IDEAS home Printed from https://ideas.repec.org/a/eco/journ2/2020-05-26.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.econjournals.com/index.php/ijeep/article/download/9513/5277
    Download Restriction: no
    File URL: https://www.econjournals.com/index.php/ijeep/article/view/9513/5277
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Umar Farooq & Muhammad Ali Jibran Qamar, 2019. "Predicting multistage financial distress: Reflections on sampling, feature and model selection criteria," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 38(7), pages 632-648, November.
    3. Thomas A. Knetsch, 2007. "Forecasting the price of crude oil via convenience yield predictions," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 26(7), pages 527-549.
    4. Bernabe, Araceli & Martina, Esteban & Alvarez-Ramirez, Jose & Ibarra-Valdez, Carlos, 2004. "A multi-model approach for describing crude oil price dynamics," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 338(3), pages 567-584.
    5. Akhmad Akhmad & Amir Amir, 2018. "Study of Fuel Oil Supply and Consumption in Indonesia," International Journal of Energy Economics and Policy, Econjournals, vol. 8(4), pages 13-20.
    6. Fardous Alom & Neil Ritson, 2012. "Asymmetric adjustment of diesel or petrol retail prices to crude oil price movements: New Zealand evidence," OPEC Energy Review, Organization of the Petroleum Exporting Countries, vol. 36(2), pages 230-245, June.
    7. Lee, John H H & King, Maxwell L, 1993. "A Locally Most Mean Powerful Based Score Test for ARCH and GARCH Regression Disturbances," Journal of Business & Economic Statistics, American Statistical Association, vol. 11(1), pages 17-27, January.
    8. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    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. 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.
    3. 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.
    4. 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.
    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. 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.
    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.

    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. 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.
    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. 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.
    4. Reitz, Stefan & Rülke, Jan & Stadtmann, Georg, 2012. "Nonlinear Expectations in Speculative Markets," VfS Annual Conference 2012 (Goettingen): New Approaches and Challenges for the Labor Market of the 21st Century 62045, Verein für Socialpolitik / German Economic Association.
    5. Reitz, Stefan & Rülke, Jan-Christoph & Stadtmann, Georg, 2012. "Nonlinear expectations in speculative markets – Evidence from the ECB survey of professional forecasters," Journal of Economic Dynamics and Control, Elsevier, vol. 36(9), pages 1349-1363.
    6. Brooks, Robert D. & Davidson, Sinclair & Faff, Robert W., 1997. "An examination of the effects of major political change on stock market volatility: the South African experience," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 7(3), pages 255-275, October.
    7. Naser, Hanan, 2016. "Estimating and forecasting the real prices of crude oil: A data rich model using a dynamic model averaging (DMA) approach," Energy Economics, Elsevier, vol. 56(C), pages 75-87.
    8. Carnero, María Ángeles & Peña, Daniel & Ruiz Ortega, Esther, 2004. "Spurious and hidden volatility," DES - Working Papers. Statistics and Econometrics. WS ws042007, Universidad Carlos III de Madrid. Departamento de Estadística.
    9. Lu-Tao Zhao & Guan-Rong Zeng & Ling-Yun He & Ya Meng, 2020. "Forecasting Short-Term Oil Price with a Generalised Pattern Matching Model Based on Empirical Genetic Algorithm," Computational Economics, Springer;Society for Computational Economics, vol. 55(4), pages 1151-1169, April.
    10. Mckenzie, Michael D., 1998. "The impact of exchange rate volatility on Australian trade flows," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 8(1), pages 21-38, January.
    11. Christiane Baumeister & Lutz Kilian, 2015. "Forecasting the Real Price of Oil in a Changing World: A Forecast Combination Approach," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 33(3), pages 338-351, July.
    12. Mohsin, Muhammad & Jamaani, Fouad, 2023. "Green finance and the socio-politico-economic factors’ impact on the future oil prices: Evidence from machine learning," Resources Policy, Elsevier, vol. 85(PA).
    13. Degiannakis, Stavros & Filis, George, 2018. "Forecasting oil prices: High-frequency financial data are indeed useful," Energy Economics, Elsevier, vol. 76(C), pages 388-402.
    14. Oliver Linton & Douglas Steigerwald, 2000. "Adaptive testing in arch models," Econometric Reviews, Taylor & Francis Journals, vol. 19(2), pages 145-174.
    15. Hughes, Anthony W. & King, Maxwell L. & Kwek, Kian Teng, 2004. "Selecting the order of an ARCH model," Economics Letters, Elsevier, vol. 83(2), pages 269-275, May.
    16. Dufour, Jean-Marie & Khalaf, Lynda & Bernard, Jean-Thomas & Genest, Ian, 2004. "Simulation-based finite-sample tests for heteroskedasticity and ARCH effects," Journal of Econometrics, Elsevier, vol. 122(2), pages 317-347, October.
    17. David Veganzones, 2022. "Corporate failure prediction using threshold‐based models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 41(5), pages 956-979, August.
    18. van Dijk, Dick & Franses, Philip Hans & Lucas, Andre, 1999. "Testing for ARCH in the Presence of Additive Outliers," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 14(5), pages 539-562, Sept.-Oct.
    19. Stefan Reitz & Jan-Christoph Rülke & Georg Stadtmann, 2009. "Are oil-price-forecasters finally right? – Regressive expectations towards more fundamental values of the oil price," WHU Working Paper Series - Economics Group 09-04, WHU - Otto Beisheim School of Management.
    20. Christian Francq & Jean-Michel Zakoïan, 2006. "Inference in GARCH when some coefficients are equal to zero," Computing in Economics and Finance 2006 64, Society for Computational Economics.

    More about this item

    Keywords

    Crude Oil Price; Heteroscedasticity; Subsidy; GARCH Model;
    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
    • 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

    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:journ2:2020-05-26. 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.