IDEAS home Printed from https://ideas.repec.org/a/gam/jjrfmx/v18y2025i7p370-d1693202.html
   My bibliography  Save this article

Analysis of the Effectiveness of Classical Models in Forecasting Volatility and Market Dynamics: Insights from the MASI and MASI ESG Indices in Morocco

Author

Listed:
  • Oumaima Hamou

    (ENCG Béni Mellal, LAREMO, University Sultane Moulay Slimane, Béni Mellal 9MCP+M4, Morocco)

  • Mohamed Oudgou

    (ENCG Béni Mellal, LAREMO, University Sultane Moulay Slimane, Béni Mellal 9MCP+M4, Morocco)

  • Abdeslam Boudhar

    (ENCG Béni Mellal, LAREMO, University Sultane Moulay Slimane, Béni Mellal 9MCP+M4, Morocco)

Abstract

This research evaluates the effectiveness of traditional models in predicting movements in the Moroccan financial market, with a focus on the MASI and MASI ESG indices. As environmental, social, and governance (ESG) criteria gain prominence in financial analysis, this study examines the strengths and limitations of conventional predictive models. The findings reveal a significant correlation between the two indices while underscoring the challenges traditional models face in effectively integrating extra-financial dimensions, particularly environmental and social factors. These limitations hinder their ability to fully capture the complexities of the Moroccan financial market, where ESG considerations are increasingly shaping economic trends. Given these constraints, the study emphasizes the need for more advanced forecasting tools, particularly models that comprehensively incorporate ESG factors. Such advancements would enhance the understanding of ongoing economic transformations and address emerging challenges. By refining these tools, predictive models could become more relevant and better equipped to meet the specific demands of Morocco’s evolving financial landscape.

