Modeling and forecasting cointegrated relationships among heavy oil and product prices

Lanza, Alessandro; Manera, Matteo; Giovannini, Massimo

doi:10.1016/j.eneco.2005.07.001

Cited by 147 publications

(83 citation statements)

References 11 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…The superiority of the model is deduced by means of the decomposition of the mean square forecast error. Lanza et al [15] investigated the relationships between 978-1-4673-6232-0/13/$31.00 ©2013 IEEE heavy crude oil and products price using co integration and error correction models and evaluated the predictive power of the specification in forecasting crude oil prices. However, previous studies described the behavior of oil price as non-linear and econometric and statistical systems are able to achieve logical results in the case of linear behavior [16].…”

Section: Related Researchmentioning

confidence: 99%

Oil price prediction using ensemble machine learning

Gabralla

Jammazi

Abraham

2013

2013 International Conference on Computing, Electrical and Electronic Engineering (Icceee)

View full text Add to dashboard Cite

Abstract-Crude oil price forecasting is a challenging task due to its complex nonlinear and chaotic behavior. During the last couple of decades, both academicians and practitioners devoted proactive knowledge to address this issue. A strand of them has focused on some key factors that may influence the crude oil price prediction accuracy. This paper extends this particular branch of recent works by considering a number of influential features as inputs to test the forecasting performance of daily WTI crude oil price covering the period 4th January 1999 through 10th October 2012. Empirical results indicate that the proposed methods are efficient and warrant further research in this field.

show abstract

Section: Related Researchmentioning

confidence: 99%

Oil price prediction using ensemble machine learning

Gabralla

Jammazi

Abraham

2013

2013 International Conference on Computing, Electrical and Electronic Engineering (Icceee)

View full text Add to dashboard Cite

show abstract

“…No cointegration relationship can be found between crude and heavy fuel oil. Lanza et al (2005) provide a comprehensive analysis of the price dynamics between 10 varieties of heavy crude oils 8 and product prices 9 in Europe and the USA during 1994-2002. They show that (i) product prices are statistically relevant in explaining short and long run adjustment in petroleum markets, and (ii) the long-run adjustment coefficients are sensitive to the gravity of the specific crude.…”

Section: Petroleum Productsmentioning

confidence: 99%

Assessing the Cross-Commodity Relationships in Energy Markets

Chevallier

Ielpo²

2014

Energy Studies Review

View full text Add to dashboard Cite

This article reviews cointegration analyses in energy markets (e.g. WTI, Brent, Gasoil, Heating Oil, Natural Gas) during 1993-2011 using daily data. The main focus lies in the determination of long-term relationships between these specific energy commodities, with the inclusion of structural breaks. The results globally point out the existence of shared trends among energy markets. The crude oil price (with the WTI as its benchmark) seems to be the leader in the price discovery process, since most of the time it triggers the adjustement towards the long term stationary equilibrium between the variables in the cointegration system.

show abstract

“…Some statisticalbased models have been widely used for crude oil prices forecasting. Typical models include the probabilistic model (Abramson and Finizza, 1995), econometric structural models (Huntington, 1994;Ye et al, 2002Ye et al, , 2005Ye et al, , 2006, co-integration analysis (Gulen, 1998), vector auto-regression models (VAR) (Mirmirani and Li, 2004), error correction models (ECM) (Lanza et al, 2005), auto-regressive integrated moving average (ARIMA) (Yu et al, 2008) and semi-parametric approach based on GARCH properties (Morana, 2001). Usually, these models can provide good prediction results when the crude oil price series under study is linear or near linear.…”

Section: Introductionmentioning

confidence: 99%

Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

Zhang

Wang

2017

EURASIA J MATH SCI T

View full text Add to dashboard Cite

Crude oil price forecasting is one of the most important topics in the field of energy research. Accordingly, numerous methods such as statistical, econometrical and intelligent approaches are applied for crude oil price forecasting. In this paper, a typical competitive learning algorithm, support vector machine (SVM), is empirically investigated to verify the feasibility and potentiality of SVM in crude oil price forecasting. For this purpose, five different prediction models, feed-forward neural networks (FNN), auto-regressive integrated moving average (ARIMA) model, fractional integrated ARIMA (ARFIMA) model, Markov-switching ARFIMA (MS-ARFIMA) model, and random walk (RW) model are used in the study. Experimental results obtained show that the SVM model outperforms the other five methods, implying that it is a fairly good candidate for crude oil price forecasting in terms of either one-step prediction or multi-step prediction.

show abstract

Modeling and forecasting cointegrated relationships among heavy oil and product prices

Cited by 147 publications

References 11 publications

Oil price prediction using ensemble machine learning

Oil price prediction using ensemble machine learning

Assessing the Cross-Commodity Relationships in Energy Markets

Assessing Potentiality of Support Vector Machine Method in Crude Oil Price Forecasting

Contact Info

Product

Resources

About