Stock market efficiency beyond the joint hypothesis: Reevaluating the Moroccan stock market Efficiency through ARFIMA-FIGARCH model

Youness Ait Oummou; Driss Omerani; Aomar Ibourk

doi:10.31893/multiscience.2026449

Keywords:

m]arket efficiency

joint hypothesis

Moroccan stock market

ARFIMA-FIGARCH

Abstract

In the context of an unstable international environment, the assessment of the state of stock market efficiency remains of paramount importance. For this reason, we intend to implement ARFIMA-FIGARCH modeling over the period from 2 January 2013 to 29 December 2023. This will enable us to reevaluate efficiency beyond the problem of its joint hypothesis in the context of the Moroccan stock market. The results of our study indicate the presence of substantial evidence supporting the existence of long memory in both returns and volatility of the Moroccan stock market. These findings call into question the foundations of the Efficient Market Hypothesis beyond the Joint hypothesis problem, while offering novel insights into the dynamics of an emerging market. The findings have a practical significance in the fields of forecasting and risk management, as the persistence of return and volatility dynamics directly affects investment strategies and policymaking. Methodologically, the employment of a rigorous ARFIMA-FIGARCH framework enhances the robustness of the analysis by accounting for non-normality and heteroskedasticity in daily financial data.
References
1. Anoruo, E., & Gil-Alana, L. A. (2010). Mean reversion, long memory and fractional integration in African stock market prices. Centre for EMEA Banking, Finance and Economics Working Paper Series, 2010(02), 1-22.
2. Arewa, A., Emmanuel, C. E., Olufemi, A. A., James, A. O., Solomon, O. O., & Joy, E. O. (2023). Long Memory Dependence over Cycles in Nigerian and South-African Stock Markets. Business and Economics Journal, 14(4), 1-11.
3. Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30.
4. Basira, K., Dhliwayo, L., Chinhamu, K., Chifurira, R., & Matarise, F. (2024). Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models. Risks 12: 73.
5. Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.
6. Boubaker, H., Saidane, B., & Ben Saad Zorgati, M. (2022). Modelling the dynamics of stock market in the gulf cooperation council countries: evidence on persistence to shocks. Financial Innovation, 8(1), 46.
7. Cavaliere, G., Nielsen, M. Ø., & Taylor, A. M. R. (2015). Bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets. Journal of Econometrics, 187(2), 557–579.
8. CHRISTODOULOU-VOLOS, C., Siokis, F. M., & Zikos, S. (2013). IS THERE LONG TERM MEMORY IN UROPEAN STOCK MARKETS? International Journal of Economic Research, 10(2).
9. Contreras Reyes, J. E., Zavala, J. E., & Idrovo Aguirre, B. J. (2024). Analyzing the selective stock price index using fractionally integrated and heteroskedastic models.
10. De Bondt, W. (2020). Investor and market overreaction: a retrospective. Review of Behavioral Finance, 12(1), 11-20.
11. De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact?. The Journal of finance, 40(3), 793-805.
12. Dum, D. Z., Meher, B. K., Bărbăcioru, I. C., Paliu-Popa, L., Gil, M. T., Simon, A. I., STEGĂROIU, C., & Florescu, I. (2023). INVESTIGATING THE EFFICACY OF ARIMA AND ARFIMA MODELS IN NIGERIA ALL SHARE INDEX MARKETS. Economic Computation & Economic Cybernetics Studies & Research, 57(3).
13. Eshun, R., & Tweneboah, G. (2025). Relationship between Macroeconomic Fundamentals in the West African Monetary Zone. Studies in Economics and Econometrics, 1–20.
14. Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105.
15. Fama, E. F. (1970). Efficient capital markets. Journal of Finance, 25(2), 383–417.
16. Fama, E. F. (2014). Two pillars of asset pricing. American Economic Review, 104(6), 1467–1485.
17. Ghalanos, A. (2020). Introduction to the rugarch package.(Version 1.3-1). Manuscript, Http://Cran. r-Project. Org/Web/Packages/Rugarch. Accessed, 11(8).
18. Granger, C. W. J., & Joyeux, R. (1980). An introduction to long‐memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15–29.
19. Hosking, J. R. M. (1981). Fractional differencing. In Biometrika (Vol. 68, Issue 1). http://biomet.oxfordjournals.org/
20. Kang, S. H., & Yoon, S.-M. (2013). Modeling and forecasting the volatility of petroleum futures prices. Energy Economics, 36, 354–362.
21. Kasman, A., Kasman, S., & Torun, E. (2009). Dual long memory property in returns and volatility: Evidence from the CEE countries’ stock markets. Emerging Markets Review, 10(2), 122–139.
22. LeRoy, S. F. (1976). Efficient capital markets: Comment. The Journal of Finance, 31(1), 139–141.
23. Maiti, M. (2021). Applied Financial Econometrics. Springer.
24. Nguyen, T., Chaiechi, T., Eagle, L., & Low, D. (2020). Growth enterprise market in Hong Kong: Efficiency evolution and long memory in return and volatility. Journal of Asian Business and Economic Studies, 27(1), 19–34.
25. Nik-Khah, E. (2023). Information at Chicago. In The Palgrave Companion to Chicago Economics (pp. 123–148). Springer.
26. Nkrumah Ababio, E., Ayertey Odonkor, A., Adu, S., & Andoh, R. (2019). Stock Returns and Long-Range Dependence. Available at SSRN 3348115.
27. OMERANI, D., AIT OUMMOU, Y., & ATITAOU, A. (2022). Efficience informationnelle et anomalies de calendrier: Etude empirique du marché boursier marocain. Alternatives Managériales Economiques, 4(4), 223-242.
28. Shiller, R. J. (1981). Do stock prices move too much to be justified by subsequent changes in dividends?.
29. Simon, H. (1957). A Behavioral Model of Rational Choice. Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting/Wiley.
30. Tripathy, N. (2022). Long memory and volatility persistence across BRICS stock markets. Research in International Business and Finance, 63, 101782.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Oummou, Y. A., Omerani, D., & Ibourk, A. (2026). Stock market efficiency beyond the joint hypothesis: Reevaluating the Moroccan stock market Efficiency through ARFIMA-FIGARCH model. Multidisciplinary Science Journal, 8(7), 2026449. https://doi.org/10.31893/multiscience.2026449

