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.
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, monetary economics.
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, business), type of debt instrument (loan, debt security), and by type of lending institution (bank, non-bank). I apply the credit component data to the financial crisis literature and the finance-growth nexus. All credit components except for non-mortgage bank loans are positively linked with the probability of financial crisis. A total private-debt-to-GDP ratio above 80 percent rapidly increases the likelihood of crisis. Growth in non-mortgage bank loans mitigates financial instability and is positively related to a shorter medium-term consumption-investment cycle and to economic growth over the entire time span of the sample. Household non-mortgage credit growth has a positive, short-term effect on economic activity. After the Second World War, the main driver of financial instability has shifted from corporate debt to household mortgage debt. In historical applications, bank loans are found to be a suboptimal proxy for private credit.
Abstract: We develop an early warning system to predict financial crises for small, open, and developed economies with independent monetary policy. We employ a multivariate logistic regression model to predict currency and banking crises in and out-of-sample. We find residential property prices and credit to the private non-financial sector to be the most significant and reliable predictors for both currency and banking crises. Current account imbalances help predict currency crisis but not banking crises. Stock prices are largely uninformative. Alongside standard macroeconomic and financial variables we include specific variables from influencing countries, like the United States, whose macroeconomic policies have the potential to induce crises in small, open economies. We find the growth rate of U.S. policy rates and the U.S. Dollar exchange rate to be indicative of banking crises but not currency crises. Our model is able to predict the Global Financial Crisis of 2007-09 out-of-sample where it assigns significantly lower crisis probabilities to economies that retrospectively were spared from disaster. Currently, Israel’s inflated house prices pose the largest threat to its financial stability.