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FORECASTING ENERGY PRODUCT PRICES

M.E. MalliarisLoyola University Chicago

S.G. MalliarisMassachusetts Institute of Technology

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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

CL HO PN HU NG

CL 1.00 - - - -

HO 0.96 1.00 - - -

PN 0.84 .88 1.00 - -

HU 0.96 .93 .85 1.00 -

NG 0.67 .73 .68 .66 1.00

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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

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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

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Correlation with Price 21 Days Away

CL HO PN HU NG

CLplus21 0.93 0.90 0.80 0.90 0.64

HOplus21 0.92 0.92 0.82 0.89 0.65

PNplus21 0.82 0.83 0.88 0.81 0.57

HUplus21 0.89 0.87 0.93 0.85 0.64

NGplus21 0.72 0.77 0.73 0.68 0.66

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MODELS

Multiple Regression K-Means clustering (cluster group used as

additional input into the neural network) Neural Network

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TOOLS

Excel– Multiple Regression

SPSS Clementine– Cluster Analysis– Neural Networks

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SPSS Clementine Screen

12

13

ClementineNN Output

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Natural Gas

1.5

2

2.5

3

3.5

4

4.5

5

5.5

1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241

NGplus21

Reg

KMeansNN

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Heating Oil

0.4

0.5

0.6

0.7

0.8

0.9

1

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241

HOplus21

Reg

KMeansNN

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Gasoline

0.4

0.5

0.6

0.7

0.8

0.9

1

1 13 25 37 49 61 73 85 97 109 121 133 145 157 169 181 193 205 217 229 241

HUplus21

Reg

KMeansNN

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Crude Oil

14

16

18

20

22

24

26

28

30

32

34

1 11 21 31 41 51 61 71 81 91 101 111 121 131 141 151 161 171 181 191 201 211 221 231 241

CLplus21

Reg

NN

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Propane

0.2

0.25

0.3

0.35

0.4

0.45

0.5

0.55

0.6

0.65

1 12 23 34 45 56 67 78 89 100 111 122 133 144 155 166 177 188 199 210 221 232 243

PNplus21

Reg

KMeansNN

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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

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Forecasting Error

Avg. Absolute Error Mean Squared Error

Reg NN Reg NN

CL 2.13 1.12 6.65 2.27

HO .06 .04 .005 .002

HU .05 .03 .004 .001

NG .41 .22 .242 .075

PN .06 .08 .005 .009

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% Correct Direction of Forecasts

Regression Neural Net

CL 40% 79%

HO 63% 72%

HU 65% 83%

NG 68% 81%

PN 68% 69%

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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