Digitální euro
Eurosystém se připravuje na potenciální vydání digitálního eura, které by doplňovalo fyzickou hotovost.
SPEECH
28 May 2026
Money in the digital age
Our goal is to preserve trust and stability as technology reshapes how money is used, says Board Member Piero Cipollone at Istituto Affari Internazionali. With our comprehensive payment strategy, we aim to modernise central bank money across retail, wholesale and cross‑border payments.
Read the full speech
MĚNOVÁ POLITIKA
28. května 2026
Záznam z dubnového měnověpolitického zasedání
Rada guvernérů ponechala úrokové sazby beze změny s odkazem na zvýšenou nejistotu v důsledku války na Blízkém východě a zvýšených rizik v oblasti inflace a růstu. Členové znovu potvrdili svůj závazek zajistit stabilizaci inflace ve střednědobém horizontu na 2% cíli.
Přečíst záznam
BLOG ECB
29. května 2026
Jak války ovlivňují očekávání spotřebitelů
Geopolitické šoky ovlivňují očekávání spotřebitelů ohledně inflace a hospodářského růstu. Tento příspěvek na blogu ECB ukazuje, jak války na Ukrajině a v Íránu ovlivňují ekonomické myšlení v sektoru domácností a jak předchozí zkušenosti zesilují reakce na následné geopolitické konflikty.
Přečtěte si blog ECB- 29 May 2026
- PRESS RELEASE
- 28 May 2026
- MONETARY POLICY ACCOUNTRelated
- 30 April 2026
- MONETARY POLICY DECISIONEnglishOTHER LANGUAGES (23) +
- 27 May 2026
- PRESS RELEASERelated
- 27 May 2026
- FINANCIAL STABILITY REVIEW
- 26 May 2026
- WEEKLY FINANCIAL STATEMENTEnglishOTHER LANGUAGES (22) +Annexes
- 26 May 2026
- WEEKLY FINANCIAL STATEMENT - COMMENTARY
- 22 May 2026
- GOVERNING COUNCIL DECISIONS - OTHER DECISIONSEnglishOTHER LANGUAGES (23) +
- 28 May 2026
- Speech by Piero Cipollone, Member of the Executive Board of the ECB, at Istituto Affari Internazionali
- 28 May 2026
- Speech by Christine Lagarde, President of the ECB, at the 28th meeting of Francophone Central Bank Governors, in Phnom Penh, Cambodia
- 27 May 2026
- Presentation slides by Luis de Guindos, Vice-President of the ECB, at the Financial Stability Review press briefing
- 22 May 2026
- Keynote speech by Philip R. Lane, Member of the Executive Board of the ECB, at the Asian Monetary Policy Forum
- 21 May 2026
- Slides by Frank Elderson, Member of the Executive Board of the ECB and Vice-Chair of the Supervisory Board of the ECB, at the University of Oxford in Oxford, United Kingdom
- 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 May 2026
- Interview with Luis de Guindos, Vice-President of the ECB, conducted by Amanda Mars on 30 April 2026
- 22 April 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 Eva Smal on 15 April 2026
- 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
- 6 May 2026
- Digitalisation is reshaping how banks pass on monetary policy. Compared with their branch‑based peers, digital banks are faster at adjusting deposit pricing for policy changes, but slower at updating their loan pricing.Details
- JEL Code
- E50 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→General
G20 : Financial Economics→Financial Institutions and Services→General
- 21 April 2026
- Artificial intelligence (AI) can help track inflation risks in real time. A new ECB model based on machine learning informs experts how likely it is that inflation will be much higher or much lower than they expect.Details
- JEL Code
- E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
- 29 May 2026
- LETTERS TO MEPS
- 28 May 2026
- WORKING PAPER SERIES - No. 3240Details
- Abstract
- Monetary policy asymmetrically affects the response of firms’ employment to an output shock and plays a role in cushioning employment adjustment over the business cycle. Combining annual firm-level data until 2020 with quarterly firm-level data until 2023 and high-frequency monetary policy surprises, we show that for a given change in output, monetary policy influences the extent to which firms hold on to labour, or “labour hoard”. Furthermore, this effect is asymmetric: a restrictive monetary policy reduces labour hoarding behaviour by 2 to 3 times more than an accommodative policy increases it. Finally, we look at the role of financing conditions and firm demographics.
