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Accurate Estimated Model of Volatility Crude Oil Price

Author

Listed:
  • Toto Gunarto

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

  • Rialdi Azhar

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

  • Novita Tresiana

    (Department of Public Administration, Faculty of Social and Political Science, Universitas Lampung, Indonesia,)

  • Supriyanto Supriyanto

    (Department of Business Administration, Faculty of Social and Political Science, Universitas Lampung, Indonesia,)

  • Ayi Ahadiat

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

Abstract

Crude oil price (COP) data are time-series data that are assessed as having both volatility and heteroscedasticity variance. One of the best models that can be applied to address the heteroscedasticity problem is GARCH (generalized autoregressive conditional heteroscedasticity) model. The purpose of this study is to construct the best-fitted model to forecast daily COP as well as to discuss the prepared recommendation for reducing the impact of daily COP movement. Daily COP data are observed for the last decade, i.e., from 2009 to 2018. The finding with the error of less than 0.0001 is AR (1) GARCH (1,1). The implementation of the model is applicable for both predicting the next 90 days for the COP and its anticipated impact in the future. Because of the increasing prediction, it is recommended that policymakers convert energy use to renewable energy to reduce the cost of oil use.

Suggested Citation

  • Toto Gunarto & Rialdi Azhar & Novita Tresiana & Supriyanto Supriyanto & Ayi Ahadiat, 2020. "Accurate Estimated Model of Volatility Crude Oil Price," International Journal of Energy Economics and Policy, Econjournals, vol. 10(5), pages 228-233.
    • Handle: RePEc:eco:journ2:2020-05-26
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    References listed on IDEAS

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    Cited by:

    1. Mohammad Benny Alexandri & Supriyanto, 2021. "The Influence of Oil Price Volatility and Price Limit in Indonesia Energy Sub-Sector for the Period Before and After Covid-19," International Journal of Energy Economics and Policy, Econjournals, vol. 11(5), pages 538-544.
    2. Ernie Hendrawaty & Rialdi Azhar & Fajrin Satria Dwi Kesumah & Sari Indah Oktanti Sembiring & Mega Metalia, 2021. "Modelling and Forecasting Crude Oil Prices during COVID-19 Pandemic," International Journal of Energy Economics and Policy, Econjournals, vol. 11(2), pages 149-154.
    3. Suripto & Supriyanto, 2021. "The Effect of the COVID-19 Pandemic on Stock Prices with the Event Window Approach: A Case Study of State Gas Companies, in the Energy Sector," International Journal of Energy Economics and Policy, Econjournals, vol. 11(3), pages 155-162.
    4. Supriyanto, 2022. "The Effect of Investment Risk, Macroeconomics on Stock Prices in IPO Companies during the Covid-19 Pandemic," GATR Journals afr209, Global Academy of Training and Research (GATR) Enterprise.
    5. Suripto, 2021. "Governance Implementation on Financial Performance," International Journal of Finance & Banking Studies, Center for the Strategic Studies in Business and Finance, vol. 10(3), pages 115-123, July.
    6. 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.
    7. Supriyanto Supriyanto & Suripto Suripto & Arif Sugiono & Putri Irmala Sari, 2021. "Impact of Oil Prices and Stock Returns: Evidence of Oil and Gas Mining Companies in Indonesia during the COVID-19 Period," International Journal of Energy Economics and Policy, Econjournals, vol. 11(4), pages 312-318.

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    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • O4 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity
    • O42 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Monetary Growth Models
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q42 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Alternative Energy Sources

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