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Study Of Stochastic Simulation Of Oil Prices

Posted on:2007-02-18Degree:MasterType:Thesis
Country:ChinaCandidate:M LinFull Text:PDF
GTID:2209360185472988Subject:Applied Mathematics
Abstract/Summary:PDF Full Text Request
Petroleum is a kind of special merchandise, and its price plays a prominent role in economy development in the world. Besides the demand and the supply of petroleum, both policy and economy influence oil price. In the past decades, the oil price hoiked or tobogganed,and changed without obvious regulation .Its theory has become a hot point in the studying field.Domestic and international experts has set up various kinds of models and theories to explain the reason why the oil price fluctuated and forecasted its tendency and fluctuation in future. However, there are still several questions for these methods : First, most researches are determinate prediction, and the result received is a definite price predicted value, but in fact, the price of petroleum has uncertainty and it is a random variable, adopting the view of randomness to imitate the distribution characteristic of the oil price is more appropriate; Second, when studying the prediction issue of oil price,we can't consider the price as it stands ,or regard oil price as the single time serial to study briefly , but should study the oil price and its influence factor as a system from a deeper level by starting with the influence factors; Third, the extremely important influence factor of oil price( such as society and politics factor) is unable to quantization accurately,only staying in qualitative analysis, so these forecasting model of oil price are not very ideal.In view of this, this text attempts to study the price of petroleum and its influence factor as a system in terms of randomicity. Main research work is as follows:(1) The quantitative method for influence factors of oil price is introduced. With the method, these influence factors that were unable to deal with can be quantitate rationally.
Keywords/Search Tags:Oil price, Stochastic simulation, Artificial neural network, Gray method, Probability distribution
PDF Full Text Request
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