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Asymmetric volatility dynamics in high frequency FTSE-100 stock index futures

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  • David McMillan
  • Alan Speight

Abstract

This paper examines whether variants of the GARCH class of model with the capacity to accommodate volatility asymmetries and volatility feedback are able to provide an adequate representation of non-linear dependency in intraday FTSE-100 stock index futures returns at the quarter-hour and hourly frequency. Significant variance asymmetry is identified, and such that negative shocks induce a greater response in volatility than equivalent positive shocks, but with the additional effect of subsequently depressing volatility at the 15-minute frequency. In the absence of financial leverage arguments in the market considered, and the absence of a statistically significant volatility feedback effect, such asymmetry is interpreted as indirect evidence for the presence of noise traders, attracted to such markets by low transaction costs and margin requirements. In contrast with previous results using intraday data, a notable absence of remaining structure in asymmetric GARCH models at the hourly frequency is found, but neither symmetric nor asymmetric models are able to fully account for nonlinear dependence at the higher intraday frequency.

Suggested Citation

  • David McMillan & Alan Speight, 2003. "Asymmetric volatility dynamics in high frequency FTSE-100 stock index futures," Applied Financial Economics, Taylor & Francis Journals, vol. 13(8), pages 599-607.
  • Handle: RePEc:taf:apfiec:v:13:y:2003:i:8:p:599-607
    DOI: 10.1080/0960310022000040715
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    Cited by:

    1. Tao, Juan & Green, Christopher J., 2012. "Asymmetries, causality and correlation between FTSE100 spot and futures: A DCC-TGARCH-M analysis," International Review of Financial Analysis, Elsevier, vol. 24(C), pages 26-37.
    2. Sang Hoon Kang & SEONG-MIN YOON, 2008. "Asymmetry and Long Memory Features in Volatility: Evidence From Korean Stock Market," Korean Economic Review, Korean Economic Association, vol. 24, pages 383-412.
    3. Ana Filipa Carvalho & Jose Sa da Costa & Jose Assis Lopes, 2006. "A systematic modelling strategy for futures markets volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 16(11), pages 819-833.
    4. T. Kalantzis & D. Papanastassiou, 2008. "Classification of GARCH time series: an empirical investigation," Applied Financial Economics, Taylor & Francis Journals, vol. 18(9), pages 759-764.

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