key drivers of changes in NPL ratios.

These ndings are broadly in line with the literature. Over long horizons (between 5 and

10 years), Espinoza and Prasad (2010), De Bock and Demyanets (2012) and Klein (2013) nd that shocks to the change in NPL ratios explain about 6%, 8% and 20% of the variance of GDP growth in their sample of countries, respectively. For the credit-to-GDP ratio (equivalent

18For comparison, Hristov, Hulsewig and Wollmershauser (2012) nd that demand shocks explain 13% of the variance of the GDP deator and 16% of lending volumes over a four year horizon in a sample of euro area countries.

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to bank lending in our model), the estimated share stands at 13% and 8% in De Bock and Demyanets (2012) and Klein (2013), respectively. For the change in the NPL ratio, Klein (2013) and De Bock and Demyanets (2012) nd that 70% and 90% of the variance is explained by the same variable, respectively.

The shock to the change in the NPL ratio explains for most countries in our model a smaller share of the variance of our variables than the literature on credit supply shocks and shocks to bank capital would suggest.19This is, however, not surprising, because the increase in NPLs has been only one of several disturbances that have in recent years a ected our sample of countries.20. However, for some countries, particularly those hardly hit by the crisis, and some variables, the share is equally important and sometimes larger than that found in the literature.

5.3 Robustness analysis

This section implements two robustness checks of the impulse-response analysis performed before to assess the reliability of our results. First, we generate impulse responses to a shock in the NPL ratio relying on a di erent ordering of the variables in the Choleski factorisation. In this new ordering, loans and NPL ratios are included in the VAR after GDP and ination and, thus, are a ected by macroeconomic variables contemporaneously. Second, we replace the changes in NPL ratios with the changes in NPL volumes and order this variable rst in the VAR.

In the rst robustness check, we relax two of the identifying assumptions made in Section

4. In particular, in that analysis we assumed that: i) It takes time to obtain a loan but once the loan is granted, it instantaneously a ects macroeconomic variables; and ii) Changes in NPL ratios move slowly, implying that GDP growth and ination a ect NPLs only with a lag. These assumptions led us to place the macroeconomic variables (real GDP growth and ination) after the lending variables and the change in the NPL ratio. By contrast, in our rst robustness check, we order loans and the NPL ratio after the two macroeconomic variables, implying a fast reply of these variables to changing macroeconomic conditions. The ordering used is as follows: real GDP growth, ination rate, annual rate of growth in bank lending volumes to non-

nancial corporations, annual rate of growth in bank lending volumes to households for house purchase, annual change in the NPL ratio, annual rate of change in real estate prices, bank lending spreads on lending to non- nancial corporations, bank lending spreads to households for house purchase, bank capital and reserves to assets ratio and monetary policy interest rate.

  1. For example, Mesonnier and Stevanovic (2017) nd that after one year, a bank capital shock accounts for some 4% of the variance of GDP growth and 11% of the variance of loan growth in the U.S. For the same country, Meeks (2012) nds that credit market shocks account for 20% of the total mean square prediction error in industrial output at a three-year horizon. Bassett, Chosak, Driscoll and Zakrajsek (2014) nd the same share at a four year horizon for credit supply shocks. Hristov et al. (2012) nd that credit supply shocks explain about 15% of the uctuations recorded in real output, 12% of loan rates and 11% of loan volumes in a sample of euro area countries over a four year horizon. However, for some countries, particularly those hardly hit by the crisis, and some variables, the share is equally important and sometimes larger than that found in the literature on credit or bank capital shocks.
  2. Other factors include, inter alia, liquidity and funding shocks, changes in bank competition, shocks to the perceived credit riskiness of borrowers and shocks to lending standards, among others.

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In the second robustness check, we replace the series of the changes in NPL ratios with the series of the changes in NPL volumes. The 90 days past due (DPD) rule discussed in Subsection 3 applies to the volume of NPLs. Indeed, as banks are allowed to classify a loan as non-performing a quarter (90 days) after a loan defaulted, we rank the NPL volume rst in the VAR, implying that it is not a ected contemporaneously by the remaining variables in the VAR. The series of NPL volumes are computed by multiplying the NPL ratios presented in Sub-section 3 by the stock of loans at the country level from the MFI Balance Sheet Statistics of the ECB, which has data for 10 of the 12 countries in our sample.21 The NPL volumes enter the VAR as annual percentage changes. In this second robustness check, we use the following ordering: the annual rate of growth in NPL volumes, real GDP growth, the ination rate, the annual rate of growth in bank lending volumes to non- nancial corporations, annual rate of growth in bank lending volumes to households for house purchase, annual rate of growth of real estate prices, bank lending spreads to non- nancial corporations, bank lending spreads to households for house purchase, bank capital and reserves to assets ratio and monetary policy rate.22

Figure 4 and Figure 5 present the results for these two extensions. Most of the results are broadly in line with those presented before, although they are somewhat weaker when assuming that loans and NPLs reacts to macroeconomic variables instantaneously. In particular, both mortgage and corporate spreads are less signi cant across countries with the new ordering. Fur- thermore, for the impact on lending, it can be observed that there are changes in the signi cance of the responses across countries (some countries with previous signi cant responses are not in- signi cant anymore and vice versa), while the total number of signi cant responses is broadly unchanged. Finally, the impact on real estate prices and real GDP growth is also unchanged across countries. Regarding the responses of the endogenous variables to a shock in NPL vol- umes, the magnitudes are not comparable with those presented before (due to the di erent scale of the change in the NPL ratio and in the NPL volumes) but results are qualitatively similar. In particular, the number of signi cant responses of corporate and mortgage spreads increases while that one of real estate prices decreases. For real GDP growth and lending, the number of signi cant responses remains broadly unchanged, but with changes for speci c countries.

All in all, this robustness analysis con rms to a large extent the results presented in Subsection 5.1. In particular, we continue to nd that banks materially deleverage their balance sheets following a shock to NPLs and the impact is stronger for non- nancial corporations than for households. The shock also leads to a decline in real GDP growth and residential property prices.

21The MFI Balance Sheet data for the loan volumes are not available for Cyprus and Estonia.

22Once the variable of interest is ordered rst in the VAR (i.e., it is assumed to be the most exogenous one), then the order of the remaining variables in the VAR is irrelevant for the calculation of response functions to a shock to that variable.

