IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2023-04-55.html
   My bibliography  Save this article

Dynamic Modeling and Analysis of Some Energy Companies of Indonesia Over the Year 2018 to 2022 By Using VAR(p)-CCC GARCH(r,s) Model: -

Author

Listed:
  • Mustofa Usman

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia,)

  • M. Komarudin

    (Deparment of Technical Information, Faculty of Engineering, Universitas Lampung, Indonesia,)

  • Nurhanurawati Nurhanurawati

    (Department of Mathematics Education, Faculty of Education, Universitas Lampung, Indonesia,)

  • Edwin Russel

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

  • Ahmad Sidiq

    (Department of Industrial Engineering, Faculty of Engineering, Universitas Malahayati, Indonesia,)

  • Warsono Warsono

    (Department of Mathematics, Faculty of Mathematics and Natural Sciences, Universitas Lampung, Indonesia,)

  • F. A.M Elfaki

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

Abstract

The stock price plays an important role in a financial market. In this research, the natural relationship of the daily stock prices of two energy companies, namely the daily stock price of ADRO and PTBA, from January 2018 to December 2022 will be discussed. The purpose of this research is to obtain the best model that fits the data of the daily share price of the two companies, ADRO and PTBA. The analysis used to model the data is the multivariate time series method. From the results of the analysis, it was found that the best model is VAR(3)-CCC-GARCH)(1,1). Based on this VAR(3)-CCC-GARCH(1,1) model, further analysis: impulse response function (IRF), granger causality, the proportion of prediction error covariances, and forecasting for the next 30 days are discussed. The granger causality test found that the ADRO and PTBA have mutual granger causality (bidirectional). The results of the IRF analysis explain: If there is a shock of one standard deviation in ADRO, ADRO and PTBA have a response. ADRO's response is positive for the next 24 days with a downward trend, while PTBA's response is positive with an upward trend; If there is a shock of one standard deviation in PTBA, PTBA itself and ADRO respond. ADRO's response is negative and weak and has a downward trend in the next 24 days, while PTBA's response is quite large and positive with a downward trend. From the forecasting results for the next 30 days (period), ADRO's closing price has decreased, and PTBA data has a downward trend for the next 30 days.

