علی عرفانی فرد

This paper predicts oil demand for road transport across 154 countries by using available data to project demand up to 2020. Oil demand for road transport accounts for more than 50% of total world oil demand. Vehicle ownership (in 1000 population) is estimated using the nonlinear Gompertz model by pooled time series cross-sections data for all countries of the world. Different saturation levels for countries and over time are estimated by using demographic and geographic variables. Oil demand is estimated as the product of vehicle stock and “oil use pervehicle” under two scenarios, In the “policy scenario” alternative fuels and technologies and new regulations are deemed to affect the oil demand. Oil demand for road transport for the sample of 154 countries is expected to be 14748 million barrels in 2020 with a 50% increase of the vehicle fleet under the business as usual scenario. However, if measures are taken to reduce per vehicle use fuel use under the "policy scenario", total demand will only increase to 11601 million barrels demonstrating a 20% savings.

This paper surveys oil futures markets and identifies relevant risks in order to propose a model that establishes an acceptable relationship between future and spot prices. Based on this mechanism the paper then proposes a framework for predicting oil prices and resultant revenues from oil sales and purchases. The framework is then used to predict prices expected revenues from oil transactions using data mining and modeling based on an adaptive expectations model and technical analysis rules. Results of three different scenarios are then compared: 1-when inputs are the outputs of adaptive expectations model 2- when inputs are derived from technical analysis rules and 3- combination of these two approaches. The results demonstrate that the combined model besides reducing uncertainty increases the predictive power of the model by respectively 70% and 10% compared to scenarios 1 and 2 respectively. In addition، applying the adaptive expectations together with technical analysis results in more precise predictions.