SafeFBDC: AI & Monetary Policy

Since the global financial crisis (GFC) of 2008/2009, central banks have been tasked with new responsibilities that include measuring systemic risk, banking regulation and supervision, digital currencies, and climate change. These responsibilities are in part a result of the collection and access to new data sources — introducing central banks to “Big Data”.

Artificial Intelligence (AI) and Machine Learning (ML) have gained importance in this process. AI and ML, in particular, can further improve the data basis for monetary policy decisions of central banks. For example, by providing more complete, immediate, and granular information to complement existing (macroeconomic) indicators. Moreover, they provide new tools to analyse big data efficiently, thereby facilitating monetary policy decision.

Objective

Between 2021- 2023, the "Artificial Intelligence and Monetary Policy Decisions" project (as part of the safe Financial Big Data Cluster (safeFBDC)) will investigate the use and importance of AI and ML in central banks and monetary policy using well-defined case studies.

The objective of the project is to improve monetary policy decision making in the Eurozone in two dimensions: (1) improve the data basis for monetary policy decisions; (2) use AI / ML methods to generate new information that is valuable for central banks and monetary policy to investigate questions related to macro analysis and forecasting, supervision of financial indicators and the assessment of financial stability risks. More broadly, these case studies will also help market participants (such as financial and non-financial institutions, regulators, academics) and other “observers” to better understand the reasons and implications of monetary policy decisions.

Project duration: 2021-2023

Project status: Ongoing

Project partners: Deloitte, TechQuartier

Associate Partner: Deutsche Bundesbank

*** NEW paper *** 

Estimating German Bank Climate Risk Exposure using the EU Emissions Trading System

Steffen,S., and Hoffner, F., 2022. Estimating German Bank Climate Risk Exposure using the EU Emissions Trading System, Research Paper.

An overview article on machine learning in central banking:

Kinywamaghana, A., and Steffen,S., 2021. A Note on the Use of Machine Learning in Central Banking, FIRE Research Paper. 

Faculty

PhD 

Research Team

• Acharya, V.V., M. Jager, S. Steffen, and L. Steinruecke, 2020. Kicking the can down the road: government interventions in the European banking sector, Review of Financial Studies, forthcoming.
• Acharya, V.V., D. Pierret, and S. Steffen, 2020. Lender of Last Resort versus Buyer of Last Resort – Evidence from the European Sovereign Debt Crisis,  Financial Markets, Institutions, and Instruments, forthcoming
• Acharya, V.V., B. Imbierowicz, D. Teichmann, and S. Steffen, 2020. Does the Lack of Financial Stability Impair the Transmission of Monetary Policy? Journal of Financial Economics, 138 (2), 342-365.
• Kirschenmann, K., J. Korte, and S. Steffen, 2020. A Zero-Risk Weight Channel of Sovereign Risk Spillovers, Journal of Financial Stability, 51.
• Große-Rueschkamp, B., S. Steffen, and D. Streitz, 2019. A Capital Structure Channel of Monetary Policy, Journal of Financial Economics, 133 (2), 357-378.
• Cai, J., F. Eidam, A. Saunders, and S. Steffen, 2018. Syndication, Interconnectedness and Systemic Risk, Journal of Financial Stability, 34, 105-120.
• Acharya, V., and S. Steffen, 2015. The “Greatest” Carry Trade Ever? Understanding Eurozone Bank Risks, Journal of Financial Economics, 115 (2), 215 – 236. (Lead Article).