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Future Natural Gas Price Forecasting Model and Its Policy Implication

Author

Listed:
  • Ambya Ambya

    (Department of Development Economics, Faculty of Economics and Business, Universitas Lampung, Indonesia)

  • Toto Gunarto

    (Department of Development Economics, Faculty of Economics and Business, Universitas Lampung, Indonesia)

  • Ernie Hendrawaty

    (Department of Management, Faculty of Economics and Business, Universitas Lampung, Indonesia,)

  • Fajrin Satria Dwi Kesumah

    (Department of Management, Faculty of Economics and Business, Universitas Lampung, Indonesia,)

  • Febryan Kusuma Wisnu

    (Department of Agriculture Engineering, Faculty of Agriculture, Universitas Lampung, Indonesia)

Abstract

Future natural gas (FNG) price is a collected data over the years and is a volatile movement in the market. In other words, FNG price is categorised as a time series data with volatility in both variance and mean, as well as most likely in some cases having heteroscedasticity problem. To come up with an estimated prediction model, some analysis tools, such as autoregressive integrated moving average (ARIMA) and generalised autoregressive conditional heteroscedasticity (GARCH), are introduced to find the best-fitted model having the smallest error value with high significance of probability value. This study aims to examine the best-fitted model that allows us to forecast FNG prices more accurately in the near future. There are 2842 observed data of daily FNG prices from 2009 to 2019 as the input of study objects. The finding suggests that the first measurement model of ARIMA (1,1,1) does not fit the model as having a non-significant probability value. Thus, it is required to check its heteroscedasticity by conducting an ARCH effect test. It is concluded that a data set has an effect of ARCH, so AR (p) GARCH (p,q) model is then tested. AR (1) GARCH (1,1) model is believed to be a best-fitted model having a significant P

Suggested Citation

  • Ambya Ambya & Toto Gunarto & Ernie Hendrawaty & Fajrin Satria Dwi Kesumah & Febryan Kusuma Wisnu, 2020. "Future Natural Gas Price Forecasting Model and Its Policy Implication," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 64-70.
    • Handle: RePEc:eco:journ2:2020-05-9
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    References listed on IDEAS

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    Cited by:

    1. Rialdi Azhar & Febryan Kusuma Wisnu & Fajrin Satria Dwi Kesuma & Widya Rizki Eka Putri & Rian Andri Prasetya, 2022. "State-space Implementation in Forecasting Carbon and Gas Prices in Commodity Markets," International Journal of Energy Economics and Policy, Econjournals, vol. 12(3), pages 280-286, May.
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    3. Saring Suhendro & Mega Matalia & Sari Indah Oktanti Sembiring, 2021. "Public Sector Policy of Estimating Model for Renewable Energy," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 609-613.

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    More about this item

    Keywords

    Future Natural Gas Price; Autoregressive Integrated Moving Average; Autoregressive Conditional Heteroscedastic Effect; Generalised Autoregressive Conditional Heteroscedasticity; Subsidy;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • H2 - Public Economics - - Taxation, Subsidies, and Revenue
    • H25 - Public Economics - - Taxation, Subsidies, and Revenue - - - Business Taxes and Subsidies
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

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