A digitális euro
Az eurorendszerben zajlanak a fizikai készpénz kiegészítőjének szánt digitális euro esetleges kibocsátásának előkészületei.
ACCOUNTABILITY
19 June 2026
Hearing at the European Parliament
President Christine Lagarde will speak on Monday, 22 June before the European Parliament’s Committee on Economic and Monetary Affairs and answer questions from its members. The hearing will be webcast live and starts at 15:00 CET.
Live webcast
RESEARCH
18 June 2026
Young Economist Prize 2026 finalists
We have selected the 10 finalists of the Young Economist Prize, whose research papers explore the role of innovation, growth and stability in shaping Europe’s future. The winner will be announced on 1 July at the ECB Forum on Central Banking in Sintra, Portugal.
Meet this year's finalists
CSALÁS
Figyelem, csalás!
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Bővebb információk- 19 June 2026
- Remarks by Frank Elderson, Member of the Executive Board of the ECB and Vice-Chair of the Supervisory Board of the ECB, and Alejandra Kindelán, Chair of the Spanish Banking Association, at the “Competitiveness for growth” event organised by the Spanish Banking Association
- 19 June 2026
- Speech by Piero Cipollone, Member of the Executive Board of the ECB, at the conference “80 years of the Italian Republic” hosted by Sapienza University, Rome
- 18 June 2026
- Slides by Piero Cipollone, Member of the Executive Board of the ECB, at the Policy Breakfast organised by Istituto per la Competitività
- 16 June 2026
- Slides by Philip R. Lane, Member of the Executive Board of the ECB, at Reuters NEXT Europe 2026 in London, United Kingdom
- 15 June 2026
- Opening speech by Christine Lagarde, President of the ECB, at the ECB conference on “Money in transition: digitalisation and innovation in payments”
- 10 June 2026
- Interview with Frank Elderson, Member of the Executive Board of the ECB and Vice-Chair of the Supervisory Board of the ECB, conducted by Daan Ballegeer and Rutger Betlem on 19 May 2026
- 31 May 2026
- Interview with Luis de Guindos, Vice-President of the ECB, conducted by Andrés Stumpf on 27 May 2026
- 26 May 2026
- Interview with Philip R. Lane, Member of the Executive Board of the ECB, conducted by Shogo Akagawa and Shiori Goso on 19 May 2026
- 26 May 2026
- Interview with Isabel Schnabel, Member of the Executive Board of the ECB, conducted by Balázs Korányi and Reinhard Becker on 21 May 2026
- 11 May 2026
- Interview with Luis de Guindos, Vice-President of the ECB, conducted by Olaf Storbeck on 7 May 2026
- 3 June 2026
- The current energy shock is significant and global, but it is also hitting a euro area economy that is more balanced than when Russia invaded Ukraine in early 2022. History and analysis show that context matters a lot for how shocks propagate to inflation.Details
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
- 2 June 2026
- The euro’s international use has grown in recent years, but largely by circumstance rather than by design. In a more contested global monetary system, Europe needs to act deliberately to strengthen the role of its currency.EnglishOTHER LANGUAGES (15) +Details
- JEL Code
- E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
- 29 May 2026
- Geopolitical shocks influence consumer expectations about inflation and growth. This blog explores how the wars in Ukraine and Iran affect the way households think about the economy and shows how the scars of past experiences amplify reactions to subsequent geopolitical conflicts.Details
- JEL Code
- E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
G10 : Financial Economics→General Financial Markets→General
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
- 26 May 2026
- The economic shock caused by the war between the United States and Iran has quickly fed into euro area firms’ expectations. Daily responses to an ECB survey show an immediate increase in expected input costs, selling prices and short-term inflation.Details
- JEL Code
- E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
G10 : Financial Economics→General Financial Markets→General
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
- 15 May 2026
- Non-bank financial institutions (NBFIs) are on the rise. This blog shows how shifts in their borrowing and investment portfolios constrain financing for euro area firms and affect the transmission of monetary policy.Details
- JEL Code
- E20 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→General
E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
- 17 June 2026
- WORKING PAPER SERIES - No. 3248Details
- Abstract
- Europe is increasingly exposed to heat waves and droughts, but their short-term economic effects across sectors remain hard to predict. This study develops climate-augmented models to predict real growth in per capita value added across 1,117 EU regions (2002–2022), by combining economic indicators with high-frequency climate data. When using machine learning (ML, Random Forest and XGBoost), climate variables improve predictions in agriculture, while gains for other sectors are limited and do not outperform economic models. Heat wave indicators consistently enhance predictive performance, whereas drought effects vary by sector. Simulations of extreme combined heat and drought scenarios suggest that agricultural annual growth could fall by 1.9 to 7.6 percentage points in most regions, whereas industry, and manufacturing in particular, is less affected, although impacts are more pronounced in Eastern Europe and the Baltic states. Overall, ML models better reflect complex climate–economic interactions, supporting their use for early warning, policy planning, and targeted adaptation.
