Jan Hannes Lang
- 17 November 2021
- FINANCIAL STABILITY REVIEW - BOXFinancial Stability Review Issue 2, 2021Details
- Abstract
- In order to assess the strength of the current residential real estate expansion, we compare recent developments in euro area housing markets with the period ahead of the global financial crisis (GFC). We find that house price dynamics, overvaluation and the risk profile of new mortgage loans are at similar levels to those observed during the height of the pre-GFC cycle in 2007. However, vulnerabilities from mortgage lending developments and household balance sheets are currently below their pre-GFC levels. We conclude that the continued build-up of vulnerabilities in residential real estate markets calls for close monitoring and possible macroprudential measures.
- JEL Code
- R31 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Real Estate Markets, Spatial Production Analysis, and Firm Location→Housing Supply and Markets
G51 : Financial Economics
P34 : Economic Systems→Socialist Institutions and Their Transitions→Financial Economics
G01 : Financial Economics→General→Financial Crises
- 25 November 2020
- FINANCIAL STABILITY REVIEW - BOXFinancial Stability Review Issue 2, 2020Details
- Abstract
- This box explores the potential macroeconomic impact of different capital buffer replenishment paths. Model simulations show that replenishing capital buffers too early or too aggressively could be counterproductive and prolong the economic downturn. While the costs of restoring capital buffers to pre-crisis levels are not excessive if the economy moves along the central projection scenario, a weaker economic environment would increase bank losses and result in a more extensive use of capital buffers. In such a scenario, a later and more gradual restoration of capital buffers would be warranted.
- JEL Code
- G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
E17 : Macroeconomics and Monetary Economics→General Aggregative Models→Forecasting and Simulation: Models and Applications
E51 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Money Supply, Credit, Money Multipliers
C68 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computable General Equilibrium Models
- 26 May 2020
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 1, 2020Details
- Abstract
- It is often maintained that the recent real estate booms in many euro area countries have been accompanied by a loosening in lending standards. However, data for a thorough cross-country assessment of lending standards have been missing. This special feature uses a novel euro area dataset from a dedicated data collection covering significant institutions supervised by ECB Banking Supervision to analyse trends in real estate lending standards and derive implications for financial stability. First, lending standards for residential real estate loans in the euro area, in particular loan-to-income ratios, eased between 2016 and 2018. Given the significant deterioration in the euro area economic outlook since the coronavirus outbreak, this vulnerability seems of particular relevance. Second, lending standards appear to be looser in countries that saw stronger real estate expansions, suggesting that real estate vulnerabilities may have been growing in some euro area countries. Third, lending standards deteriorated less in countries with borrower-based macroprudential policies in place, highlighting the importance of early macroprudential policy action to help prevent the build-up of real estate vulnerabilities.
- 12 May 2020
- WORKING PAPER SERIES - No. 2405Details
- 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
- 29 October 2019
- MACROPRUDENTIAL BULLETIN - ARTICLE - No. 9Details
- 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
- 14 February 2019
- OCCASIONAL PAPER SERIES - No. 219Details
- Abstract
- This paper presents a tractable, transparent and broad-based domestic cyclical systemic risk indicator (d-SRI) that captures risks stemming from domestic credit, real estate markets, asset prices, and external imbalances. The d-SRI increases on average several years before the onset of systemic financial crises, and its early warning properties for euro area countries are superior to those of the total credit-to-GDP gap. In addition, the level of the d-SRI around the start of financial crises is highly correlated with measures of subsequent crisis severity, such as GDP declines. Model estimates suggest that the d-SRI has significant predictive power for large declines in real GDP growth three to four years down the line, as it precedes shifts in the entire distribution of future real GDP growth and especially of its left tail. The d-SRI therefore provides useful information about both the probability and the likely cost of systemic financial crises many years in advance. Given its timely signals, the d-SRI is a useful analytical tool for macroprudential policymakers.
- 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
- 13 November 2018
- WORKING PAPER SERIES - No. 2194Details
- Abstract
- This paper proposes a semi-structural approach to identifying excessive household credit developments. Using an overlapping generations model, a normative trend level for the real household credit stock is derived that depends on four fundamental economic factors: real potential GDP, the equilibrium real interest rate, the population share of the middle-aged cohort, and institutional quality. Semi-structural household credit gaps are obtained as deviations of the real household credit stock from this fundamental trend level. Estimates of these credit gaps for 12 EU countries over the past 35 years yield long credit cycles that last between 15 and 25 years with amplitudes of around 20%. The early warning properties for financial crises are superior compared to credit gaps that are obtained from purely statistical filters. The proposed semistructural household credit gaps could therefore provide useful information for the formulation of countercyclical macroprudential policy, especially because they allow for economic interpretation of observed credit developments.
