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Modeling Volatility in Emerging Stock Markets Of India And China

Author

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  • Prashant Joshi

    (Shrimad Rajchandra Institute of Management and Computer Application, Surat)

Abstract

The study investigated the stock market volatility in the emerging stock markets of India and China using daily closing price from 1st January, 2005 to 12th May, 2009. The results detect the presence of non-linearity through BDSL test while conditional Heteroscedasticity is identified through ARCH-LM test. The findings reveal that the GARCH(1,1) model successfully captures nonlinearity and volatility clustering. The analysis suggests that the persistence of volatility in Chinese stock market is more than Indian stock market.

Suggested Citation

  • Prashant Joshi, 2010. "Modeling Volatility in Emerging Stock Markets Of India And China," Journal of Quantitative Economics, The Indian Econometric Society, vol. 8(1), pages 86-94, January.
    • Handle: RePEc:jqe:jqenew:v:8:y:2010:i:1:p:86-94
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    File URL: http://www.jqe.co.in/journals/JQE_v8_n1_2010_p5.pdf
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    References listed on IDEAS

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    1. Nelson, Daniel B., 1990. "ARCH models as diffusion approximations," Journal of Econometrics, Elsevier, vol. 45(1-2), pages 7-38.
    2. Dickey, David A & Fuller, Wayne A, 1981. "Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root," Econometrica, Econometric Society, vol. 49(4), pages 1057-1072, June.
    3. Thierry Ane, 2006. "Short and long term components of volatility in Hong Kong stock returns," Applied Financial Economics, Taylor & Francis Journals, vol. 16(6), pages 439-460.
    4. Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
    5. Sunil Poshakwale & Victor Murinde, 2001. "Modelling the volatility in East European emerging stock markets: evidence on Hungary and Poland," Applied Financial Economics, Taylor & Francis Journals, vol. 11(4), pages 445-456.
    6. T. Ane, 2006. "Short and long term components of volatility in Hong Kong stock returns," Post-Print hal-00170780, HAL.
    7. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
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    Cited by:

    1. Soumya Ganguly & Amalendu Bhunia, 2022. "Testing volatility and relationship among BRICS stock market returns," SN Business & Economics, Springer, vol. 2(8), pages 1-15, August.

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    More about this item

    Keywords

    Volatility clustering; nonlinearity; BDSL; GARCH;
    All these keywords.

    JEL classification:

    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading
    • C32 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes; State Space Models

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