European Central Bank - eurosystem
Search Options
Home Media Explainers Research & Publications Statistics Monetary Policy The €uro Payments & Markets Careers
Sort by

Paul Hiebert

Macro Prud Policy&Financial Stability


Systemic Risk&Financial Institutions

Current Position

Head of Division

Fields of interest

Financial Economics,Macroeconomics and Monetary Economics,International Economics


Other current responsibilities

Visiting professor, College of Europe (Bruges campus)


Co-lead, ECB / European Systemic Risk Board Project Team on Climate Risk (with Jean Boissinot)


M.A. Economics, McGill University

Professional experience

Advisor, Monetary and Capital Markets Department, International Monetary Fund


Deputy Head, Financial Stability Surveillance Division, European Central Bank


Head of Fiscal Analysis Section, Fiscal Policies Division, European Central Bank


Principal Economist, Euro Area Macroeconomic Developments Division, European Central Bank


Senior/ Principal Economist, External Developments Division, European Central Bank


Visiting Fellow, Reserve Bank of Australia


Economist, Fiscal Policies Division, European Central Bank


Economist, Economic Forecasting Division, Department of Finance, Government of Canada


Economist, Fiscal Policy Division, Department of Finance, Government of Canada

17 July 2019
This occasional paper describes how the financial stability and macroprudential policy functions are organised at the ECB. Financial stability has been a key policy function of the ECB since its inception. Macroprudential policy tasks were later conferred on the ECB by the Single Supervisory Mechanism (SSM) Regulation. The paper describes the ECB’s macroprudential governance framework in the new institutional set-up. After reviewing the concept and origins of systemic risk, it reflects on the emergence of macroprudential policy in the aftermath of the financial crisis, its objectives and instruments, as well as specific aspects of this policy area in a monetary union such as the euro area. The ECB’s responsibilities required new tools to be developed to measure systemic risk at financial institution, country and system-wide level. The paper discusses selected analytical tools supporting financial stability surveillance and assessment work, as well as macroprudential policy analysis at the ECB. The tools are grouped into three broad areas: (i) methods to gauge the state of financial instability or prospects of near-term systemic stress, (ii) measures to capture the build-up of systemic risk focused on country-level financial cycle measurement and early warning methods, and (iii) the ECB stress testing framework for macroprudential purposes.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
F36 : International Economics→International Finance→Financial Aspects of Economic Integration
G20 : Financial Economics→Financial Institutions and Services→General
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
K23 : Law and Economics→Regulation and Business Law→Regulated Industries and Administrative Law
14 September 2015
We introduce a methodology to characterise financial cycles combining a novel multivariate spectral approach to identifying common cycle frequencies across a set of indicators, and a time varying aggregation emphasising systemic developments. The methodology is applied to 13 European Union countries as well a synthetic euro area aggregate, based on a quarterly dataset spanning 1970-2013. Results suggest that credit and asset prices share cyclical similarities, which, captured by a synthetic financial cycle, outperform the credit-to-GDP gap in predicting systemic banking crises on a horizon of up to three years. Financial cycles tend to be long, particularly in upswing phases and with important dispersion across country cases. Concordance of financial and business cycles is observed only 2/3 of the time. While a similar degree of concordance for financial cycles is apparent across countries, heterogeneity is high
JEL Code
E30 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→General
E40 : Macroeconomics and Monetary Economics→Money and Interest Rates→General
C54 : Mathematical and Quantitative Methods→Econometric Modeling→Quantitative Policy Modeling
27 November 2014
Financial Stability Review Issue 2, 2014
This special feature discusses ways of measuring financial cycles for macro-prudential policymaking. It presents some estimates and empirical characteristics of financial cycles. Existing studies on financial cycle measurement remain quite nascent in comparison with the voluminous literature on business cycles. In this context, two approaches – turning point and spectral analysis – are used to capture financial and business cycles at the country level. The results of the empirical analysis suggest that financial cycles tend to be more volatile than business cycles in the euro area, albeit with strong cross-country heterogeneity. Both aspects underscore the relevance of robust financial cycle estimates for macro-prudential policy design in euro area countries.
JEL Code
G00 : Financial Economics→General→General
1 October 2010
