Ei saatavilla suomeksi
Bruno Buchetti
- 19 October 2026
- WORKING PAPER SERIES - No. 3302Details
- Abstract
- General-purpose technologies create large opportunities but make their distribution across firms difficult to assess in real time. We use banks as interpreters of the rise of generative AI by studying lender-specific probability-of-default (PD) estimates for private firms in four large euro-area economies. As public attention to AI rises after ChatGPT, banks lower assessed default risk for borrowers in highly AI-exposed industries by about 5%. The reassessment is weaker for labor-intensive firms and stronger among lenders with pre-existing industry expertise. Yet the favorable industry-level effect is accompanied by a systematic reordering within product markets: banks reassess firms with stronger pre-ChatGPT market positions less favorably relative to their competitors, compressing the risk advantage associated with market leadership. Credit allocation moves in the same direction, with market leaders experiencing relatively lower drawn and committed credit, more relationship exits, and fewer new lenders. The reordering also predicts subsequent bad-credit events beyond banks’ consensus PDs. Internal bank assessments therefore provide a forward-looking measure of how generative AI is expected to reshape competitive risk.
- JEL Code
- D82 : Microeconomics→Information, Knowledge, and Uncertainty→Asymmetric and Private Information, Mechanism Design
D83 : Microeconomics→Information, Knowledge, and Uncertainty→Search, Learning, Information and Knowledge, Communication, Belief
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
O33 : Economic Development, Technological Change, and Growth→Technological Change, Research and Development, Intellectual Property Rights→Technological Change: Choices and Consequences, Diffusion Processes
- 11 June 2025
- WORKING PAPER SERIES - No. 3060Details
- Abstract
- This study examines how dismantling Mafia-connected firms affects banks’ lending practices. Using a unique dataset of 667 such firms and loan-level data from the European Central Bank, our analysis shows that anti-Mafia operations precede an increase in bank loans to businesses that operate in areas that are directly affected by these actions. Specifically, overall loan volumes increase by approximately 0.8 percent, which translates to an increase of €1.38 billion in bank loans to these firms. The effect increases to 1.2 percent in areas that have experienced extensive Mafia activities, amounting to €2.76 billion in bank loans, and to 2.1 percent in areas that were once dominated by Mafia-connected firms that were engaged in rent extraction, amounting to €3.62 billion in bank loans. Borrowing costs rise concurrently, driven by heightened perceptions of risk following exposure of Mafia infiltration. Cross-sectional analyses indicate that banks’ responses vary significantly because non-local and foreign banks and banks with no prior exposure to Mafia-affiliated firms face increased challenges related to their lack of local knowledge. Removal of Mafia-connected firms also correlates with improved productivity in affected municipalities, underscoring financial institutions’ dynamic responses to the eradication of organized crime and the potential for economic revitalization in post-Mafia environments.
- JEL Code
- E52 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Monetary Policy
E58 : Macroeconomics and Monetary Economics→Monetary Policy, Central Banking, and the Supply of Money and Credit→Central Banks and Their Policies
G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
- 21 April 2025
- WORKING PAPER SERIES - No. 3050Details
- Abstract
- The primary objective of this study is to explore the dynamic relationships between equity returns or volatility and sentiment factors in European markets during both the periods preceding the COVID-19 pandemic, the COVID-19 itself, and the Russia-Ukraine war. We achieve this by applying the network methodology initially introduced by Diebold & Yilmaz (2014), along with its extensions based on realized measures and generalized forecast error variance decomposition, as proposed by Baruník & Křehlík (2018) and Chatziantoniou et al. (2023). Additionally, we investigate how the global sentiment factor influences the overall connectedness index by employing a quantile-on-quantile approach, following the methods outlined by Sim & Zhou (2015) and Bouri et al. (2022). To conduct our analysis, we utilize daily-frequency data encompassing the period from January 1, 2011, to December 31, 2023, covering the entirety of the COVID-19 pandemic in 2020 and the Russia-Ukraine conflict in 2022 across six European stock indices. Our primary discovery is the interconnectedness of both returns and sentiment. Furthermore, our resultsindicate that during the COVID-19 and Russia-Ukraine war, there is a notable increase in volatility spillovers among the analyzed stock indices, driven by the heightened interconnectedness between stock market returns.
- JEL Code
- G11 : Financial Economics→General Financial Markets→Portfolio Choice, Investment Decisions
G12 : Financial Economics→General Financial Markets→Asset Pricing, Trading Volume, Bond Interest Rates
G14 : Financial Economics→General Financial Markets→Information and Market Efficiency, Event Studies, Insider Trading
G40 : Financial Economics
- 13 March 2024
- WORKING PAPER SERIES - No. 2916Details
- Abstract
- Using pan-European credit register data, we analyze the sharp increase in public guaranteed lending (PGL) during the COVID-19 loan guarantee programs and show that banks leverage PGL to expand lending to low-emission firms. This behavior is driven by industries less affected by COVID-19, banks with ”browner” portfolios, and younger firms. Notably, compared to high-emission firms, banks’ internal risk assessments in PGL to low-emission firms are less frequently updated and exhibit weaker predictive power for future credit quality deterioration, indicating lax monitoring efforts. These findings highlight the additional information production costs associated with green lending and shed light on why banks may be slow to transition to greener portfolios.
- JEL Code
- G20 : Financial Economics→Financial Institutions and Services→General
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