Short Term Forecasting of Natural Gas Price using Wavelet Decomposition

doi:10.12972/ksmer.2014.51.2.260

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2014 Vol.51, Issue 2 Preview Page Next Page

Research Paper

Short Term Forecasting of Natural Gas Price using Wavelet Decomposition Wavelet 분해를 이용한 단기 천연가스 가격 예측: 진정환·김진수
Junghwan Jin, Jinsoo Kim

30 April 2014. pp. 260-270

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Abstract

Although natural gas contracts have been done based on oil prices, many recent research argued that the natural gas and oil prices are decoupled. Therefore, it is necessary to analyze the natural gas price as an independent price. In the light of this, we forecasted the short term natural gas price using wavelet decomposition method. In order to compare a forecasting power, three approaches are applied. The first approach is ARIMA only case. We applied an ARIMA into the original data. The second one is wavelet-ARIMA case. In this case, ARIMA is applied into the approximation. In the third approach, we implement wavelet-GARCH analysis. In this approach, an approximation is treated same as second case, but GARCH is applied to details. As results, the wavelet-applied approaches are better than only ARIMA case for the forecasting power. Consequently, we could confirm the usefulness of wavelet-applied approaches in natural gas price forecasting.

Keywords

Natural gas price forecasting

Wavelet decomposition

ARIMA

Wavelet-GARCH

많은 경우 천연가스 계약은 원유 가격에 연동되어 책정되어 왔지만 현재는 천연가스 시장 가격이 원유 가격과 탈동조화(decoupling)되었다는 연구가 꾸준히 발표되고 있다. 따라서 천연가스 가격에 대한 별도의 연구의 필요성이 높아지고 있다. 이에 본 연구에서는 wavelet 분해 분석을 사용하여 단기 천연가스 가격 예측을 수행하였다. 예측 결과를 비교하기 위하여 ARIMA 모형 만을 적용한 경우, 분해 성분 중 근사 성분에 ARIMA 모형을 적용한 경우, 근사 성분에는 ARIMA, 세부 성분에는 GARCH 모형을 적용한 경우를 설정하였다. 비교 결과 ARIMA만을 이용하여 예측한 결과보다 wavelet 분해를 이용한 경우, 특히 wavelet-GARCH 모형의 예측 오차가 약 3.8% 줄어든 것으로 나타나 천연가스 가격의 단기 예측에 있어서 wavelet 분해 방법론의 유용성을 확인할 수 있었다.

키워드

천연가스 가격 예측

Wavelet 분해

ARIMA 모형

Wavelet-GARCH 모형

References

Ahmad, S., Popopla, A. and Ahmad, K., 2005, “Wavelet-based multiresolution forecasting,” University of Surrey, Technical report.
Belén, M.-B., Ramos, S.B. and Veiga, H., 2013, Correlations between oil and stock markets: A wavelet-based approach, Universidad Carlos III de Madrid, Working paper.
Brown, S.-P. and Yucel, M.-K., 2007, What drives natural gas prices?, Federal Reserve Bank of Dallas, Dallas, Texas, USA, Working paper.
Energy Charter Secretariat, 2007, Putting a price on energy; international pricing mechanisms for oil and gas, Energy Chart Secretariat, Brussels, Belgium, pp. 99-200.
Engle, R.F., 1982, “Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation,” Econometrica, Vol. 50, pp. 987-1007.
Hartley, P., 2013, The future of long-term LNG contracts, James A. Baker III Institute for Public Policy of Rice University.
Hwang, K., Kim, J., Hwang, Y. and Heo, E., 2012, “Decoupling of International Natural Gas Prices from Crude Oil Prices,” J. of the Korean Society for Geosystem Engineering, Vol. 49, No. 3, pp. 338-349.
Hwang, Y. and Kim, N., 2012, “An analysis on wealth transfer between oil exporting and importing countries under the oil price fluctuation,” J. of Korea Trade, Vol. 37, No. 3, pp. 217-234.
IEA, 2011, World Energy Outlook, International Energy Agency, Paris.
Lee, D. and Kang, E., 2012, The effect of oil price increase on Korean economy, Korea Institute for International Economic Policy.
Liu, H., Tian, H.-q., Pan, D.-f. and Li, Y.-f., 2013, “Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks,” Applied Energy, Vol. 107, No. 0, pp. 191-208.
Miyamoto, A. and Ishiguro, C., 2009, A new paradigm for natural gas pricing in Asia: a perspective on market value, NG 28. Oxford Institute for Energy Studies, Oxford, UK.
Reboredo, J.C. and Rivera-Castro, M.A., 2013, “A wavelet decomposition approach to crude oil price and exchange rate dependence,” Economic Modelling, Vol. 32, No. 0, pp. 42-57.
Soltani, S., 2002, “On the use of the wavelet decomposition for time series prediction,” Neurocomputing, Vol. 48, No. 1-4, pp. 267-277.
Stern, J., 2007, Is there a rationale for the continuing link to oil product prices in Continental European long-term gas contracts, NG 19. Oxford Institute for Energy Studies, Oxford, UK.
Shumway, H.-R. and Stoffer, S.-D., 2010, Time Series Analysis and Its Applications, Springer, London.
Yousefi, S., Weinreich, I. and Reinarz, D., 2005, “Wavelet- based prediction of oil prices,” Chaos, Solitons & Fractals, Vol. 25, No. 2, pp. 265-275.

