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Modeling Volatility for the Chinese Equity Markets

Author

Listed:
  • Frank J. Fabozzi

    (Frederick Frank Adjunct Professor of Finance School of Management, Yale University)

  • Radu Tunaru

    (Senior Lecturer in Financial Economics, London Metropolitan University)

  • Tony Wu

    (Senior Analyst, Jutian Securities Company Ltd)

Abstract

A series of GARCH models are investigated for the volatility of the Chinese equity data from the Shenzhen and Shanghai markets. There has been empirical evidence of volatility clustering, contrary to findings in previous studies. Each market contains different GARCH models which fit well. The models are used to test for a spill-over effect between the two Chinese markets, an example of volatility transmission within one country and between two equity exchanges. Our testing suggests that there is no volatility transmission between the two markets.

Suggested Citation

  • Frank J. Fabozzi & Radu Tunaru & Tony Wu, 2004. "Modeling Volatility for the Chinese Equity Markets," Annals of Economics and Finance, Society for AEF, vol. 5(1), pages 79-92, May.
    • Handle: RePEc:cuf:journl:y:2004:v:5:i:1:p:79-92
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    References listed on IDEAS

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    Cited by:

    1. Wölfle, Marco, 2007. "Price Discovery for Cross-Listed Securities from Emerging Eastern European Countries," ZEW Discussion Papers 07-067, ZEW - Leibniz Centre for European Economic Research.
    2. Li, Johnny Siu-Hang & Ng, Andrew C.Y. & Chan, Wai-Sum, 2015. "Managing financial risk in Chinese stock markets: Option pricing and modeling under a multivariate threshold autoregression," International Review of Economics & Finance, Elsevier, vol. 40(C), pages 217-230.
    3. Jacobsen, Brian J. & Liu, Xiaochun, 2008. "China's segmented stock market: An application of the conditional international capital asset pricing model," Emerging Markets Review, Elsevier, vol. 9(3), pages 153-173, September.
    4. Lidija Dedi & Burhan F. Yavas, 2016. "Return and volatility spillovers in equity markets: An investigation using various GARCH methodologies," Cogent Economics & Finance, Taylor & Francis Journals, vol. 4(1), pages 1266788-126, December.
    5. Sebastián Cano-Berlanga & José-Manuel Giménez-Gómez, 2018. "On Chinese stock markets: How have they evolved over time?," Annals of Operations Research, Springer, vol. 266(1), pages 499-510, July.
    6. Vlasova, E. & Luo, D., 2022. "Volatility spillover between the Russia-India-China triad and the United States: A multivariate generalized autoregressive conditional heteroscedasticity analysis," Journal of the New Economic Association, New Economic Association, vol. 54(2), pages 111-128.
    7. Ida Farida & Faurani Santi Singagerda, 2021. "Volatilitiy of World Food Commodity Prices and Renewable Fuel Standard Policy," International Journal of Energy Economics and Policy, Econjournals, vol. 11(1), pages 516-527.
    8. Cano Berlanga, Sebastian & Giménez Gómez, José M. (José Manuel), 2016. "On Chinese stock markets: How have they evolved along time?," Working Papers 2072/267085, Universitat Rovira i Virgili, Department of Economics.
    9. Wang, Yajing & Liang, Fang & Wang, Tianyi & Huang, Zhuo, 2020. "Does measurement error matter in volatility forecasting? Empirical evidence from the Chinese stock market," Economic Modelling, Elsevier, vol. 87(C), pages 148-157.
    10. Yavas, Burhan F. & Dedi, Lidija, 2016. "An investigation of return and volatility linkages among equity markets: A study of selected European and emerging countries," Research in International Business and Finance, Elsevier, vol. 37(C), pages 583-596.

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    More about this item

    Keywords

    Emerging markets; Volatility clustering; GARCH-M; IGARCH; TAGARCH; Spill-over effect;
    All these keywords.

    JEL classification:

    • C51 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Model Construction and Estimation
    • G14 - Financial Economics - - General Financial Markets - - - Information and Market Efficiency; Event Studies; Insider Trading

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