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5.4 Structural out of sample scenario analysis

This section reports the results of a structural, out-of-sample scenario analysis which assesses the impact of two dierent paths of NPL ratios over the period 2017Q4 to 2020Q3. This exercises provides a quantitative illustration of the possible benets associated with a further decline in NPL ratios in euro area countries. In this analysis, we focus on the six most relevant variables in the VAR and on the six countries that exhibited the most sizable increase in NPL ratios during the crisis, namely Cyprus, Ireland, Spain, Italy, Greece and Portugal. Under a baseline scenario, the out of sample change in the NPL ratio for each country is assumed to equal the average change recorded over the last four quarters of the historical data.23 Under an adverse scenario, the out of sample change in the NPL ratio is assumed to equal zero. In both cases, the remaining variables in the VAR are projected conditional on the assumed evolution of the change in the NPL ratio, following the methodology proposed by Antolin-Diaz, Petrella and Rubio-Ramrez (2018). In particular, the forecasts are computed assuming that only the structural shock to the change in the NPL ratio adjusts to ensure the new path for the conditioning variable.24 This structural scenario is more meaningful than the one proposed by Waggoner and Zha (1999), who assume that all the structural shocks adjust to ensure the evolution of the conditioning variable. Applied to our model, it would mean assessing the most likely set of circumstances under which the change in the NPL ratio evolves as prescribed. Instead, we are interested in computing the paths of the endogenous variables in the VAR which are consistent with a sequence of shocks to the change in the NPL ratio per se.

The observed and out of sample evolution of the change in NPL ratios for the two paths and the six countries are depicted in Figure 6. By construction, the gap between the baseline and the adverse changes in the NPL ratio depends on how strongly the variable evolved in the last four quarters of our sample. This gap is the widest for Cyprus, followed by Ireland, Portugal, Italy and then Spain and Greece. These assumptions result in dierent levels of the NPL ratio at the end of the forecast horizon. Under the baseline, the level of the NPL ratio is expected to decline (with respect to the starting point) by 10.7 percentage points in Cyprus,

9.7 percentage points in Ireland, 5 percentage points in Portugal, 3.2 percentage points in Italy and 1.6 percentage points in both Spain and Greece.

The out of sample deviation between the baseline and adverse conditional forecasts of the variables is reported in Figure 7. The countries are reported in the columns, while the variables are depicted in the rows. A positive value implies that the baseline forecast exhibits a higher value than the adverse one. The results show, as expected, that a further reduction in NPL ratios would have a positive impact on both the macroeconomic and the banking variables.

23More specically, it is assumed that the out of sample change in the NPL ratio is equal to -3.6% for Cyprus, -0.5% for Spain, -0.5% for Greece, -3.2% for Ireland, -1.1% for Italy and -1.7% for Portugal.

24Restricting the value of a variable in the VAR over a certain period of time gives rise to a system of conditions that the structural disturbances should verify. Waggoner and Zha (1999) show that the distribution of these restricted structural disturbances is normal. Jarocinski (2010) proposed a numerically more ecient method that avoids drawing the shocks from that distribution. See Dieppe et al. (2016) for more details.

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At the end of the forecast horizon, the annual rate of growth of mortgage lending under the baseline scenario is between 1.4 (Italy) and 2.9 (Ireland) percentage points higher than under the adverse scenario, while the annual rate of growth of corporate lending increases faster by between 0.9 (Spain) and 4.4 (Ireland) percentage points. Bank lending spreads are narrower, by between 0.2 and 0.6 percentage points for mortgages and by between 0.2 and 0.8 for loans to non- nancial corporations under the baseline scenario. Stronger lending and lower spreads lead to higher residential real estate prices, with annual rates of growth being between 1.6 (Italy) and 6.7 (Cyprus) percentage points higher under the baseline than the adverse. Finally, the rate of growth of real GDP is higher by between 0.5 (Italy) and 1.6 (Ireland) percentage points. Overall, this structural out-of-sample forecast illustrates that a further reduction in NPL ratios can generate signi cant medium-term economic bene ts in euro area countries.

Non-performing loan (NPL) ratios increased substantially in many euro area countries since the onset of the global nancial crisis. Despite a gradual decline from the peak in 2014, NPL ratios still remained a key problem in several euro area countries at the end of 2017. High NPL ratios can impair the stability of the banking system and its ability to lend to the real economy. Therefore, in particular, for highly bank-dependent economies such as the euro area, the necessity to deal with elevated NPL ratios is unquestionable.

Against this background, we quantify the impact of an exogenous increase in the change in NPL ratios on economic and banking sector developments in twelve euro area countries. Given the relatively short time series available for NPL ratios and the large number of parameters to be estimated, we estimate a panel Bayesian VAR model with hierarchical priors that allows for country-speci c coecients. The variables included in the panel VAR are economic activity (which is a proxy for the repayment capacity of borrowers), ination, the monetary policy rate, real estate prices, bank lending volumes both to non- nancial corporations and to households for house purchase, bank lending spreads to these two sectors, the ratio of capital and reserves over total assets and the change in NPL ratios. We estimate the impact of the shock using a Choleski factorisation approach. In particular, we use as an identi cation strategy the fact that NPLs generally do not respond within a quarter to endogenous shocks, as banks are allowed to classify a loan as non-performing only a quarter after default.

We illustrate the impact of this shock relying on three sets of results. Looking rst at the impulse response functions, we nd that an exogenous increase in the change in NPL ratios depresses bank lending, widens lending spreads and leads to a fall in real GDP growth and residential real estate prices and an easing of monetary policy. The responses of ination and capital and reserves over total assets vary across countries. However, the latter signi cantly increases in Cyprus, Spain, Ireland, Italy, Lithuania and Portugal. The reason is that this variable includes provisions for impairments which materially increased in these countries dur-

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ing the crisis. These results are robust to a change in the ordering of the variables in the Choleski factorisation (whereby bank loans and the NPL ratio are aected contemporaneously by macroeconomic variables) and also when including in the VAR the annual rate of growth in NPL volumes (instead of the NPL ratio) and order it rst in the VAR. The forecast error variance decomposition also shows that shocks to the change in NPL ratios explain a large share of the variance of the variables in the VAR, particularly for those countries that experienced a large increase in NPL ratios during the crisis. Finally, a three-year structural out of sample forecast analysis provides quantitative evidence that a further reduction of NPL ratios can produce signicant economic benets in euro area countries.