Information

Publisher :The Korean Society of Mineral and Energy Resources Engineers
Publisher(Ko) :한국자원공학회
Journal Title :Journal of the Korean Society of Mineral and Energy Resources Engineers
Journal Title(Ko) :한국자원공학회지
Volume : 51
No :2
Pages :260-270
DOI :https://doi.org/10.12972/ksmer.2014.51.2.260

[1] Ahmad, S., Popopla, A. and Ahmad, K., 2005, “Wavelet-based multiresolution forecasting,” University of Surrey, Technical report.

[2] Belén, M.-B., Ramos, S.B. and Veiga, H., 2013, Correlations between oil and stock markets: A wavelet-based approach, Universidad Carlos III de Madrid, Working paper.

[3] Brown, S.-P. and Yucel, M.-K., 2007, What drives natural gas prices?, Federal Reserve Bank of Dallas, Dallas, Texas, USA, Working paper.

[4] Energy Charter Secretariat, 2007, Putting a price on energy; international pricing mechanisms for oil and gas, Energy Chart Secretariat, Brussels, Belgium, pp. 99-200.

[5] Engle, R.F., 1982, “Autoregressive conditional heteroskedasticity with estimates of the variance of United Kingdom inflation,” Econometrica, Vol. 50, pp. 987-1007.

[6] Hartley, P., 2013, The future of long-term LNG contracts, James A. Baker III Institute for Public Policy of Rice University.

[7] Hwang, K., Kim, J., Hwang, Y. and Heo, E., 2012, “Decoupling of International Natural Gas Prices from Crude Oil Prices,” J. of the Korean Society for Geosystem Engineering, Vol. 49, No. 3, pp. 338-349.

[8] Hwang, Y. and Kim, N., 2012, “An analysis on wealth transfer between oil exporting and importing countries under the oil price fluctuation,” J. of Korea Trade, Vol. 37, No. 3, pp. 217-234.

[9] IEA, 2011, World Energy Outlook, International Energy Agency, Paris.

[10] Lee, D. and Kang, E., 2012, The effect of oil price increase on Korean economy, Korea Institute for International Economic Policy.

[11] Liu, H., Tian, H.-q., Pan, D.-f. and Li, Y.-f., 2013, “Forecasting models for wind speed using wavelet, wavelet packet, time series and Artificial Neural Networks,” Applied Energy, Vol. 107, No. 0, pp. 191-208.

[12] Miyamoto, A. and Ishiguro, C., 2009, A new paradigm for natural gas pricing in Asia: a perspective on market value, NG 28. Oxford Institute for Energy Studies, Oxford, UK.

[13] Reboredo, J.C. and Rivera-Castro, M.A., 2013, “A wavelet decomposition approach to crude oil price and exchange rate dependence,” Economic Modelling, Vol. 32, No. 0, pp. 42-57.

[14] Soltani, S., 2002, “On the use of the wavelet decomposition for time series prediction,” Neurocomputing, Vol. 48, No. 1-4, pp. 267-277.

[15] Stern, J., 2007, Is there a rationale for the continuing link to oil product prices in Continental European long-term gas contracts, NG 19. Oxford Institute for Energy Studies, Oxford, UK.

[16] Shumway, H.-R. and Stoffer, S.-D., 2010, Time Series Analysis and Its Applications, Springer, London.

[17] Yousefi, S., Weinreich, I. and Reinarz, D., 2005, “Wavelet- based prediction of oil prices,” Chaos, Solitons & Fractals, Vol. 25, No. 2, pp. 265-275.

Journal of the Korean Society of Mineral and Energy Resources Engineers ISSN:2288-0291(Print) 2288-2790(Online) 한국자원공학회지

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