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Marco Forletta

29 October 2019
MACROPRUDENTIAL BULLETIN - ARTICLE - No. 9
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Abstract
Cyclical systemic risk tends to build up well ahead of financial crises and is measured best by credit and asset price dynamics. This article shows that high levels of cyclical systemic risk lead to large downside risks to the bank-level return on assets three to five years ahead. Hence, exuberant credit and asset price dynamics tend to increase considerably the likelihood of large future bank losses. Given the tight link between bank losses and reductions in bank capital, the results presented in this article can be used to quantify the level of “Bank capital-at-risk” (BCaR) for a banking system. BCaR is a useful tool for macroprudential policy makers as it helps to quantify how much additional bank resilience could be needed if imbalances unwind and systemic risk materialises.
JEL Code
G01 : Financial Economics→General→Financial Crises
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
12 May 2020
WORKING PAPER SERIES - No. 2405
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Abstract
This paper studies the impact of cyclical systemic risk on future bank profitability for a large representative panel of EU banks between 2005 and 2017. Using linear local projections we show that high current levels of cyclical systemic risk predict large drops in the average bank-level return on assets (ROA) with a lead time of 3-5 years. Based on quantile local projections we further show that the negative impact of cyclical systemic risk on the left tail of the future bank-level ROA distribution is an order of magnitude larger than on the median. Given the tight link between negative profits and reductions in bank capital, our method can be used to quantify the level of “Bank capital-at-risk” for a given banking system, akin to the concept of “Growth-at-risk”. We illustrate how the method can inform the calibration of countercyclical macroprudential policy instruments.
JEL Code
G01 : Financial Economics→General→Financial Crises
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages