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Forecasting stock market volatility with non-linear GARCH models: a case for China

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  • Weixian Wei
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    Abstract

    This paper studies the performance of the GARCH model and two of its non-linear modifications to forecast China's weekly stock market volatility. The models are the Quadratic GARCH and the Glosten, Jagannathan and Runkle models which have been proposed to describe the often observed negative skewness in stock market indices. It is found that the QGARCH model is best when the estimation sample does not contain extreme observations such as the stock market crash, and that the GJR model cannot be recommended for forecasting.

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    File URL: http://www.informaworld.com/openurl?genre=article&doi=10.1080/13504850110053266&magic=repec&7C&7C8674ECAB8BB840C6AD35DC6213A474B5
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    Bibliographic Info

    Article provided by Taylor & Francis Journals in its journal Applied Economics Letters.

    Volume (Year): 9 (2002)
    Issue (Month): 3 ()
    Pages: 163-166

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    Handle: RePEc:taf:apeclt:v:9:y:2002:i:3:p:163-166

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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. Yasemin Ulu, 2005. "Out-of-sample forecasting performance of the QGARCH model," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(6), pages 387-392, November.
    3. C. James Hueng, 2006. "Short-sales constraints and stock return asymmetry: evidence from the Chinese stock markets," Applied Financial Economics, Taylor & Francis Journals, vol. 16(10), pages 707-716.

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