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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

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.

Suggested Citation

  • Chen, Carl R. & Su, Yuli & Huang, Ying, 2008. "Hourly index return autocorrelation and conditional volatility in an EAR-GJR-GARCH model with generalized error distribution," Journal of Empirical Finance, Elsevier, vol. 15(4), pages 789-798, September.
  • Handle: RePEc:eee:empfin:v:15:y:2008:i:4:p:789-798
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    Cited by:

    1. Daniele Coin, 2017. "A goodness-of-fit test for Generalized Error Distribution," Temi di discussione (Economic working papers) 1096, Bank of Italy, Economic Research and International Relations Area.
    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. Shi, Yanlin & Ho, Kin-Yip & Liu, Wai-Man, 2016. "Public information arrival and stock return volatility: Evidence from news sentiment and Markov Regime-Switching Approach," International Review of Economics & Finance, Elsevier, vol. 42(C), pages 291-312.
    4. Warren Dean & Robert Faff, 2011. "Feedback trading and the behavioural ICAPM: multivariate evidence across international equity and bond markets," Applied Financial Economics, Taylor & Francis Journals, vol. 21(22), pages 1665-1678.
    5. 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.
    6. 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.
    7. Hou, Yang & Li, Steven, 2014. "The impact of the CSI 300 stock index futures: Positive feedback trading and autocorrelation of stock returns," International Review of Economics & Finance, Elsevier, vol. 33(C), pages 319-337.
    8. Kinnunen, Jyri, 2017. "Dynamic cross-autocorrelation in stock returns," Journal of Empirical Finance, Elsevier, vol. 40(C), pages 162-173.
    9. 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.

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