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

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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 CLHOPNHUNG CL HO PN HU NG

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 CLHOPNHUNG CLplus HOplus PNplus HUplus NGplus

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

13 Clementine NN Output

14 Natural Gas

15 Heating Oil

16 Gasoline

17 Crude Oil

18 Propane

19 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

20 Forecasting Error Avg. Absolute ErrorMean Squared Error RegNNRegNN CL HO HU NG PN

21 % Correct Direction of Forecasts RegressionNeural Net CL40%79% HO63%72% HU65%83% NG68%81% PN68%69%

22 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