Daniel Dieckelmann is a PhD student of Economics at the John F. Kennedy Institute of Freie Universität Berlin. His fields of research are quantitative macro-financial history, financial stability and systemic risk. He studied Information Systems and Economics in Cologne, Heidelberg and Copenhagen. For three years he worked on financial crises predictions and systemic risk analysis in fin-tech and at the European Central Bank. Daniel is interested in machine learning, statistics and programming and applies in his work quantitative methodologies to historical data. His current research seeks to enhance the understanding and prediction of financial crises through discovering and employing new historical macro-financial data.
In 2019, Daniel stayed at the Bank of Israel as a visiting researcher and currently he is a visiting PhD student and research assistant at Cornell University.
Money, Banking, and Financial Crises: A historical North American perspective, undergraduate economics, Freie Universität Berlin
Introductory Statistics, undergraduate economics, Heidelberg University
Daniel Dieckelmann conducts research at the intersection of quantitative economic history and macro-financial stability. Specifically, he investigates how employing historical data enhances our understanding of financial crises and their prediction.
Fields: financial stability, (quantitative) macro-financial history, empirical macroeconomics, systemic risk, monetary economics.
First supervisor: Prof. Dr. Max Steinhardt, Freie Universität Berlin
Second supervisor: Prof. Matthew Baron, PhD, Cornell University
Third supervisor: Priv.-Doz. Dr. Till Strohsal, Freie Universität Berlin
Abstract: This paper presents an early warning system for predicting banking crises specifically tailored to developed small open economies. The model considers two sources of financial instability: Domestic macro-financial imbalances and exposure to foreign banking systems with high crisis risk. Exposure of small open economies is measured by their total cross-border bank claims against foreign countries relative to GDP and weighted by the domestic risk of banking crisis in the foreign economies. A combined system that captures both national and foreign-induced risks outperforms conventional domestic early warning models. Further, the system correctly predicts crisis incidence out-of-sample for every small open economy in the sample prior to the Global Financial Crisis. Low banking exposure to highly leveraged foreign economies explains the resilience of many small open economies during the recent crisis.
Abstract: This paper examines whether the functional differentiation of private credit matters with respect to financial stability and economic growth. I present new historical data on total private credit to the non-financial sector in the United States for the past 120 years. The new series is disaggregated by type of borrower (household, businesses) and by type of loan (mortgage, non-mortgage). I find that no single credit components outperforms the others in predicting financial crises. However, total private credit levels above 80% rapidly increase their probability. Household non-mortgage credit has a positive short-term effect on income growth that is offset by a subsequent negative effect over the medium run. Business non-mortgage credit depresses income growth and puts the economy on a lower output level. Reversely, income growth induces growth in business debt, especially after the Second World War. The effects of mortgage credit remain ambiguous both in terms of financial stability and economic growth.