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Structural Breaks and GARCH Models of Stock Return Volatility: The Case of South Africa

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Author Info

  • Ali Babikir

    (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa)

  • Rangan Gupta

    ()
    (Department of Economics, University of Pretoria)

  • Chance Mwabutwa

    ()
    (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa)

  • Emmanuel Owusu-Sekyere

    ()
    (Department of Economics, University of Pretoria and South African Treasury, Pretoria, South Africa)

Abstract

This paper investigates the empirical relevance of structural breaks in forecasting stock return volatility using both in-sample and out-of-sample tests and daily returns for the Johannesburg Stock Exchange (JSE) All Share Index from 07/02/1995 to 08/25/2010. We find evidence of structural breaks in the unconditional variance of the stock returns series over the period, with high levels of persistence and variability in the parameter estimates of the GARCH (1, 1) model across the sub-samples defined by the structural breaks. This indicates that structural breaks are empirically relevant to stock return volatility in South Africa. In out-of-sample tests, we find that combining forecasts from different benchmark and competing models that accommodate structural breaks in volatility improves the accuracy of volatility forecasting. Furthermore, for shorter horizons, the MS-GARCH model better captures asymmetry in stock return volatility than the GJR-GARCH (1, 1) model, which better suited to longer horizons, but in general, the asymmetric models fail to outperform the GARCH (1,1) model.

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Bibliographic Info

Paper provided by University of Pretoria, Department of Economics in its series Working Papers with number 201030.

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Length: 23 pages
Date of creation: Dec 2010
Date of revision:
Handle: RePEc:pre:wpaper:201030

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Keywords: stock return volatility; structural breaks; in-sample tests; out-of-sample tests; GARCH Models;

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References

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Citations

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Cited by:
  1. 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, Taylor & Francis Journals, vol. 24(17), pages 1159-1166, September.
  2. Tao Xiong & Yukun Bao & Zhongyi Hu, 2014. "Multiple-output support vector regression with a firefly algorithm for interval-valued stock price index forecasting," Papers 1401.1916, arXiv.org.
  3. Tse, Chin-Bun & Rodgers, Timothy & Niklewski, Jacek, 2014. "The 2007 financial crisis and the UK residential housing market: Did the relationship between interest rates and house prices change?," Economic Modelling, Elsevier, Elsevier, vol. 37(C), pages 518-530.
  4. Ezzat, Hassan, 2013. "Long Memory Processes and Structural Breaks in Stock Returns and Volatility: Evidence from the Egyptian Exchange," MPRA Paper 51465, University Library of Munich, Germany.

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