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Chaos in Natural Gas Futures?

Victor Chwee

Year: 1998
Volume: Volume19
Number: Number 2
DOI: 10.5547/ISSN0195-6574-EJ-Vol19-No2-10
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Technical analysis using charting techniques to forecast future price trends can be difficult due to the volatile and unpredictable nature of futures market. Alternatively, the emergence of chaos theory seeks to find order in random looking futures price behavior. Hence, this paper tests for the presence of nonlinearity and chaos using the NYMEX 1 -month, 2-month, 3-month, and 6-month daily natural gas settlement prices, from April 1990 to September 1996. In doing so, we use the BDS statistic of Brock, Dechert, and Scheinkman (1987) for nonlinearity testing and then proceed to compute the Lyapunov spectra to determine to what degree futures data resemble a chaotic system. Although the results indicate the presence of nonlinearity, they fail to provide significant evidence of deterministic chaos.

The North American Natural Gas Liquids Markets are Chaotic

Apostolos Serletis and Periklis Gogas

Year: 1999
Volume: Volume20
Number: Number 1
DOI: 10.5547/ISSN0195-6574-EJ-Vol20-No1-5
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In this paper we test for deterministic chaos (i.e., nonlinear deterministic processes which look random) in seven Mont Belview, Texas, hydrocarbon markets, using monthly data from 1985:1 to 1996:12--the markets are those of ethane, propane, normal butane, iso-butane, naptha, crude oil, and natural gas. In doing so, we use the Lyapunov exponent estimator of Nychka, Ellner, Gallant, and McCaffrey (1992). We conclude that there is evidence, consistent with a chaotic nonlinear generation process in all five natural gas liquids markets.

Forecasting Nonlinear Crude Oil Futures Prices

Saeed Moshiri and Faezeh Foroutan

Year: 2006
Volume: Volume 27
Number: Number 4
DOI: 10.5547/ISSN0195-6574-EJ-Vol27-No4-4
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The movements in oil prices are very complex and, therefore, seem to be unpredictable. However, one of the main challenges facing econometric models is to forecast such seemingly unpredictable economic series. Traditional linear structural models have not been promising when used for oil price forecasting. Although linear and nonlinear time series models have performed much better in forecasting oil prices, there is still room for improvement. If the data generating process is nonlinear, applying linear models could result in large forecast errors. Model specification in nonlinear modeling, however, can be very case dependent and time-consuming.In this paper, we model and forecast daily crude oil futures prices from 1983 to 2003, listed in NYMEX, applying ARIMA and GARCH models. We then test for chaos using embedding dimension, BDS(L), Lyapunov exponent, and neural networks tests. Finally, we set up a nonlinear and flexible ANN model to forecast the series. Since the test results indicate that crude oil futures prices follow a complex nonlinear dynamic process, we expect that the ANN model will improve forecasting accuracy. A comparison of the results of the forecasts among different models confirms that this is indeed the case.

Nonlinear Dynamics in Energy Futures

Mariano Matilla-García

Year: 2007
Volume: Volume 28
Number: Number 3
DOI: 10.5547/ISSN0195-6574-EJ-Vol28-No3-2
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This paper studies the possible nonlinear and chaotic nature of three energy futures: natural gas, unleaded gasoline and light crude oil. Nonlinearity is analyzed using the generalized BDS statistic, along with Kaplan�s test. The results show that nonlinearity cannot be rejected. The null hypothesis of chaos is then investigated via the stability of the largest Lyapunov exponent. Evidence of chaos is found in futures returns. Global modelling techniques, like genetic algorithms, have been used in order to estimate potential motion equations. In addition, short term forecasts in futures price movements have been conducted with these estimated equations. The results show that although forecast errors are statistically smaller than those computed with other stochastic approaches, further research on these topics needs to be done.

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