- JEL Code
- E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
J23 : Labor and Demographic Economics→Demand and Supply of Labor→Labor Demand
E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles - Network
- Challenges for Monetary Policy Transmission in a Changing World Network (ChaMP)
- 27 May 2026
- WORKING PAPER SERIES - No. 3239Details
- Abstract
- We examine recent changes in stock market participation using newly available survey data from eleven euro area countries over the period 2020–2024. The evidence points to substantial turnover, with around 10% of non-stockholders entering the market each year, and more than 20% of stockholders exiting. New entrants tend to have lower education, income, financial literacy, and risk tolerance than established investors, indicating a shift in the composition of market participants. We also highlight the growing importance of cryptocurrency investments among retail investors. Overall, these findings shed new light on evolving household financial behavior and its implications for market participation and financial stability.
- JEL Code
- D14 : Microeconomics→Household Behavior and Family Economics→Household Saving; Personal Finance
E21 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Consumption, Saving, Wealth
G51 : Financial Economics
- 27 May 2026
- WORKING PAPER SERIES - No. 3238Details
- Abstract
- We propose a new model in which relationship-specific effects or shocks are identified in a bipartite network under mild covariance restrictions, generalizing the influential Abowd et al. (1999) framework. For example, separate demand shocks are identified for each bank from which a firm borrows. We show how previous approaches break down when confronted with such heterogeneity, while our novel identification strategy yields a simple estimator that is consistent and asymptotically normal, under weaker network density assumptions than previous approaches. The methodology performs well in empirically-calibrated simulations. We apply our approach to identify relationship-level credit demand and supply shocks for thousands of firms and banks across nine Euro-area countries and three distinct economic episodes. We formally reject the Abowd et al. (1999) assumptions in nearly every country-period and show that within-firm/bank shock variation is of comparable scale to between firm/bank variation. We document considerable bias in Abowd et al. (1999) style estimates and associated regressions, while finding significant deleterious effects of the post-2022 monetary contraction on exposed firms. We highlight novel heterogeneity in the transmission of monetary policy.
- JEL Code
- C33 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Panel Data Models, Spatio-temporal Models
C58 : Mathematical and Quantitative Methods→Econometric Modeling→Financial Econometrics
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G30 : Financial Economics→Corporate Finance and Governance→General - Network
- Challenges for Monetary Policy Transmission in a Changing World Network (ChaMP)
- 27 May 2026
- FINANCIAL STABILITY REVIEWAnnexes
- 27 May 2026
- FINANCIAL STABILITY REVIEW
Related- 27 May 2026
- PRESS RELEASE
- 27 May 2026
- FINANCIAL STABILITY REVIEW - BOXFinancial Stability Review Issue 1, 2026Details
- Abstract
- Liquidity mismatches in open-ended funds can generate systemic risk when redemption pressures meet illiquid markets, potentially triggering fire-sale spirals and spillovers to banks and other financial institutions. This box uses a system-wide agent-based model of the European financial system to assess the macroprudential impact of liquidity management tools in open-ended investment funds. The analysis evaluates two types of tool applied to second-round redemptions under the adverse scenario of the 2025 EU-wide stress test: redemption gates, which limit withdrawals, and anti-dilution levies, which pass liquidation costs on to redeeming investors. The results suggest that appropriately calibrated redemption gates can redistribute liquidity pressure away from more fragile and less liquid funds towards more resilient funds, thereby reducing the risk of destabilising fire sales while only marginally restricting aggregate liquidity. Anti-dilution levies generate meaningful liquidity transfers to funds facing high liquidation costs and are particularly effective in reducing tail losses in the fund sector. Both tools have limited effects on banks’ capital ratios, indicating that they do not materially constrain banks’ access to liquidity. Overall, the findings suggest that strictly and consistently implemented liquidity management tools can strengthen the resilience of investment funds and reduce systemic spillover risks, although potential incentives for pre-emptive redemptions due to gates or anti-dilution levies remain outside the model’s scope.
