Elements of forecasting applied to crude oil prices

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Last updated: April 16, 2019

The goal of our project is to study crude oil prices over the last 20 years and discover any tendencies the data follows. Crude oil prices are extremely volatile due to OPEC and the constant strife in the Middle East. It will be interesting to see how these conflicts effect prices around the world and how much lag there is between the incident and the respective change in price. We expect crude oil prices to be seasonal with a peak in winter and valley in summer.

Also, we want to investigate if there are any trends such as prices rising towards the end of the week.Based on the spot prices of crude oil, we hope to find a decline in prices during economic recessions and a rise during periods of economic growth. In addition, we can compare the oil prices between Oklahoma and Europe. It will also be interesting to study whether Oklahoma has smaller weekly spikes during the year than in Europe. The data was collected daily over a period from June 2nd, 1986 through March 9th, 2004 by the United States Department of Energy.The world’s most popular energy source is an imminent factor in determining the well being of a nation’s economy. Petroleum has become the driving force behind the world’s economy since the mid 1900’s when it became the more efficient energy substitute for coal.

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As the world economy continues to expand at an exponential rate, there are many industry policies that cause shocks and unrest due to the overall importance of energy on the quality of life on both macro and microeconomic scales. From the middle-class American family relying on low gasoline prices for transportation, to the factory in London deciding on how much insulation would maximize cost efficiency, the dependence for petroleum-based products is an underlying priority for all households and firms. Between cars, trucks, and airliners, petroleum makes up roughly 97 percent of the fuel in which the United States relies on for transportation. From an economic standpoint, the importance of the oil industry is evident through the $487 billion in revenues from the leading twenty oil companies in America in 2001 (API (2004)). With fuel and energy acting as the backbone for transportation, homes, and factories, a country’s standard of living is highly dependent on the oil market.

The forces of supply and demand have directly affected world economies even with the slightest shifts and movements along the respective curves. The world demand for petroleum has increased rigorously over the last half-century, and it has been the responsibility of the Organization of Petroleum Exporting Countries (OPEC) to determine the appropriate supply, thereby creating a price for the product in the marketplace for commodities. Specifically, we focus on the market for light, sweet crude oil. The price reflected on the multiple mercantile exchanges in the U.S. are per barrel, and include the costs for exploration, oil reserves, extraction, shipping, refining, and pipeline usage. OPEC tries to maintain the average price band for a basket of crude at $22 to $28 per barrel (Williams (2003)).

However, there are many factors that cause price fluctuations in both the short and long-terms. These oil price shocks can result from disruptions in supply and demand caused by such factors as seasonality, political turmoil, OPEC capacity, and weather. The costs associated with these shocks can lead to reduced output, idle factories, layoffs, higher costs, increased political tension; all of which significantly alter the economic blueprint of a nation’s society (Leiby (2003)). Because of the underlying importance of the industry on world economic stability, it is imperative that a continued effort is placed on developing world wide policies that aim at maximizing utility. It is the focus of our research to uncover and understand the ebbs and flows of the petroleum industry by applying the elements of forecasting to crude oil prices over the last twenty years.This graphical depiction of crude oil prices from WTRG Economics displays the general fluctuations and underlying shocks adjusted in 2000 dollars.

First, this report will provide a detailed description of the data, including the source and frequency of the sample. Second, it will extend an explanation of the empirical methods used to create a best fit model. Thirdly, the outcomes of the study are displayed, followed by the resulting conclusions. We hope that through this exposition, one can uncover a basic comprehension of how the crude market fluctuates through periods of differing duration along with attaining an understanding of where the market is destined in the future.The data collected was the spot prices of crude oil measured in dollars per barrel. Data from May 20, 1987 was used because that was the first date the prices were available for both the locations; Cushing, Oklahoma and Europe. The data ends at March 9, 2004 because that was the last date available.

The frequency of the data is five day weeks (no weekends) beginning with Wednesday May 20th. The data was obtained from the government agency the Energy Information Administration, which reports to the U.S. Department of Energy, and the website is http//www.eia.

doe.gov/oil_gas/petroleum/info_glance/prices.html.

At first glance, the data seemed extremely thorough, but a closer inspection revealed missing data points. December 25th and January 1st were never included most likely due to these being holidays every year and government officials not working on those days to collect the data. Still, Thanksgiving was always included as were other popular holidays, so it still remains unclear why Christmas and New Years were left out. Also, two other data points from each year were continually left out, but these two days were random. They almost always occurred in either April or May. Again, the reason these days were not included or not recorded is uncertain. To model the data in eviews, the full range of data points is needed. Therefore, the two days surrounding each missing data point were averaged and then substituted to give a full range of data.

This was done for about 60 data points out of over 4000; as a result, it is assumed that these averaged data points did not have an effect on the modeling of the data.Empirical work on the data began by reviewing these graphs:An interesting observation on the government came from an attempt to acquire more information about the data. The excel spreadsheet of the data came with a phone number and email address of the government official who serves as the data administrator. The phone number was out of service and the person never responded to emails. This sounds like a typical government official. In addition, the data has not been updated since March 10th.

Again, it seems the data administrator does not have the time to keep up with these records. One would believe the crude oil prices to be an important statistic for many companies making important decisions and also for government representatives trying to make policies. If this data is not updated regularly, then these companies and government officials are making judgments with out of date data and therefore, these decisions are not as precise as they could be.

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