Seminar in presence

Building BL26/B – Sala Consiglio (ground floor)
Department of Management, Economics and Industrial Engineering
Via R. Lambruschini 4/B, 20156 Milano

Lucia Alessi
European Commission

Abstract:

The first part of the seminar will be devoted to a general discussion of the main challenges posed by climate stress testing, with particular reference to the long-term nature of the shocks against the short-term focus of stress-testing exercises, and to data issues in the context of both climate physical and transition risks.
The second part of the seminar will focus on the paper by Alessi, Di Girolamo, Pagano and Petracco (2022). This is an empirical application, where a stylized simulation model is used to assess the potential impact of transition risk on banks’ balance sheets and to establish a basis for calibrating relevant prudential instruments. The paper shows that even in the short run, a fire-sale mechanism could amplify an initially contained shock on high-carbon assets into a systemic crisis with significant losses for the EU banking sector. A capital add-on of 0.9% RWA on average would be sufficient to protect the system.

Lucia Alessi is Team Leader at the Joint Research Centre of the European Commission, where she is responsible for research in support of EU policymaking in the field of finance, including banking and financial stability, corporate finance, as well as digital and sustainable finance. From 2007 to 2015 she worked at the European Central Bank, where she served in various DGs, including Research, Economics, and Macroprudential Policy and Financial Stability. Lucia chairs the EC Sustainable Finance Research Forum. Her more recent research focuses on the assessment of the greenness of financial assets, climate risk and sustainability in financial markets, and the development of climate stress tests for financial institutions. On systemic risk more generally, she has worked on early warning systems for financial crises, including sovereign crises. Earlier in her career, she worked on econometric models for big data, with a focus on dynamic factor models.

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