ECB Working Paper Series No 2411 / May 2020

23

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ECB Working Paper Series No 2411 / May 2020

26

Table 1: Data sources

Variable

Source

Real GDP growth

ECB SDW

Ination

ECB SDW

RRE prices

ECB SDW

Euribor

ECB SDW

Corporate loans

ECB BSI

Mortgage loans

ECB BSI

Corporate spread

ECB MIR and SDW

Mortgage spread

ECB MIR and SDW

Capital and reserves ratio

ECB BSI

Change in NPL ratio

IMF FSI, Banque de France, Banco de Espa~na,

Central Bank of Cyprus, Irish Central Statistics Oce, Bankscope

Percentage change in NPL volumes

NPL volumes are obtained by multiplying

the country-level NPL ratio

by the stock of loans from the ECB BSI

Table 2: Summary statistics

Variable

Obs

Mean

Std. Dev.

Min

Max

Real GDP growth

564

1.0

4.0

-17.5

12.0

Ination

564

1.6

1.6

-3.1

10.6

RRE prices

564

1.8

10.5

-40.3

57.5

Euribor

564

1.4

1.7

-0.3

5.0

Corporate loans

564

4.6

12.0

-20.2

67.5

Mortgage loans

564

5.7

12.3

-33.0

87.4

Corporate spread

564

2.5

1.4

0.2

6.6

Mortgage spread

564

2.3

1.1

-0.3

5.0

Capital and reserves ratio

564

12.3

10.9

2.7

68.8

Change in NPL ratio

564

0.8

3.3

-8.2

27.2

Percentage change in NPL volumes

470

23.6

60.9

-39.1

369.3

ECB Working Paper Series No 2411 / May 2020

27

2020 May / 2411 No Series Paper Working ECB

Table 3: Correlation matrix among the variables included in the panel VAR

Real GDP

Ination

RRE

Euribor

Corporate

Mortgage

Corporate

Mortgage

Capital and

Change in

Change in

growth

prices

loans

loans

spread

spread

reserves ratio

NPL ratio

NPL levels

Real GDP growth

1

Ination

0.15***

1

RRE prices

0.79***

0.26***

1

Euribor

0.15***

0.52***

0.25***

1

Corporate loans

0.35***

0.57***

0.59***

0.69***

1

Mortgage loans

0.40***

0.49***

0.62***

0.52***

0.78***

1

Corporate spread

-0.29***

-0.30***

-0.32***

-0.47***

-0.36***

-0.27***

1

Mortgage spread

-0.33***

-0.42***

-0.39***

-0.74***

-0.55***

-0.44***

0.69***

1

Capital and reserves ratio

0.01

-0.27***

-0.11**

-0.37***

-0.26***

-0.25***

0.61***

0.42***

1

Change in NPL ratio

-0.55***

-0.19***

-0.47***

-0.09*

-0.18***

-0.16***

0.39***

0.33***

0.15***

1

Percentage change in NPL volumes

-0.49***

0.17***

-0.31***

0.25***

0.17***

0.10**

-0.04

-0.08

-0.17***

0.54***

1

Note: The data sample spans from 2006Q1 to 2017Q3. (***), (**) and (*) denote statistical signi cance at the 1%, 5% and 10% levels, respectively. Data for the percentage change in NPL volumes are not available for Cyprus and Estonia.

Sources: Data constructed based on the IMF, ECB, Banque de France, Banco de Espa~na, Central Bank of Cyprus, Central Statistics Oce of Ireland and Bankscope.

Figure 1: Non-performing loan ratios

AT

4

2

0

2008 2010 2012 2014 2016

EE

6

4

2

0

2008 2010 2012 2014 2016

BE

60

4

40

2

20

0

0

2008 2010 2012 2014 2016

FR

4

40

2

20

0

0

2008 2010 2012 2014 2016

CY

2008 2010 2012 2014 2016

GR

2008 2010 2012 2014 2016

30

IE

20

20

10

10

0

0

2008 2010 2012 2014 2016

NL

20

3

2

10

1

0

0

2008 2010 2012 2014 2016

IT

30

20

10

0

2008 2010 2012 2014 2016

PT

10

5

0

2008 2010 2012 2014 2016

LT

2008 2010 2012 2014 2016

ES

2008 2010 2012 2014 2016

Note: The data sample spans from 2006Q1 to 2017Q3. The displayed NPL ratios are based on data sourced from the IMF FSI, Banque de France, Banco de Espa~na, Central Bank of Cyprus and Bankscope.

ECB Working Paper Series No 2411 / May 2020

29

Figure 2: Response to a shock to the change in the NPL ratio

loans

0.1

AT

0.1

Corporate

-0

-0.1

-0.2

-0.3

0

3

6

9

12 15

0.4

CY

0.1

-0.9

-0.4

-2.3

-0.9

0

3

6

9

12 15

EE

0.1

-0.1

-0.3

0

3

6

9

12 15

ES

0.1

-0.1

-0.2

0

3

6

9

12 15

loans

0.2

0.1

0

0.1

0

0.1

Mortgage

0.1

-0

-0.8

-0.2

-0.2

-0.1

-0.1

-0.2

-1.5

-0.5

-0.3

-0.3

ratio

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.3

0.3

4.8

0.4

0.4

0.1

in NPL

0.1

0.1

2.4

0.2

0.2

0.1

Change

-0

-0

0

-0

-0

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

growth

0.2

0.2

0.1

0

0.1

0

-0

-0

-0.4

-0.7

-0.1

-0.1

GDP

-0.2

-0.2

-1

-1.4

-0.2

-0.3

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

0.1

0.3

0

0

0

Inflation

-0

0.1

0

-0.1

-0.1

-0

-0.1

-0

-0.2

-0.2

-0.1

-0.1

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

prices

0.7

0

-0.3

-0.1

-0.1

0

0.2

-0.3

-2.2

-1.7

-0.5

-0.3

RRE

-0.3

-0.6

-4.2

-3.3

-0.9

-0.6

spread

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.1

0.1

0.4

0.1

0.1

0.1

Mortgage

0

0

0.2

0

0

-0

-0

-0

0

-0

-0

-0.1

spread

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

0

0.4

0.1

0.1

0

Corporate

0

0

0.2

0

0

0

-0

3

6

9

-0

0

3

6

9

12 15

0

3

6

9

-0

0

3

6

9

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

12 15

0

12 15

12 15

0

ratio

0.2

0.1

2.1

0.3

0.3

0.2

0.1

0.2

Capital

0

1.1

0

0.1

0

-0.1

0.1

-0.2

0

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

0.1

0.1

0

0.1

0.1

Euribor

-0.1

-0

-0.2

-0.1

-0

-0

-0.1

-0.1

-0.5

-0.2

-0.1

-0.1

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

Note: The Figure reports the responses of the endogenous variables to one standard deviation shock to the change in NPL ratios. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points. Responses are reported for 4 years (16 quarters) after the shock (assumed to take place at time 0). The median of the accepted draws is plotted together with the 16% and 84% Bayesian credibility bands.

