Time series models to obtain the barrel crude oil prices.

Enos Nobuo Sato, Carlos Teixeira, Beck Nader, Giorgio De Tomi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The use of time series as an additional tool in decision making for the oil industry has been established as a mechanism for predicting the behavior of crude oil price. Especially in Brazil, after the discovery in this decade of the pre-salt reservoirs, the estimate of the price of a crude oil barrel through the use of modern techniques can minimize risks in exploration and production of oil. The more appropriate pricing for crude oil aims to minimize the risks to the economic activity for both exporters and importers of oil. This paper presents six different methods for obtaining crude oil future prices i.e. Multiple regression(MR), Holt´s method (HM), Holt-Winter(HW), Kalman filter (KF), Auto-Regression/Moving-Average (ARIMA) and stochastic simulation based on the use of the Monte Carlo method (SMC). The methods are compared to determine their advantages and disadvantages against each other, seeking to determine which of the generated models has the best potential to determine the future fair price of a barrel of oil. As a result, the most appropriate methodology capable of projecting a more precise future barrel oil fair price was determined, among the six alternatives studied.

Publication series

NameMaterials Science Forum
Volume805
ISSN (Print)02555476

Other

Other56 Brazilian Ceramic Conference, COLAOB 2012, Latin American Cong. of Artificial Organs and Biomaterials, 2012, 7th International Conference on Intelligent Processing and Manufacturing of Materials, IPMM, 2012, Brazilian Surface Treatments and Exhibition, EBRATS 2012, Ptech - 8th International Latin American Conference on Powder Technology, 2011
CountryBrazil
CityFoz de Iguaçu
Period2/9/123/9/12

Fingerprint

Petroleum
crude oil
Time series
Oils
Crude oil
oils
regression analysis
decision making
Kalman filters
Brazil
winter
Monte Carlo method
economics
Monte Carlo methods
Salts
Decision making
industries
methodology
salts
Economics

Keywords

  • Forecast
  • Modeling
  • Oil Prices
  • Time series

ASJC Scopus subject areas

  • Materials Science(all)
  • Condensed Matter Physics
  • Mechanical Engineering
  • Mechanics of Materials

Cite this

Sato, E. N., Teixeira, C., Nader, B., & Tomi, G. D. (2015). Time series models to obtain the barrel crude oil prices. In Materials Science Forum (Vol. 805, pp. 422-428). (Materials Science Forum; Vol. 805). Trans Tech Publications Ltd. https://doi.org/10.4028/www.scientific.net/MSF.805.422

Time series models to obtain the barrel crude oil prices. / Sato, Enos Nobuo; Teixeira, Carlos; Nader, Beck; Tomi, Giorgio De.

Materials Science Forum. Vol. 805 Trans Tech Publications Ltd, 2015. p. 422-428 (Materials Science Forum; Vol. 805).

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Sato, EN, Teixeira, C, Nader, B & Tomi, GD 2015, Time series models to obtain the barrel crude oil prices. in Materials Science Forum. vol. 805, Materials Science Forum, vol. 805, Trans Tech Publications Ltd, pp. 422-428, 56 Brazilian Ceramic Conference, COLAOB 2012, Latin American Cong. of Artificial Organs and Biomaterials, 2012, 7th International Conference on Intelligent Processing and Manufacturing of Materials, IPMM, 2012, Brazilian Surface Treatments and Exhibition, EBRATS 2012, Ptech - 8th International Latin American Conference on Powder Technology, 2011, Foz de Iguaçu, Brazil, 2/9/12. https://doi.org/10.4028/www.scientific.net/MSF.805.422
Sato EN, Teixeira C, Nader B, Tomi GD. Time series models to obtain the barrel crude oil prices. In Materials Science Forum. Vol. 805. Trans Tech Publications Ltd. 2015. p. 422-428. (Materials Science Forum). https://doi.org/10.4028/www.scientific.net/MSF.805.422
Sato, Enos Nobuo ; Teixeira, Carlos ; Nader, Beck ; Tomi, Giorgio De. / Time series models to obtain the barrel crude oil prices. Materials Science Forum. Vol. 805 Trans Tech Publications Ltd, 2015. pp. 422-428 (Materials Science Forum).
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