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Hourly index return autocorrelation and conditional volatility in an EAR-GJR-GARCH model with generalized error distribution

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  • Chen, Carl R.
  • Su, Yuli
  • Huang, Ying
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    Abstract

    We study the autocorrelation and conditional volatility of the hourly Dow Jones Industrial Index return data from October 1974 to September 2002 using an exponential asymmetric AR-GARCH specification with a generalized error distribution. Our findings document a positive autocorrelation in hourly return data in the early years of the sampling period, but the autocorrelation turns negative after 1986 and the negative shock causes more impact on the conditional volatility. This latter period evidence stands in contrast to prior findings employing lower frequency and/or earlier year data. In addition, our results present some evidence of a negative relation between autocorrelation and conditional volatility before 1986 (albeit weaker than prior findings), but this negative relationship disappears after 1986.

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    Bibliographic Info

    Article provided by Elsevier in its journal Journal of Empirical Finance.

    Volume (Year): 15 (2008)
    Issue (Month): 4 (September)
    Pages: 789-798

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    Handle: RePEc:eee:empfin:v:15:y:2008:i:4:p:789-798

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

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    References

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    Cited by:
    1. Lin, Shih-Kuei & Wang, Shin-Yun & Tsai, Pei-Ling, 2009. "Application of hidden Markov switching moving average model in the stock markets: Theory and empirical evidence," International Review of Economics & Finance, Elsevier, vol. 18(2), pages 306-317, March.
    2. Majumder, Debasish, 2013. "Towards an efficient stock market: Empirical evidence from the Indian market," Journal of Policy Modeling, Elsevier, vol. 35(4), pages 572-587.
    3. Majumder, Debasish, 2012. "When the market becomes inefficient: Comparing BRIC markets with markets in the USA," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 84-92.
    4. Stoyanov, Stoyan V. & Rachev, Svetlozar T. & Fabozzi, Frank J., 2013. "CVaR sensitivity with respect to tail thickness," Journal of Banking & Finance, Elsevier, vol. 37(3), pages 977-988.

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