ECB Working Paper Series No 2411 / May 2020

30

Figure 3: Response to a shock to the change in the NPL ratio (cont.)

loans

0.4

GR

0

IE

0

IT

0.1

LT

0.1

NL

0.2

PT

Corporate

-0.1

-0.7

-0.3

-0.9

-0.1

-0.2

-0.5

-1.4

-0.6

-1.8

-0.4

-0.6

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

loans

0.2

0.1

0.1

0

0

0.1

Mortgage

-0.1

-0.3

-0

-0.4

-0.2

-0.1

-0.3

-0.6

-0.2

-0.8

-0.3

-0.3

ratio

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

1.4

2.5

0.3

2.1

0.3

1.5

in NPL

0.7

1.3

0.2

1.1

0.1

0.7

Change

-0

0

0

-0

-0

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

growth

0.6

0

0.1

0.1

0.1

0.1

0.2

-0.6

-0.2

-0.7

-0.1

-0.1

GDP

-0.3

-1.2

-0.4

-1.5

-0.3

-0.2

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.1

0.1

0.1

0.3

0.1

0.1

Inflation

-0

-0

0

0.1

0

-0

-0.2

-0.1

-0

-0.2

-0

-0.1

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

prices

0.5

0.3

0.3

0.1

0.5

-0.4

-0.2

-1.4

0

-1.4

-0.2

-0.2

RRE

-0.9

-2.4

-0.3

-3

-0.6

-1

spread

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.1

0.2

0.1

0.2

0.1

0.1

0.1

Mortgage

0.1

0.1

0.1

0

0.1

0

0

0

-0

-0.1

-0

spread

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0.1

0.2

0.1

0.2

0.1

0.1

0.1

Corporate

0.1

0.1

0.1

0

0

0

0

0

-0.1

-0

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

0

3

6

9

0

3

6

9

12 15

3

6

9

12 15

12 15

12 15

0

ratio

0.9

0.9

0.2

1

0.1

0.5

Capital

0.3

0.4

0.1

0.5

0

0.2

-0.3

-0.1

-0

-0.1

-0.1

-0

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

-0

0

0.1

0

Euribor

-0

-0.1

-0.2

-0.1

-0.1

0

-0.1

-0.2

-0.3

-0.2

-0.3

-0.1

-0.2

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

0

3

6

9

12 15

Note: The Figure reports the responses of the endogenous variables to one standard deviation shock to the change in NPL ratios. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points. Responses are reported for 4 years (16 quarters) after the shock (assumed to take place at time 0). The median of the accepted draws is plotted together with the 16% and 84% Bayesian credibility bands.

ECB Working Paper Series No 2411 / May 2020

31

Table 4: Forecast error variance decomposition (shock to the change in the NPL ratio)

NPL shock

Variable

Year

AT

BE

CY

EE

ES

FR

GR

IE

IT

LT

NL

PT

Corporate

1st

0%

0%

0%

1%

0%

0%

0%

1%

2%

1%

0%

1%

loans

2nd

1%

0%

6%

2%

0%

1%

1%

6%

5%

5%

0%

2%

3rd

1%

0%

14%

2%

1%

1%

1%

9%

6%

7%

1%

4%

4th

1%

0%

17%

2%

1%

1%

1%

10%

6%

8%

1%

5%

Mortgage

1st

0%

0%

2%

2%

1%

0%

0%

0%

0%

1%

0%

0%

loans

2nd

1%

0%

6%

2%

2%

0%

0%

1%

0%

2%

0%

1%

3rd

1%

0%

11%

2%

2%

1%

0%

1%

0%

2%

0%

1%

4th

1%

0%

14%

2%

3%

1%

1%

1%

0%

3%

0%

2%

Change

1st

81%

71%

95%

55%

87%

56%

88%

93%

80%

88%

80%

94%

in NPL ratio

2nd

61%

53%

94%

39%

71%

38%

80%

92%

66%

83%

61%

92%

3rd

51%

40%

94%

34%

63%

28%

76%

91%

58%

81%

48%

90%

4th

47%

32%

93%

33%

59%

24%

74%

91%

55%

80%

41%

89%

Ination

1st

3%

9%

12%

3%

2%

3%

2%

2%

5%

4%

1%

1%

2nd

3%

5%

14%

6%

2%

4%

3%

2%

5%

3%

1%

3%

3rd

3%

4%

15%

6%

2%

4%

3%

3%

5%

4%

1%

3%

4th

3%

4%

17%

6%

2%

4%

3%

4%

5%

4%

2%

3%

GDP growth

1st

1%

2%

14%

16%

3%

8%

2%

23%

6%

9%

2%

2%

2nd

2%

3%

28%

15%

2%

7%

2%

22%

5%

10%

4%

2%

3rd

2%

2%

33%

15%

2%

6%

2%

22%

5%

10%

4%

3%

4th

2%

2%

33%

14%

3%

5%

2%

22%

5%

10%

3%

3%

Mortgage

1st

2%

1%

8%

4%

5%

1%

4%

16%

3%

5%

1%

3%

spread

2nd

2%

1%

18%

3%

4%

1%

4%

21%

5%

10%

2%

6%

3rd

2%

1%

23%

2%

4%

1%

4%

21%

6%

11%

2%

7%

4th

2%

1%

24%

2%

4%

1%

4%

21%

7%

12%

2%

7%

Corporate

1st

1%

2%

10%

4%

5%

4%

4%

22%

11%

3%

3%

2%

spread

2nd

1%

2%

19%

5%

5%

4%

4%

32%

13%

7%

4%

4%

3rd

1%

3%

25%

5%

5%

4%

5%

36%

15%

9%

5%

6%

4th

1%

3%

29%

5%

5%

4%

6%

38%

16%

10%

5%

7%

RRE prices

1st

1%

4%

30%

11%

6%

5%

2%

25%

2%

4%

4%

3%

2nd

1%

5%

49%

11%

7%

5%

5%

36%

3%

7%

6%

6%

3rd

1%

5%

55%

11%

7%

5%

7%

40%

4%

8%

7%

8%

4th

2%

5%

56%

12%

7%

5%

8%

41%

4%

9%

6%

9%

Capital and

1st

7%

1%

15%

3%

6%

4%

1%

7%

2%

3%

3%

3%

reserves ratio

2nd

9%

1%

28%

2%

11%

6%

2%

23%

4%

7%

2%

8%

3rd

9%

1%

37%

2%

14%

8%

2%

32%

6%

10%

2%

12%

4th

9%

1%

42%

2%

15%

9%

2%

38%

8%

12%

3%

15%

Euribor

1st

2%

1%

4%

5%

1%

1%

3%

19%

6%

3%

1%

2%

2nd

2%

1%

17%

5%

1%

1%

3%

24%

9%

8%

1%

4%

3rd

2%

1%

26%

4%

1%

1%

4%

25%

10%

10%

1%

6%

4th

2%

1%

32%

4%

1%

1%

5%

26%

11%

11%

1%

8%

Note: The table reports the share of the variance of the variables in the VAR which is explained by a shock to the change in the NPL ratio over a horizon of 16 quarters. The median of the accepted draws of the variance decomposition from the posterior distribution is reported.