[1] Anoruo, E., & Gil-Alana, L. A. (2010). Mean reversion, long memory and fractional integration in African stock market prices. Centre for EMEA Banking, Finance and Economics Working Paper Series, 2010(02), 1-22.

[2] Arewa, A., Emmanuel, C. E., Olufemi, A. A., James, A. O., Solomon, O. O., & Joy, E. O. (2023). Long Memory Dependence over Cycles in Nigerian and South-African Stock Markets. Business and Economics Journal, 14(4), 1-11.

[3] Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 74(1), 3–30.

[4] Basira, K., Dhliwayo, L., Chinhamu, K., Chifurira, R., & Matarise, F. (2024). Estimation and Prediction of Commodity Returns Using Long Memory Volatility Models. Risks 12: 73.

[5] Bollerslev, T. (1986). Generalized autoregressive conditional heteroskedasticity. Journal of Econometrics, 31(3), 307–327.

[6] Boubaker, H., Saidane, B., & Ben Saad Zorgati, M. (2022). Modelling the dynamics of stock market in the gulf cooperation council countries: evidence on persistence to shocks. Financial Innovation, 8(1), 46.

[7] Cavaliere, G., Nielsen, M. Ø., & Taylor, A. M. R. (2015). Bootstrap score tests for fractional integration in heteroskedastic ARFIMA models, with an application to price dynamics in commodity spot and futures markets. Journal of Econometrics, 187(2), 557–579.

[8] CHRISTODOULOU-VOLOS, C., Siokis, F. M., & Zikos, S. (2013). IS THERE LONG TERM MEMORY IN UROPEAN STOCK MARKETS? International Journal of Economic Research, 10(2).