- JEL Code
- C53 : Mathematical and Quantitative Methods→Econometric Modeling→Forecasting and Prediction Methods, Simulation Methods
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
Q54 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Environmental Economics→Climate, Natural Disasters, Global Warming
R15 : Urban, Rural, Regional, Real Estate, and Transportation Economics→General Regional Economics→Econometric and Input?Output Models, Other Models
- 16 June 2026
- WORKING PAPER SERIES - No. 3247Details
- Abstract
- We assess the effectiveness of supervisory activities in mitigating credit risk stemming from banks’ commercial real estate portfolios. We analyse two activity types deployed by European banking supervisors: (a) on-site inspections, which assess in depth banks’ risk-taking and internal controls, but can only be selectively applied, and (b) off-site targeted reviews, which survey risk management practices across institutions, are less intrusive but are applied more widely. Using quarterly confidential supervisory data for large euro area banks between 2020 and 2024, we employ a Difference-in-Differences framework with an event-study design to capture the effects of these activities on the coverage ratio of banks’ commercial real estate portfolios. We find that on-site inspections are followed by persistent increases in coverage ratios, while targeted reviews are associated with immediate improvements which are significant but short-lived. The results highlight the complementary nature of the two activity types, which have different outreach possibilities and effects.
- JEL Code
- 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
R30 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Real Estate Markets, Spatial Production Analysis, and Firm Location→General
C23 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Panel Data Models, Spatio-temporal Models
- 16 June 2026
- WORKING PAPER SERIES - No. 3246Details
- Abstract
- How well does innovation diffuse across geographic boundaries? To shed light on this question, we present a large-scale field experiment involving 3,300 firms across twelve European Union countries. We elicit firms’ perceptions of the share of similar firms in their own country that had invested in artificial intelligence (AI), as well as the corresponding share among similar firms in Germany, France, and Italy. We randomly provide half of the sample with accurate information about both domestic and foreign AI investment. We show that firms substantially underestimate competitors’ current AI investment, both domestically and abroad, and that they update their expectations about competitors’ future AI investment in response to the information treatment. The treatment also causes a statistically significant increase in firms’ own expected AI investment rate. We find strong strategic complementarities within borders: a 1 pp increase in the expected share of domestic peers investing in AI raises a firm’s own expected AI investment rate by 0.570 pp. These complementarities are absent across borders: the effect of an increase in the expected share of foreign peers investing in AI on a firm’s own expected AI investment rate is statistically insignificant. Overall, our evidence shows that innovation diffusion and strategic complementarities in AI investment are much stronger domestically than internationally.
- JEL Code
- O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes
D22 : Microeconomics→Production and Organizations→Firm Behavior: Empirical Analysis
C93 : Mathematical and Quantitative Methods→Design of Experiments→Field Experiments
L21 : Industrial Organization→Firm Objectives, Organization, and Behavior→Business Objectives of the Firm
- 16 June 2026
- STATISTICS PAPER SERIES - No. 53Details
- Abstract
- This report summarises the stylised facts from the 2023 wave of the Eurosystem Household Finance and Consumption Survey, which provides household-level data collected in a harmonised way in 20 euro area countries, as well as in the Czech Republic and Hungary, for a sample of almost 90,000 households. When compared with previous waves, the 2023 results are strongly affected by the temporary surge in inflation experienced in the euro area between 2021 and 2023. Since asset prices had not caught up with the price level by the time the survey was conducted, the real value of both assets and liabilities decreased for most households. In terms of net wealth, therefore, reductions were more pronounced for asset-rich households, while poorer, more indebted households tended to lose less, and sometimes benefit, from the price level increase. For real gross incomes, due to the slow adjustment of nominal wages, declines were recorded in the lower and middle parts of the distribution, including the median household, while those in the top income decile experienced gains. Expenditures on food were mostly stable in the aggregate, while those on utilities recorded some increase.