Suggested Citation

  • Mustofa Usman & M. Komarudin & Nurhanurawati Nurhanurawati & Edwin Russel & Ahmad Sidiq & Warsono Warsono & F. A.M Elfaki, 2023. "Dynamic Modeling and Analysis of Some Energy Companies of Indonesia Over the Year 2018 to 2022 By Using VAR(p)-CCC GARCH(r,s) Model: -," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 542-554, July.
    • Handle: RePEc:eco:journ2:2023-04-55
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/14364/7422
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijeep/article/view/14364
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Helmut Lütkepohl, 2005. "New Introduction to Multiple Time Series Analysis," Springer Books, Springer, number 978-3-540-27752-1, September.
    2. Engle, Robert F. & Kroner, Kenneth F., 1995. "Multivariate Simultaneous Generalized ARCH," Econometric Theory, Cambridge University Press, vol. 11(1), pages 122-150, February.
    3. Mustofa Usman & M. Komarudin & Munti Sarida & Wamiliana Wamiliana & Edwin Russel & Mahatma Kufepaksi & Iskandar Ali Alam & Faiz A.M. Elfaki, 2022. "Analysis of Some Variable Energy Companies by Using VAR(p)-GARCH(r,s) Model : Study From Energy Companies of Qatar over the Years 2015 2022," International Journal of Energy Economics and Policy, Econjournals, vol. 12(5), pages 178-191, September.
    4. Nairobi Nairobi & Edwin Russel & Ambya Ambya & Arif Darmawan & Mustofa Usman & Wamiliana Wamiliana, 2020. "Dynamic Modeling Data Export Oil and Gas and Non-Oil and Gas by ARMA(2,1)-GARCH(1,1) Model: Study of Indonesian s Export over the Years 2008-2019," International Journal of Energy Economics and Policy, Econjournals, vol. 10(6), pages 175-184.
    5. Warsono Warsono & Edwin Russel & Wamiliana Wamiliana & Widiarti Widiarti & Mustofa Usman, 2019. "Modeling and Forecasting by the Vector Autoregressive Moving Average Model for Export of Coal and Oil Data (Case Study from Indonesia over the Years 2002-2017)," International Journal of Energy Economics and Policy, Econjournals, vol. 9(4), pages 240-247.
    6. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    7. Bollerslev, Tim & Engle, Robert F & Wooldridge, Jeffrey M, 1988. "A Capital Asset Pricing Model with Time-Varying Covariances," Journal of Political Economy, University of Chicago Press, vol. 96(1), pages 116-131, February.
    8. Cheong, Chin Wen, 2009. "Modeling and forecasting crude oil markets using ARCH-type models," Energy Policy, Elsevier, vol. 37(6), pages 2346-2355, June.
    9. Chai, Jian & Xing, Li-Min & Zhou, Xiao-Yang & Zhang, Zhe George & Li, Jie-Xun, 2018. "Forecasting the WTI crude oil price by a hybrid-refined method," Energy Economics, Elsevier, vol. 71(C), pages 114-127.
    10. Warsono Warsono & Edwin Russel & Almira Rizka Putri & Wamiliana Wamiliana & Widiarti Widiarti & Mustofa Usman, 2020. "Dynamic Modeling Using Vector Error-correction Model: Studying the Relationship among Data Share Price of Energy PGAS Malaysia, AKRA, Indonesia, and PTT PCL-Thailand," International Journal of Energy Economics and Policy, Econjournals, vol. 10(2), pages 360-373.
    11. Engle, Robert F. & Granger, C. W. J. & Kraft, Dennis, 1984. "Combining competing forecasts of inflation using a bivariate arch model," Journal of Economic Dynamics and Control, Elsevier, vol. 8(2), pages 151-165, November.
    12. Herrera, Ana María & Hu, Liang & Pastor, Daniel, 2018. "Forecasting crude oil price volatility," International Journal of Forecasting, Elsevier, vol. 34(4), pages 622-635.
    13. Bollerslev, Tim, 1990. "Modelling the Coherence in Short-run Nominal Exchange Rates: A Multivariate Generalized ARCH Model," The Review of Economics and Statistics, MIT Press, vol. 72(3), pages 498-505, August.
    14. Warsono Warsono & Edwin Russels & Wamiliana Wamiliana & Widiarti Widiarti & Mustofa Usman, 2019. "Vector Autoregressive with Exogenous Variable Model and its Application in Modeling and Forecasting Energy Data: Case Study of PTBA and HRUM Energy," International Journal of Energy Economics and Policy, Econjournals, vol. 9(2), pages 390-398.
    15. Engle, Robert F, 1982. "Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation," Econometrica, Econometric Society, vol. 50(4), pages 987-1007, July.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. E. Ramos-P'erez & P. J. Alonso-Gonz'alez & J. J. N'u~nez-Vel'azquez, 2020. "Forecasting volatility with a stacked model based on a hybridized Artificial Neural Network," Papers 2006.16383, arXiv.org, revised Aug 2020.
