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Application of GARCH Model to Forecast Data and Volatility of Share Price of Energy (Study on Adaro Energy Tbk, LQ45)

Author

Listed:
  • Erica Virginia

    (Department of Accounting, President University, Cikarang-Bekasi, Indonesia)

  • Josep Ginting

    (Department of Accounting, President University, Cikarang-Bekasi, Indonesia,)

  • Faiz A.M. Elfaki

    (Department of Mathematics, Statistics and Physics, College of Arts and Sciences, Qatar University, Qatar.)

Abstract

Most of the times, Economic and Financial data not only become highly volatile but also show heterogeneous variances (heteroscedasticity). The common method of the Box Jenkins cannot be used for data modeling as the method has an effect of heteroscedasticity (ARCH effects). One of the usable methods to overcome the effect of heteroscedasticity is GARCH model. The aim of this study is to find the best model to estimate the parameters, to predict the share price, and to forecast the volatility of data share price of Adaro Energy Tbk, Indonesia, from January 2014 to December 2016. The study also discuss the Window Dressing. The best model which fits the data is identified as AR(1)-GARCH (1,1). The application of this best model for forecasting the share price of Adaro Energy Tbk, Indonesia, for the next 30 days showed very promising results and the Mean Absolute Percentage Error (MAPE) was determined as 2.16%.

Suggested Citation

  • Erica Virginia & Josep Ginting & Faiz A.M. Elfaki, 2018. "Application of GARCH Model to Forecast Data and Volatility of Share Price of Energy (Study on Adaro Energy Tbk, LQ45)," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 131-140.
    • Handle: RePEc:eco:journ2:2018-03-19
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    References listed on IDEAS

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    1. repec:cup:cbooks:9781107034662 is not listed on IDEAS
    2. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    3. Chan, Kalok & Fong, Wai-Ming, 2000. "Trade size, order imbalance, and the volatility-volume relation," Journal of Financial Economics, Elsevier, vol. 57(2), pages 247-273, August.
    4. Chia Chun Lo & Konstantinos Skindilias & Andreas Karathanasopoulos, 2016. "Forecasting Latent Volatility through a Markov Chain Approximation Filter," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 35(1), pages 54-69, January.
    5. Brooks,Chris, 2014. "Introductory Econometrics for Finance," Cambridge Books, Cambridge University Press, number 9781107661455, December.
    6. Beaver, William & Lambert, Richard & Morse, Dale, 1980. "The information content of security prices," Journal of Accounting and Economics, Elsevier, vol. 2(1), pages 3-28, March.
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    Cited by:

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    3. Yee-Fan Tan & Lee-Yeng Ong & Meng-Chew Leow & Yee-Xian Goh, 2021. "Exploring Time-Series Forecasting Models for Dynamic Pricing in Digital Signage Advertising," Future Internet, MDPI, vol. 13(10), pages 1-24, September.
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    5. 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.
    6. Mustofa Usman & Luvita Loves & Edwin Russel & Muslim Ansori & Warsono Warsono & Widiarti Widiarti & Wamiliana Wamiliana, 2022. "Analysis of Some Energy and Economics Variables by Using VECMX Model in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 12(2), pages 91-102, March.
    7. 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.
    8. Emma Viviani & Luca Di Persio & Matthias Ehrhardt, 2021. "Energy Markets Forecasting. From Inferential Statistics to Machine Learning: The German Case," Energies, MDPI, vol. 14(2), pages 1-33, January.
    9. Rialdi Azhar & Fajrin Satria Dwi Kesumah & Ambya Ambya & Febryan Kusuma Wisnu & Edwin Russel, 2020. "Application of Short-term Forecasting Models for Energy Entity Stock Price (Study on Indika Energi Tbk, JII)," International Journal of Energy Economics and Policy, Econjournals, vol. 10(1), pages 294-301.

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    More about this item

    Keywords

    Volatility; heteroscedasticity; ARCH effect; GARCH model; Window Dressing;
    All these keywords.

    JEL classification:

    • C5 - Mathematical and Quantitative Methods - - Econometric Modeling
    • 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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