Author(s): Nkemnole E Bridget and Wulu J Trarso

Abstract: The constant concern in commodities market, particularly in oil price, has necessitated accurate models to aid in the generation of relatively good synthetic oil price data. Oil prices are subject to high volatility and fluctuations; and this is a nagging challenge, as it becomes difficult to deduce the changing trends of oil prices in the middle - and long - terms; and. hence, this affects the prediction of oil price levels in the short-term. Basically, traditional methodologies in the estimation of oil volatility have mostly been attentive to the problem of predicting oil price levels and seeking assessment of the best estimates. This research employs Weibull distribution and limit probability of a Markov Chain for a statistical modelling of Brent crude oil prices.Basically, we obtained the pattern and fluctuations of Brent crude oil prices and confirmed that there is an existence of a Markov chain in the observed oil price series. We then used the limit probability of a Markov Chain to deduce the changing trends of the oil prices. Subsequently, we carried out a probability distribution analysis and discovered that the oil price series conform to a Weibull distribution when compared to other probability distributions (e.g., Gamma, Lognormal, and Gumbel Max). Empirical analysis of the Weibull distribution and the limit probability of a Markov Chain of oil price series reveal that there are changing trends of oil prices from the short-term to the middle- and long-terms, respectively. The proposed models does not only indicate specific trend changes in a variety of oil price states over different terms, but also, establishes a consideration for assessing a trend deduction model of fluctuating oil prices.

Pages: 115-130 | Views: 657 | Downloads: 16

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