Volatilität, Wachstum und Finanzkrisen
Diese Forschungsgruppe erforscht – auch vor dem Hintergrund der jüngsten Krisen – den Zusammenhang zwischen finanziellen und monetären Größen, realwirtschaftlichen Schwankungen und langfristigem Wirtschaftswachstum.
IWH-Datenprojekt: Financial Stability Indicators in Europe
ForschungsclusterFinanzstabilität und Regulierung
01.2017 ‐ 12.2018
Early-warning Models for Systemic Banking Crises
Deutsche Forschungsgemeinschaft (DFG)
01.2018 ‐ 12.2018
International Monetary Policy Transmission
Does Machine Learning Help us Predict Banking Crises?
in: Journal of Financial Stability, im ErscheinenPublikation lesen
Optimizing Policymakers’ Loss Functions in Crisis Prediction: Before, Within or After?
in: Macroeconomic Dynamics, im Erscheinen
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.
Sign Restrictions, Structural Vector Autoregressions, and Useful Prior Information
in: Econometrica, im Erscheinen
This paper makes the following original contributions to the literature. (i) We develop a simpler analytical characterization and numerical algorithm for Bayesian inference in structural vector autoregressions (VARs) that can be used for models that are overidentified, just‐identified, or underidentified. (ii) We analyze the asymptotic properties of Bayesian inference and show that in the underidentified case, the asymptotic posterior distribution of contemporaneous coefficients in an n‐variable VAR is confined to the set of values that orthogonalize the population variance–covariance matrix of ordinary least squares residuals, with the height of the posterior proportional to the height of the prior at any point within that set. For example, in a bivariate VAR for supply and demand identified solely by sign restrictions, if the population correlation between the VAR residuals is positive, then even if one has available an infinite sample of data, any inference about the demand elasticity is coming exclusively from the prior distribution. (iii) We provide analytical characterizations of the informative prior distributions for impulse‐response functions that are implicit in the traditional sign‐restriction approach to VARs, and we note, as a special case of result (ii), that the influence of these priors does not vanish asymptotically. (iv) We illustrate how Bayesian inference with informative priors can be both a strict generalization and an unambiguous improvement over frequentist inference in just‐identified models. (v) We propose that researchers need to explicitly acknowledge and defend the role of prior beliefs in influencing structural conclusions and we illustrate how this could be done using a simple model of the U.S. labor market.
On the Empirics of Reserve Requirements and Economic Growth
in: Journal of Macroeconomics, 2019
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.
Structural Interpretation of Vector Autoregressions with Incomplete Identification: Revisiting the Role of Oil Supply and Demand Shocks
in: American Economic Review, Nr. 5, 2019
Traditional approaches to structural vector autoregressions (VARs) can be viewed as special cases of Bayesian inference arising from very strong prior beliefs. These methods can be generalized with a less restrictive formulation that incorporates uncertainty about the identifying assumptions themselves. We use this approach to revisit the importance of shocks to oil supply and demand. Supply disruptions turn out to be a bigger factor in historical oil price movements and inventory accumulation a smaller factor than implied by earlier estimates. Supply shocks lead to a reduction in global economic activity after a significant lag, whereas shocks to oil demand do not.
Fiscal Policy and Fiscal Fragility: Empirical Evidence from the OECD
in: IWH-Diskussionspapiere, Nr. 13, 2019
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.
An Evaluation of Early Warning Models for Systemic Banking Crises: Does Machine Learning Improve Predictions?
in: IWH-Diskussionspapiere, Nr. 2, 2019
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.
Did the Swiss Exchange Rate Shock Shock the Market?
in: IWH-Diskussionspapiere, Nr. 9, 2018
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.
Sovereign Stress, Banking Stress, and the Monetary Transmission Mechanism in the Euro Area
in: IWH-Diskussionspapiere, Nr. 3, 2018
In this paper, we investigate to what extent sovereign stress and banking stress have contributed to the increase in the level and in the heterogeneity of non-financial firms’ financing costs in the Euro area during the European debt crisis and how both have affected the monetary transmission mechanism. Employing a large firm-level data set containing two million observations, we are able to identify the effect of government bond yield spreads (sovereign stress) and the share of non-performing loans (banking stress) on firms‘ financing costs in a panel model by assuming that idiosyncratic shocks to individual firms are uncorrelated with country-specific variables. We find that the two sources of stress have increased firms’ financing costs controlling for country and firm-specific factors. Moreover, we estimate both to have significantly impaired the monetary transmission mechanism.
Inflation Dynamics During the Financial Crisis in Europe: Cross-sectional Identification of Long-run Inflation Expectations
in: IWH-Diskussionspapiere, Nr. 10, 2017
We investigate drivers of Euro area inflation dynamics using a panel of regional Phillips curves and identify long-run inflation expectations by exploiting the crosssectional dimension of the data. Our approach simultaneously allows for the inclusion of country-specific inflation and unemployment-gaps, as well as time-varying parameters. Our preferred panel specification outperforms various aggregate, uni- and multivariate unobserved component models in terms of forecast accuracy. We find that declining long-run trend inflation expectations and rising inflation persistence indicate an altered risk of inflation expectations de-anchoring. Lower trend inflation, and persistently negative unemployment-gaps, a slightly increasing Phillips curve slope and the downward pressure of low oil prices mainly explain the low inflation rate during the recent years.