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G17 : Financial Economics→General Financial Markets→Financial Forecasting and Simulation
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
- 27 May 2026
- FINANCIAL STABILITY REVIEW - BOXFinancial Stability Review Issue 1, 2026Details
- Abstract
- The box examines the drivers of euro area investment fund flows into equity markets, with a particular focus on funds investing in currently highly valued US segments. Investment funds play a central role in channelling euro area capital into US equities, making them the key intermediary for assessing investment flows into assets with elevated valuations. Using a BVAR model, the analysis identifies US macroeconomic factors, most notably the AI-driven investment boom, as the dominant driver of recent inflows from the euro area into US equity markets, while deteriorating global risk sentiment has exerted offsetting downward pressure. More accommodative monetary conditions in both the United States and the euro area have supported inflows in recent years. The analysis also shows that flows into US technology equity funds are significantly more sensitive to macroeconomic and monetary shocks, as well as global risk sentiment, than flows into broader equity funds. This makes such funds particularly vulnerable to sudden and disorderly redemptions in the event of adverse developments. The findings highlight the risks to financial stability should these supportive drivers suddenly reverse, particularly through spillovers to euro area markets and wealth effects on euro area investors.
- JEL Code
- E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G15 : Financial Economics→General Financial Markets→International Financial Markets
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
- 27 May 2026
- FINANCIAL STABILITY REVIEW - BOXFinancial Stability Review Issue 1, 2026Details
- Abstract
- Following more than a decade of persistently depressed valuations, euro area banks’ price-to-book (P/B) ratios have been on an upward curve since late 2022, with the most pronounced rise seen during 2025. The 2025 surge has raised questions about the sustainability of high valuations going forward and the risks for financial stability. This box investigates the main drivers behind the marked increase in euro area bank valuations and the factors explaining the remaining gap with US banks. Using a Vector Error Correction Model (VECM), the analysis decomposes P/B ratios into macroeconomic, bank-specific and market factors. It finds that higher short-term interest rates (via restored deposit franchise values), improved bank profitability and elevated payout ratios (dividends and share buybacks) were the primary drivers of the 2022-25 increase. The remaining valuation gap with US banks stems mainly from weaker euro area macroeconomic conditions rather than bank fundamentals. While valuations appear broadly aligned with fundamentals, they remain vulnerable to negative growth surprises or rising risk premia.
- JEL Code
- G21, G12, E44 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
- 27 May 2026
- FINANCIAL STABILITY REVIEW - BOXFinancial Stability Review Issue 1, 2026Details
- Abstract
- The box examines euro area investors’ activity in euro area government bond (EGB) markets in response to changes in the slope of the yield curve. Investors are unevenly exposed to different maturities along the EGB yield curve, with non-bank financial institutions playing a particularly important role at the long end. Exploiting granular data on sectoral holdings of sovereign bonds, the analysis shows that euro area investors in EGBs react more strongly to changes in yields in other maturity segments than to changes in yields for the bonds they currently hold. As a result, shifts in the yield curve may trigger portfolio rebalancing across maturities, with non-banks acting as stabilisers of the long end of the curve. The box also documents foreign hedge fund activity in euro area government bond futures markets, highlighting persistent short positions in ultra-long maturities throughout 2025.
- JEL Code
- G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
G15 : Financial Economics→General Financial Markets→International Financial Markets
G22 : Financial Economics→Financial Institutions and Services→Insurance, Insurance Companies, Actuarial Studies
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
- 27 May 2026
- FINANCIAL STABILITY REVIEW - BOXFinancial Stability Review Issue 1, 2026Details
- Abstract
- Recent geopolitical and geoeconomic events − including the war in the Middle East, with disruptions to oil and energy supplies amplifying uncertainty − have heightened risks to global growth, inflation and financial stability. This box evaluates the impact of geoeconomic risks on euro area financial stability using a new composite indicator that integrates geopolitical, trade and financial market dimensions. Estimates based on a quantile vector autoregression reveal that spikes in geoeconomic risks significantly dampen economic activity, exacerbate financial stress and weaken financial cycles. Persistent geoeconomic stress poses asymmetric downside risks to real GDP growth and financial stability, thus challenging macroeconomic conditions in the euro area.
