Forecasting the Nominal Brent Oil Price with VARs

Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All?

November 25, 2015

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Format: Chicago

Benjamin Beckers, and Samya Beidas-Strom. Forecasting the Nominal Brent Oil Price with VARs—One Model Fits All?, (USA: International Monetary Fund, 2015) accessed November 8, 2024

Disclaimer: This Working Paper should not be reported as representing the views of the IMF.The views expressed in this Working Paper are those of the author(s) and do not necessarily represent those of the IMF or IMF policy. Working Papers describe research in progress by the author(s) and are published to elicit comments and to further debate

Summary

We carry out an ex post assessment of popular models used to forecast oil prices and propose a host of alternative VAR models based on traditional global macroeconomic and oil market aggregates. While the exact specification of VAR models for nominal oil price prediction is still open to debate, the bias and underprediction in futures and random walk forecasts are larger across all horizons in relation to a large set of VAR specifications. The VAR forecasts generally have the smallest average forecast errors and the highest accuracy, with most specifications outperforming futures and random walk forecasts for horizons up to two years. This calls for caution in reliance on futures or the random walk for forecasting, particularly for near term predictions. Despite the overall strength of VAR models, we highlight some performance instability, with small alterations in specifications, subsamples or lag lengths providing widely different forecasts at times. Combining futures, random walk and VAR models for forecasting have merit for medium term horizons.

Subject: Commodities, Econometric analysis, Economic forecasting, Financial institutions, Futures, Oil, Oil prices, Prices, Vector autoregression

Keywords: Brent price, Factor VAR, Forecasting, Forecasting model, Futures, Global, Intercept term, Lag length, Medium-term oil price forecasting model, North America, Null hypothesis, Oil, Oil price, Oil prices, RAC price series, Random walk model, Rival VAR specification, Trended oil price model, VAR forecast, VAR model, VAR system, VARs, Vector autoregression, WP, WTI crude

Publication Details

Pages:

32
Volume:

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DOI:

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Issue:

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Series:

Working Paper No. 2015/251
Stock No:

WPIEA2015251
ISBN:

9781513524276
ISSN:

1018-5941

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