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Long Memory In Southern African Stock Markets

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  • Keith Jefferis
  • Pako Thupayagale

Abstract

The paper examines long memory in equity returns and volatility for stock markets in Botswana, South Africa and Zimbabwe using the ARFIMA-FIGARCH model in order to assess the efficiency of these markets in processing information. The findings are diverse. Significant long memory is demonstrated in the equity returns of Botswana; while, in South Africa this result is not statistically different from zero. For Zimbabwe returns are characterised by an anti-persistent process. Furthermore, all the markets investigated provide evidence of long memory in volatility with the exception of Botswana where there is no evidence of volatility persistence and hence the return from taking risk in this market cannot be predicted on the basis of previous values. Copyright (c) 2008 The Authors. Journal compilation (c) 2008 Economic Society of South Africa.

Suggested Citation

  • Keith Jefferis & Pako Thupayagale, 2008. "Long Memory In Southern African Stock Markets," South African Journal of Economics, Economic Society of South Africa, vol. 76(3), pages 384-398, September.
  • Handle: RePEc:bla:sajeco:v:76:y:2008:i:3:p:384-398
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    Cited by:

    1. CHIA-LIN CHANG & MICHAEL McALEER & ROENGCHAI TANSUCHAT, 2012. "Modelling Long Memory Volatility In Agricultural Commodity Futures Returns," Annals of Financial Economics (AFE), World Scientific Publishing Co. Pte. Ltd., vol. 7(02), pages 1-27.
    2. Lumengo Bonga-Bonga & Jamela Hoveni, 2011. "Volatility Spillovers between the Equity Market and Foreign Exchange Market in South Africa," Working Papers 252, Economic Research Southern Africa.
    3. Emmanuel Numapau Gyamfi & Kwabena Kyei & Kwabena Kyei, 2016. "Long - Memory Persistence in African Stock Markets," EuroEconomica, Danubius University of Galati, issue 1(35), pages 83-91, may.
    4. Quinton Morris & Gary Van vuuren & Paul Styger, 2009. "Further Evidence Of Long Memory In The South African Stock Market," South African Journal of Economics, Economic Society of South Africa, vol. 77(1), pages 81-101, March.
    5. Cifter, Atilla, 2012. "Volatility Forecasting with Asymmetric Normal Mixture Garch Model: Evidence from South Africa," Journal for Economic Forecasting, Institute for Economic Forecasting, vol. 0(2), pages 127-142, June.
    6. Lumengo Bonga-Bonga & Jamela Hoveni, 2013. "Volatility Spillovers between the Equity Market and Foreign Exchange Market in South Africa in the 1995-2010 Period," South African Journal of Economics, Economic Society of South Africa, vol. 81(2), pages 260-274, June.
    7. Hiremath, Gourishankar S & Bandi, Kamaiah, 2010. "Long Memory in Stock Market Volatility:Evidence from India," MPRA Paper 48519, University Library of Munich, Germany.
    8. Goodness C. Aye & Mehmet Balcilar & Rangan Gupta & Nicholas Kilimani & Amandine Nakumuryango & Siobhan Redford, 2014. "Predicting BRICS stock returns using ARFIMA models," Applied Financial Economics, Taylor & Francis Journals, vol. 24(17), pages 1159-1166, September.
    9. Assefa, Tibebe A. & Mollick, André Varella, 2014. "African stock market returns and liquidity premia," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 32(C), pages 325-342.
    10. Muteba Mwamba, John Weirstrass & Webb, Daniel, 2014. "The predictability of asset returns in the BRICS countries: a nonparametric approach," MPRA Paper 72880, University Library of Munich, Germany, revised 15 Nov 2014.

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