Volatilität, Wachstum und Finanzkrisen

Diese Forschungsgruppe analysiert die Entstehung von Instabilitäten im Finanzsystem und die realökonomischen Konsequenzen von Finanzkrisen. Dabei werden kausale Reaktionen gesamtwirtschaftlicher Größen auf makroökonomische Schocks identifiziert. Frühwarnmodelle beschreiben das zyklische Auftreten von Vulnerabilitäten im Finanzsystem.

IWH-Datenprojekt: Financial Stability Indicators in Europe

Forschungscluster
Finanzstabilität und Regulierung

Ihr Kontakt

Juniorprofessor Dr. Gregor von Schweinitz
Juniorprofessor Dr. Gregor von Schweinitz
Mitglied - Abteilung Makroökonomik
Nachricht senden +49 345 7753-744 Persönliche Seite

PROJEKTE

01.2018 ‐ 12.2018

International Monetary Policy Transmission

Deutsche Bundesbank

Juniorprofessor Dr. Gregor von Schweinitz

01.2017 ‐ 12.2018

Early-warning Models for Systemic Banking Crises

Deutsche Forschungsgemeinschaft (DFG)

Juniorprofessor Dr. Gregor von Schweinitz

Referierte Publikationen

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The Appropriateness of the Macroeconomic Imbalance Procedure for Central and Eastern European Countries

Geraldine Dany-Knedlik Martina Kämpfe Tobias Knedlik

in: Empirica, im Erscheinen

Abstract

The European Commission’s Scoreboard of Macroeconomic Imbalances is a rare case of a publicly released early warning system. It was published first time in 2012 by the European Commission as a reaction to public debt crises in Europe. So far, the Macroeconomic Imbalance Procedure takes a one-size-fits-all approach with regard to the identification of thresholds. The experience of Central and Eastern European Countries during the global financial crisis and in the resulting public debt crises has been largely different from that of other European countries. This paper looks at the appropriateness of scoreboard of the Macroeconomic Imbalances Procedure of the European Commission for this group of catching-up countries. It is shown that while some of the indicators of the scoreboard are helpful to predict crises in the region, thresholds are in most cases set too narrow since it largely disregarded the specifics of catching-up economies, in particular higher and more volatile growth rates of various macroeconomic variables.

Publikation lesen

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Does Machine Learning Help us Predict Banking Crises?

Johannes Beutel Sophia List Gregor von Schweinitz

in: Journal of Financial Stability, im Erscheinen

Publikation lesen

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Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?

Peter Sarlin Gregor von Schweinitz

in: Macroeconomic Dynamics, im Erscheinen

Abstract

Recurring financial instabilities have led policymakers to rely on early-warning models to signal financial vulnerabilities. These models rely on ex-post optimization of signaling thresholds on crisis probabilities accounting for preferences between forecast errors, but come with the crucial drawback of unstable thresholds in recursive estimations. We propose two alternatives for threshold setting with similar or better out-of-sample performance: (i) including preferences in the estimation itself and (ii) setting thresholds ex-ante according to preferences only. Given probabilistic model output, it is intuitive that a decision rule is independent of the data or model specification, as thresholds on probabilities represent a willingness to issue a false alarm vis-à-vis missing a crisis. We provide real-world and simulation evidence that this simplification results in stable thresholds, while keeping or improving on out-of-sample performance. Our solution is not restricted to binary-choice models, but directly transferable to the signaling approach and all probabilistic early-warning models.

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Trade Effects of Silver Price Fluctuations in 19th-Century China: A Macro Approach

Makram El-Shagi Lin Zhang

in: China Economic Journal, 2020

Abstract

We assess the role of silver price fluctuations in Chinese trade and GDP during the late Qing dynasty, when China still had a bimetallic (silver/copper) monetary system, in which silver was mostly used for international trade. Using a structural VAR (SVAR) with blockwise recursive identification, we identify the impact of silver price shocks on the Chinese economy from 1867, when trade data became available, to 1910, one year before the Qing dynasty collapsed. We find that silver price shocks had a sizable impact on both imports and exports but only a very minor effect on the trade balance, only a marginal impact on growth, and almost no effect on domestic prices. Stronger effects were partly mitigated by inelastic export quantities. Generally, the effect of silver price shocks, while considerable, was only short-lived, displaying no persistence in either direction. We find that the bimetallic system in Qing China might have mitigated a potential positive effect of silver depreciation but did not reverse the effect, which – contrary to claims made in the previous literature – was responsible for neither the worsening trade balance nor the inflation and the quickly increasing imports that occurred during our sample period.

