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Daniel Dieckelmann


PhD Candidate

Lansstraße 5-9
14195 Berlin

Daniel Dieckelmann is a PhD candidate of Economics at the John F. Kennedy Institute of Freie Universität Berlin.

His research fields are Macro-Finance, Economic History, Financial Stability, and Systemic Risk.

With a double background in Information Systems and Economics, Daniel is further interested in machine learning and the history of economic thought. His current research explores the quantitative history of banking crises and the possibility of their prediction.

Daniel has worked for the European Central Bank, in the financial sector, and for tech start-ups.

During his PhD studies, Daniel has been hosted by Cornell University, the Bank of Israel, and the Institute of New Economic Thinking for research stays.

Download Daniel’s CV here.


Booms, Busts, and Business Cycles: Economic Growth and Financial Development in Canada, Mexico, and the United States.

Intermediate undergraduate economics, Freie Universität Berlin

Download Syllabus (2020, preliminary)

2018-2019 & 2020:
Money, Banking, and Financial Crises: A historical North American perspective.

Intermediate undergraduate economics, Freie Universität Berlin

Download Syllabus (2020)

Introductory Statistics.

Undergraduate economics, Heidelberg University

Mentoring Team:

Prof. Dr. Max Steinhardt, Freie Universität Berlin (First advisor)

Prof. Matthew Baron, PhD, Cornell University (Second advisor)

Prof. Dr. Moritz Schularick, University of Bonn

Priv.-Doz. Dr. Till Strohsal, Freie Universität Berlin


Working Papers:


Cross-Border Lending and the International Transmission of Banking Crises

Collaboration with the Bank of Israel

Abstract: This paper introduces a new transmission channel of banking crises where sizable cross-border bank claims on foreign countries with high domestic crisis risk enable contagion to the home economy. This asset-side channel opposes traditional views that see banking crises originating from either domestic credit booms or from cross-border borrowing. I propose a combined model that predicts banking crises using both domestic and foreign factors. For developed economies, the channel is predictive of crises irrespective of other types of capital flows, while it is entirely inactive for emerging economies. I show that policy makers can significantly enhance current early warning models by incorporating exposure-based risk from cross-border lending.

Download paper

The Historical Banking Crisis Database, 1870-2016

with Matthew Baron, Cornell University

Abstract: We construct a new quantitative and narrative database of banking crises, covering 46 countries since 1870, and show two ways in which modern banking crises are different from past crises. First, although unlevered bank equity losses are lower for post-1945 crises (relative to pre-1914 crises), bank equity losses are considerably greater, due to higher bank leverage. Second, in the pre-1914 period, bank equity returns are sensitive to trade, commodity, and monetary gold shocks, but less so to real estate returns and past credit booms---whereas the reverse is true in the post-1945 period. In particular, few pre-1914 banking crises are credit booms gone bust.


Laissez Faire or Laissez Faire Faillite? 150 Years of Bank Bailouts

Work in progress.


Making Predictions Possible: Financial Fragility, Market Sentiment, and Banking Crises

Abstract: Using quarterly historical data for the past 120 years, I show that real-time estimates of financial fragility fail to predict banking crises in the United States while market sentiment indicators—driven particularly by sharp reversals in corporate securities issuance relative to GDP—predict banking crises several quarters ahead of time. Despite recent support from cross-sectional studies, credit cycle measures such as the credit-to-GDP gap do not have sufficient predictive power that a policy maker historically could have used ex ante. A recently proposed triggers-plus-vulnerabilities interpretation of the credit cycle seems unlikely to hold in light of my findings.

Dahlem Research School
Deutsche Forschungsgemeinschaft