1 / 22

FORECASTING ENERGY PRODUCT PRICES

This study focuses on forecasting 5 energy product prices using daily spot price data from 1997 to 2002. The research utilizes multiple regression, K-means clustering, and neural networks as tools to predict prices 21 days ahead. Results show variable importance and forecasting errors. Conclusions highlight the complexity of energy price prediction.

kstauffer
Download Presentation

FORECASTING ENERGY PRODUCT PRICES

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. FORECASTING ENERGY PRODUCT PRICES M.E. Malliaris Loyola University Chicago S.G. Malliaris Massachusetts Institute of Technology

  2. The Problem • Forecasting 5 interrelated energy products using price data from all five of them • Crude Oil [CO] • Heating Oil [HO] • Gasoline [HU] • Natural Gas [NG] • Propane [PN]

  3. Yield from a Barrel of Crude Oil

  4. Natural Gas Breakdown

  5. Correlation Among Variable Prices

  6. ORIGINAL DATA • Daily spot prices for each of the five variables from December 1997 through November 2002 [5 years] • The first four years were used for the training set • The last year was used for validation

  7. Variables Inputs • Daily closing price of the 5 products • Percent change in price from previous day • Standard deviation over 5 previous days • Standard deviation over 21 previous days Output • Daily price 21 trading days away

  8. Correlation with Price 21 Days Away

  9. MODELS • Multiple Regression • K-Means clustering (cluster group used as additional input into the neural network) • Neural Network

  10. TOOLS • Excel • Multiple Regression • SPSS Clementine • Cluster Analysis • Neural Networks

  11. SPSS Clementine Screen

  12. ClementineNN Output

  13. Natural Gas

  14. Heating Oil

  15. Gasoline

  16. Crude Oil

  17. Propane

  18. Variables Used • The number of variables used in each of the final regression models ranged from 9 to 14 • Only NG appeared in every regression model • The most significant variables in the NN models had little agreement among them • The CL model had no variable in the top five in common with any other model’s top five

  19. Forecasting Error

  20. % Correct Direction of Forecasts

  21. Some Conclusions • In some cases, there is enough information contained in a simple set of price data to allow effective forecasting • The ability to predict the price of a source good does not imply an ability to predict the price of that good’s byproducts

More Related