Forecasting oil price trends using wavelets and hidden Markov models
AbstractThe crude oil price is influenced by a great number of factors, most of which interact in very complex ways. For this reason, forecasting it through a fundamentalist approach is a difficult task. An alternative is to use time series methodologies, with which the price's past behavior is conveniently analyzed, and used to predict future movements. In this paper, we investigate the usefulness of a nonlinear time series model, known as hidden Markov model (HMM), to predict future crude oil price movements. Using an HMM, we develop a forecasting methodology that consists of, basically, three steps. First, we employ wavelet analysis to remove high frequency price movements, which can be assumed as noise. Then, the HMM is used to forecast the probability distribution of the price return accumulated over the next F days. Finally, from this distribution, we infer future price trends. Our results indicate that the proposed methodology might be a useful decision support tool for agents participating in the crude oil market.
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Bibliographic InfoArticle provided by Elsevier in its journal Energy Economics.
Volume (Year): 32 (2010)
Issue (Month): 6 (November)
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Web page: http://www.elsevier.com/locate/eneco
Oil price trends Forecasting Hidden Markov model Wavelets;
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