- JEL Code
- F51 : International Economics→International Relations, National Security, and International Political Economy→International Conflicts, Negotiations, Sanctions
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G01 : Financial Economics→General→Financial Crises
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes
- 27 May 2026
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 1, 2026Details
- Abstract
- This special feature examines policy-assignment dilemmas facing macroprudential authorities when housing markets boom: which instruments work best, on which objectives, and in combination with which other tools? It does so by revitalising Mundell’s Principle of Effective Market Classification, the policy-space analogue of Ricardo’s comparative advantage principle, and by applying it to macroprudential policy. The analysis uses a novel G-search literature-search algorithm and an AI-supported, replicable data-extraction system to assemble estimates of policy-impact parameters from the empirical literature. It then distinguishes standard, instrument-by-instrument evidence, from jointly estimated policy-impact parameters, which are needed to account for rival instruments acting in the same empirical setting. Three findings emerge. First, the results confirm earlier meta-analytic evidence that macroprudential policy moderates household credit growth more clearly than it does house price growth, that tightening has more visible effects than loosening, and that instruments differ in their strengths and weaknesses. Second, joint estimates sharpen policy-assignment analysis by revealing how relative effects change when instruments are assessed together rather than alone. Third, applying the Mundell framework identifies instrument pairings that satisfy necessary conditions for substitutability or complementarity. Overall, the menu of options available to effectively tame housing market booms is wide, provided instruments are assigned to objectives by their relative – not absolute – effectiveness.
- JEL Code
- C83 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Survey Methods, Sampling Methods
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G28 : Financial Economics→Financial Institutions and Services→Government Policy and Regulation
R31 : Urban, Rural, Regional, Real Estate, and Transportation Economics→Real Estate Markets, Spatial Production Analysis, and Firm Location→Housing Supply and Markets
- 27 May 2026
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 1, 2026Details
- Abstract
- Corporate bankruptcies in the euro area have been on the rise, but the aggregate asset quality of banks’ corporate lending has remained broadly stable. This special feature analyses this divergence and its implications for financial stability. It shows that rising bankruptcies may partly be explained by the normalisation of firm turnover since the COVID-19 pandemic, albeit with marked cross-country unevenness. At the same time, firm-level evidence suggests that balance sheet and profitability challenges are concentrated in a vulnerable tail of firms, but have remained stable for the average euro area company. Structural changes in corporate financing, including a declining reliance on bank loans and a larger role for equity, debt securities and non-bank lending, imply that a greater share of corporate risk might be outside the banking system. The analysis also shows that broadly stable aggregate asset quality reflects diverging trends in loan performance across countries and firm sizes, as well as banks’ proactive management of non-performing loans. Overall, it does not find any systematic evidence for banks delaying the recognition of non-performing loans in their loan books. Instead, the analysis indicates that weaker firm fundamentals result in a higher probability of bank exposures being reclassified from performing to non-performing.
