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The realized volatility of FTSE‐100 futures prices

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  • Nelson M. P. C. Areal
  • Stephen J. Taylor

Abstract

Five‐minute returns from FTSE‐100 index futures contracts are used to obtain accurate estimates of daily index volatility from January 1986 to December 1998. These realized volatility measures are used to obtain inferences about the distributional and autocorrelation properties of FTSE‐100 volatility. The distribution of volatility measured daily is similar to lognormal while the volatility time series has persistent positive autocorrelation that displays long‐memory effects. The distribution of daily returns standardized using the measures of realized volatility is shown to be close to normal, unlike the unconditional distribution. © 2002 Wiley Periodicals, Inc. Jrl Fut Mark 22:627–648, 2002

Suggested Citation

  • Nelson M. P. C. Areal & Stephen J. Taylor, 2002. "The realized volatility of FTSE‐100 futures prices," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 22(7), pages 627-648, July.
  • Handle: RePEc:wly:jfutmk:v:22:y:2002:i:7:p:627-648
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    Cited by:

    1. Michiel de Pooter & Martin Martens & Dick van Dijk, 2008. "Predicting the Daily Covariance Matrix for S&P 100 Stocks Using Intraday Data—But Which Frequency to Use?," Econometric Reviews, Taylor & Francis Journals, vol. 27(1-3), pages 199-229.
    2. Koopman, Siem Jan & Jungbacker, Borus & Hol, Eugenie, 2005. "Forecasting daily variability of the S&P 100 stock index using historical, realised and implied volatility measurements," Journal of Empirical Finance, Elsevier, vol. 12(3), pages 445-475, June.
    3. Martin Martens & Dick van Dijk & Michiel de Pooter, 2004. "Modeling and Forecasting S&P 500 Volatility: Long Memory, Structural Breaks and Nonlinearity," Tinbergen Institute Discussion Papers 04-067/4, Tinbergen Institute.
    4. Ozcan Ceylan, 2015. "Limited information-processing capacity and asymmetric stock correlations," Quantitative Finance, Taylor & Francis Journals, vol. 15(6), pages 1031-1039, June.
    5. repec:sbe:breart:v:35:y:2015:i:1:a:21453 is not listed on IDEAS
    6. Christophe Chorro & Florian Ielpo & Benoît Sévi, 2017. "The contribution of jumps to forecasting the density of returns," Université Paris1 Panthéon-Sorbonne (Post-Print and Working Papers) halshs-01442618, HAL.
    7. Renò, Roberto & Rizza, Rosario, 2003. "Is volatility lognormal? Evidence from Italian futures," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 322(C), pages 620-628.
    8. Ahmad Sarlak & Zahra Talei, 2016. "Impact of High-Frequency Trading on the Stock Returns of Large and Small Companies in the Tehran Stock Exchange," International Journal of Economics and Finance, Canadian Center of Science and Education, vol. 8(4), pages 216-228, April.
    9. Allen, David E. & McAleer, Michael & Scharth, Marcel, 2011. "Monte Carlo option pricing with asymmetric realized volatility dynamics," Mathematics and Computers in Simulation (MATCOM), Elsevier, vol. 81(7), pages 1247-1256.
    10. Apostolos Kourtis & Raphael N. Markellos & Lazaros Symeonidis, 2016. "An International Comparison of Implied, Realized, and GARCH Volatility Forecasts," Journal of Futures Markets, John Wiley & Sons, Ltd., vol. 36(12), pages 1164-1193, December.
    11. repec:spr:portec:v:16:y:2017:i:2:d:10.1007_s10258-017-0131-3 is not listed on IDEAS
    12. Chen Xilong & Ghysels Eric & Wang Fangfang, 2011. "HYBRID GARCH Models and Intra-Daily Return Periodicity," Journal of Time Series Econometrics, De Gruyter, vol. 3(1), pages 1-28, February.
    13. repec:spr:elmark:v:27:y:2017:i:3:d:10.1007_s12525-017-0254-5 is not listed on IDEAS
    14. R. P. Brito & H. Sebastião & P. Godinho, 2017. "Portfolio choice with high frequency data: CRRA preferences and the liquidity effect," Portuguese Economic Journal, Springer;Instituto Superior de Economia e Gestao, vol. 16(2), pages 65-86, August.
    15. Torben G. Andersen & Luca Benzoni, 2008. "Realized volatility," Working Paper Series WP-08-14, Federal Reserve Bank of Chicago.

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