Do High-Frequency Financial Data Help Forecast Oil Prices? The Midas Touch at Work
By Christiane Baumeister, Pierre Guérin, Lutz Kilian. 2015
132 citations recorded by the source corpus.
Subfields: Economics and Econometrics, Renewable Energy, Sustainability and the Environment
View Paper: https://www.semanticscholar.org/paper/ba65a4e014831fb230dfc27ff81b6d6f4a4120ac
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
- Stavros Degiannakis *
- George Filis
- Vipin Arora
- Yue‐Jun Zhang
- Jin-li Wang
- D. Broadstock
- Ioannis Chatziantoniou
- Perry Sadorsky
In Renewable Energy, Sustainability and the Environment
- Stavros Degiannakis *
- George Filis
- Yue‐Jun Zhang
- Vipin Arora
- Jin-li Wang
- Perry Sadorsky
- Ronald A. Ratti
- Muhammad Shahbaz
Citations in the corpus
- Do high-frequency stock market data help forecast crude oil prices? Evidence from the MIDAS models (2019, methodology): “To evaluate the out-of-sample forecasting accuracy, we use theMSPE as a measure of forecast performance (Wang et al., 2015; Baumeister et al., 2015; Chen, 2014), which are specified as Eq.”
- Oil Prices and Stock Markets: A Review of the Theory and Empirical Evidence (2018, methodology): “Contrary to the findings by Baumeister et al. (2015), Degiannakis and Filis (2017b) show that the use of high frequency stock market data (daily) provides incremental predictive accuracy to oil price forecasts, and incremental directional accuracy.”