European Central Bank

European temporary migration in a two country DSGE model

Free movement of labour across borders can influence business cycle dynamics in the affected countries. This paper studies the macroeconomic implications of temporary migration using a two-country dynamic stochastic general equilibrium model calibrated to represent the “old” EU Member States (EU15) and the “new” Member States (NMS12). The model introduces fully endogenous temporary migration and combines it with search-and-matching frictions in labour markets.

Gas market shocks: tracing the effect on euro area inflation expectations

This paper examines the impact of natural gas market shocks on gas market dynamics, inflation expectations and realized inflation in the Euro Area using a BVAR model. Our contribution lies in a novel identification strategy that distinguishes between various types of shocks of unprecedented detail, leverages weekly rather than monthly data, and extends the analysis to both market-based headline and core inflation expectations.

Gas market shocks: tracing the effect on euro area inflation expectations

This paper examines the impact of natural gas market shocks on gas market dynamics, inflation expectations and realized inflation in the Euro Area using a BVAR model. Our contribution lies in a novel identification strategy that distinguishes between various types of shocks of unprecedented detail, leverages weekly rather than monthly data, and extends the analysis to both market-based headline and core inflation expectations.

European temporary migration in a two country DSGE model

Free movement of labour across borders can influence business cycle dynamics in the affected countries. This paper studies the macroeconomic implications of temporary migration using a two-country dynamic stochastic general equilibrium model calibrated to represent the “old” EU Member States (EU15) and the “new” Member States (NMS12). The model introduces fully endogenous temporary migration and combines it with search-and-matching frictions in labour markets.

Physical climate risk, credit risk and lending activity

We study how physical climate risk shapes bank lending activity and credit quality by combining high-resolution Copernicus flood geospatial maps with loan-level AnaCredit data. We exploit four major European floods (2021–2024) in a spatial regression discontinuity design comparing firms located just inside versus just outside flood boundaries (within 300–500 meters). We find that immediately after floods there is an increase by about 3.5 to 5% in lending, driven by liquidity demand, followed by a contraction of similar magnitude in the subsequent quarter.

Ex Machina: financial stability in the age of artificial intelligence

Does artificial intelligence (AI) pose a threat to financial stability? We study AI investor behavior, specifically Q-learning and large language model (LLM) investors, in a mutual fund redemption problem with economic and strategic uncertainty. Different AI architectures generate systematically different outcomes. Q-learning investors coordinate well but under default risk exhibit excessive redemption that amplifies fragility. LLM investors internalize equilibrium structure but display belief heterogeneity, weakening coordination and predictability.

Physical climate risk, credit risk and lending activity

We study how physical climate risk shapes bank lending activity and credit quality by combining high-resolution Copernicus flood geospatial maps with loan-level AnaCredit data. We exploit four major European floods (2021–2024) in a spatial regression discontinuity design comparing firms located just inside versus just outside flood boundaries (within 300–500 meters). We find that immediately after floods there is an increase by about 3.5 to 5% in lending, driven by liquidity demand, followed by a contraction of similar magnitude in the subsequent quarter.

Ex Machina: financial stability in the age of artificial intelligence

Does artificial intelligence (AI) pose a threat to financial stability? We study AI investor behavior, specifically Q-learning and large language model (LLM) investors, in a mutual fund redemption problem with economic and strategic uncertainty. Different AI architectures generate systematically different outcomes. Q-learning investors coordinate well but under default risk exhibit excessive redemption that amplifies fragility. LLM investors internalize equilibrium structure but display belief heterogeneity, weakening coordination and predictability.

Sectoral interconnectedness in the euro area economies: insights from network analysis

Using who-to-whom data for the last quarter of 2024, I build networks of financial interconnections in the euro area countries. After representing them in chord diagrams, I consider centrality metrics and find that banks dominate, with four exceptions: Cyprus, Ireland, Luxembourg and Malta. In these countries, other financial institutions and investment funds are at the core, with limited links to domestic sectors and strong ones with the rest of the world.

Disciplining digital risk: evidence from cyber stress tests

Investment in cybersecurity in an interconnected banking system has public-good proper- ties: positive externalities can generate systemic underinvestment. Using confidential supervi- sory data from the European Central Bank, we first identify “laggard” European banks that underinvest relative to their cyber-risk profiles, and then examine how supervisory scrutiny af- fects their incentives to invest. We exploit the 2024 ECB Cyber Resilience Stress Test (CyRST) as a quasi-natural experiment.

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