ECB Working Paper Series No 2411 / May 2020

32

Figure 4: Response to a shock to the change in the NPL ratio with new ordering

GDP growth Corporate loans Mortgage loans

RRE prices Mortgage spreadCorporate spread

AT

0.05

0

-0.05

0 2 4 6 8 101214

0.02

BE

0

-0.02

-0.04

-0.06

0 2 4 6 8 101214

0

CY

-0.5

0 2 4 6 8 101214

0

EE

-0.1

-0.2

-0.3

0 2 4 6 8 101214

0

ES

-0.1

-0.2

0 2 4 6 8 101214

0

FR -0.05

-0.1

0 2 4 6 8 101214

0

GR

-0.1

-0.2

0 2 4 6 8 101214

0

IE

-0.2

-0.4

0 2 4 6 8 101214

0.08

IT

0.06

0.04

0.02

0

-0.02

0 2 4 6 8 101214

0.2

LT

0

-0.2

0 2 4 6 8 101214

0

NL

-0.05

-0.1

0 2 4 6 8 101214

0

PT

-0.1

-0.2

0 2 4 6 8 101214

0.05

0

-0.05

-0.1

-0.15

0 2 4 6 8 101214

0.05

0

-0.05

-0.1

-0.15

0 2 4 6 8 101214

0.5

0

-0.5

-1

-1.5

0 2 4 6 8 101214

0.1

0

-0.1

-0.2

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0.1

0

-0.1

0 2 4 6 8 101214

0

-0.2

-0.4

-0.6

-0.8

0 2 4 6 8 101214

0

-0.5

-1

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0.4

0.2

0

-0.2

-0.4

-0.6

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0.2

0

-0.2

-0.4

-0.6

0 2 4 6 8 101214

0.1

0

-0.1

0 2 4 6 8 101214

0.1

0.05

0

-0.05

0 2 4 6 8 101214

0

-0.5

-1

0 2 4 6 8 101214

0.1

0

-0.1

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0

-0.2

-0.4

0 2 4 6 8 101214

0

-0.2

-0.4

0 2 4 6 8 101214

0.1

0

-0.1

0 2 4 6 8 101214

0.2

0

-0.2

-0.4

0 2 4 6 8 101214

0

-0.1

-0.2

0 2 4 6 8 101214

0

-0.2

-0.4

0 2 4 6 8 101214

0.4

0.2

0

-0.2

-0.4

0 2 4 6 8 101214

0

-0.1

-0.2

-0.3

0 2 4 6 8 101214

0

-1

-2

0 2 4 6 8 101214

0

-1

-2

0 2 4 6 8 101214

0

-0.5

0 2 4 6 8 101214

0.1

0

-0.1

-0.2

0 2 4 6 8 101214

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

0 2 4 6 8 101214

0

-0.5

-1

-1.5

0 2 4 6 8 101214

0.4

0.2

0

0 2 4 6 8 101214

2

1

0

0 2 4 6 8 101214

0.1

0

-0.1

-0.2

-0.3

0 2 4 6 8 101214

0.4

0.2

0

-0.2

-0.4

-0.6

-0.8

0 2 4 6 8 101214

0.04

0.02

0

0 2 4 6 8 101214

0.04

0.02

0

0 2 4 6 8 101214

0.1

0.05

0

0 2 4 6 8 101214

0.06

0.04

0.02

0

-0.02

0 2 4 6 8 101214

0.06

0.04

0.02

0

0 2 4 6 8 101214

0

-0.05

-0.1

0 2 4 6 8 101214

0.1

0.05

0

0 2 4 6 8 101214

0.15

0.1

0.05

0

0 2 4 6 8 101214

0.04

0.02

0

-0.02

0 2 4 6 8 101214

0.05

0

-0.05

0 2 4 6 8 101214

0.02

0

-0.02

-0.04

0 2 4 6 8 101214

0.06

0.04

0.02

0

0 2 4 6 8 101214

0.01

0

-0.01

-0.02

0 2 4 6 8 101214

0.01

0

-0.01

-0.02

0 2 4 6 8 101214

0.1

0.05

0

-0.05

0 2 4 6 8 101214

0.06

0.04

0.02

0

-0.02

0 2 4 6 8 101214

0.06

0.04

0.02

0

0 2 4 6 8 101214

0.02

0.01

0

-0.01

0 2 4 6 8 101214

0.1

0.05

0

0 2 4 6 8 101214

0.1

0.05

0

0 2 4 6 8 101214

0.04

0.02

0

0 2 4 6 8 101214

0

-0.05

-0.1

0 2 4 6 8 101214

0.03

0.02

0.01

0

0 2 4 6 8 101214

0.04

0.02

0

-0.02

0 2 4 6 8 101214

Note: The Figure reports the responses of selected endogenous variables to one standard deviation shock to the change in NPL ratios. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points. Responses are reported for 4 years (16 quarters) after the shock (assumed to take place at time 0). The median of the accepted draws is plotted together with the 16% and 84% Bayesian credibility bands.