[9] Contreras Reyes, J. E., Zavala, J. E., & Idrovo Aguirre, B. J. (2024). Analyzing the selective stock price index using fractionally integrated and heteroskedastic models.

[10] De Bondt, W. (2020). Investor and market overreaction: a retrospective. Review of Behavioral Finance, 12(1), 11-20.

[11] De Bondt, W. F., & Thaler, R. (1985). Does the stock market overreact?. The Journal of finance, 40(3), 793-805.

[12] Dum, D. Z., Meher, B. K., Bărbăcioru, I. C., Paliu-Popa, L., Gil, M. T., Simon, A. I., STEGĂROIU, C., & Florescu, I. (2023). INVESTIGATING THE EFFICACY OF ARIMA AND ARFIMA MODELS IN NIGERIA ALL SHARE INDEX MARKETS. Economic Computation & Economic Cybernetics Studies & Research, 57(3).

[13] Eshun, R., & Tweneboah, G. (2025). Relationship between Macroeconomic Fundamentals in the West African Monetary Zone. Studies in Economics and Econometrics, 1–20.

[14] Fama, E. F. (1965). The behavior of stock-market prices. The Journal of Business, 38(1), 34–105.

[15] Fama, E. F. (1970). Efficient capital markets. Journal of Finance, 25(2), 383–417.

[16] Fama, E. F. (2014). Two pillars of asset pricing. American Economic Review, 104(6), 1467–1485.

[17] Ghalanos, A. (2020). Introduction to the rugarch package.(Version 1.3-1). Manuscript, Http://Cran. r-Project. Org/Web/Packages/Rugarch. Accessed, 11(8).

[18] Granger, C. W. J., & Joyeux, R. (1980). An introduction to long‐memory time series models and fractional differencing. Journal of Time Series Analysis, 1(1), 15–29.

[19] Hosking, J. R. M. (1981). Fractional differencing. In Biometrika (Vol. 68, Issue 1). http://biomet.oxfordjournals.org/

[20] Kang, S. H., & Yoon, S.-M. (2013). Modeling and forecasting the volatility of petroleum futures prices. Energy Economics, 36, 354–362.

[21] Kasman, A., Kasman, S., & Torun, E. (2009). Dual long memory property in returns and volatility: Evidence from the CEE countries’ stock markets. Emerging Markets Review, 10(2), 122–139.

[22] LeRoy, S. F. (1976). Efficient capital markets: Comment. The Journal of Finance, 31(1), 139–141.

[23] Maiti, M. (2021). Applied Financial Econometrics. Springer.

[24] Nguyen, T., Chaiechi, T., Eagle, L., & Low, D. (2020). Growth enterprise market in Hong Kong: Efficiency evolution and long memory in return and volatility. Journal of Asian Business and Economic Studies, 27(1), 19–34.

[25] Nik-Khah, E. (2023). Information at Chicago. In The Palgrave Companion to Chicago Economics (pp. 123–148). Springer.

[26] Nkrumah Ababio, E., Ayertey Odonkor, A., Adu, S., & Andoh, R. (2019). Stock Returns and Long-Range Dependence. Available at SSRN 3348115.

[27] OMERANI, D., AIT OUMMOU, Y., & ATITAOU, A. (2022). Efficience informationnelle et anomalies de calendrier: Etude empirique du marché boursier marocain. Alternatives Managériales Economiques, 4(4), 223-242.

[28] Shiller, R. J. (1981). Do stock prices move too much to be justified by subsequent changes in dividends?.

[29] Simon, H. (1957). A Behavioral Model of Rational Choice. Models of Man, Social and Rational: Mathematical Essays on Rational Human Behavior in a Social Setting/Wiley.

[30] Tripathy, N. (2022). Long memory and volatility persistence across BRICS stock markets. Research in International Business and Finance, 63, 101782.

Abstract

References

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