ECB monetary policy and commodity prices

Abstract

We assess the impact of ECB monetary policy on global aggregate and sectoral commodity prices over 2001–2019. We employ an SVAR model and separately assess periods before and after the global financial crisis. Our key results indicate that contractionary monetary policy shocks have positive effects on commodity prices during both conventional and unconventional monetary policy periods, indicating the effectiveness of unconventional monetary policy tools. The largest impact is documented on energy (fuel) and food commodities. Our results also suggest that the effect of ECB monetary policy on commodity prices transmits through the exchange rate channel, which influences European market demand.

Frequency volatility connectedness and portfolio hedging of U.S. energy commodities

“…Further, since oil prices influence the cost of production and transportation, affecting a wide range of consumer goods and services, understanding the connectedness among oil-based commodities helps economists and policymakers anticipate potential inflationary pressures and assess the overall impact on consumers and the broader economy. This implication is important for major central banks as Aliyev and Kočenda (2023) show that the ECB policy affects fuel (oil) commodity prices.…”

Financial Crime and Punishment: A Meta-Analysis

“…Consequently, the most frequent reasons for excluding the selected studies were the following: 1) event studies out of our scope on (partly) unintentional financial crimes, other corporate scandals or crimes, impact of regulatory changes, impact of financial crimes when committed, over the fraud period (see Figure 2), spillovers of financial crimes on sector peers, or too specific financial crimes (case studies such as Enron or the U.S. stock option backdating scandal in 2006), 2) methodological problems, 8 3) theoretical articles on financial crimes (models or literature reviews), and 4) experimental articles on financial misconducts.…”

“…(𝐶)𝐴𝐴𝑅𝐷𝑠 are comparable between articles as, by construction, they all use the same event study methodology (see Appendix 1 for methodological details). Two main methodological differences between (and within) studies are the length of the event windows 8 For example, higher-than-daily frequencies (weekly, monthly, quarterly, yearly) with usually a longer-term perspective (longer event windows), unpublished estimates of event studies or statistical significance indication, estimations of the costs of financial crimes with other methodologies than event studies (difference in difference with a sector perspective, no specification on the methodology used), and event studies on other variables than returns (volatility, volume of trades, spreads, interest rates, bonds, ratings, bank loans, systemic risk, sales, top management with equity compensation, wages, bonuses, careers, etc. ).…”

“…We estimate Eq. ( 2) based on four estimators, as in Iwasaki and Kočenda (2017): cluster-robust OLS (columns [5]), cluster-robust WLS (columns [6]), random-effects model estimated by the maximum likelihood (columns [7]) and the population-averaged generalized estimating equation (columns [8]).…”