Suggested Citation

  • Oumaima Hamou & Mohamed Oudgou & Abdeslam Boudhar, 2025. "Analysis of the Effectiveness of Classical Models in Forecasting Volatility and Market Dynamics: Insights from the MASI and MASI ESG Indices in Morocco," JRFM, MDPI, vol. 18(7), pages 1-43, July.
    • Handle: RePEc:gam:jjrfmx:v:18:y:2025:i:7:p:370-:d:1693202
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1911-8074/18/7/370/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1911-8074/18/7/370/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Robert G. Eccles & Ioannis Ioannou & George Serafeim, 2014. "The Impact of Corporate Sustainability on Organizational Processes and Performance," Management Science, INFORMS, vol. 60(11), pages 2835-2857, November.
    2. Geert Bekaert & Campbell R. Harvey, 2000. "Foreign Speculators and Emerging Equity Markets," Journal of Finance, American Finance Association, vol. 55(2), pages 565-613, April.
    3. Roll, Richard, 1977. "A critique of the asset pricing theory's tests Part I: On past and potential testability of the theory," Journal of Financial Economics, Elsevier, vol. 4(2), pages 129-176, March.
    4. Robert F. Engle & Simone Manganelli, 2004. "CAViaR: Conditional Autoregressive Value at Risk by Regression Quantiles," Journal of Business & Economic Statistics, American Statistical Association, vol. 22, pages 367-381, October.
    5. 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. Geert Bekaert & Campbell R. Harvey, 2000. "Capital Flows and the Behavior of Emerging Market Equity Returns," NBER Chapters, in: Capital Flows and the Emerging Economies: Theory, Evidence, and Controversies, pages 159-194, National Bureau of Economic Research, Inc.
    2. repec:wyi:journl:002087 is not listed on IDEAS
    3. Ra l De Jes s Guti rrez & Lidia E. Carvajal Guti rrez & Oswaldo Garcia Salgado, 2023. "Value at Risk and Expected Shortfall Estimation for Mexico s Isthmus Crude Oil Using Long-Memory GARCH-EVT Combined Approaches," International Journal of Energy Economics and Policy, Econjournals, vol. 13(4), pages 467-480, July.
    4. CARPANTIER, Jean - François, 2010. "Commodities inventory effect," LIDAM Discussion Papers CORE 2010040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
    5. Teulon, Frédéric & Guesmi, Khaled & Mankai, Selim, 2014. "Regional stock market integration in Singapore: A multivariate analysis," Economic Modelling, Elsevier, vol. 43(C), pages 217-224.
    6. Buczyński Mateusz & Chlebus Marcin, 2018. "Comparison of Semi-Parametric and Benchmark Value-At-Risk Models in Several Time Periods with Different Volatility Levels," Financial Internet Quarterly (formerly e-Finanse), Sciendo, vol. 14(2), pages 67-82, June.
    7. Bai, Ye & Chow, Darien Yan Pang, 2017. "Shanghai-Hong Kong Stock Connect: An analysis of Chinese partial stock market liberalization impact on the local and foreign markets," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 50(C), pages 182-203.
    8. Weronika Ormaniec & Marcin Pitera & Sajad Safarveisi & Thorsten Schmidt, 2022. "Estimating value at risk: LSTM vs. GARCH," Papers 2207.10539, arXiv.org.
    9. Hartz, Christoph & Mittnik, Stefan & Paolella, Marc S., 2006. "Accurate Value-at-Risk forecast with the (good old) normal-GARCH model," CFS Working Paper Series 2006/23, Center for Financial Studies (CFS).
    10. Turan Bali & Panayiotis Theodossiou, 2007. "A conditional-SGT-VaR approach with alternative GARCH models," Annals of Operations Research, Springer, vol. 151(1), pages 241-267, April.
    11. Roberto Buizza & James W. Taylor, 2004. "A comparison of temperature density forecasts from GARCH and atmospheric models," Journal of Forecasting, John Wiley & Sons, Ltd., vol. 23(5), pages 337-355.
    12. Wagner Piazza Gaglianone & Luiz Renato Lima & Oliver Linton & Daniel R. Smith, 2011. "Evaluating Value-at-Risk Models via Quantile Regression," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(1), pages 150-160, January.
    13. Wu, Qi & Yan, Xing, 2019. "Capturing deep tail risk via sequential learning of quantile dynamics," Journal of Economic Dynamics and Control, Elsevier, vol. 109(C).
    14. Mohamed El Hedi Arouri & Mondher Bellalah & Duc Khuong Nguyen, 2010. "The comovements in international stock markets: new evidence from Latin American emerging countries," Applied Economics Letters, Taylor & Francis Journals, vol. 17(13), pages 1323-1328.
    15. Guillaume Coqueret, 2016. "Empirical properties of a heterogeneous agent model in large dimensions," Post-Print hal-02088097, HAL.
    16. Tim Bollerslev, 2008. "Glossary to ARCH (GARCH)," CREATES Research Papers 2008-49, Department of Economics and Business Economics, Aarhus University.
    17. Ngene, Geoffrey & Post, Jordin A. & Mungai, Ann N., 2018. "Volatility and shock interactions and risk management implications: Evidence from the U.S. and frontier markets," Emerging Markets Review, Elsevier, vol. 37(C), pages 181-198.
    18. Chen, Cathy W.S. & Hsu, Hsiao-Yun & Watanabe, Toshiaki, 2023. "Tail risk forecasting of realized volatility CAViaR models," Finance Research Letters, Elsevier, vol. 51(C).
    19. Wilson Calmon & Eduardo Ferioli & Davi Lettieri & Johann Soares & Adrian Pizzinga, 2021. "An Extensive Comparison of Some Well‐Established Value at Risk Methods," International Statistical Review, International Statistical Institute, vol. 89(1), pages 148-166, April.
    20. Ben Rejeb, Aymen & Arfaoui, Mongi, 2016. "Financial market interdependencies: A quantile regression analysis of volatility spillover," Research in International Business and Finance, Elsevier, vol. 36(C), pages 140-157.
    21. Po-Cheng Wu & Cheng-Kun Kuo & Chih-Wei Lee, 2012. "Evaluation Of Multi-Asset Value At Risk: Evidence From Taiwan," Global Journal of Business Research, The Institute for Business and Finance Research, vol. 6(4), pages 23-34.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;

    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:gam:jjrfmx:v:18:y:2025:i:7:p:370-:d:1693202. 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.