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

Baumeister, Christiane; Guérin, Pierre; Kilian, Lutz

doi:10.1016/j.ijforecast.2014.06.005

Cited by 121 publications

(40 citation statements)

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“…The forecasting models considered in that study included a vector autoregressive (VAR) forecast, forecasts based on the spread between oil futures prices and the spot price of oil, forecasts based on non-oil industrial commodity prices, and forecasts based on a time-varying parameter (TVP) model of the spreads between the U.S. spot prices of gasoline and heating oil and the spot price of crude oil. More recent work by Baumeister, Guérin and Kilian (2014), which explored the predictive content of high-frequency data from financial and energy markets, uncovered evidence that an important additional source of real-time information about future oil prices is the cumulative change in U.S. crude oil inventories. In the current paper, we extend the set of models to be combined to include the latter forecast, which performs particularly well at horizons between one and two years.…”

Section: Introductionmentioning

confidence: 99%

Are there gains from pooling real-time oil price forecasts?

2014

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. iii Terms of use: Documents in AbstractThe answer as to whether there are gains from pooling real-time oil price forecasts depends on the objective. The approach of combining five of the leading forecasting models with equal weights dominates the strategy of selecting one model and using it for all horizons up to two years. Even more accurate forecasts, however, are obtained when allowing the forecast combinations to vary across forecast horizons. While the latter approach is not always more accurate than selecting the single most accurate forecasting model by horizon, its accuracy can be shown to be much more stable over time. The mean-squared prediction error of real-time pooled forecasts is between 3% and 29% lower than that of the no-change forecast and its directional accuracy as high as 73%. Our results are robust to alternative oil price measures and apply to monthly as well as quarterly forecasts. We illustrate how forecast pooling may be used to produce real-time forecasts of the real and the nominal price of oil in a format consistent with that employed by the U.S. Energy Information Administration in releasing its short-term oil price forecasts, and we compare these forecasts during key historical episodes. JEL classification: Q43, C53 Bank classification: Econometric and statistical methods; International topics RésuméLa combinaison de modèles de prévision en temps réel du prix du pétrole présente-t-elle des avantages? La réponse dépend de l'objectif. La méthode consistant à mettre en commun cinq des principaux modèles de prévision en leur attribuant un poids uniforme prime sur la stratégie visant à choisir un seul modèle et à l'utiliser à des horizons allant jusqu'à deux ans. Il est même possible d'accroître la justesse des prévisions en faisant varier les combinaisons de modèles selon les horizons. Bien que cette approche ne produise pas systématiquement des prévisions plus exactes que celles obtenues en sélectionnant le modèle qui offre la meilleure qualité prédictive à chaque horizon, la précision des projections fournies se révèle beaucoup plus stable au fil du temps. En effet, les prévisions en temps réel réunies présentent une erreur quadratique moyenne de 3 % à 29 % inférieure à celle d'une marche aléatoire et indiquent avec exactitude le sens des variations dans 73 % des cas. Ces résultats ne dépendent pas de la mesure du prix du pétrole retenue et s'appliquent tant aux prévisions mensuelles qu'aux prévisions trimestrielles. Les auteu...

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Section: Introductionmentioning

confidence: 99%

Are there gains from pooling real-time oil price forecasts?

2014

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“…The results show that the MSMF-VAR model is valid for estimating the cyclical state of economic activity. In their study of whether financial high-frequency data can help predict the true price of the oil market, Baumeister et al [17] stated that the MIDAS model is superior to the real-time MF-VAR model in terms of the accuracy of predicting oil price using high-frequency financial data.…”

Section: Literature Reviewmentioning

confidence: 99%

The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data

et al. 2018

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Presently, the total supply of crude oil is sufficient, but short-term supply and demand imbalances and regional imbalances still exist. The effect of crude oil supply security and price impact cannot be ignored. As the world's largest oil importer, China is highly dependent on foreign oil. Therefore, the fluctuation of international oil prices may impact the development of China's various industries in a significant and differential way. However, because the available data have different frequencies, much of the recent research that addresses the effect of oil prices on industry development need to replace, split, or merge the original data, resulting in loss of the information from the original data. Using the mixed data sampling model (MIDAS(m,K,h)-AR(1)) with the first-order lag autoregressive terms of the interpreted variables, this study builds a mixed data model to investigate the effect of oil price volatility on the output of China's industries. This study expands the extant research by financial market fluctuations and macroeconomic analysis, and at the same time makes short-term predictions on the output of China's seven main industries. The analysis results show that the mixed data regression model brings the original information contained in different frequency data into the model analysis, and utilizes the latest high frequency data of the explanatory variables to perform real-time short-term prediction of low-frequency interpreted variables. This method improves the timeliness of forecasting macroeconomic indicators and the accuracy of short-term forecasts. The empirical results show that the spot price of international crude oil has a significant and differential impact on the outputs of the seven industries in China. Among them, oil price fluctuation has the greatest impact on the output of China's financial industry.

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“…This index is designed as the successor to the Baltic Freight Index. The BDI is frequently viewed as a leading indicator of future global trade demand and economic growth, as the goods that are shipped are raw materials and thus give an indication of the demand for primary production inputs (see, for instance, Kilian (2012, 2014); Baumeister et al (2015) who also use the BDI as a measure of global trade activity to forecast oil prices).…”

Section: A23 Risk Aversion Measures and Global Trade Activitymentioning

confidence: 99%

Heterogeneous agents, the financial crisis and exchange rate predictability

Bunčić

Piras²

2016

Journal of International Money and Finance

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Highlights:• we build an empirical heterogeneous agent model for 6 currencies • individual agent forecasts are constructed from DMA framework • our daily out-of-sample R2 relative to RW can be as high as 1.41% and highly significant • our model forecasts yield annualized Sharpe ratios of up to 1.1 and performance fees above 400 basis points • our predictability results break down after February 2009, are strongest after Lehman Brothers collapse  We are grateful to Andrea Vedolin (LSE) for providing the yield curve factor loadings for Japan and Switzerland. We would like to thank AbstractWe construct an empirical heterogeneous agent model which optimally combines forecasts from fundamentalist and chartist agents and evaluates its out-of-sample forecast performance using daily data covering an overall period from January 1999 to June 2014 for six of the most widely traded currencies. We use daily financial data such as level, slope and curvature yield curve factors, equity prices, as well as risk aversion and global trade activity measures in the fundamentalist agent's predictor set to obtain a proxy for the market's view on the state of the macroeconomy. Chartist agents rely upon standard momentum, moving average and relative strength index technical indicators in their predictor set. Individual agent specific forecasts are constructed using a flexible dynamic model averaging framework and are then aggregated into a model combined forecast using a forecast combination regression. We show that our empirical heterogeneous agent model produces statistically significant and sizable forecast improvements over a random walk benchmark, reaching out-of-sample R 2 values of 1.41, 1.07, 0.99 and 0.74 percent at the daily one-step ahead horizon for 4 out of the 6 currencies that we consider. Forecast gains remain significant for horizons up to three-days ahead. The forecast improvements are largely realised before and around the time of the Lehman Brothers collapse. We show further that our model combined forecasts produce economic value to a mean variance investor, yielding annualized Sharpe ratios of around 1.1 and annualized performance fees in excess of 400 basis points.

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

Cited by 121 publications

References 46 publications

Are there gains from pooling real-time oil price forecasts?

Are there gains from pooling real-time oil price forecasts?

The Conductive and Predictive Effect of Oil Price Fluctuations on China’s Industry Development Based on Mixed-Frequency Data

Heterogeneous agents, the financial crisis and exchange rate predictability

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