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Forecasting
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Forecasting Commodity Markets
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Forecasting Commodity Markets

A special issue of Forecasting (ISSN 2571-9394). This special issue belongs to the section "Forecasting in Economics and Management".

Deadline for manuscript submissions: closed (31 August 2021) | Viewed by 13968

Special Issue Editors

Prof. Dr. Michał Rubaszek

E-Mail Website
Guest Editor
Head of Financial Markets Modelling Unit, SGH Warsaw School of Economics, al. Niepodległości 162, 02-554 Warsaw, Poland
Interests: exchange rates; commodity prices; time series econometrics; Bayesian econometrics; DSGE models
Prof. Gazi Salah Uddin

E-Mail Website
Guest Editor
Division of Economics, Department of Management and Engineering (IEI), Linköping University, SE-581 83 Linköping, Sweden
Interests: commodity market; time series econometrics; applied macroeconomics and financial market integration
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

We all know that fluctuations in commodity prices exert a tremendous impact on the global economy, but also everyday life of many individuals. For that reason, understanding commodity price dynamics and the ability to formulate their reliable forecasts are important to take better economic policy or investment decisions.

The key question we ask in this SI is whether it is possible to develop a method that can be successfully applied in forecasting commodity prices. We welcome submissions:

- Discussing short and long-term forecasting;

- Focusing on individual as well as a broader range of commodities;

- Using traditional econometric models as well as machine learning methods or technical analysis.

The ultimate goal is to identify methods that allow us to better understand the dynamics of commodity markets and hence which can be exploited in practice in decision-taking process.

Prof. Dr. Michał Rubaszek
Prof. Gazi Salah Uddin
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Forecasting is an international peer-reviewed open access quarterly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1800 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • forecasting
  • commodity prices
  • time series models
  • machine learning methods
  • nonlinear models
  • technical analysis

Published Papers (4 papers)

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Research

32 pages, 888 KiB  
Article
Switching Coefficients or Automatic Variable Selection: An Application in Forecasting Commodity Returns
by Massimo Guidolin and Manuela Pedio
Forecasting 2022, 4(1), 275-306; https://doi.org/10.3390/forecast4010016 - 18 Feb 2022
Viewed by 2207
Abstract
In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and [...] Read more.
In this paper, we conduct a thorough investigation of the predictive ability of forward and backward stepwise regressions and hidden Markov models for the futures returns of several commodities. The predictive performance relative a standard AR(1) benchmark is assessed under both statistical and economic loss functions. We find that the evidence that either stepwise regressions or hidden Markov models may outperform the benchmark under standard statistical loss functions is rather weak and limited to low-volatility regimes. However, a mean-variance investor that adopts flexible forecasting models (especially stepwise predictive regressions) when building her portfolio, achieves large benefits in terms of realized Sharpe ratios and mean-variance utility compared to an investor employing AR(1) forecasts. Full article
(This article belongs to the Special Issue Forecasting Commodity Markets)
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13 pages, 904 KiB  
Article
Forecasting Commodity Prices: Looking for a Benchmark
by Marek Kwas and Michał Rubaszek
Forecasting 2021, 3(2), 447-459; https://doi.org/10.3390/forecast3020027 - 19 Jun 2021
Cited by 8 | Viewed by 4988
Abstract
The random walk, no-change forecast is a customary benchmark in the literature on forecasting commodity prices. We challenge this custom by examining whether alternative models are more suited for this purpose. Based on a literature review and the results of two out-of-sample forecasting [...] Read more.
The random walk, no-change forecast is a customary benchmark in the literature on forecasting commodity prices. We challenge this custom by examining whether alternative models are more suited for this purpose. Based on a literature review and the results of two out-of-sample forecasting experiments, we draw two conclusions. First, in forecasting nominal commodity prices at shorter horizons, the random walk benchmark should be supplemented by futures-based forecasts. Second, in forecasting real commodity prices, the random walk benchmark should be supplemented, if not substituted, by forecasts from the local projection models. In both cases, the alternative benchmarks deliver forecasts of comparable and, in many cases, of superior accuracy. Full article
(This article belongs to the Special Issue Forecasting Commodity Markets)
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16 pages, 1038 KiB  
Article
A Time-Varying Gerber Statistic: Application of a Novel Correlation Metric to Commodity Price Co-Movements
by Bernardina Algieri, Arturo Leccadito and Pietro Toscano
Forecasting 2021, 3(2), 339-354; https://doi.org/10.3390/forecast3020022 - 16 May 2021
Cited by 3 | Viewed by 2536
Abstract
This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the [...] Read more.
This study investigates the daily co-movements in commodity prices over the period 2006–2020 using a novel approach based on a time-varying Gerber correlation. The statistic is computed considering a set of probabilities estimated via non-traditional models that give a time-varying structure to the measure. The results indicate that there are several co-movements across commodities, that these co-movements change over time, and that they are tendentially positive. Conditional auto-regressive multithreshold logit models show higher forecasting accuracy for agricultural returns, while dynamic conditional correlation models are more accurate for energy products and metals. The proposed models are shown to be superior in terms of forecasting power to the benchmark method which is based on estimating the Gerber correlation moving a rolling window. Full article
(This article belongs to the Special Issue Forecasting Commodity Markets)
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16 pages, 971 KiB  
Article
Modeling Post-Liberalized European Gas Market Concentration—A Game Theory Perspective
by Hassan Hamie, Anis Hoayek and Hans Auer
Forecasting 2021, 3(1), 1-16; https://doi.org/10.3390/forecast3010001 - 28 Dec 2020
Cited by 3 | Viewed by 2958
Abstract
The question of whether the liberalization of the gas industry has led to less concentrated markets has attracted much interest among the scientific community. Classical mathematical regression tools, statistical tests, and optimization equilibrium problems, more precisely non-linear complementarity problems, were used to model [...] Read more.
The question of whether the liberalization of the gas industry has led to less concentrated markets has attracted much interest among the scientific community. Classical mathematical regression tools, statistical tests, and optimization equilibrium problems, more precisely non-linear complementarity problems, were used to model European gas markets and their effect on prices. In this research, the parametric and nonparametric game theory methods are employed to study the effect of the market concentration on gas prices. The parametric method takes into account the classical Cournot equilibrium test, with assumptions on cost and demand functions. However, the non-parametric method does not make any prior assumptions, a factor that allows greater freedom in modeling. The results of the parametric method demonstrate that the gas suppliers’ behavior in Austria and The Netherlands gas markets follows the Nash–Cournot equilibrium, where companies act rationally to maximize their payoffs. The non-parametric approach validates the fact that suppliers in both markets follow the same behavior even though one market is more liquid than the other. Interestingly, our findings also suggest that some of the gas suppliers maximize their ‘utility function’ not by only relying on profit, but also on some type of non-profit objective, and possibly collusive behavior. Full article
(This article belongs to the Special Issue Forecasting Commodity Markets)
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