IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v17y2024i5p182-d1385163.html
   My bibliography  Save this article

Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM Models

Author

Listed:
  • Mohammed Alhagyan

    (Mathematics Department, College of Humanities and Science, Prince Sattam bin Abdulaziz University, Al-Kharj 11912, Saudi Arabia)

Abstract

Future index prices are viewed as a critical issue for any trader and investor. In the literature, various models have been developed for forecasting index prices. For example, the geometric Brownian motion (GBM) model is one of the most popular tools. This work examined four types of GBM models in terms of the presence of memory and the kind of volatility estimations. These models include the classical GBM model with memoryless and constant volatility assumptions, the SVGBM model with memoryless and stochastic volatility assumptions, the GFBM model with memory and constant volatility assumptions, and the SVGFBM model with memory and stochastic volatility assumptions. In this study, these models were utilized in an empirical study to forecast the future index price of the energy sector in the Saudi Stock Exchange Market. The assessment was led by utilizing two error standards, the mean square error (MSE) and mean absolute percentage error (MAPE). The results show that the SVGFBM model demonstrates the highest accuracy, resulting in the lowest MSE and MAPE, while the GBM model was the least accurate of all the models under study. These results affirm the benefits of combining memory and stochastic volatility assumptions into the GBM model, which is also supported by the findings of numerous earlier studies. Furthermore, the findings of this study show that GFBM models are more accurate than GBM models, regardless of the type of volatility. Furthermore, under the same type of memory, the models with a stochastic volatility assumption are more accurate than the corresponding models with a constant volatility assumption. In general, all models considered in this work showed a high accuracy, with MAPE ≤ 10%. This indicates that these models can be applied in real financial environments. Based on the results of this empirical study, the future of the energy sector in Saudi Arabia is forecast to be predictable and stable, and we urge financial investors and stockholders to trade and invest in this sector.

Suggested Citation

  • Mohammed Alhagyan, 2024. "Forecasting the Performance of the Energy Sector at the Saudi Stock Exchange Market by Using GBM and GFBM Models," JRFM, MDPI, vol. 17(5), pages 1-10, April.
    • Handle: RePEc:gam:jjrfmx:v:17:y:2024:i:5:p:182-:d:1385163
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/17/5/182/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/17/5/182/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Chenyu Han & Yiming Wang & Yingying Xu, 2020. "Nonlinearity and efficiency dynamics of foreign exchange markets: evidence from multifractality and volatility of major exchange rates," Economic Research-Ekonomska Istraživanja, Taylor & Francis Journals, vol. 33(1), pages 731-751, January.
    2. Ashok Bardhan & Raša Karapandža & Branko Urošević, 2006. "Valuing Mortgage Insurance Contracts in Emerging Market Economies," The Journal of Real Estate Finance and Economics, Springer, vol. 32(1), pages 9-20, February.
    3. Kim, Kyong-Hui & Kim, Nam-Ung & Ju, Dong-Chol & Ri, Ju-Hyang, 2020. "Efficient hedging currency options in fractional Brownian motion model with jumps," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 539(C).
    4. Hull, John C & White, Alan D, 1987. "The Pricing of Options on Assets with Stochastic Volatilities," Journal of Finance, American Finance Association, vol. 42(2), pages 281-300, June.
    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. Amit K. Sinha, 2021. "The reliability of geometric Brownian motion forecasts of S&P500 index values," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 40(8), pages 1444-1462, December.
    2. Amit Sinha, 2024. "Daily and Weekly Geometric Brownian Motion Stock Index Forecasts," JRFM, MDPI, vol. 17(10), pages 1-22, September.
    3. Peter Carr & Liuren Wu, 2014. "Static Hedging of Standard Options," Journal of Financial Econometrics, Oxford University Press, vol. 12(1), pages 3-46.
    4. Söderlind, Paul, 2009. "The C-CAPM without ex post data," Journal of Macroeconomics, Elsevier, vol. 31(4), pages 721-729, December.
    5. Damir Filipovi'c & Martin Larsson, 2017. "Polynomial Jump-Diffusion Models," Papers 1711.08043, arXiv.org, revised Jul 2019.
    6. René Garcia & Richard Luger & Eric Renault, 2000. "Asymmetric Smiles, Leverage Effects and Structural Parameters," Working Papers 2000-57, Center for Research in Economics and Statistics.
    7. Christoffersen, Peter & Heston, Steven & Jacobs, Kris, 2010. "Option Anomalies and the Pricing Kernel," Working Papers 11-17, University of Pennsylvania, Wharton School, Weiss Center.
    8. Peng He, 2012. "Option Portfolio Value At Risk Using Monte Carlo Simulation Under A Risk Neutral Stochastic Implied Volatility Model," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 6(5), pages 65-72.
    9. Martin Melecky, 2012. "Choosing The Currency Structure Of Foreign‐Currency Debt: A Review Of Policy Approaches," Journal of International Development, John Wiley & Sons, Ltd., vol. 24(2), pages 133-151, March.
    10. José Valentim Machado Vicente & Jaqueline Terra Moura Marins, 2019. "A Volatility Smile-Based Uncertainty Index," Working Papers Series 502, Central Bank of Brazil, Research Department.
    11. Bjork, Tomas, 2009. "Arbitrage Theory in Continuous Time," OUP Catalogue, Oxford University Press, edition 3, number 9780199574742, Decembrie.
    12. Feil, Jan-Henning & Musshoff, Oliver, 2013. "Investment, disinvestment and policy impact analysis in the dairy sector: a real options approach," Structural Change in Agriculture/Strukturwandel im Agrarsektor (SiAg) Working Papers 159229, Humboldt University Berlin, Department of Agricultural Economics.
    13. Romuald N. Kenmoe S & Carine D. Tafou, 2014. "The Implied Volatility Analysis: The South African Experience," Papers 1403.5965, arXiv.org.
    14. Nour Meddahi, 2002. "A theoretical comparison between integrated and realized volatility," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 17(5), pages 479-508.
    15. F. Gonzalez Miranda & N. Burgess, 1997. "Modelling market volatilities: the neural network perspective," The European Journal of Finance, Taylor & Francis Journals, vol. 3(2), pages 137-157.
    16. Francesco Caravenna & Jacopo Corbetta, 2015. "The asymptotic smile of a multiscaling stochastic volatility model," Papers 1501.03387, arXiv.org, revised Jul 2017.
    17. Semih Yon & Cafer Erhan Bozdag, 2014. "Test of Log-Normal Process with Importance Sampling for Options Pricing," Proceedings of Economics and Finance Conferences 0401571, International Institute of Social and Economic Sciences.
    18. Hardin, Erin M. & Bryant, Henry L., 2016. "A Real Option Approach to Valuing and Hedging Cropland Obligations," 2016 Annual Meeting, February 6-9, 2016, San Antonio, Texas 230148, Southern Agricultural Economics Association.
    19. Hilscher, Jens, 2007. "Is the corporate bond market forward looking?," Working Paper Series 800, European Central Bank.
    20. Peter Christoffersen & Ruslan Goyenko & Kris Jacobs & Mehdi Karoui, 2018. "Illiquidity Premia in the Equity Options Market," The Review of Financial Studies, Society for Financial Studies, vol. 31(3), pages 811-851.

    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:gam:jjrfmx:v:17:y:2024:i:5:p:182-:d:1385163. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.