Oil price and oil price volatility forecasts are of major importance, given the oil market is well crowded by participants who proceed to decisions based on these forecasts (e.g. oil traders, monetary policy authorities, etc). The current state-of-the-art forecasting...
Oil price and oil price volatility forecasts are of major importance, given the oil market is well crowded by participants who proceed to decisions based on these forecasts (e.g. oil traders, monetary policy authorities, etc). The current state-of-the-art forecasting techniques, though, (i) involve a trade-off among internal consistency, forecasting accuracy and easiness to communicate aspects and (ii) do not use ultra-high frequency data. Thus, this exciting and innovative project aimed to develop new econometric model frameworks to forecast oil price and oil price volatility, which would be successful in enhancing internal consistency, forecasting accuracy and easiness to communicate extracting added-value (in terms of predictability) information from the ultra-high sampling frequency. In addition, this project aimed to use the forecasted oil price volatility to predict the economic policy uncertainty in Europe, given that oil price shocks exert significant impact on the effectiveness of economic policy.
The conclusions of the action were very encouraging and opened new avenues of research in the line of research which concerns the forecasts of oil prices, oil price volatility and economic policy uncertainty. In particular, we show that the information extracted from the exogenous financial and commodities assets volatilities and returns (i.e. the â€œinformation channelsâ€ by which asset classes impact the oil market behavior), based on ultra-high frequency data, provide significant incremental predictive gains on oil prices, oil price volatility and economic policy uncertainty.
The first research objective concerned the development of a new econometric model framework, which would allow the estimation and forecast of oil price volatility and the time-varying correlations of oil prices and asset classes (such as stocks, bonds, foreign exchange, commodities and macroeconomic indicators). The second research objective was to develop new econometric model framework that allows the forecast of oil prices. The third research objective of the project was to increase the practical relevance of the forecasting model frameworks by forecasting the level of economic policy uncertainty in Europe based on oil price volatility, as well as, other asset volatilities.
We show that the information extracted from the exogenous financial and commodities assets volatilities and returns (i.e. the â€œinformation channelsâ€ by which asset classes impact the oil market behavior), based on ultra-high frequency data, provide significant incremental predictive gains on both the oil prices and oil price volatility. Thus, accurate forecasts cannot be obtained unless the various â€œinformation channelsâ€ through which different asset classes impact oil price and its volatility, are considered. More importantly we show that the forecasting gains are materially higher during economic turbulent periods, which are the periods that urgently call for more accurate forecasts. Furthermore, we report that the â€œinformation channelsâ€ of European and global financial markets volatility, as well as, global economic uncertainty provide significantly higher predictive information to the European economic policy uncertainty.
The conclusions of the project were widely disseminated using various means, as suggested in the projectâ€™s proposal. We presented the findings of the project in staff seminar series in Greek and international academic institutions, as well as, in public talks at the Bank of Greece. In addition, we presented the findings in three international conferences. The Marie Sklodowska-Curie researcher, George Filis, has successfully accomplished the project, delivered the three working packages, and submitted papers in international journals that have been produced during the fellowship. The working papers of the projects were also made available to the wider public via the projectâ€™s website. The Community support of the Marie Sklodowska-Curie Action is being acknowledged in publications and presentations.
ENEFOR has made a significant contribution beyond the current state-of-the-art in forecasting oil price volatility, oil prices and economic policy uncertainty.
In particular, we have added to the scarce literature of oil price realized volatility forecasting using the current state-of-the-art Heterogeneous Autoregressive -Realized Volatily (HAR-RV) model, which we mainly extended in two very important ways. First, we investigated for the first time whether the â€œinformation channelsâ€ by which different asset classesâ€™ volatilities can impact oil price volatility, could also improve oil price volatility forecasts (we named the model HAR-RV-X, where X denoted the exogenous asset classesâ€™ volatilities). Second, we provided for the first time a method that handles exogenous variables in a HAR model in order to proceed with the forecasts. In addition to these major contributions, we also assessed the forecasting accuracy of the HAR-RV-X models based on each individual asset class, their combined forecasts, as well as the forecast-averaging. We further assessed for the first time whether the forecasting accuracy of the HAR-RV-X models can be improved using the time-varying correlations between oil price volatility and the remaining asset classesâ€™ volatilities.
In terms of oil price forecasting, ENEFOR showed for the first time that the Mixed-Data Sampling (MIDAS) models using either daily asset classesâ€™ volatilities or asset classesâ€™ returns, which are constructed from ultra-high frequency data, exhibit significantly higher predictive ability and directional accuracy compared to the current state-of-the-art models for oil price forecasting (e.g. VAR and BVAR models). More specifically, our MIDAS models with the daily realized volatilities of the exogenous assets classes provide predictive gains relatively to the no-change forecast at the level of 75% at the 12-month ahead forecasting horizon. Even more, our MIDAS models also exhibit a very high directional accuracy, especially up to 6-months ahead.
The last working package of ENEFOR we forecasted for the first time the European economic policy uncertainty, showing that even in this case the information extracted from ultra-high frequency of various asset classes provides predictive gains relatively to the no-change forecast.
So far the project has managed to achieve the intended short-run impact. The fellow gained proficiency on the forecasting techniques using ultra high frequency data, which are essential to tackle other energy related forecasting issues. The project also allowed him to develop state-of-the-art programming skills, as well as, consultancy and policy formulation skills, which are necessary to develop other forecasting models, as well as, to obtain additional consultancy experience. The fellow has also strengthened his affiliations with the Greek academic and non-academic sector, which allowed him to expand his research network and obtain consulting experience. He also improved his public outreach profile and effectiveness through the presentations of the projectâ€™s output to staff seminars and public talks. Finally, the project provided the fellow with the opportunity to enhance his reputation in the academia and, thus, gain greater visibility, given the prestige of the Marie Sklodowska-Curie fellowship.
The medium- and long-run impacts that are anticipated to be realized are those reported in the projectâ€™s proposal.
More info: http://enefor.eu.