ECB Working Paper Series No 2411 / May 2020

33

Figure 5: Response to a shock to the annual rate of change in NPL volumes

GDP growth

0.2

AT

0

-0.2

-0.4

0 2 4 6 8 101214

0.2

BE

0

-0.2

-0.4

0 2 4 6 8 101214

0.2

ES

0

-0.2

-0.4

0 2 4 6 8 101214

0.1

FR

0

-0.1

-0.2

-0.3

0 2 4 6 8 101214

0.6

GR

0.4

0.2

0

-0.2

0 2 4 6 8 101214

0

IE

-0.5

-1

0 2 4 6 8 101214

0

IT

-0.2

-0.4

0 2 4 6 8 101214

0

LT

-1

-2

0 2 4 6 8 101214

0 2 4 6 8 101214

0.2

PT

0

-0.2

-0.4

0 2 4 6 8 101214

Corporate loans Mortgage loans

RRE prices Mortgage spreadCorporate spread

0.8

0.15

0.06

0

0.6

0.5

0.1

0.04

0.4

-0.5

0

0.05

0.02

0.2

-1

0

-0.5

0

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

1

0

0.4

0.2

0.1

0.2

0

0

0.1

0.05

-1

-0.2

-1

-0.4

0

-2

-0.6

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.2

0

0.15

0.05

-0.5

0

-0.5

0.1

0

0.05

-0.2

-1

-0.05

-0.4

-1

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.2

0.2

0.1

0.04

-0.2

0

0

-0.2

-0.2

-0.4

0.05

0.02

-0.4

-0.4

-0.6

-0.6

-0.6

0

0

-0.8

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.5

0.8

0.2

-0.5

0.6

0.1

-1

0

0.4

0.1

-1.5

0.2

-0.5

0

0

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.5

0

0.2

0.15

0

0.1

-1

-0.5

-1

0.1

0.05

-1

-2

0

-1.5

-2

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0.6

0.8

0.08

0.08

0.06

0.4

0.6

0

0.06

0.04

0.2

0.4

0.04

-0.2

0.02

0

0.2

0.02

0

0

-0.2

-0.4

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0

0

0.4

-1

0.2

-0.5

-1

-2

0.2

0.1

-1

-2

-3

-1.5

0

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

1

0.2

0.15

0.08

-0.5

0

0.1

0.06

0.5

0.04

-1

-0.2

0.05

0.02

0

-0.4

0

0

-1.5

-0.02

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0.4

0.6

0.1

-0.5

0.2

0.4

0.1

0

0.2

0.05

-1

-0.2

0

-0.4

0 2 4 6 8 101214

0 2 4 6 8 101214

0

0

0 2 4 6 8 101214

0 2 4 6 8 101214

0 2 4 6 8 101214

Note: The Figure reports the responses of selected endogenous variables to one standard deviation shock to the annual rate of change in NPL volumes. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points. Responses are reported for 4 years (16 quarters) after the shock (assumed to take place at time 0). The median of the accepted draws is plotted together with the 16% and 84% Bayesian credibility bands.

ECB Working Paper Series No 2411 / May 2020

34

Figure 6: Observed and assumed out-of-sample baseline and adverse change in NPL ratios for the structural scenario analysis

CY

25

20

15

10

5

0

-5

2005

2007

2009

2011

2013

2015

2017

2019

ES

10

5

0

-5

2005

2007

2009

2011

2013

2015

2017

2019

2.5

ES

2

1.5

1

0.5

0

-0.5

-1

-1.5

-2

2005

2007

2009

2011

2013

2015

2017

2019

IE

3

2

1

0

-1

-2

-3

-4

-5

-6

2005

2007

2009

2011

2013

2015

2017

2019

GR

10

8

6

4

2

0

-2

2005

2007

2009

2011

2013

2015

2017

2019

PT

6

4

2

0

-2

-4

-6

-8

2005

2007

2009

2011

2013

2015

2017

2019

Note: The data sample spans from 2006Q1 to 2017Q3. The out-of-sample assumptions for the baseline and adverse paths for the change in NPL ratios span from 2017Q4 to 2020Q3. The data are sourced from the IMF FSI, Banque de France, Banco de Espa~na, Central Bank of Cyprus and Bankscope.

ECB Working Paper Series No 2411 / May 2020

35

Figure 7: Difference in the structural scenario forecasts between the baseline and the adverse path for the main variables included in the panel VAR

CY

loans

6.00

1.00

4.00

0.50

Corporate

2.00

0.00

0.00

-0.50

1 2 3 4 5 6 7 8 9 101112

loans

2.00

2.00

1.00

Mortgage

1.00

0.00

0.00

-1.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

0.50

0.00

-0.50

-1.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

IE

IT

6.00

2.00

2.00

4.00

1.00

1.00

2.00

0.00

0.00

0.00

-1.00

-1.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

3.00

2.00

4.00

2.00

2.00

1.00

1.00

0.00

0.00

0.00

-2.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

PT

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

growth

1.50

1.00

1.50

3.00

1.50

1.50

1.00

1.00

2.00

1.00

1.00

GDP

0.50

0.50

0.50

1.00

0.50

0.50

0.00

0.00

0.00

0.00

0.00

0.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

10.00

2.00

4.00

6.00

2.00

4.00

prices

4.00

1.00

2.00

5.00

1.00

2.00

RRE

2.00

0.00

0.00

0.00

0.00

0.00

0.00

-1.00

-2.00

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

spread

0.00

0.00

0.00

0.00

0.00

0.50

-0.10

-0.10

Mortgage

-0.20

-0.50

-0.50

0.00

-0.20

-0.20

-0.30

-0.30

-0.40

-1.00

-1.00

-0.50

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

spread

0.00

0.00

0.00

0.00

0.00

0.20

-0.20

-0.10

-0.20

0.00

Corporate

-0.50

-0.50

-0.40

-0.20

-0.40

-0.20

-0.60

-0.30

-0.60

-1.00

-1.00

-0.40

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

1 2 3 4 5 6 7 8 9 101112

Note: The gure reports the di erence between the baseline and the adverse structural scenario forecasts of the main variables in the panel VAR. Under both the baseline the adverse assumption, the forecasts for the variables in the VAR are computed assuming that only the structural shock to the change in NPL ratios adjusts to ensure the conditioning path for this variable. Real GDP growth, headline ination, residential real estate prices and corporate and mortgage loans are expressed in annual growth rates. The Euribor, bank lending spreads, the change in NPL ratios and the capital and reserves-to-asset ratio are expressed in percentage points.

ECB Working Paper Series No 2411 / May 2020

36

Acknowledgements

We thank seminar participants at the European Central Bank, the Bank of England and ADB-ECB Workshop on NPL Resolution in Asia and Europe for helpful comments and suggestions. We also thank Björn van Roye, Dejan Krusec, Lorenzo Ricci and Paolo Fioretti for useful discussions. Paola Antilici, Marija Deipenbrock, Marco Forletta and Alexandros Kouris provided excellent research assistance. We are solely responsible for any errors that remain. The findings, views and interpretations expressed herein are those of the authors and should not be attributed to the Joint Vienna Institute, the Eurosystem, the European Central Bank, its Executive Board, or its management.

