The substantial fluctuations in oil prices in the wake of the COVID-19 pandemic and the Russian invasion of Ukraine have highlighted the importance of tail events in the global market for crude oil which call for careful risk assessment. In this paper we focus on forecasting tail risks in the oil market by setting up a general empirical framework that allows for flexible predictive distributions of oil prices that can depart from normality. This model, based on Bayesian additive regression trees, remains agnostic on the functional form of the conditional mean relations and assumes that the shocks are driven by a stochastic volatility model. We show that our nonparametric approach improves in terms of tail forecasts upon three competing models: quantile regressions commonly used for studying tail events, the Bayesian VAR with stochastic volatility, and the simple random walk. We illustrate the practical relevance of our new approach by tracking the evolution of predictive densities during three recent economic and geopolitical crisis episodes, by developing consumer and producer distress indices that signal the build-up of upside and downside price risk, and by conducting a risk scenario analysis for 2024.

This paper provides new evidence of herding due to non- and fundamental information in global equity markets. Using quantile regressions applied to daily data for 33 countries, we investigate herding during the Eurozone crisis, China’s market crash in 2015–2016, in the aftermath of the Brexit vote and during the Covid-19 Pandemic. We find significant evidence of herding driven by non-fundamental information in case of negative tail market conditions for most countries. This study also investigates the relationship between herding and systemic risk, suggesting that herding due to fundamentals increases when systemic risk increases more than when driven by non-fundamentals. Granger causality tests and Johansen’s vector error-correction model provide solid empirical evidence of a strong interrelationship between herding and systemic risk, entailing that herding behavior may be an ex-ante aspect of systemic risk, with a more relevant role played by herding based on fundamental information in increasing systemic risk.

Wie bedeutend ist das menschliche Element für die Genauigkeit empirischer Erkenntnisse in den Wirtschaftswissenschaften? Die Unsicherheit empirischer Schätzungen wird üblicherweise als ein statistisches Phänomen betrachtet. Unbekannte Parameter einer Grundgesamtheit werden anhand einer Stichprobe geschätzt, deren Erzeugung zu so genannten Standardfehlern führt. Forschende treffen jedoch viele unbeobachtete Entscheidungen, die nicht per se richtig oder falsch sind, sich aber auf das Ergebnis der Schätzung auswirken. Beispiele hierfür sind die Wahl der Software, die Art der Datenbereinigung oder die Spezifikation der Kontrollvariablen, um nur einige zu nennen. Wir haben an einem großen crowd-basierten Feldexperiment teilgenommen, bei dem sich herausstellte, dass dieser evidenzgenerierende Prozess von Forscher zu Forscher stark variiert, wodurch eine neue Art von Unsicherheit entsteht: so genannte Nicht-Standardfehler (NSE). 164 Teams von Finanzökonominnen und Finanzökonomen testeten sechs Hypothesen an einer identischen Stichprobe von Finanzmarktdaten. Das wichtigste Ergebnis ist, dass die Nicht-Standardfehler beträchtlich sind und die gleiche Größenordnung haben wie die Standardfehler, dass sie aber nach einem anonymen Begutachtungsprozess deutlich abnehmen. Wer sich von Wirtschaftsforschern beraten lässt, sollte sich daher darüber im Klaren sein, dass die Entscheidungen der einzelnen Forschenden die empirische Evidenz mit einer nicht unerheblichen Unsicherheit behaften. Gleichzeitig scheint eine der Veröffentlichung vorausgehende Begutachtung der Ergebnisse durch wissenschaftliche Kollegen (peer-review) die Anfälligkeit für diese Art von Unsicherheit zu verringern.

Abstract In Germany, employers used to pay union members and non-members in a plant the same union wage in order to prevent workers from joining unions. Using recent administrative data, we investigate which workers in firms covered by collective bargaining agreements still individually benefit from these union agreements, which workers are not covered anymore and what this means for their wages. We show that about 9 per cent of workers in plants with collective agreements do not enjoy individual coverage (and thus the union wage) anymore. Econometric analyses with unconditional quantile regressions and firm-fixed-effects estimations demonstrate that not being individually covered by a collective agreement has serious wage implications for most workers. Low-wage non-union workers and those at low hierarchy levels particularly suffer since employers abstain from extending union wages to them in order to pay lower wages. This jeopardizes unions' goal of protecting all disadvantaged workers.

This paper investigates whether non-base compensation contributes to the gender pay gap (GPG). Using administrative data from Germany, we find in wage decompositions that lower bonus payments to women explain about 10 per cent of the gap at the mean and at different quantiles of the unconditional wage distribution whereas the lower prevalence of shift premia and overtime pay among women is unimportant. Among managers, the contribution of bonuses to the mean gap more than doubles and is steadily rising as one moves up the wage distribution. Our findings suggest that gender differences in bonuses are an important contributor to the GPG, particularly in top jobs.

Niedriglohnsektor und steigende Lohnungleichheit sind seit langem dominierende Themen am Arbeitsmarkt. Dieser Artikel legt nahe, dass die Verhandlungsmacht der Arbeitnehmer von der Existenz von Betriebsräten und Tarifverträgen abhängt und dass sich vor allem betriebliche Mitbestimmung positiv auf Löhne auswirkt. Während Mitbestimmung die zwischenbetriebliche Lohnungleichheit erhöht, wird sie durch Tarifverträge reduziert.

It is widely claimed that “the German saver” suffers (i.e. generates significantly lower returns on her savings) in the low interest environment that Germany currently experiences relative to a high interest rate environment. With “low interest rate environment”, the observers tend to mean “low policy rates”, i.e. the European Central Bank’s (ECB) main refinancing rate.

This paper estimates relative risk aversion using the observed shares of risky assets and characteristics of households from the Household Finance and Consumption Survey of the European Central Bank. Given that the risky share is a fractional response variable belonging to [0, 1], this paper proposes a censored fractional response estimation method using extremal quantiles to approximate the censoring thresholds. Considering that participation in risky asset markets is costly, I estimate both the heterogeneous relative risk aversion and participation cost using a working sample that includes both risky asset holders and non-risky asset holders by treating the zero risky share as the result of heterogeneous self-censoring. Estimation results show lower participation costs and higher relative risk aversion than what was previously estimated. The estimated median relative risk aversions of eight European countries range from 4.6 to 13.6. However, the results are sensitive to households’ perception of the risky asset market return and volatility.

In this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). For four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies) we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions.

In this paper, we develop a state-dependent sensitivity value-at-risk (SDSVaR) approach that enables us to quantify the direction, size, and duration of risk spillovers among financial institutions as a function of the state of financial markets (tranquil, normal, and volatile). Within a system of quantile regressions for four sets of major financial institutions (commercial banks, investment banks, hedge funds, and insurance companies) we show that while small during normal times, equivalent shocks lead to considerable spillover effects in volatile market periods. Commercial banks and, especially, hedge funds appear to play a major role in the transmission of shocks to other financial institutions. Using daily data, we can trace out the spillover effects over time in a set of impulse response functions and find that they reach their peak after 10 to 15 days.