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Miha Leber

11 October 2013
The use of macro stress tests to assess bank solvency has developed rapidly over the past few years. This development was reinforced by the financial crisis, which resulted in substantial losses for banks and created general uncertainty about the banking sector's loss-bearing capacity. Macro stress testing has proved a useful instrument to help identify potential vulnerabilities within the banking sector and to gauge its resilience to adverse developments. To support its contribution to safeguarding financial stability and its financial sector-related work in the context of EU/IMF Financial Assistance Programmes, and looking ahead to the establishment of the Single Supervisory Mechanism (SSM), the ECB has developed a top-down macro stress testing framework that is used regularly for forward-looking bank solvency assessments. This paper comprehensively presents the main features of this framework and illustrates how it can be employed for various policy analysis purposes.
JEL Code
C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
D85 : Microeconomics→Information, Knowledge, and Uncertainty→Network Formation and Analysis: Theory
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
E47 : Macroeconomics and Monetary Economics→Money and Interest Rates→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
G01 : Financial Economics→General→Financial Crises
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
28 May 2015
Financial Stability Review Issue 1, 2015
The weight of non-performing exposures (NPEs) on the balance sheets of European banks is a cause for concern for policy-makers; yet resolving the issue presents a number of challenges. This special feature presents an overview of the scale of the NPE problem, highlights several operational aspects that are critical for effectively resolving the problem, and outlines the merits of various resolution strategies.
JEL Code
G00 : Financial Economics→General→General
10 July 2019
We assess the effects of regulatory caps in the loan-to-value (LTV) ratio using agent-based models (ABMs). Our approach builds upon a straightforward ABM where we model the interactions of sellers, buyers and banks within a computational framework that enables the application of LTV caps. The results are first presented using simulated data and then we calibrate the probability distributions based on actual European data from the HFCS survey. The results suggest that this approach can be viewed as a useful alternative to the existing analytical frameworks for assessing the impact of macroprudential measures, mainly due to the very few assumptions the method relies upon and the ability to easily incorporate additional and more complex features related to the behavioral response of borrowers to such measures.
JEL Code
D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance
D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions
E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
R21 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Household Analysis→Housing Demand