- JEL Code
- D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis
D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance
D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions - Network
- Household Finance and Consumption Network (HFCN)
- 16 June 2026
- STATISTICS PAPER SERIES - No. 52Details
- Abstract
- This report summarises the methodologies used in the fifth wave of the Eurosystem Household Finance and Consumption Survey, which provides household-level data collected in a harmonised way in the 20 euro area countries at the time of collection, as well as in the Czech Republic and Hungary. The total sample size comprises almost 90,000 households. Although the survey does not refer to the same time period in all countries, the most common reference period for the data is 2023. The report presents the methodologies applied in areas such as data collection, sample design, weighting, imputation and variance estimation. It also addresses statistical disclosure control issues and analyses issues that may have an effect on the comparability of the survey data across countries and waves.
- JEL Code
- D12 : Microeconomics→Household Behavior and Family Economics→Consumer Economics: Empirical Analysis
D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance
D31 : Microeconomics→Distribution→Personal Income, Wealth, and Their Distributions - Network
- Household Finance and Consumption Network (HFCN)
- 15 June 2026
- WORKING PAPER SERIES - No. 3245Details
- Abstract
- This paper re-examines foreign direct investment motives in the ‘FDI gravity’ model (Kleinert and Toubal, 2010), focusing on the role of distance. More precisely, we investigate whether aggregate and pooled gravity models for FDI obscure relevant heterogeneities across sectors. This is possible through the novel MREID dataset, which provides us with FDI data at the 2-digit NAICS level for 184 countries over the period 2010 to 2020. Our results reveal that aggregate and pooled models mask significant sector heterogeneities in two aspects: (i) in the importance of horizontal versus vertical FDI motives, and (ii) in the distance elasticity. Distance is negatively correlated with FDI on an aggregate level, which is robust to multiple econometric specifications, but exhibits significant sector heterogeneity. Our analysis suggests the presence of complex sector-specific components that cannot easily be explained with standard economic rationales.
- JEL Code
- F21 : International Economics→International Factor Movements and International Business→International Investment, Long-Term Capital Movements
F23 : International Economics→International Factor Movements and International Business→Multinational Firms, International Business
C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
- 15 June 2026
- DISCUSSION PAPER SERIES - No. No. 30Details
- Abstract
- The paper documents models used to analyse the interactions and trade-offs between price and financial stability at the European Central Bank. The paper describes a simple conceptual framework to think about the short- and medium-term trade-offs between price and financial stability. Short-term trade-offs arise whenever current inflationary pressure is high, but the financial system is experiencing stress. Medium-term trade-offs arise whenever current inflationary pressure is low, but risk is building up in the financial system. We document four main sets of models used to quantify trade-offs: time series models, balance sheet models, credit risk models and DSGE models with banking and financial frictions.