- JEL Code
- E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E51 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Money Supply, Credit, Money Multipliers
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
G01 : Financial Economics→General→Financial Crises
D15 : Microeconomics→Household Behavior and Family Economics
- 11 October 2018
- WORKING PAPER SERIES - No. 2182Details
- Abstract
- This paper proposes a framework for deriving early-warning models with optimal out-of-sample forecasting properties and applies it to predicting distress in European banks. The main contributions of the paper are threefold. First, the paper introduces a conceptual framework to guide the process of building early-warning models, which highlights and structures the numerous complex choices that the modeler needs to make. Second, the paper proposes a flexible modeling solution to the conceptual framework that supports model selection in real-time. Specifically, our proposed solution is to combine the loss function approach to evaluate early-warning models with regularized logistic regression and cross-validation to find a model specification with optimal real-time out-of-sample forecasting properties. Third, the paper illustrates how the modeling framework can be used in analysis supporting both microand macro-prudential policy by applying it to a large dataset of EU banks and showing some examples of early-warning model visualizations.
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G33 : Financial Economics→Corporate Finance and Governance→Bankruptcy, Liquidation
C52 : Mathematical and Quantitative Methods→Econometric Modeling→Model Evaluation, Validation, and Selection
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
- 19 June 2018
- WORKING PAPER SERIES - No. 2160Details
- Abstract
- This paper uses data on bilateral foreign exposures of domestic banking systems in order to construct early warning models for financial crises that take into account cross-country spill-overs of vulnerabilities. The empirical results show that incorporating cross-country financial linkages can improve the signalling performance of early warning models. The relative usefulness increases from 65% to 87% and the AUROC from 0.89 to 0.97 when weighted foreign variables are added to domestic variables in a multivariate logit early warning model. The findings of the paper also suggest that global variables still play a role in predicting financial crises, even when foreign variables are controlled for, which could suggest that both cross-country spill-overs and contagion are important factors for driving financial crises. A parsimonious model with nine variables that combines domestic, foreign and global variables yields an out-of-sample relative usefulness of 0.82 with Type I and Type II errors of 0.11 and 0.07.
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
F37 : International Economics→International Finance→International Finance Forecasting and Simulation: Models and Applications
F65 : International Economics→Economic Impacts of Globalization→Finance
- 24 May 2018
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 1, 2018Details
- Abstract
- This special feature presents a tractable, transparent and broad-based cyclical systemic risk indicator (CSRI) that captures risks stemming from domestic credit, real estate markets, asset prices, external imbalances and cross-country spillovers. The CSRI increases on average several years before the onset of systemic financial crises and its level is highly correlated with measures of crisis severity. Model estimates suggest that high values of the CSRI contain information about large declines in real GDP growth three to four years down the road, as it precedes shifts in the entire distribution of future real GDP growth and especially of its left tail. Given its timely signals, the CSRI is a useful analytical tool for macroprudential policymakers to complement other existing analytical tools.
- JEL Code
- G00 : Financial Economics→General→General
- 31 July 2017
- OCCASIONAL PAPER SERIES - No. 194Details
- 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- 31 July 2017
- ANNEX
- 20 June 2017
- WORKING PAPER SERIES - No. 2079Details
- Abstract
- This paper addresses the tradeoff between additional loss-absorbing capacity and potentially higher bank risk-taking associated with the introduction of the Basel III Leverage Ratio. This is addressed in both a theoretical and empirical setting. Using a theoretical micro model, we show that a leverage ratio requirement can incentivise banks that are bound by it to increase their risk-taking. This increase in risk-taking however, should be more than outweighed by the benefits of higher capital and therefore increased lossabsorbing capacity, thereby leading to more stable banks. These theoretical predictions are tested and confirmed in an empirical analysis on a large sample of EU banks. Our baseline empirical model suggests that a leverage ratio requirement would lead to a significant decline in the distress probability of highly leveraged banks.
- JEL Code
- 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
- 24 May 2017
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 1, 2017Details
- Abstract
- Excessive credit growth and leverage have been key drivers of past financial crises, notably the recent global financial crisis. For the appropriate setting of countercyclical macroprudential policy instruments, it is therefore important to identify periods of excessive credit developments at an early stage. This special feature discusses the standard statistical method for computing credit gaps and compares it with an alternative approach to measuring credit excesses based on fundamental economic factors. Theory-based credit gaps could provide a useful complement to statistical measures of cyclical systemic risk.
- JEL Code
- G00 : Financial Economics→General→General
- 25 November 2015
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 2, 2015Details
- Abstract
- The Basel III leverage ratio aims to constrain the build-up of excessive leverage in the banking system and to enhance bank stability. Concern has been raised, however, that the non-risk-based nature of the leverage ratio could incentivise banks to increase their risk-taking. This special feature presents theoretical considerations and empirical evidence for EU banks that a leverage ratio requirement should only lead to limited additional risk-taking relative to the induced benefits of increasing loss-absorbing capacity, thus resulting in more stable banks.
- JEL Code
- G00 : Financial Economics→General→General