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Patrizio Pagano

28 January 2009
This paper documents the existence of a significant forecast error on crude oil futures. We interpret it as a risk premium, which, in part, could have been explained by means of a real-time US business cycle indicator, such as the degree of capacity utilization in manufacturing. This result is robust to the specification of the estimating equation and to the considered business cycle indicator. An out-of-the-sample prediction exercise reveals that futures adjusted to take into account this time-varying component produce significantly better forecasts than those of unadjusted futures, of futures adjusted for the average forecast error and of the random walk, particularly at horizons of more than 6 months.
JEL Code
E37 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Forecasting and Simulation: Models and Applications
E44 : Macroeconomics and Monetary Economics→Money and Interest Rates→Financial Markets and the Macroeconomy
G13 : Financial Economics→General Financial Markets→Contingent Pricing, Futures Pricing
Q4 : Agricultural and Natural Resource Economics, Environmental and Ecological Economics→Energy
5 August 2010
Global monetary conditions have often been cited as a driving factor of commodity prices. This paper investigates the empirical relationship between US monetary policy and commodity prices by means of a standard VAR system, commonly used in analysing the effects of monetary policy shocks. The results suggest that expansionary US monetary policy shocks drove up the broad commodity price index and all of its components. While these effects are significant, they however do not appear to be overwhelmingly large. This finding is also confirmed under different identification strategies for the monetary policy shock.
JEL Code
E31 : Macroeconomics and Monetary Economics→Prices, Business Fluctuations, and Cycles→Price Level, Inflation, Deflation
E40 : Macroeconomics and Monetary Economics→Money and Interest Rates→General
C32 : Mathematical and Quantitative Methods→Multiple or Simultaneous Equation Models, Multiple Variables→Time-Series Models, Dynamic Quantile Regressions, Dynamic Treatment Effect Models, Diffusion Processes