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The crude oil is both a commodity and a financial asset. As there are many factors affecting the crude oil spot and futures markets, the analysis of the relationship between major factors of these markets is complicated. The main objective of this paper is to investigate the relationship between the price of crude oil in spot and futures market and identify the effect of the crude oil inventory and the interest-adjusted basis risk on these price changes. The monthly data of WTI spot and futures prices, WTI crude oil inventory and interest-adjusted basis risk are from EIA (Energy Information Administration) database. The data period is from January 1986 to December 2010. Due to the unpredictable volatilities and uncertainties in variables, the GARCH error process models are used. Empirical results show that there is a positive, strong and significant relationship between the spot crude oil price changes and futures prices. Additionally, the basis risk changes can affect the spot and futures crude oil prices up to three lags. Also, crude oil inventory changes have a negative effect on the spot crude oil price changes with one lag.

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Developing models for accurate natural gas spot price forecasting is critical because these forecasts are useful in determining a range of regulatory decisions covering both supply and demand of natural gas or for market participants. A price forecasting modeler needs to use trial and error to build mathematical models (such as ANN) for different input combinations. This is very time consuming since the modeler needs to calibrate and test different model structures with all the likely input combinations. In addition, there is no guidance about how many data points should be used in the calibration and what accuracy the best model is able to achieve. In this study, the Gamma Test has been used for the first time as a mathematically nonparametric nonlinear smooth modeling tool to choose the best input combination before calibrating and testing models. Then, several nonlinear models have been developed efficiently with the aid of the Gamma test, including regression models Local Linear Regression (LLR), Dynamic Local Linear Regression (DLLR) and Artificial Neural Networks (ANN) models. We used daily, weekly and monthly spot prices in Henry Hub from Jan 7, 1997 to Mar 20, 2012 for modeling and forecasting. Comparing the results of regression models show that DLLR model yields higher correlation coefficient and lower MSError than LLR and will make steadily better predictions. The calibrated ANN models specify the shorter the period of forecasting, the more accurate results will be. Therefore, the forecasting model of daily spot prices with ANN can interpret a fine view. Moreover, the ANN models have superior performance compared with LLR and DLLR. Although ANN models present a close up view and a high accuracy of natural gas spot price trend forecasting in different timescales, its ability in forecasting price shocks of the market is not notable.