Long-Memory Models in Testing the Efficiency Market Hypothesis of the Algerian Exchange Market



The purpose of this study is to examine the Efficiency Market Hypothesis (EMH) from the perspective of the Algerian exchange rate market. We apply different tests of dependence, long memory, volatility clustering and unit root tests over the three main Algerian exchange rate returns series vis–à-vis the US Dollar, the Euro, and the British Pound. Empirical findings suggest that combined Autoregressive Moving Average (ARMA)-Fractionally Integrated Generalized Autoregressive Conditional Heteroskedastic (FIGARCH) models were the most appropriate to represent the behavior of exchange rate returns. We also compare the predictive qualities of the estimated models and the Random Walk (RW) in terms of out-of-sample forecasting. The results are held to imply the rejection of the EMH in the Algerian exchange rate market. Therefore, the exchange rates fluctuations can be predicted, which may help public authorities intervene in the exchange market and assess the consequences of different economic policies.


Afzal, A., & Sibbertsen, P. (2022). Long Memory, Spurious Memory: Persistence in Range-Based Volatility of Exchange Rates. Open Economies Review, 2(1), 1-23. https://doi.org/10.1007/s11079-022-09686-2

Azad, A. S. (2009). Random walk and efficiency tests in the Asia-Pacific foreign exchange markets: Evidence from the post-Asian currency crisis data. Research in International Business and Finance, 23(2), 322-338. https://doi.org/10.1016/j.ribaf.2008.11.001

Baillie, R. T., Bollerslev, T., & Mikkelsen, H. O. (1996). Fractionally integrated generalized autoregressive conditional heteroskedasticity. Journal of econometrics, 74(1), 3-30. https://doi.org/10.1016/S0304-4076(95)01749-6

Barkoulas, J., Barilla, A., & Wells, W. (2016). Long-memory exchange rate dynamics in the Euro era. Chaos, Solitons and Fractals, 86, 92–100. https://doi.org/10.1016/j.chaos.2016.02.007

Beine, M., & Laurent, S. (2003). Central bank interventions and jump in double long memory models of daily exchange rate. Journal of Empirical Finance, 10(5), 641-660. https://doi.org/10.1016/S0927-5398(03)00009-4

Bollerslev, T., Chou, R., Jayaraman, N., & Kroner, K. (1991). Les modèles ARCH en finance: Un point sur la théorie et les résultats empiriques. Annales d'Économie et de Statistique, 24, 1-59.

Booth, G., Kaen, F., & Koveos, P. (1982). R/S analysis of foreign exchange rates under two international monetary regimes. Journal of Monetary Economics 10(3), 407-415. https://doi.org/10.1016/0304-3932(82)90035-6

Canales-Kriljenko, J. (2004). Foreign exchange market organization in selected developing and transition economies: Evidence from a survey (IMF Working paper WP/04/4). https://www.imf.org/external/pubs/ft/wp/2004/wp0404.pdf

Caporale, G., & Gil-Alana, L. (2010). Long memory and volatility dynamics in the US Dollar Exchange Rate (Brunel university Economics and Finance Working Paper No. 1003).https://citeseerx.ist.psu.edu/viewdoc/download?doi=

Caporale, G., & Gil-Alana, L. (2013). Long memory and fractional integration in high frequency data on the US Dollar/British Pound spot exchange rate (DIW Berlin Discussion Paper No. 1294). https://ideas.repec.org/p/diw/diwwpp/dp1294.html

Cheung, Y. (1993). Long memory in foreign exchange rates. Journal of Business & Economic Statistics, 11(1), 93-101. https://doi.org/10.2307/1391309

Chung, C. F. (1999). Estimating the fractionally intergated GARCH model. National Taiwan University.

Engel, R. F., & Kroner, K. F. (1995). Multivariate simultaneous generalized ARCH. Econometric Theory, 11(1), 122-150. https://www.jstor.org/stable/3532933

Engle, R. (1982). Autoregressive conditional heteroscedasticity with estimates of the variance of United Kingdom inflation. Econometrica, 50(4), 987-1007. https://doi.org/10.2307/1912773

Fama, E. (1965). The behavior of stock market prices. Journal of Business, 30(1), 383–417. http://www.e-m-h.org/Fama65.pdf

Floros, C. (2008). Long memory in exchange rates: International evidence. The International Journal of Business and Finance Research, 2(1), 31-39. http://www.theibfr2.com/RePEc/ibf/ijbfre/ijbfr-v2n1-2008/IJBFR-V2N1-2008-3.pdf

Gao, G., Ho, K. Y., & Shi, Y. (2020). Long memory or regime switching in volatility? Evidence from high-frequency returns on the U.S. stock indices. Pacific-Basin Finance Journal, 61(1), 101059. https://doi.org/10.1016/j.pacfin.2018.08.013