Ivan Huljak

Croatian National Bank, Zagreb, Croatia; email: ivan.huljak@hnb.hr

Reiner Martin

European Central Bank, Frankfurt am Main, Germany; Joint Vienna Institute, Vienna, Austria; email: rmartin@jvi.org

Diego Moccero

European Central Bank, Frankfurt am Main, Germany; email: diego.moccero@ecb.europa.eu

Cosimo Pancaro

European Central Bank, Frankfurt am Main, Germany; email: cosimo.pancaro@ecb.europa.eu

© European Central Bank, 2020

Postal address 60640 Frankfurt am Main, Germany

Telephone +49 69 1344 0

Website www.ecb.europa.eu

All rights reserved. Any reproduction, publication and reprint in the form of a different publication, whether printed or produced electronically, in whole or in part, is permitted only with the explicit written authorisation of the ECB or the authors.

This paper can be downloaded without charge from www.ecb.europa.eu, from the Social Science Research Network electronic library or from RePEc: Research Papers in Economics. Information on all of the papers published in the ECB Working Paper Series can be found on the ECB’s website.

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ISBN 978-92-899-4054-2

ISSN 1725-2806

doi:10.2866/44778

QB-AR-20-063-EN-N

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ECB – European Central Bank published this content on 15 May 2020 and is solely responsible for the information contained therein. Distributed by Public, unedited and unaltered, on 15 May 2020 09:14:08 UTC

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Bad Credit

Can My Cosigner Take My Car?

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Cosigners don’t get any rights to the vehicle they signed the loan for. However, if the cosigner is trying to take your car, it may be time to take some action.

Cosigners and Ownership

Can My Cosigner Take My Car?Cosigners can’t take the vehicle they cosigned for because their name isn’t listed on the title. A cosigner isn’t responsible for making the monthly payments, maintaining car insurance, or really anything else. Cosigners simply lend you their good credit score to help you get approved for the auto loan, and if you can’t make payments, the lender can require them to pick up the slack.

Since you’re the primary borrower on the vehicle and your name is listed on the car’s title, you have ownership rights. Your cosigner can’t come to your residence and take possession of the vehicle – even if they’re the one making the car payments right now.

If you do default on the loan and the vehicle is repossessed, the cosigner still can’t take the car.

But My Cosigner Did Take My Car!

If your cosigner did somehow take your keys and your vehicle without permission, it’s considered theft. If you want to take action, you can report the car as stolen.

However, a better first step is probably contacting the cosigner and letting them know that they don’t have any ownership rights (if you want to maintain a relationship with them). You can ask them to return the vehicle and explain that their name isn’t on the title.

Removing a Cosigner From a Car Loan

If things are dicey with your cosigner, then it may be time to consider removing them from the auto loan. The easiest way to remove a cosigner is by refinancing.

Refinancing is when you replace your current loan with another one. You can work with your current lender or another one, but most borrowers look for another lender to refinance with.

You don’t need a perfect credit score to refinance your car loan – it just has to be good or better than it was when you first got the loan. Another common requirement of refinancing is that you’ve had the loan for at least one year.

Other common requirements for refinancing are:

  • You’ve stayed current on payments throughout the loan
  • You have equity or your loan balance is equal to the vehicle’s value
  • Your car has less than 100,000 miles and is less than 10 years old

Most borrowers usually refinance to lower their loan payments. Since you’re replacing your current auto loan with another one, many borrowers try to qualify for lower interest rates or extend their loan to lower their payments. If your credit score has improved, you may even be able to get a better interest rate and remove your cosigner!

Can’t Refinance to Remove the Cosigner?

Refinancing isn’t in the cards for everyone. However, another efficient way to remove a cosigner is by selling the car. Cosigners don’t have to be present at the sale of the vehicle, since they don’t have to sign the title to transfer ownership.

If you sell the car and get an offer large enough to cover the entire balance of your loan, you and the cosigner can walk away from the auto loan scot-free.

However, many borrowers need cosigners because their credit score isn’t the best. If you want to sell your vehicle to remove your cosigner, but you’re worried you can’t get a car loan by yourself, consider a subprime auto loan for your next vehicle.

Bad Credit Auto Loans

Since many traditional car lenders don’t work with borrowers who have poor credit histories or lower credit scores, they often ask them to bring a cosigner. But what if you don’t want a cosigner (or can’t get one) on your next auto loan? Enter subprime car loans.

Subprime lenders are teamed up with special finance dealerships, and they operate remotely. When you apply for financing with a special finance dealer, you work with the special finance manager who acts as the middleman between you and the lender.

You need documents to prove you’re ready to take on an auto loan – typical things like check stubs, proof of residency, valid driver’s license, a down payment, and other assorted items depending on your credit situation. If you qualify, the lender determines what your maximum car payment can be, and you choose a vehicle you qualify for from there.

What sets subprime auto loans apart from traditional car loans is that they assist borrowers in tough credit situations and offer the opportunity for credit repair. Some in-house financing dealerships that don’t check credit reports don’t report their auto loans, which means your timely payments don’t improve your credit score.

Finding a Car Dealership Near You

The best way to improve your credit score is by paying all your bills on time. Payment history is the most influential piece of the credit score pie. There are many lenders willing to work with bad credit borrowers, you just have to know where to look!

Here at Auto Credit Express, we’ve already done the searching, and we’ve created a nationwide network of dealers that are signed up with subprime lenders. Get matched to a dealership in your area, with no cost and no obligation, by filling out our car loan request form.

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Bad Credit

United Way hoping to raise thousands of dollars on Giving Tuesday –

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“We have partnered with Carter Meyers Associates in the community and developed what we call Driving Lives Forward, an automobile loan program to help families that maybe have no credit or bad credit to access resources to have an affordable loan to purchase a reliable used car,” said Barbara Hutchinson, the Vice President of Community Impact for the United Way of Greater Charlottesville.

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Loans for Bad Credit: Alternatives to High-Interest Loans

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In the face of unexpected events, most Americans don’t have enough cash to cover their needs. Statistics estimate that more than half of all Americans have less than $1,000 in a savings account.

It’s challenging to get through everyday life without expecting anything to go wrong. Any emergency — be it a car accident, a hospital visit, or even a broken refrigerator — will put Americans in trouble.

To add insult to injury, poor credit can make an emergency even more challenging. That’s where installment loans come in.

For consumers that have a bad credit score (below 630), installment loans can be the best option to get quick money. Installment loan funds are distributed all at once. Afterward, the repayment of the installment loan follows either a fixed monthly payment.

Online installment loans are ideal for emergencies as access to fast cash. Here’s everything you need to know before taking out an installment loan.

