Do high-frequency financial data help forecast oil prices? The MIDAS touch at work : [version november 20, 2013]

The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency realtime VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil

Do high-frequency financial data help forecast oil prices? The MIDAS touch at work

The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models may be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, especially changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred MIDAS model reduces the MSPE by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 82 percent. This MIDAS forecast also is more accurate than a mixed-frequency realtime VAR forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil

Frequency selection in globally unstable round jets

International audienceThe self-sustained formation of synchronized ring vortices in hot subsonic jets is investigated by direct numerical simulation of the axisymmetric equations of motion. The onset of global instability and the global frequency of synchronized oscillations are examined as functions of the ambient-to-jet temperature ratio and the initial jet shear layer thickness. The numerical results are found to follow the predictions from nonlinear global instability theory; global instability sets in as the unperturbed flow is absolutely unstable over a region of finite streamwise extent at the inlet, and the global frequency near the global instability threshold corresponds to the absolute frequency of the inlet profile. In strongly supercritical thin shear layer jets, however, the simulations display global frequencies well above the absolute frequency, in agreement with experimental results. The inner structure of rolled-up vortices in hot jets displays fine layers of positive and negative vorticity that are produced and maintained by the action of the baroclinic torque. © 2007 American Institute of Physics

Do high-frequency financial data help forecast oil prices? The MIDAS touch at work

The substantial variation in the real price of oil since 2003 has renewed interest in the question of how to forecast monthly and quarterly oil prices. There also has been increased interest in the link between financial markets and oil markets, including the question of whether financial market information helps forecast the real price of oil in physical markets. An obvious advantage of financial and energy market data in forecasting oil prices is their availability in real time on a daily or weekly basis. We investigate whether mixed-frequency models can be used to take advantage of these rich data sets. We show that, among a range of alternative high-frequency predictors, changes in U.S. crude oil inventories produce substantial and statistically significant real-time improvements in forecast accuracy. The preferred mixed-data sampling (MIDAS) model reduces the mean-squared prediction error by as much as 16 percent compared with the no-change forecast and has statistically significant directional accuracy as high as 80 percent. This MIDAS forecast also is more accurate than a mixed-frequency real-time vector autoregressive forecast, but not systematically more accurate than the corresponding forecast based on monthly inventories. We conclude that typically not much is lost by ignoring high-frequency financial data in forecasting the monthly real price of oil.La variation considérable des prix réels du pétrole depuis 2003 a ravivé l’intérêt porté aux méthodes de prévision des cours mensuels et trimestriels de ce produit. On a aussi observé un regain d’intérêt pour l’étude du lien entre les marchés financiers et pétroliers : à ce titre, les chercheurs se sont demandé si l’information en provenance des marches financiers aide à prédire les prix réels du pétrole sur les marchés au comptant. Les données des marchés financiers et énergétiques présentent un avantage évident pour prévoir les cours du pétrole : elles sont accessibles en temps réel selon une fréquence quotidienne ou hebdomadaire. Nous cherchons donc à déterminer le pouvoir de prevision de ces riches ensembles de données en utilisant des modèles avec données à fréquence mixte. Nous montrons que, parmi toute une gamme de prédicteurs de haute fréquence, les variations des stocks de pétrole brut aux États-Unis améliorent de manière appréciable et statistiquement significative, en temps réel, l’exactitude des prévisions. Le modèle d’échantillonnage de données de fréquence mixte (MIDAS) privilégié peut réduire l’erreur quadratique moyenne de prévision dans une proportion allant jusqu’à 16 %, si l’on compare avec la prévision du modèle de marche aléatoire. Il permet également de prévoir avec exactitude le sens des variations dans 80 % des cas, et cela de manière statistiquement significative. Les prévisions établies grâce à ce modèle MIDAS sont également plus justes que celles issues d’un modèle vectoriel autorégressif basé sur des données de fréquence mixte en temps réel, mais elles ne sont pas systématiquement plus exactes que les projections correspondantes fondées sur les stocks mensuels. Nous concluons que, généralement, le fait de ne pas tenir compte des données financières de haute fréquence dans la prévision des prix mensuels réels du pétrole a une incidence négligeable

Aerodynamic sound generation by global modes in hot jets

International audienceThe acoustic field generated by the synchronized vortex street in self-excited hot subsonic jets is investigated via direct numerical simulation of the compressible equations of motion in an axisymmetric geometry. The simulation simultaneously resolves both the aerodynamic near field and the acoustic far field. Self-sustained near-field oscillations in the present flow configurations have been described as nonlinear global modes in an earlier study. The associated acoustic far field is found to be that of a compact dipole, emanating from the location of vortex roll-up. A far-field solution of the axisymmetric Lighthill equation is derived, on the basis of the source term formulation of Lilley (AGARD-CP, vol. 131, 1974, pp. 13.1-13.12). With the near-field source distributions obtained from the direct numerical simulations, the Lighthill solution is in good agreement with the far-field simulation results. Fluctuations of the enthalpy flux within the jet are identified as the dominant aeroacoustic source. Superdirective effects are found to be negligible. © 2010 Cambridge University Press