Abstract
Crude oil is an important energy commodity to mankind. Several causes have made crude oil prices to be volatile. The fluctuation of crude oil prices has affected many related sectors and stock market indices. Hence, forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise. In this study, daily crude oil prices data was obtained from WTI dated 2 January to 29 May 2015. We used Markov Model (MM) approach in forecasting the crude oil prices. In this study, the analyses were done using EViews and Maple software where the potential of this software in forecasting daily crude oil prices time series data was explored. Based on the study, we concluded that MM model is able to produce accurate forecast based on a description of history patterns in crude oil prices.
Highlights
Crude oil is a complex mixture consisting of more than 200 organic compounds, especially hydrocarbons [1] mostly alkenes and smaller fraction aromatics
Forecasting the crude oil prices is essential to avoid the future prices of the non-renewable natural resources to rise
We concluded that Markov Model (MM) model is able to produce accurate forecast based on a description of history patterns in crude oil prices
Summary
Crude oil is a complex mixture consisting of more than 200 organic compounds, especially hydrocarbons [1] mostly alkenes and smaller fraction aromatics. The decline in crude oil price during the recession was due to a noticeable slowdown in global economic activity. Soft global demand caused prices for goods and services in addition to crude oil to fall suddenly [10]. These movements propose that only a portion of the decline in 2014 is likely due to fragile global economic activity. A substantial amount of research has been published in recent times and is continuing to find an optimal prediction model for crude oil price [12, 13]. Some researchers have used fuzzy systems to develop a model to forecast crude oil price behaviour. We locate pattern(s) from the past datasets that match with today’s crude oil price behaviour, interpolate these two datasets with appropriate neighbouring price elements and forecast tomorrow’s crude oil price
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