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Financial volatility forecasting with range-based autoregressive volatility model

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  • Li, Hongquan
  • Hong, Yongmiao
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    Abstract

    The classical volatility models, such as GARCH, are return-based models, which are constructed with the data of closing prices. It might neglect the important intraday information of the price movement, and will lead to loss of information and efficiency. This study introduces and extends the range-based autoregressive volatility model to make up for these weaknesses. The empirical results consistently show that the new model successfully captures the dynamics of the volatility and gains good performance relative to GARCH model.

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    Bibliographic Info

    Article provided by Elsevier in its journal Finance Research Letters.

    Volume (Year): 8 (2011)
    Issue (Month): 2 (June)
    Pages: 69-76

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    Handle: RePEc:eee:finlet:v:8:y:2011:i:2:p:69-76

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    Web page: http://www.elsevier.com/locate/frl

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    Keywords: Volatility modeling Price range Forecasting performance Intraday information GARCH;

    References

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    1. Yang, Dennis & Zhang, Qiang, 2000. "Drift-Independent Volatility Estimation Based on High, Low, Open, and Close Prices," The Journal of Business, University of Chicago Press, vol. 73(3), pages 477-91, July.
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    8. Sassan Alizadeh & Michael W. Brandt & Francis X. Diebold, 2002. "Range-Based Estimation of Stochastic Volatility Models," Journal of Finance, American Finance Association, vol. 57(3), pages 1047-1091, 06.
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    Cited by:
    1. repec:wyi:journl:002202 is not listed on IDEAS
    2. Louzis, Dimitrios P. & Xanthopoulos-Sisinis, Spyros & Refenes, Apostolos P., 2011. "The role of high frequency intra-daily data, daily range and implied volatility in multi-period Value-at-Risk forecasting," MPRA Paper 35252, University Library of Munich, Germany.
    3. Zheng, Tingguo & Zuo, Haomiao, 2013. "Reexamining the time-varying volatility spillover effects: A Markov switching causality approach," The North American Journal of Economics and Finance, Elsevier, vol. 26(C), pages 643-662.
    4. Bayraci, Selcuk & Demiralay, Sercan, 2013. "Conditional Autoregregressive Range (CARR) Based Volatility Spillover Index For the Eurozone Markets," MPRA Paper 51909, University Library of Munich, Germany.
    5. Piotr Fiszeder & Grzegorz Perczak, 2013. "A new look at variance estimation based on low, high and closing prices taking into account the drift," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 67(4), pages 456-481, November.
    6. Koutmos, Dimitrios & Song, Wei, 2014. "Speculative dynamics and price behavior in the Shanghai Stock Exchange," Research in International Business and Finance, Elsevier, vol. 31(C), pages 74-86.

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