Do NPL affect Lending?
ECB Paper estimates impact of NPL porfolio on Business Cycle and Lending
A recent paper published by European Central Benks tried to estimate the impact of non-performing loan (NPL) ratios on aggregate banking sector variables and the macroeconomy.
The non-performing loan (NPL) ratio in the euro area increased from around 3% at the onset of the global financial 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 significantly reduced borrowers’ capacity to service their debt.
At the same time, the fast increase in NPL ratios was also significantly influenced 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 reflecting the different macroeconomic conditions and diverse structural features (the efficiency 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 affected by the recent economic and f inancial 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 affect the soundness of the banking system and its ability to lend to the real economy through three main channels.
First, high nonperforming loans reduce bank profits. 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 profitable 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.
The paper contributes 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 first quarter of 2006 and the third quarter of 2017. More specifically, 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-offs, supervisory actions that incentivise banks to work out these loans (by offering restructuring solutions to clients) and other policy initiatives which deal with NPLs’ workouts and defaults associated with poor enforcement mechanisms.
The analysis applies a Bayesian model 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-specific coefficients. 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 efficient use of the data. The variables included in the panel VAR are economic activity (which is a proxy for the repayment capacity of borrowers), inflation, the monetary policy rate, real estate prices, bank lending volumes both to non-financial 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.
The Authors use the Choleski factorisation, a recursive technique commonly adopted in the literature, to identify the shock to NPLs. In particular, the fact that NPLs generally do not respond within a quarter to endogenous shocks is used as an identification strategy, as banks are allowed to classify a loan as non-performing only a quarter after default.
A relevant result is 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 reservesto-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-financial 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 quantifies 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 benefits in terms of improved macroeconomic and financial 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 affected 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 first variable.
Link:
https://www.ecb.europa.eu/pub/pdf/scpwps/ecb.wp2411~839bc74726.en.pdf