Is it possible to accurately forecast the evolution of Brent crude oil prices? An answer based on parametric and nonparametric ... View Full Text


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Article Info

DATE

2019-03-15

AUTHORS

Marcos Álvarez-Díaz

ABSTRACT

Can we accurately predict the Brent oil price? If so, which forecasting method can provide the most accurate forecasts? To unravel these questions, we aim at predicting the weekly Brent oil price growth rate by using several forecasting methods that are based on different approaches. Basically, we assess and compare the out-of-sample performances of linear parametric models (the ARIMA, the ARFIMA and the autoregressive model), a nonlinear parametric model (the GARCH-in-Mean model) and different nonparametric data-driven methods (a nonlinear autoregressive artificial neural network, genetic programming and the nearest-neighbor method). The results obtained show that (1) all methods are capable of predicting accurately both the value and the directional change in the Brent oil price, (2) there are no significant forecasting differences among the methods and (3) the volatility of the series could be an important factor to enhance our predictive ability. More... »

PAGES

1-21

Journal

TITLE

Empirical Economics

ISSUE

N/A

VOLUME

N/A

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s00181-019-01665-w

DOI

http://dx.doi.org/10.1007/s00181-019-01665-w

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1112780280


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58 schema:description Can we accurately predict the Brent oil price? If so, which forecasting method can provide the most accurate forecasts? To unravel these questions, we aim at predicting the weekly Brent oil price growth rate by using several forecasting methods that are based on different approaches. Basically, we assess and compare the out-of-sample performances of linear parametric models (the ARIMA, the ARFIMA and the autoregressive model), a nonlinear parametric model (the GARCH-in-Mean model) and different nonparametric data-driven methods (a nonlinear autoregressive artificial neural network, genetic programming and the nearest-neighbor method). The results obtained show that (1) all methods are capable of predicting accurately both the value and the directional change in the Brent oil price, (2) there are no significant forecasting differences among the methods and (3) the volatility of the series could be an important factor to enhance our predictive ability.
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