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Piotr Kusmierczyk

27 July 2022
OCCASIONAL PAPER SERIES - No. 297
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Abstract
This paper looks at the macroeconomic impact of the two policies proposed by ECB Banking Supervision to tackle the high share of non-performing loans (NPLs) on the balance sheets of euro area banks. The first is the coverage expectations for new NPLs set out in the Addendum to the ECB’s NPL Guidance, which aim to prevent the build-up of new NPLs, and the second is the coverage expectations for legacy NPLs, which target the reduction of already existing stocks of NPLs. The impact assessment of the package is analysed via a semi-structural model, the Banking Euro Area Stress Test (BEAST). The coverage expectations for NPLs are found to be effective in reducing banks’ NPLs. The phase-in of the policies can temporarily reduce bank profitability owing to increased loan loss provisioning targets. However, over a longer time horizon, lower NPL ratios reduce uncertainty and enable banks to access cheaper funding in the markets, ultimately benefiting lending and output growth. Furthermore, the coverage expectations can also moderately but persistently reduce procyclicality in the banking system.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
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
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
20 December 2019
STATISTICS PAPER SERIES - No. 32
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Abstract
This paper describes the Macroprudential Database (MPDB) of the European CentralBank (ECB), which is an important component of the ECB’s Statistical DataWarehouse. After explaining the rationale for creating the MPDB, the paper illustrateshow it supports the macroprudential analysis conducted by the European System ofCentral Banks (ESCB), the European Systemic Risk Board (ESRB) and the nationalauthorities of the Single Supervisory Mechanism (SSM) and the European Union. Thestructure of the database and a broad overview of available indicators are thenpresented, with a description of the relevant confidentiality issues. Examples illustratehow the MPDB is used for monitoring purposes and econometric modelling. Finally,the paper discusses remaining data gaps and expected future enhancements of thedatabase.
JEL Code
C82 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Macroeconomic Data, Data Access
E60 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→General
31 July 2017
OCCASIONAL PAPER SERIES - No. 194
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Abstract
This paper presents a new database for financial crises in European countries, which serves as an important step towards establishing a common ground for macroprudential oversight and policymaking in the EU. The database focuses on providing precise chronological definitions of crisis periods to support the calibration of models in macroprudential analysis. An important contribution of this work is the identification of financial crises by combining a quantitative approach based on a financial stress index with expert judgement from national and European authorities. Key innovations of this database are (i) the inclusion of qualitative information about events and policy responses, (ii) the introduction of a broad set of non-exclusive categories to classify events, and (iii) a distinction between event and post-event adjustment periods. The paper explains the two-step approach for identifying crises and other key choices in the construction of the dataset. Moreover, stylised facts about the systemic crises in the dataset are presented together with estimations of output losses and fiscal costs associated with these crises. A preliminary assessment of the performance of standard early warning indicators based on the new crises dataset confirms findings in the literature that multivariate models can improve compared to univariate signalling models.
JEL Code
G01 : Financial Economics→General→Financial Crises
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
E60 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→General
H12 : Public Economics→Structure and Scope of Government→Crisis Management
Annexes