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On the Empirics of Reserve Requirements and Economic Growth

Jesús Crespo Cuaresma Gregor von Schweinitz Katharina Wendt

in: Journal of Macroeconomics, June 2019

Abstract

Reserve requirements, as a tool of macroprudential policy, have been increasingly employed since the outbreak of the great financial crisis. We conduct an analysis of the effect of reserve requirements in tranquil and crisis times on long-run growth rates of GDP per capita and credit (%GDP) making use of Bayesian model averaging methods. Regulation has on average a negative effect on GDP in tranquil times, which is only partly offset by a positive (but not robust effect) in crisis times. Credit over GDP is positively affected by higher requirements in the longer run.

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Arbeitspapiere

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Energy Markets and Global Economic Conditions

Christiane Baumeister Dimitris Korobilis Thomas K. Lee

in: NBER Working Paper, Nr. 27001, 2020

Abstract

This paper evaluates alternative indicators of global economic activity and other market fundamentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. We find that world industrial production is one of the most useful indicators that has been proposed in the literature. However, by combining measures from a number of different sources we can do even better. Our analysis results in a new index of global economic conditions and new measures for assessing future tightness of energy demand and expected oil price pressures.

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Drawing Conclusions from Structural Vector Autoregressions Identified on the Basis of Sign Restrictions

Christiane Baumeister James D. Hamilton

in: NBER Working Paper No. 26606, 2020

Abstract

This paper discusses the problems associated with using information about the signs of certain magnitudes as a basis for drawing structural conclusions in vector autoregressions. We also review available tools to solve these problems. For illustration we use Dahlhaus and Vasishtha's (2019) study of the effects of a U.S. monetary contraction on capital flows to emerging markets. We explain why sign restrictions alone are not enough to allow us to answer the question and suggest alternative approaches that could be used.

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Fiscal Policy and Fiscal Fragility: Empirical Evidence from the OECD

Makram El-Shagi Gregor von Schweinitz

in: IWH-Diskussionspapiere, Nr. 13, 2019

Abstract

In this paper, we use local projections to investigate the impact of consolidation shocks on GDP growth, conditional on the fragility of government finances. Based on a database of fiscal plans in OECD countries, we show that spending shocks are less detrimental than tax-based consolidation. In times of fiscal fragility, our results indicate strongly that governments should consolidate through surprise policy changes rather than announcements of consolidation at a later horizon.

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An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?

Johannes Beutel Sophia List Gregor von Schweinitz

in: IWH-Diskussionspapiere, Nr. 2, 2019

Abstract

This paper compares the out-of-sample predictive performance of different early warning models for systemic banking crises using a sample of advanced economies covering the past 45 years. We compare a benchmark logit approach to several machine learning approaches recently proposed in the literature. We find that while machine learning methods often attain a very high in-sample fit, they are outperformed by the logit approach in recursive out-of-sample evaluations. This result is robust to the choice of performance measure, crisis definition, preference parameter, and sample length, as well as to using different sets of variables and data transformations. Thus, our paper suggests that further enhancements to machine learning early warning models are needed before they are able to offer a substantial value-added for predicting systemic banking crises. Conventional logit models appear to use the available information already fairly effciently, and would for instance have been able to predict the 2007/2008 financial crisis out-of-sample for many countries. In line with economic intuition, these models identify credit expansions, asset price booms and external imbalances as key predictors of systemic banking crises.

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Did the Swiss Exchange Rate Shock Shock the Market?

Manuel Buchholz Gregor von Schweinitz Lena Tonzer

in: IWH-Diskussionspapiere, Nr. 9, 2018

Abstract

The Swiss National Bank abolished the exchange rate floor versus the Euro in January 2015. Based on a synthetic matching framework, we analyse the impact of this unexpected (and therefore exogenous) shock on the stock market. The results reveal a significant level shift (decline) in asset prices in Switzerland following the discontinuation of the minimum exchange rate. While adjustments in stock market returns were most pronounced directly after the news announcement, the variance was elevated for some weeks, indicating signs of increased uncertainty and potentially negative consequences for the real economy.

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