- JEL Code
- E32 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Business Fluctuations, Cycles
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G01 : Financial Economics→General→Financial Crises
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill
G33 : Financial Economics→Corporate Finance and Governance→Bankruptcy, Liquidation
- 27 May 2026
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 1, 2026Details
- Abstract
- Financial stability communication is challenging because its task is not to forecast financial crises, let alone predict their precise timing. Rather, it is to identify vulnerabilities and explain how the financial system is likely to fare should it be confronted with adverse shocks. Great care is needed in this endeavour, because the sentiment of financial stability communication can influence market perceptions and risk assessments, as well as broader economic and financial outcomes. Given the presence of this potential feedback loop, the task of financial stability communication at the ECB has long been guided by a broad concept of financial stability: the smooth allocation of financial resources, effective management of risk by financial institutions and the capacity of the financial system to absorb shocks. Using the messages conveyed in the ECB’s Financial Stability Review over two decades, this special feature compares dictionary-based, FinBERT and prompt-based AI approaches to extracting financial stability sentiment. It finds broad co-movement across methods, while the GPT-based filter isolates sentences that contain explicit risk assessments, capturing subtle shifts in tone and context that were previously difficult to quantify. Used carefully, such tools can support risk monitoring and drafting consistency over time, but they remain complementary to expert judgement, vulnerability analysis and stress testing, rather than substitutes for it. A deep-dive box in the special feature also shows how AI can be used to systematically extract information from financial news to create an indicator for the severity and probability of triggers (SPOT) for financial stability risks.
- JEL Code
- 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
C81 : Mathematical and Quantitative Methods→Data Collection and Data Estimation Methodology, Computer Programs→Methodology for Collecting, Estimating, and Organizing Microeconomic Data, Data Access
- 26 May 2026
- WORKING PAPER SERIES - No. 3237Details
- Abstract
- This paper studies the employment effects of carbon pricing under the European Union’s Emissions Trading System (EU-ETS). I refer to standard methods from the literature to define and measure the environmental properties of jobs along two dimensions: how “green” a job is, and how polluting it is. I then leverage a series of shocks to EU-ETS prices to estimate their dynamic impacts on employment. The panel local projections estimates reveal that an exogenous 1% increase in EU-ETS prices leads to a roughly 0.2% decline in employment after one and a half years. Impacts on employment in more polluting jobs are estimated to be even stronger, while impacts on employment in greener jobs are also estimated to be negative, albeit less pronounced. Two factors play an important role in shaping these responses: the allocation of free emissions allowances and the stringency of employment protection legislation. When relatively fewer emissions are covered by free allowances, the negative employment effects of EU-ETS price shocks are stronger. Similarly, when employment protection is greater, the estimated impact is more muted. Average weekly hours of work is found to be an additional margin along which EU-ETS prices impact employment yet the estimated effects are relatively small and short-lived. Together, these findings underscore the economic consequences of carbon pricing, offering valuable insights for policymakers balancing climate objectives with labour market considerations.
- JEL Code
- E24 : Macroeconomics and Monetary Economics→Consumption, Saving, Production, Investment, Labor Markets, and Informal Economy→Employment, Unemployment, Wages, Intergenerational Income Distribution, Aggregate Human Capital
J21 : Labor and Demographic Economics→Demand and Supply of Labor→Labor Force and Employment, Size, and Structure
H23 : Public Economics→Taxation, Subsidies, and Revenue→Externalities, Redistributive Effects, Environmental Taxes and Subsidies
Q54 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Environmental Economics→Climate, Natural Disasters, Global Warming
Q58 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Environmental Economics→Government Policy
- 26 May 2026
- WORKING PAPER SERIES - No. 3236Details
- Abstract
- This paper develops a sequential deep learning algorithm for solving dynamic stochastic general equilibrium (DSGE) models. The algorithm trains a deep neural network to approximate the model’s policy functions across four progressive phases: steady-state anchoring, exploration around the steady state, simulation on the ergodic set, and Monte Carlo integration of stochastic expectations. Training requires no pre-computed starting approximation: the network initialises from the analytically known steady state and constructs its training data endogenously, resolving the circularity between the training distribution and the solution. A systematic comparison across network architectures shows that shallow, moderately wide networks with an intermediate steady-state penalty consistently deliver the best accuracy at the lowest computational cost. We apply the method to a two-country open-economy model and show that large tariff shocks generate non-linearities that local methods cannot reproduce even at higher orders.
