This paper proposes an alternative approach based on a genetic algorithm and neural network (GA–NN) for the prediction of the West Texas Intermediate (WTI) crude oil price. Comparative simulation results suggested that the proposed GA–NN approach is better than the baseline algorithms in terms of prediction accuracy and computational efficiency. Mann–Whitney test results indicated that the WTI ...

In this study, a multiscale neural network learning paradigm based on empirical mode decomposition (EMD) is proposed for crude oil price prediction. In this learning paradigm, the original price series are first decomposed into various independent intrinsic mode components (IMCs) with a range of frequency scales. Then the internal correlation structures of different IMCs are explored by neural ...

This paper examines the forward pricing efficiency of the local crude palm oil (CPO) futures market. In an efficient market, the relevant signal to be used by -the producers, traders and processors is simply the futures price. The forward pricing efficiency is measured in terms of the forecasting ability of Malaysian crude palm oil futures price on physical price. The relative predictive power ...

I use daily futures price data to examine the behavior of natural gas and crude oil price volatility in the U.S. since 1990. I test whether there has been a significant trend in volatility, whether there was a short-term increase in volatility during the time of the Enron collapse, and whether natural gas and crude oil price volatilities are interrelated. I also measure the persistence of shock...

ƒ In addition to accurate forecasts of the price of oil, policy-makers are interested in evaluating the risks associated with the baseline forecast to gauge the implications of alternative oil price paths for the economic outlook. A structural model of the global oil market can be used to develop risk scenarios for oil price forecasts, based on hypothetical assumptions about future demand and s...

According to International Energy Outlook 2007 the total world demand of energy is projected to increase through 2030 about 95% for the non-OECD region and 24% for OECD nations. Crude oil is one of the most critical energy commodities while with coal and natural gas are projected to provide roughly the 86% share of the total US primary energy supply in 2030. In this paper, we use wavelet neural...

eiarnination the oil market and review the major oil importer countries, is a major need for guidance and marketing strategy for crude oil exporting countries. so that estimating the oil demand for importer countries and analysis the effective factors on their oil demand, can be an important factor in planning and policy making for oil exporting countries in order to sell oil. so iran is not an...

This study seeks to explain why crude oil prices fluctuate, the main cause being the quota regime, which characterises the OPEC agreements. Given that the Saudi oil supply is inelastic in the short term, a shock in the oil market is accommodated by an immediate price change. In contrast, a dominant firm behaviour in the long term causes an output change, which is accompanied by a smaller price ...

This paper proposes a new method for crude oil price forecasting based on support vector machine (SVM). The procedure of developing a support vector machine model for time series forecasting involves data sampling, sample preprocessing, training & learning and out-of-sample forecasting. To evaluate the forecasting ability of SVM, we compare its performance with those of ARIMA and BPNN. The expe...