Two contradictory strands of the rating literature criticize that rating agencies merely follow the market on the one hand, and emphasizing that rating changes affect capital movements on the other hand. Both focus on explaining rating levels rather than the timing of rating announcements. Contrarily, we explicitly differentiate between a decision to assess a country and the actual rating decision. We show that this differentiation significantly improves the estimation of the rating function. The three major rating agencies treat economic fundamentals similarly, while differing in their response to other factors such as strategic considerations. This reconciles the conflicting literature.

We evaluate alternative indicators of global economic activity and other market funda-mentals in terms of their usefulness for forecasting real oil prices and global petroleum consumption. World industrial production is one of the most useful indicators. However, by combining measures from several different sources we can do even better. Our analysis results in a new index of global economic conditions and measures for assessing future energy demand and oil price pressures. We illustrate their usefulness for quantifying the main factors behind the severe contraction of the global economy and the price risks faced by shale oil producers in early 2020.

The Swiss National Bank abolished the exchange rate floor versus the Euro in January 2015. Using a synthetic matching framework, we analyze the impact of this unexpected (and therefore exogenous) policy change on the stock market. The results reveal a significant level shift (decline) in asset prices following the discontinuation of the minimum exchange rate. As a novel finding in the literature, we document that the exchange‐rate elasticity of Swiss asset prices is around −0.75. Differentiating between sectors of the Swiss economy, we find that the industrial, financial and consumer goods sectors are most strongly affected by the abolition of the minimum exchange rate.

This paper evaluates the predictive content of a set of alternative monthly indicators of global economic activity for nowcasting and forecasting quarterly world real GDP growth using mixed-frequency models. It shows that a recently proposed indicator that covers multiple dimensions of the global economy consistently produces substantial improvements in forecasting accuracy, while other monthly measures have more mixed success. Specifically, the best-performing model yields impressive gains with MSPE reductions of up to 34% at short horizons and up to 13% at long horizons relative to an autoregressive benchmark. The global economic conditions indicator also contains valuable information for assessing the current and future state of the economy for a set of individual countries and groups of countries. This indicator is used to track the evolution of the nowcasts for the U.S., the OECD area, and the world economy during the COVID-19 pandemic and the main factors that drive the nowcasts are quantified.

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.

This paper investigates the propagation of technology news shocks within and across industrialised economies. We construct quarterly utilisation-adjusted total factor productivity (TFP) for thirteen OECD countries. Based on country-specific structural vector autoregressions (VARs), we document that (i) the identified technology news shocks induce a quite homogeneous response pattern of key macroeconomic variables in each country; and (ii) the identified technology news shock processes display a significant degree of correlation across several countries. Contrary to conventional wisdom, we find that the US are only one of many different sources of technological innovations diffusing across advanced economies. Technology news propagate through the endogenous reaction of monetary policy and via trade-related variables. That is, our results imply that financial markets and trade are key channels for the dissemination of technology.

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.

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.

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.

The experience of Central and Eastern European countries (CEEC) during the global financial crisis and in the resulting European debt crises has been largely different from that of other European countries. This paper looks at the specifics of the CEEC in recent history and focuses in particular on the appropriateness of the Macroeconomic Imbalances Procedure for this group of countries. In doing so, the macroeconomic situation in the CEEC is highlighted and macroeconomic problems faced by these countries are extracted. The findings are compared to the results of the Macroeconomic Imbalances Procedure of the European Commission. It is shown that while the Macroeconomic Imbalances Procedure correctly identifies some of the problems, it understates or overstates other problems. This is due to the specific construction of the broadened surveillance procedure, which largely disregarded the specifics of catching-up economies.