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Forecasting index volatility: sampling interval and non-trading effects

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  • David Walsh
  • Glenn Yu-Gen Tsou

Abstract

A detailed comparison is made of volatility forecasting techniques on Australian value-weighted indices. The techniques compared are the naive approach (historical volatility), an improved extreme-value method (IEV), the ARCH/GARCH class of models and an exponentially weighted moving average (EWMA) of volatility. The study suggests that the EWMA technique appears to be the best volatility forecasting technique, closely followed by the appropriate GARCH specification. Both the IEV and historical volatility approaches are poor by comparison. The diversification benefit that arises from indices with larger numbers of stocks appears to make forecasting the volatility of larger indices more accurate. However, as the sampling interval is reduced, the non-trading effects evident in the larger indices start to counteract this benefit.

Suggested Citation

  • David Walsh & Glenn Yu-Gen Tsou, 1998. "Forecasting index volatility: sampling interval and non-trading effects," Applied Financial Economics, Taylor & Francis Journals, vol. 8(5), pages 477-485.
  • Handle: RePEc:taf:apfiec:v:8:y:1998:i:5:p:477-485 DOI: 10.1080/096031098332772
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    Cited by:

    1. Degiannakis, Stavros & Floros, Christos, 2013. "Modeling CAC40 volatility using ultra-high frequency data," Research in International Business and Finance, Elsevier, vol. 28(C), pages 68-81.
    2. Stephen L. Lee & Simon Stevenson, 2001. "Time Weighted Portfolio Optimisation," ERES eres2001_207, European Real Estate Society (ERES).
    3. Matei, Marius, 2010. "Risk analysis in the evaluation of the international investment opportunities. Advances in modelling and forecasting volatility for risk assessment purposes," Working Papers of Institute for Economic Forecasting 100201, Institute for Economic Forecasting.
    4. Kim-Leng Goh & Kim-Lian Kok, 2006. "Beating the Random Walk: Intraday Seasonality and Volatility in a Developing Stock Market," International Journal of Business and Economics, College of Business and College of Finance, Feng Chia University, Taichung, Taiwan, vol. 5(1), pages 41-59, April.
    5. Frijters, Paul, 2002. "The non-parametric identification of lagged duration dependence," Economics Letters, Elsevier, pages 289-292.
    6. Stavros Degiannakis & Evdokia Xekalaki, 2007. "Assessing the performance of a prediction error criterion model selection algorithm in the context of ARCH models," Applied Financial Economics, Taylor & Francis Journals, pages 149-171.

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