- JEL Code
- C45 : Mathematical and Quantitative Methods→Econometric and Statistical Methods: Special Topics→Neural Networks and Related Topics
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
C68 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computable General Equilibrium Models
E13 : Macroeconomics and Monetary Economics→General Aggregative Models→Neoclassical
F13 : International Economics→Trade→Trade Policy, International Trade Organizations
- 26 May 2026
- FINANCIAL STABILITY REVIEW - ARTICLEFinancial Stability Review Issue 1, 2026Details
- Abstract
- Recent stress in parts of the US private credit market − including concerns about exposures in the software sector and redemption pressure in semi-liquid vehicles − has led to renewed focus on possible financial stability risks stemming from private credit and the potential relevance of such risks for the euro area. This special feature looks at the exposure of the euro area financial system to private credit. Using available commercial, public and proprietary data, it finds that euro area financial institutions appear to have limited direct exposure to private credit. This makes it unlikely that private credit in isolation could be a source of systemic financial instability at present. However, insurance corporations and pension funds in particular could, in an adverse scenario, face more material second-round revaluation losses from broader spillovers to leveraged loans, high-yield bonds and equities. Private credit could promote long-term growth by channelling funds from long-term investors to innovative firms, thereby supporting the objectives of the EU’s savings and investments union. The market should nonetheless be monitored closely, especially in view of worsening credit quality, possible expansion into retail-oriented structures and a potential role of private credit in AI-related financing. Reducing private credit’s opacity, addressing data gaps and working towards a harmonised definition of private credit at a global level would avoid a potential underestimation of direct exposures and enable risk to be assessed more completely.
- JEL Code
- G20 : Financial Economics→Financial Institutions and Services→General
G21 : Financial Economics→Financial Institutions and Services→Banks, Depository Institutions, Micro Finance Institutions, Mortgages
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
G32 : Financial Economics→Corporate Finance and Governance→Financing Policy, Financial Risk and Risk Management, Capital and Ownership Structure, Value of Firms, Goodwill
- 25 May 2026
- SURVEY OF MONETARY ANALYSTS
- 22 May 2026
- EURO AREA BALANCE OF PAYMENTS AND INTERNATIONAL INVESTMENT POSITION STATISTICS - QUALITY REPORTAnnexes
- 22 May 2026
- EURO AREA BALANCE OF PAYMENTS AND INTERNATIONAL INVESTMENT POSITION STATISTICS - QUALITY REPORT
- 21 May 2026
- RESEARCH BULLETIN - No. 143Details
- Abstract
- Artificial intelligence (AI) is rapidly transforming financial decision-making. To explore the implications for financial stability we ran simulation-based experiments on two different AI architectures. We found that Q-learning algorithms, a form of reinforcement learning, achieved a high degree of coordination, but were prone to bank run-like dynamics. In contrast, large language models , which rely on contextual reasoning, were less prone to such runs but generated heterogeneous and unpredictable behaviour. This suggests that AI architecture is itself a source of financial instability: algorithms operating in the same environment, pursuing the same goals, yield fundamentally different outcomes for financial stability
- JEL Code
- G01 : Financial Economics→General→Financial Crises
G23 : Financial Economics→Financial Institutions and Services→Non-bank Financial Institutions, Financial Instruments, Institutional Investors
C63 : Mathematical and Quantitative Methods→Mathematical Methods, Programming Models, Mathematical and Simulation Modeling→Computational Techniques, Simulation Modeling
- 20 May 2026
- OTHER PUBLICATIONAnnexes
- 20 May 2026
- SURVEY ON CREDIT TERMS AND CONDITIONS IN EURO-DENOMINATED SECURITIES FINANCING AND OTC DERIVATIVES MARKETS
Úrokové sazby
| Vkladová facilita | 2,00 % |
| Hlavní refinanční operace (s pevnou sazbou) | 2,15 % |
| Marginální zápůjční facilita | 2,40 % |
Míra inflace
Více o inflaciSměnné kurzy
| USD | US dollar | 1.1644 | |
| JPY | Japanese yen | 185.45 | |
| GBP | Pound sterling | 0.86723 | |
| CHF | Swiss franc | 0.9111 |
NEJSTE ODBORNÍK? PODÍVEJTE SE NA STRÁNKY „ECB VYSVĚTLUJE“
VÍCE INFORMACÍ
Co je to Evropská centrální banka?