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Volatility persistence, long memory and time-varying unconditional mean: Evidence from 10 equity indices

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  • McMillan, David G.
  • Ruiz, Isabel

Abstract

This paper re-examines evidence of volatility persistence and long memory in the light of potential time-variation in the unconditional mean of the volatility series. Daily equity volatility is generally regarded as exhibiting long memory, however, recent evidence has suggested that long memory may be a spurious finding arising from neglected breaks or time-variation in unconditional variance. The results presented here suggested that long memory is apparent when analysed on the assumption that unconditional variance is constant. However, both breakpoint tests and a moving average application suggest that unconditional variance exhibits substantial, although slow moving, time-variation. The apparent long-memory property largely disappears when this time-variation is taken into account. A modification of the GARCH model to allow for mean variation generates improved volatility forecasting performance, but only over long horizon. At the daily level the assumption of a constant unconditional variance does not seem to affect forecasts.

Suggested Citation

  • McMillan, David G. & Ruiz, Isabel, 2009. "Volatility persistence, long memory and time-varying unconditional mean: Evidence from 10 equity indices," The Quarterly Review of Economics and Finance, Elsevier, vol. 49(2), pages 578-595, May.
  • Handle: RePEc:eee:quaeco:v:49:y:2009:i:2:p:578-595
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    Cited by:

    1. David G. McMillan & Pako Thupayagale, 2009. "The efficiency of African equity markets," Studies in Economics and Finance, Emerald Group Publishing, vol. 26(4), pages 275-292, October.
    2. Wang, Yudong & Wei, Yu & Wu, Chongfeng, 2010. "Auto-correlated behavior of WTI crude oil volatilities: A multiscale perspective," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 389(24), pages 5759-5768.
    3. Jean-Christophe Statnik & David Verstraete, 2015. "Price dynamics in agricultural commodity markets: a comparison of European and US markets," Empirical Economics, Springer, vol. 48(3), pages 1103-1117, May.
    4. Li, Ziran & Sun, Jiajing & Wang, Shouyang, 2013. "Amplitude-Duration-Persistence Trade-off Relationship for Long Term Bear Stock Markets," MPRA Paper 54177, University Library of Munich, Germany.
    5. Chatzikonstanti, Vasiliki & Venetis, Ioannis A., 2015. "Long memory in log-range series: Do structural breaks matter?," Journal of Empirical Finance, Elsevier, vol. 33(C), pages 104-113.
    6. Chkili, Walid & Hammoudeh, Shawkat & Nguyen, Duc Khuong, 2014. "Volatility forecasting and risk management for commodity markets in the presence of asymmetry and long memory," Energy Economics, Elsevier, vol. 41(C), pages 1-18.
    7. Al-Shboul, Mohammad & Anwar, Sajid, 2016. "Fractional integration in daily stock market indices at Jordan's Amman stock exchange," The North American Journal of Economics and Finance, Elsevier, vol. 37(C), pages 16-37.
    8. Vivian, Andrew & Wohar, Mark E., 2012. "Commodity volatility breaks," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 22(2), pages 395-422.
    9. Degiannakis, Stavros & Floros, Christos & Dent, Pamela, 2013. "Forecasting value-at-risk and expected shortfall using fractionally integrated models of conditional volatility: International evidence," International Review of Financial Analysis, Elsevier, vol. 27(C), pages 21-33.
    10. Cevik, Emrah Ismail & Topaloğlu, Gültekin, 2014. "Volatilitede uzun hafıza ve yapısal kırılma: Borsa Istanbul örneği
      [Long memory and structural breaks on volatility: evidence from Borsa Istanbul]
      ," MPRA Paper 71485, University Library of Munich, Germany, revised 2014.

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