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Volatility dynamics in high frequency financial data: an empirical investigation of the Australian equity returns

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  • G. Mujtaba Mian
  • Christopher Adam

Abstract

The behaviour of volatility for intraday high frequency returns of the ASX equity index is examined. It is found that volatility of the Australian equities follows an L-shaped curve over the trading day that is distinct from the U-shaped pattern commonly documented by previous studies on other markets. While GARCH model remains useful in capturing volatility clustering for high frequency returns, the intraday deterministic volatility seasonals need to be carefully accounted for before carrying out an analysis of the volatility dynamics. Moreover, the frequently documented asymmetric effect of positive and negative shocks to volatility disappears for returns recorded at higher frequencies.

Suggested Citation

  • G. Mujtaba Mian & Christopher Adam, 2001. "Volatility dynamics in high frequency financial data: an empirical investigation of the Australian equity returns," Applied Financial Economics, Taylor & Francis Journals, vol. 11(3), pages 341-352.
    • Handle: RePEc:taf:apfiec:v:11:y:2001:i:3:p:341-352
    DOI: 10.1080/096031001300138744
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    Cited by:

    1. Chu, Carlin C.F. & Lam, K.P., 2011. "Modeling intraday volatility: A new consideration," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 21(3), pages 388-418, July.
    2. Manolis Kavussanos & Ilias Visvikis, 2008. "Hedging effectiveness of the Athens stock index futures contracts," The European Journal of Finance, Taylor & Francis Journals, vol. 14(3), pages 243-270.
    3. William T. Lin & Shih-Chuan Tsai & David S. Sun, 2012. "Search Costs and Investor Trading Activity: Evidence from Limit Order Books," Emerging Markets Finance and Trade, Taylor & Francis Journals, vol. 48(3), pages 4-30, May.
    4. Lien, Donald & Yang, Li, 2005. "Availability and settlement of individual stock futures and options expiration-day effects: evidence from high-frequency data," The Quarterly Review of Economics and Finance, Elsevier, vol. 45(4-5), pages 730-747, September.
    5. Jayawardena, Nirodha I. & Todorova, Neda & Li, Bin & Su, Jen-Je, 2016. "Forecasting stock volatility using after-hour information: Evidence from the Australian Stock Exchange," Economic Modelling, Elsevier, vol. 52(PB), pages 592-608.
    6. Chiang, Thomas C. & Yu, Hai-Chin & Wu, Ming-Chya, 2009. "Statistical properties, dynamic conditional correlation and scaling analysis: Evidence from Dow Jones and Nasdaq high-frequency data," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 388(8), pages 1555-1570.
    7. Riza Erdugan & Nada Kulendran & Riccardo Natoli, 2019. "Incorporating financial market volatility to improve forecasts of directional changes in Australian share market returns," Financial Markets and Portfolio Management, Springer;Swiss Society for Financial Market Research, vol. 33(4), pages 417-445, December.
    8. Todorova, Neda & Souček, Michael, 2014. "Overnight information flow and realized volatility forecasting," Finance Research Letters, Elsevier, vol. 11(4), pages 420-428.
    9. Andrew C. Worthington, 2009. "Political Cycles in the Australian Stock Market since Federation," Australian Economic Review, The University of Melbourne, Melbourne Institute of Applied Economic and Social Research, vol. 42(4), pages 397-409, December.

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