Gil-Alana, L. A., & Sauci, L. (2018). Long memory and mean reversion in real exchange rates in Latin America. Applied Economics, 50(29), 3148-3155. https://doi.org/10.1080/00036846.2017.1418076

Graves , T., Gramacy, R., Watkins, N., & Franzke, C. (2017). A Brief History of Long Memory: Hurst, Mandelbrot and the Road to ARFIMA, 1951–1980. Entropy, 19(9), 1-21. https://doi.org/10.3390/e19090437

Hakkio, C. S. (1986). Does the exchange rate follow a random walk? A Monte Carlo study offour tests for a random walk. Journal of International Money and Finance, 5(2), 221-229. https://doi.org/10.1016/0261-5606(86)90043-4

Jensen, M. (1978). Some anomalous evidence regarding market efficiency. Journal of Financial Economics, 6(3), 95-101. https://doi.org/10.1016/0304-405X(78)90025-9

Kumar, A. (2014). Testing for long memory in volatility in the Indian forex market. Economic Annals, 59(203), 75-90. http://www.ekof.bg.ac.rs/wp-content/uploads/2014/04/318-1.pdf

Lee, C., & Chou, P. (2013). Real exchange rate behavior: Nonlinearity and breaks. International Review of Economics & Finance, 27(1), 125-133. https://doi.org/10.1016/j.iref.2012.09.007

Lillo, F., & Farmer, J. (2004). The long memory of the efficient market. Studies in in Nonlinear Dynamics & Econometrics, 8(3), 1-33. https://doi.org/10.2202/1558-3708.1226

Liu, R., & Lux, T. (2005). Long memory in financial time series: Estimation of the bivariate multi-fractal model and its application for Value-at-Risk. The University of Kiel.

Lothian, R., & Taylor, M. (1998). Real exchange rate behavior. Journal of International Money and Finance, 16(6), 945-954. https://doi.org/10.1016/S0261-5606(97)00014-4

Meese, R., & Rogoff, K. (1983). Empirical exchange rate models of the seventies, Do they fit out of sample. Journal of International Economics, 14(2), 3-24. https://doi.org/10.1016/0022-1996(83)90017-X

Mensi, W., Hammoudeh, S., & Yonn, S. (2014). Structural breaks and long memory in modeling and forecasting volatility of foreign exchange markets of oil exporters: The importance of scheduled and unscheduled news announcements. International Review of Economics & Finance, 30(1), 101-119. https://doi.org/10.1016/j.iref.2013.10.004

Morana, C., & Beltratti, A. (2004). Structural change and long-range dependence in volatility of exchange rates: either, neither or both? Journal of Empirical Finance 11(5), 629–658. https://doi.org/10.1016/j.jempfin.2003.03.002

Ohanissian, A., Russell, J., & Tsay, R. (2008). True or spurious long memory? A new test. Journal of Business & Economic Statistics, 26(2), 161-175. https://www.jstor.org/stable/27638972

Parikh, A., & Wakerly, E. (2000). Real exchange rates and unit root tests. Review of World Economics, 136(1), 478-490. https://doi.org/10.1007/BF02707290

Rossi, B. (2013). Exchange rate predictability. Journal of Economic Literature 51(4), 1063-1119. https://www.jstor.org/stable/23644817

Tan, Z., Fu, Y., Cheng, H., & Liu, J. (2020). Stock prices' long memory in China and the United States. International Journal of Emerging Markets, 17(5), 1292-1314. https://doi.org/10.1108/IJOEM-11-2019-0921

Tripathy, N. (2022). Long memory and volatility persistence across BRICS stock markets. Research in International Business and Finance, 63, 101782. https://doi.org/10.1016/j.ribaf.2022.101782

Tschernig, R. (1994). Long memory in foreign exchange rates revisited (Institute of statistics and econometrics, Humboldt university of Berlin Discussion Paper No. 46). https://ideas.repec.org/p/zbw/sfb373/199446.html

Turkyilmaz, S., & Balibey, M. (2014). Long memory behavior in the returns of Pakistan stock market: ARFIMA-FIGARCH models. International Journal of Economics and Financial Issues, 4(2), 400-410. https://www.econjournals.com/index.php/ijefi/article/view/784/pdf

Varneskov, R., & Perron, P. (2017). Combining long memory and level shifts in modelling and forecasting the volatility of asset returns. Quantitative Finance, 18(3), 371-393. https://doi.org/10.1080/14697688.2017.1329591




How to Cite

BENZAI, Y., AOUAD, H. S., & DJELLOULI, N. (2022). Long-Memory Models in Testing the Efficiency Market Hypothesis of the Algerian Exchange Market. Management Dynamics in the Knowledge Economy, 10(4), 376–390. Retrieved from https://www.managementdynamics.ro/index.php/journal/article/view/487