- JEL Code
- E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
- 15 June 2026
- CLIMATE-RELATED FINANCIAL DISCLOSURES
- 15 June 2026
- CLIMATE-RELATED FINANCIAL DISCLOSURES
- 15 June 2026
- SURVEY OF MONETARY ANALYSTS - AGGREGATE RESULTS
- 12 June 2026
- LETTERS TO MEPS
- 12 June 2026
- TARGET SERVICES ANNUAL REPORT
- 12 June 2026
- AMI-SECO REPORT
- 11 June 2026
- MACROECONOMIC PROJECTIONS FOR THE EURO AREAEnglishOTHER LANGUAGES (21) +Annexes
- 11 June 2026
- MACROECONOMIC PROJECTIONS FOR THE EURO AREA
- 3 June 2026
- WORKING PAPER SERIES - No. 3244Details
- Abstract
- We study how sector-specific fiscal policy propagates in an economy with heterogeneous households and production networks. We develop a multisector New Keynesian model in which input-output linkages interact with differences in households’ marginal propensities to consume (MPCs). We show that fiscal multipliers depend on sectors’ positions in the production network, as network linkages reallocate income across households with heterogeneous consumption responses. We derive an intersectoral Keynesian cross and introduce an MPC-augmented network multiplier that jointly characterize the transmission of fiscal shocks. The interaction between heterogeneous consumption responses and production networks is non-additive: network linkages can either amplify or attenuate fiscal transmission depending on how income is redistributed across households. Fiscal policy is most effective when spending is directed toward labor-intensive, downstream sectors that employ a large share of high-MPC households. Using data from the Survey of Consumer Finances, we document substantial sectoral heterogeneity in household balance sheets and in the prevalence of hand-to-mouth households. Calibrating the model to the U.S. economy, we find sizable variation in sectoral fiscal multipliers and significant distributional effects of government spending.
- JEL Code
- D57 : Microeconomics→General Equilibrium and Disequilibrium→Input?Output Tables and Analysis
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
E62 : Macroeconomics and Monetary Economics→Macroeconomic Policy, Macroeconomic Aspects of Public Finance, and General Outlook→Fiscal Policy
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
- 3 June 2026
- WORKING PAPER SERIES - No. 3243Details
- Abstract
- Using a recent and comprehensive data set covering nine of the most actively traded currencies on a monthly basis from 1995 to 2024, this paper explores the presence and potential drivers of herding behaviour in foreign exchange rate forecasts. The dataset features an average of 40–50 forecasters per currency, representing a broader range of currencies, a longer time frame, and a larger cross section of forecasters than is commonly found in the FX herding literature. Our results provide mixed evidence on herding, where the balance tends towards anti-herding conclusions.While some revision-based tests suggest herding when current consensus forecasts are used, this evidence weakens considerably when lagged information is employed. In contrast, forecast-error based tests, Bernhardt et al. statistics, and over-reaction regressions more often point to anti-herding, particularly at longer horizons. Overall, we interpret the findings as suggesting thatdifferences among forecasters are largely attributable to heterogeneous views, noise, or idiosyncratic error rather than systematic convergence toward the consensus. When alternative explanations for expectation formation or revisions are considered, the main findings remain unchanged across a wide range of measures, including different types of uncertainty and FX predictors such as the forward premium, the real exchange rate, and the depreciation rate.
- JEL Code
- C10 : Mathematical and Quantitative Methods→Econometric and Statistical Methods and Methodology: General→General
C22 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models &bull Diffusion Processes
F31 : International Economics→International Finance→Foreign Exchange
F47 : International Economics→Macroeconomic Aspects of International Trade and Finance→Forecasting and Simulation: Models and Applications
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
- 2 June 2026
- WORKING PAPER SERIES - No. 3242Details
- Abstract
- This paper examines whether differences in the composition of investment help explain economic growth disparities in the EU and other advanced economies from 1996 to 2021. While overall investment levels in the EU and the US are broadly similar, the EU invests less in intangible and tangible ICT capital. This difference in composition is associated with part of the EU’s productivity gap with the US. Employing panel fixed effects and local projection methods, we find that intangible and tangible ICT investments -particularly in communications equipment, R&D, and other intellectual property products- are associated with higher GDP per capita growth than other forms of investment. To quantify these differences, we construct a novel investment efficiency ratio that relates the estimated economic growth contribution of each asset to its share in total investment. The results are robust across empirical methods, country samples, and time periods, and reveal substantial heterogeneity: the growth association of ICT-related investment is stronger in countries with higher income levels and greater human capital. Overall, the findings suggest that improving the allocation and efficiency of investment, rather than simply increasing its volume, is key to enhancing long-term growth.