This paper presents a parsimonious model for forecasting and analysing euro area house prices and their interrelations with the macroeconomy. A quarterly vector error correction model is estimated over 1970-2009 using supply and demand forces central to the determination of euro area house prices in equilibrium and their dynamics: housing investment, real disposable income per capita and a mixed maturity measure of the real interest rate. In addition to house price forecasts using the resulting reduced form equation, a structural decomposition of the system is obtained employing a common trends framework of King, Plosser, Stock, and Watson (1991), which allows for the identification and economic interpretation of permanent and transitory shocks. The main results are twofold. First, the reduced form model tracks closely turning points in house prices when examining out-of-sample one- and two- step ahead forecasts. Moreover, the model suggests that euro area housing was overvalued in recent years, implying a period of stagnation to bring housing valuation back in line with its modelled fundamentals. Second, housing demand and financing cost shocks appear to have contributed strongly to the dynamism in euro area house prices over the sample period. While much of the increase appears to reflect a permanent component, a transitory component has also contributed from 2005 onwards. Specification tests suggest a robustness of the small model to alternative specifications, along with validity of the long-run restrictions.
JEL Code
R21 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Household Analysis→Housing Demand
R31 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Real Estate Markets, Spatial Production Analysis, and Firm Location→Housing Supply and Markets
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
8 June 2010
This paper examines the time varying dispersion in city house price levels across the four biggest euro area countries compared with those in the United States. Using available city-level data over the period 1987-2008, it tests for price convergence and analyses key factors explaining price differentials in a panel regression framework including per capita income, population and relative distances. Results indicate limited evidence of convergence in city-level house prices despite synchronised cycles in the national aggregates for most countries since the 1990s. There is an important role for income differentials in explaining city-level house price dispersion in Germany, France, and the US (but not in Italy or Spain once unobserved city factors are taken into account). At the same time, population differences across cities play a role, though this appears to be associated with amenities specific to a particular location. In general, there has been a lower dispersion of city-level house prices in the four largest euro area economies compared with the US in conjunction with a lower estimated income elasticity for house price differentials. The results, particularly for income, appear to be robust to restricting the analysis to large urban centres.
JEL Code
R21 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Household Analysis→Housing Demand
R31 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Real Estate Markets, Spatial Production Analysis, and Firm Location→Housing Supply and Markets
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
6 March 2009
This paper empirically assesses the prospects for house price spillovers in the euro area, where co-movement in house prices across countries may be particularly relevant given a general trend with monetary union toward increasing linkages in trade, financial markets, and general economic conditions. The application involves a Global VAR for three housing demand variables (real house prices, real per capita disposable income, and the real interest rate) on the basis of quarterly data for 10 euro area countries (Belgium, Germany, Ireland, Spain, France, Italy, the Netherlands, Austria, Portugal and Finland) over the period 1989-2007. The results suggest limited house price spillovers in the euro area, with evidence of some overshooting in the first 1-3 years after the shock, followed by a long run aggregate euro area impact of country-specific changes in real house prices related in part to the country's economic weight. This contrasts with the impacts of a shock to domestic long- term interest rates, with the latter causing a permanent shift in house prices after around 3 years. Underlying this aggregate development are rather heterogeneous house price spillovers at the country level, with a strong importance for economic weight in the euro area in governing their general magnitude, while geographic proximity appears to also play a role.
JEL Code
R21 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Household Analysis→Housing Demand
R31 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Real Estate Markets, Spatial Production Analysis, and Firm Location→Housing Supply and Markets
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
3 March 2009
We apply a dynamic dividend-discount model to analyse unexpected housing returns in a panel of eight euro area countries which together comprise 90% of euro area GDP. The application of this model allows for a de-composition of house price movements into movements in rent (cash-flow) and expected return news components. The empirical application of the model involves the estimation of a panel vector autoregressive model (VAR) for four variables –excess return to housing, rents, the real interest rate and real disposable per capita income– using quarterly data over the period 1985-2007. This empirical investigation yields two main findings. First, the bulk of the variability of house price move-ments in the panel of countries analysed can be attributed to movements in the rental yield. Indeed, perturbations to rents appear to result in a one-to-one analogous movement in house prices over the long term once controlling for changes in expected returns. Second, evidence from the dynamic profile of shocks along with the negative co-movement between changing rental yield expectations and changing expected returns on housing assets would suggest that euro area house prices overreact to news.
JEL Code