Online Installment Loan Basics

Installment loans are actually a broad category that includes many different kinds of loans, such as mortgage loans, car loans, and other personal loans. They tend to be long-term loans that require credit checks.

Payday loans are another type of installment loan. However, its structure is different. They must be repaid over a shorter period, have higher interest rates, but require no credit checks.

Installment Loans

As stated above:

  • Installment loans deliver quick cash in one lump sum

  • Installment loans require a credit check

  • Installment loans describe many different loan types

Furthermore, installment loan terms depend on the type of loan and can range from 3 years for car loans to 30 years for mortgage loans. In contrast, a personal installment loan lasts for approximately 12 months.

To get approval for any of the above loans, the individual will be subject to a credit check (more to know on that here: https://www.wisegeek.com/what-is-a-credit-check.htm ) and a fairly long application process.

Installment loans offer an APR of 36% or below, and user payments can be made online, over the phone, or by check.

Another advantage of installment loans is that they help borrowers improve their credit rating — as long as they pay on-time. It provides immediate access to cash, while at the same time, it’s a means to an end toward recovering a bad credit score.

How well individuals can do often depends on the terms of the installment loan that they receive. Keep reading to get more advice on how to choose an installment loan that is right for you.

Choosing an Online Lender

Like any other loan, picking a lender requires a fair amount of research and work. It’s not going to be a simple task, and there are several factors that individuals need to look out for when picking the right loans.

Below are the most important features that individuals need to keep in mind when choosing an online installment loan.

Compare Rates

All the different installment loan options out there are going to offer different percentage rates. These range from 6% to 36%, and you should sift through all possible options to get the most favorable rates.

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Ideally, individuals should opt for the lowest rates to ensure that the monthly payments are as low as possible.

Online lenders can offer potential borrowers their interest rates ahead of time. This usually requires a soft credit check, which does not impact a borrower’s credit score.

However, applicants need to be careful, as different vendors have different requirements. Understanding these requirements will help avoid any mishaps.

Understand All Fees

Every vendor has different fees, these fees might have different names like an “organization fee” or a “service fee,” but they generally range from 1% to 6%.

In contrast, other vendors might charge a prepayment fee for early repayment. Under no circumstances should a borrower agree to a loan deal before the lender discloses all fees.

It’s up to the individual to be as vigilant as possible because certain vendors will keep some fees to themselves, and may not disclose them until the last minute.

To avoid any excess costs in the future, make sure to go over the contract in its entirety.

Choosing Manageable Terms

Installment loans offer a lot of advantages for needy consumers:

But, one thing to remember is this: the longer the loan term, the higher the interest individuals will pay. Taking longer terms might give borrowers more time to pay, but it also means that borrowers will have to pay more interest.

In contrast, shorter terms are harder to manage, but it means paying a lower interest rate. When choosing the right installment loan, individuals should calculate the monthly payment based on the term length.

Many online vendors offer software that automatically calculates the amount. Everyone should employ a strategy to assess different term lengths to see what monthly payments are the most manageable.

Vendor Perks

Not all vendors are the same, and it’s already established that they offer different rates for different prices. However, while already offering different rates, vendors also offer different perks — specific features tailored to the individual.

If the individual is consolidating debt, certain lenders will send loan money to creditors on behalf of the loanee. Other vendors offer the ability to change due dates or provide hardship plans if the borrower encounters any financial difficulties.

It’s crucial to consider all these factors before taking out an installment loan. It’s best to have everything working to your advantage with an already poor credit history before taking out an installment loan.

Our Top Picks for Online Installment Loans

There are hundreds of online installment loan options out there, and looking through all of them can be a hassle. Furthermore, those new to the industry won’t be able to identify scams or loans meant to exploit.

Upgrade

Upgrade is one of the best installment loan vendors for those with a bad credit score. They accept a minimum score of 600 and will provide potential applicants with an offer in minutes. Their APR rates range from 7.99-35.97% depending on the amount, duration, and purpose of the loan. Users can easily apply for loans and get ideas on rates using the company’s website.

Simple Fast Loans

Simple Fast Loans is also among the best installment loan vendors for individuals that have a bad credit score. They offer loans ranging from $200-$3,000. These loans have terms up to 5 years.There’s no prepayment penalty, and applicants will also get next day funding. To get an idea of the rates, users can easily apply using their website.

LendingPoint

For users that have a credit score below 600, a great option to choose is LendingPoint. They accept a minimum credit score of 585 and offer loans for $2,000-$25,000. The APR rates for these loans are on the higher side ranging from 9.99-35.99%. Money becomes available to the applicant the next day, and there’s no prepayment penalty on the loan.

Avant

Another installment loan vendor for users that don’t have a good credit score is Avant. They require a minimum credit score of 580 and offer loans for $2,000-$35,000. The APR rate is between 9.95-35.99%, and they offer the ability to change payment dates. However, applicants will have to pay a loan origination fee, and there’s no option to include a co-signer on the loan.

Online Financing Options to Avoid

Online installment loans are a great option for individuals with bad credit scores, and, if used correctly, are a way to improve credit scores.

However, the same can’t be said for all online financing options, and certain ones are important to avoid.

Payday Loans

Payday loans function similarly to installment loans. In addition, they have recently been rebranded as short-term installment loans.

The loans are usually under $1,000 and are due on the next payday. With payday loans, an individual will have to either submit a post-dated check or provide access to the bank account.

It might sound relatively okay, but the issue with payday loans is that it’s nearly impossible to pay them back. Lenders will let individuals roll over the loans with more interest to pay the next day. Interest rates are typically 400% APR on these loans, and individuals get caught in the payday loan debt cycle.

No Credit Check Loans

These loans might seem like a good idea for those with bad credit scores, but they’re essentially just a debt cycle. The combination of high-interest rates, short terms, and lump sum repayment means that borrowers are stuck in a cycle of ever-increasing debt. It’s best to find loans that offer some sort of credit check and security to get the best terms.

Upfront fees

While certain loans might require a small percentage to process the application, some can be a complete red flag. There are plenty of up-front loan scams, and there are several signs that borrowers need to address. If a vendor asks for money upfront, then there’s a good chance it’s a scam.

Additionally, these issues tend to arise the most with vendors that don’t offer credit checks. Lastly, do enough research to recognize an offer that seems too good to be true.

Conclusion

Keeping all these things in mind, online installment loans are the best option for borrowers with a bad credit score. They are a useful resource and, if managed correctly, are a path to recovering a good credit score.

This article does not necessarily reflect the opinions of the editors or management of EconoTimes



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