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Volatility forecasts: the role of asymmetric and long-memory dynamics and regional evidence

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  • Twm Evans
  • David McMillan

Abstract

This article seeks to examine the forecasting performance of nine competing models for daily volatility for stock market returns of 33 economies. Whilst volatility is an important variable in many financial applications including those relating to areas of risk management there exits little consensus with regard to the most appropriate model. The results of this article seek to bring some closure to the debate. Our results suggest that in 70% of our cases the GARCH-class of model provide the best forecasts and in particular models that account for either asymmetry or long-memory dynamics. Outwith the GARCH-class, the moving average model provides reasonable forecasts.

Suggested Citation

  • Twm Evans & David McMillan, 2007. "Volatility forecasts: the role of asymmetric and long-memory dynamics and regional evidence," Applied Financial Economics, Taylor & Francis Journals, vol. 17(17), pages 1421-1430.
  • Handle: RePEc:taf:apfiec:v:17:y:2007:i:17:p:1421-1430
    DOI: 10.1080/09603100601007149
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    References listed on IDEAS

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    Cited by:

    1. Liu, Hung-Chun & Chiang, Shu-Mei & Cheng, Nick Ying-Pin, 2012. "Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 78-91.
    2. Jian Zhou & Zhixin Kang, 2011. "A Comparison of Alternative Forecast Models of REIT Volatility," The Journal of Real Estate Finance and Economics, Springer, vol. 42(3), pages 275-294, April.
    3. Wamg, Jianxin, 2011. "Forecasting Volatility in Asian Stock Markets: Contributions of Local, Regional, and Global Factors," Asian Development Review, Asian Development Bank, vol. 28(2), pages 32-57.

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