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Derrick Kanngiesser
- 16 September 2019
- MACROPRUDENTIAL BULLETIN - ARTICLE - No. 8Details
- Abstract
- How do changes in bank capital requirements affect bank lending, lending spreads and the broader macroeconomy? The answer to this question is important for calibrating and assessing macroprudential policies. There is, however, relatively little empirical evidence to answer this question in the case of the euro area countries. This article contributes to filling this gap by studying the effects of changes in economic bank capital buffers in the four largest euro area countries. We use bank-level data and macroeconomic information to estimate a bank-internal, target level of economic capital ratio, i.e. the capital ratio that a bank would like to hold considering its own characteristics (size, profitability, risk aversion of its creditors, risk exposure, etc.) and macroeconomic conditions (expected GDP growth, etc.). Economic bank capital buffers are then computed as the difference between the current and the target economic capital ratio. However, due to adjustment costs, banks cannot adjust the actual capital ratio to the target level instantaneously. As a result, a change in the target capital ratio will result in an instantaneous change in the economic capital buffer. These buffers are aggregated at the country level and included in a panel Bayesian vector auto regressive (VAR) model. With the VAR, it is then possible to compute the response of macroeconomic and banking variables to a change in the buffer. The idea is that changes in economic capital buffers mimic the effects a change in regulatory capital requirements would have on the economy. We find that a negative economic capital buffer shock, i.e. a decline in actual capital ratios below the target level, leads to a modest decline in output and prices and to a larger decline in bank lending growth. By affecting the difference between actual and target economic capital ratios, these findings suggest that countercyclical capital-based macroprudential policy measures can be useful to dampen the financial cycle.
- JEL Code
- C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
- 19 June 2017
- WORKING PAPER SERIES - No. 2077Details
- Abstract
- We contribute to the empirical literature on the impact of shocks to bank capital in the euro area by estimating a Bayesian VAR model identied with sign restrictions. The variables included in the VAR are those typically used in monetary policy analysis, extended to include aggregate banking sector variables. We estimate two shocks affecting the euro area economy, namely a demand shock and a shock to bank capital. The main findings of the paper are as follows: i) Impulse-response analysis shows that in response to a shock to bank capital, banks boost capital ratios by reducing their relative exposure to riskier assets and by adjusting lending to a larger extent than they increase the level of capital and reserves per se; ii) Historical shock decomposition analysis shows that bank capital shocks have contributed to increasing capital ratios since the crisis, impairing bank lending growth and contributing to widen bank lending spreads; and iii) counterfactual analysis shows that higher capital ratios pre-crisis would have helped dampening the euro area credit and business cycle. This suggests that going forward the use of capital-based macroprudential policy instruments may be helpful to avoid a repetition of the events seen since the start of the global financial crisis.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
C11 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→Bayesian Analysis: General