    2. Bessler, Wolfgang & Leonhardt, Alexander & Wolff, Dominik, 2016. "Analyzing hedging strategies for fixed income portfolios: A Bayesian approach for model selection," International Review of Financial Analysis, Elsevier, vol. 46(C), pages 239-256.
    3. Halkos, George & Tzirivis, Apostolos, 2018. "Effective energy commodities’ risk management: Econometric modeling of price volatility," MPRA Paper 90781, University Library of Munich, Germany.
    4. Suhejla Hoti & Felix Chan & Michael McAleer, 2003. "Structure and Asymptotic Theory for Multivariate Asymmetric Volatility: Empirical Evidence for Country Risk Ratings," CIRJE F-Series CIRJE-F-203, CIRJE, Faculty of Economics, University of Tokyo.
    5. Ruili Sun & Tiefeng Ma & Shuangzhe Liu & Milind Sathye, 2019. "Improved Covariance Matrix Estimation for Portfolio Risk Measurement: A Review," JRFM, MDPI, vol. 12(1), pages 1-34, March.
    6. Duchesne, Pierre, 2006. "Testing for multivariate autoregressive conditional heteroskedasticity using wavelets," Computational Statistics & Data Analysis, Elsevier, vol. 51(4), pages 2142-2163, December.
    7. W. Kwan & W. K. Li & K. W. Ng, 2010. "A Multivariate Threshold Varying Conditional Correlations Model," Econometric Reviews, Taylor & Francis Journals, vol. 29(1), pages 20-38.
    8. Chia-Lin Chang & Yiying Li & Michael McAleer, 2018. "Volatility Spillovers between Energy and Agricultural Markets: A Critical Appraisal of Theory and Practice," Energies, MDPI, vol. 11(6), pages 1-19, June.
    9. Pierre Giot & Sébastien Laurent, 2003. "Value-at-risk for long and short trading positions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 18(6), pages 641-663.
    10. Sébastien Laurent & Luc Bauwens & Jeroen V. K. Rombouts, 2006. "Multivariate GARCH models: a survey," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 21(1), pages 79-109.
    11. Resende, Paulo Angelo Alves & Dorea, Chang Chung Yu, 2016. "Model identification using the Efficient Determination Criterion," Journal of Multivariate Analysis, Elsevier, vol. 150(C), pages 229-244.
    12. Hakim, Abdul & McAleer, Michael, 2009. "Forecasting conditional correlations in stock, bond and foreign exchange markets," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 79(9), pages 2830-2846.
    13. Yilmaz, Tolgahan, 2010. "Improving Portfolio Optimization by DCC And DECO GARCH: Evidence from Istanbul Stock Exchange," MPRA Paper 27314, University Library of Munich, Germany.
    14. Pelletier, Denis, 2006. "Regime switching for dynamic correlations," Journal of Econometrics, Elsevier, vol. 131(1-2), pages 445-473.
    15. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
    16. Yudong Wang & Li Liu, 2016. "Crude oil and world stock markets: volatility spillovers, dynamic correlations, and hedging," Empirical Economics, Springer, vol. 50(4), pages 1481-1509, June.
    17. Shi Chen & Cathy Yi-Hsuan Chen & Wolfgang Karl Hardle, 2020. "A first econometric analysis of the CRIX family," Papers 2009.12129, arXiv.org.
    18. Nelson, Daniel B., 1996. "Asymptotic filtering theory for multivariate ARCH models," Journal of Econometrics, Elsevier, vol. 71(1-2), pages 1-47.
    19. Halkos, George E. & Tsirivis, Apostolos S., 2019. "Effective energy commodity risk management: Econometric modeling of price volatility," Economic Analysis and Policy, Elsevier, vol. 63(C), pages 234-250.
    20. Marçal, Emerson Fernandes & Pereira, Pedro L. Valls, 2008. "Testing the Hypothesis of Contagion Using Multivariate Volatility Models," Brazilian Review of Econometrics, Sociedade Brasileira de Econometria - SBE, vol. 28(2), November.

    More about this item

    Keywords

    VAR(p) model; CCC GARCH(r s) model; Impulse Response Function; Granger causality; varaiance decomposition; forecasting;
    All these keywords.

    JEL classification:

    • C53 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Forecasting and Prediction Models; Simulation Methods
    • Q4 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy
    • Q47 - Agricultural and Natural Resource Economics; Environmental and Ecological Economics - - Energy - - - Energy Forecasting

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eco:journ2:2023-04-55. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Ilhan Ozturk (email available below). General contact details of provider: http://www.econjournals.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.