R21 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Household Analysis→Housing Demand
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
23 February 2007
We empirically analyse the response of US manufacturing labour market variables to various shocks, notably to trade openness and technology. The econometric approach involves an application of the recently developed global VAR (GVAR) methodology of D¶ees, DiMauro, Pesaran, and Smith (2005) to 12 manufacturing industries over the period 1977-2003. This frame-work allows for an assessment of both shocks to weakly exogenous variables and intra-industry spillovers. In this vein, beyond a standard set of labour-market related variables (employment, real compensation, productivity and capital stock) and exogenous factors (a sector-specific measure of trade openness, along with common technology and oil price shocks), specific measures of manufacturing-wide variables are included for each sector. Generalised impulse responses indicate that increased trade openness negatively affects real compensation, has negligible employment effects and leads to higher labour productivity. These impacts, however, are relatively weaker those induced by technology shocks, with the latter positively and significantly affecting both real compensation and employment. There is also evidence of positive spill-overs across industries from sector-specific employment and productivity shocks. Impact elasticities suggest strong intra-sectoral linkages for employment and capital stock formation, contrasting with weak linkages for what concerns real compensation and productivity.
JEL Code
F16 : International Economics→Trade→Trade and Labor Market Interactions
J01 : Labor and Demographic Economics→General→Labor Economics: General
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes
1 October 2002
This paper presents an optimal fiscal policy response to address the basic trade-off between the automatic stabilisation properties of budgets and the long run fiscal positions. The framework is an overlapping generations model la Weil (1989), extended to account for stochastic endowments and borrowing constrained households. A benign government chooses over the optimal degree of responsiveness of net taxes to individual incomes, an optimal measure of long-run public debt, or both, in order to smooth households' consumption across states of nature. In the presence of a deficit constraint for the government, the results unambiguously point to the desire for lower debt levels than those currently prevailing in order to enable a more effective hedging of personal income uncertainty by means of more active fiscal stabilisers. Citizens in economies exhibiting more pronounced cycles will favour less automatic stabilisation combined with a more aggressive policy of debt reduction.
JEL Code
H31 : Public Economics→Fiscal Policies and Behavior of Economic Agents→Household
H63 : Public Economics→National Budget, Deficit, and Debt→Debt, Debt Management, Sovereign Debt
E63 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Comparative or Joint Analysis of Fiscal and Monetary Policy, Stabilization, Treasury Policy
1 July 2002
In this paper, we present a model-based method for identifying fiscal closure rules in stochastic macroeconomic models. The methodology is based on the stability analysis of the model at hand, with an endogenous derivation of a reaction on the part of the fiscal authority to state variables in the model. The rule achieves the dual aim of imposing solvency on the fiscal sector and generating a state-contingent dynamic adjustment in a framework consistent with the properties of the model. Up to now, fiscal rules in leading large-scale macroeconomic forecasting models have been imposed exogenously, and in this sense are not necessarily compatible with the formulation of other sectors of these models. An example of the derivation procedure, including some illustrative results, is provided using a small calibrated macro model.
JEL Code
C5 : Mathematical and Quantitative Methods→Econometric Modeling
E6 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook
C62 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Existence and Stability Conditions of Equilibrium
Journal of International Money and Finance
  • Schüler, Y., Hiebert, P., Peltonen T.
Journal of Financial Stability
  • Hiebert, P., Jaccard, I., Schüler, Y.
Journal of Urban Economics
  • Hiebert, P., Sydow. M.
Journal of Housing Economics
  • Vansteenkiste, I., Hiebert, P.
Applied Economics
  • Vansteenkiste, I., Hiebert, P.
Economic Modelling
  • Hiebert, P., Pérez, J., Rostagno, M.
Journal of Policy Modeling
  • Pérez, J., PaulHiebert, P.
Systemic Risk in the Financial Sector: Ten Years after the Great Crash
  • Feldman, R. Hiebert, P.
Globalization, regionalism and economic interdependence,
  • Hiebert, P., Vansteenkiste, I.
Banca d’Italia: The Impact of Fiscal Policy
  • Hiebert, P., Vidal, J.-P., Lamo, A., Romero, D.
The Stability and Growth Pact – The Fiscal Architecture of EMU
  • Rostagno, M., Pérez, J., Hiebert, P.
Banca d’Italia: Fiscal Sustainability
  • Hiebert, P, Pérez, J.
  • Hiebert P
Joint report of the ECB / European Systemic Risk Board
  • Hiebert P and M Després (eds)
Paper of the Basel Committee on Banking Supervision Paper of the Task Force on Climate-related Risks (April)
  • Laurent Clerc and Paul Hiebert (eds)
International Monetary Fund Working Paper No. WP/19/295
  • Caparusso, J., Chen, Y., Dattels, P., Goel, R., and Hiebert, P.
European Systemic Risk Board
  • Hiebert, P, Després, M. (Editors)
University of Nottingham Globalisation and Economic Policy Centre Research Paper No. 14
  • Anderton. R., Hiebert, P.
Reserve Bank of Australia Research Discussion Paper Series 2006-07
  • Hiebert, P.