Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach

24 Pages Posted: 10 Jan 2020

See all articles by Davide Ferrari

Davide Ferrari

Free University of Bozen-Bolzano, Faculty of Economics and Management

Francesco Ravazzolo

Free University of Bozen-Bolzano

Joaquin Vespignani

University of Tasmania - School of Economics and Finance

Multiple version iconThere are 2 versions of this paper

Date Written: 2019-12-20

Abstract

This paper focuses on forecasting quarterly energy prices of commodities, such as oil, gas and coal, using the Global VAR dataset proposed by Mohaddes and Raissi (2018). This dataset includes a number of potentially informative quarterly macroeconomic variables for the 33 largest economies, overall accounting for more than 80% of the global GDP. To deal with the information in this large database, we apply a dynamic factor model based on a penalized maximum likelihood approach that allows us to shrink parameters to zero and to estimate sparse factor loadings. The estimated latent factors show considerable sparsity and heterogeneity in the selected loadings across variables. When the model is extended to predict energy commodity prices up to four periods ahead, results indicate larger predictability relative to the benchmark random walk model for 1-quarter ahead for all energy commodities. In our application, the largest improvement in terms of prediction accuracy is observed when predicting gas prices from 1 to 4 quarters ahead.

Keywords: Energy Prices, Forecasting, Dynamic Factor Model, Sparse Estimation, Penalized Maximum Likelihood

JEL Classification: C1;, C5;, C8;, E3;, Q4

Suggested Citation

Ferrari, Davide and Ravazzolo, Francesco and Vespignani, Joaquin, Forecasting Energy Commodity Prices: A Large Global Dataset Sparse Approach (2019-12-20). Globalization and Monetary Policy Institute Working Paper No. 376, Available at SSRN: https://ssrn.com/abstract=3517055 or http://dx.doi.org/10.24149/gwp376

Davide Ferrari

Free University of Bozen-Bolzano, Faculty of Economics and Management ( email )

Sernesiplatz 1
Bozen-Bolzano, BZ 39100
Italy

Francesco Ravazzolo (Contact Author)

Free University of Bozen-Bolzano ( email )

Sernesiplatz 1
Bozen-Bolzano, BZ 39100
Italy

Joaquin Vespignani

University of Tasmania - School of Economics and Finance ( email )

Commerce Building,
Sandy Bay Campus
Sandy Bay, TAS, Tasmania 7005
Australia

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