- JEL Code
- E22 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Capital, Investment, Capacity
O47 : Economic Development, Technological Change, and Growth→Economic Growth and Aggregate Productivity→Measurement of Economic Growth, Aggregate Productivity, Cross-Country Output Convergence
O50 : Economic Development, Technological Change, and Growth→Economywide Country Studies→General
J24 : Labor and Demographic Economics→Demand and Supply of Labor→Human Capital, Skills, Occupational Choice, Labor Productivity
C23 : Mathematical and Quantitative Methods→Single Equation Models, Single Variables→Panel Data Models, Spatio-temporal Models
- 2 June 2026
- THE INTERNATIONAL ROLE OF THE EURO - BOXThe international role of the euro 2026Details
- Abstract
- This box presents novel analytical indicators to assess the euro’s global appeal derived from newly developed currency breakdowns in the euro area international investment position (IIP). By 2025, the euro accounted for one-third of euro area cross-border assets and two-thirds of liabilities, with the euro’s share of liabilities rising from 54% in 2015 to 66% in 2025, reflecting its growing attractiveness. Factors such as the availability of assets, trade intensity and positive business sentiment towards Europe correlate with this trend. Policies fostering trade openness, safe asset supply, and macroeconomic stability could further enhance the euro’s global role.
- JEL Code
- F20 : International Economics→International Factor Movements and International Business→General
F31 : International Economics→International Finance→Foreign Exchange
F41 : International Economics→Macroeconomic Aspects of International Trade and Finance→Open Economy Macroeconomics
- 2 June 2026
- THE INTERNATIONAL ROLE OF THE EURO - BOXThe international role of the euro 2026Details
- Abstract
- This box examines the evolving role of euro-denominated sovereign debt as a global safe asset. Using the government basis to estimate convenience yields earned by foreign investors, the results indicate that the foreign convenience yield on German government bonds has increased in recent years. Notably, the majority of this yield is attributable to foreign investors’ preference for euro currency exposure rather than the bonds’ safety or liquidity. However, the foreign convenience yield remains substantially below that of US Treasuries. The analysis further shows that larger and more liquid sovereign debt markets tend to generate higher convenience yields, while the euro’s international reserve currency role may be constrained by the limited supply and fragmentation of highly rated euro area government debt. Although EU bonds have grown rapidly, their temporary and fragmented structure limits their safe-asset properties. Establishing a genuine European safe asset could strengthen the euro’s international role, improve market liquidity and support the financing of European public goods.
- JEL Code
- G15 : Financial Economics→General Financial Markets→International Financial Markets
F31 : International Economics→International Finance→Foreign Exchange
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
F41 : International Economics→Macroeconomic Aspects of International Trade and Finance→Open Economy Macroeconomics
F32 : International Economics→International Finance→Current Account Adjustment, Short-Term Capital Movements
- 2 June 2026
- THE INTERNATIONAL ROLE OF THE EURO - BOXThe international role of the euro 2026Details
- Abstract
- Safe-haven currencies provide investors with protection during periods of heightened market uncertainty and financial stress. This box examines the behaviour of the euro during geopolitical and policy-related risk-off episodes in 2025 and early 2026, including tariff announcements and the outbreak of war in the Middle East. While traditional safe-haven currencies such as the US dollar, Swiss franc and Japanese yen continued to attract inflows during periods of acute stress, the euro also displayed characteristics typically associated with safe-haven assets, reflecting changing perceptions of the euro area’s resilience and external position. The analysis highlights that strengthening and further integrating euro area capital markets would help insulate the euro area from exchange rate volatility and support the euro’s evolution into a stronger global international currency.
- JEL Code
- F31 : International Economics→International Finance→Foreign Exchange
G15 : Financial Economics→General Financial Markets→International Financial Markets
Kamatlábak
| Betéti rendelkezésre állás | 2,25 % |
| Irányadó refinanszírozási műveletek (rögzített kamatláb) | 2,40 % |
| Aktív oldali rendelkezésre állás | 2,65 % |
Inflációs ráta
Bővebben az inflációrólÁrfolyam
| USD | US dollar | 1.1467 | |
| JPY | Japanese yen | 184.88 | |
| GBP | Pound sterling | 0.86653 | |
| CHF | Swiss franc | 0.9248 |
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