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

Beckers, Benjamin; Beidas-Strom, Samya

doi:10.5089/9781513524276.001

Cited by 9 publications

(3 citation statements)

References 18 publications

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“…VAR C aggregates VAR B variables casting energy demand, as in Trujillo- Barrera et al (2012) and Mallory et al (2012). VAR D sums to its specification some monetary variables, such as inflation, following Beckers & Beidas-Strom (2015) and Albuquerquemello et al (2018). Models E and F specification investigates substitution effects on grains and cereals inflation, as proposed by McPhail et al…”

Section: Methodology and Datamentioning

confidence: 99%

The role of transition regime models for corn prices forecasting

Albuquerquemello

Medeiros

Jesús

et al. 2022

Rev. Econ. Sociol. Rural

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Given the relevance of corn for food and fuel industries, analysts and scholars are constantly comparing the forecasting accuracy of econometric models. These exercises test not only for the use of new approaches and methods, but also for the addition of fundamental variables linked to the corn market. This paper compares the accuracy of different usual models in financial macro-econometric literature for the period between 1995 and 2017. The main contribution lies in the use of transition regime models, which accommodate structural breaks and perform better for corn price forecasting. The results point out that the best models as those which consider not only the corn market structure, or macroeconomic and financial fundamentals, but also the non-linear trend and transition regimes, such as threshold autoregressive models.

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Section: Methodology and Datamentioning

confidence: 99%

The role of transition regime models for corn prices forecasting

Albuquerquemello

Medeiros

Jesús

et al. 2022

Rev. Econ. Sociol. Rural

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“…Since the total oil inventory level measures the balance between oil production and demand, the model could reflect the changing market conditions of crude oil prices. Beckers et al (2015) incorporated relevant variables into the vector auto-regression model. These variables include consumer price index (CPI) inflation, dollar exchange rates, US threemonth and ten-year Treasury yields, spreads between long-term and short-term interest rates, oil supply and demand from major producers or regions, global industrial production, the Organisation for Economic Co-operation and Development (OECD) inventory demand and other variables.…”

Section: Literature Reviewmentioning

confidence: 99%

A study of univariate forecasting methods for crude oil price

Cheng

Chu

Hsu³

2021

MABR

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PurposeThis paper aims to compare different univariate forecasting methods to provide a more accurate short-term forecasting model on the crude oil price for rendering a reference to manages.Design/methodology/approachSix different univariate methods, namely the classical decomposition model, the trigonometric regression model, the regression model with seasonal dummy variables, the grey forecast, the hybrid grey model and the seasonal autoregressive integrated moving average (SARIMA), have been used.FindingsThe authors found that the grey forecast is a reliable forecasting method for crude oil prices.Originality/valueThe contribution of this research study is using a small size of data and comparing the forecasting results of the six univariate methods. Three commonly used evaluation criteria, mean absolute error (MAE), root mean squared error (RMSE) and mean absolute percent error (MAPE), were adopted to evaluate the model performance. The outcome of this work can help predict the crude oil price.

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“…Two recent crude oil datasets MCX INR Daily and CFD USD Real Time is collected from [31] http://in.investing.com/commodities/crude-oil spanning from 12 th December 2011 to 1 st August 2016 and 1 st July 2011 to 1 st August 2016 respectively out of which MCX INR Daily is in Indian rupees and other is in American dollar. Similarly two other datasets BRENT and WTI [32][33][34] are collected from https://fred.stlouisfed.org/series spanning from 20th May 1987 to 29th August 2016 and 2nd January 1986 to 29th August 2016 respectively, the description of the dataset can be found in http://www.eia.gov/dnav/pet/TblDefs/pet_pri_spt_tbldef2.asp. Table 1 shows the statistics of all four datasets.…”

Section: Data Set Descriptionmentioning

confidence: 99%

A Novel Look Back N Feature Approach towards Prediction of Crude Oil Price

Nayak¹,

Kuhoo²,

Mishra³

et al. 2018

IJET

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Prediction of crude oil prices in advance can play a significant role in the global economy. Change in crude oil price affect wide range of application for economic and risk projection. Crude oil price forecasting is a challenging task due to its complex nonlinear and chaotic behavior. During the last decade’s researcher have designed many classification algorithm for crude oil prediction. The major challenge for any unsupervised dataset is to define a class label for every feature of its dataset. This paper, propose a new novel technique, look back N feature (LBNF) algorithm to discover class label. Later the classifier support vector machine (SVM) with k-nearest neighbor (k-NN) has been used to classify the current feature vector to predict the crude indices one day, one weak, one month in advance. We have checked our algorithm with standard recent MCX INR Daily and CFD USD Real Time crude oil dataset. To prove the effectiveness of proposed algorithm we have compared it with recent Grey wave forecasting method and the experimental result is found to be better than this method.

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

Abstract: Working Papers describe research in progress by the author(s) and are published to elicit comments and to encourage debate. The views expressed in IMF Working Papers are those of the author(s) and do not necessarily represent the views of the IMF, its Executive Board, or IMF management.

Cited by 9 publications

References 18 publications

The role of transition regime models for corn prices forecasting

The role of transition regime models for corn prices forecasting

A study of univariate forecasting methods for crude oil price

A Novel Look Back N Feature Approach towards Prediction of Crude Oil Price

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