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Forecasting UK stock market volatility

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  • David McMillan
  • Alan Speight
  • Owain Apgwilym

Abstract

The paper analyses the forecasting performance of a variety of statistical and econometric models of UK FTA All Share and FTSE100 stock index volatility at the monthly, weekly and daily frequencies under both symmetric and asymmetric loss functions. Under symmetric loss, results suggest that the random walk model provides vastly superior monthly volatility forecasts, while random walk, moving average, and recursive smoothing models provide moderately superior weekly volatility forecasts, and GARCH, moving average and exponential smoothing models provide marginally superior daily volatility forecasts. If attention is restricted to one forecasting method for all frequencies, the most consistent forecasting performance is provided by moving average and GARCH models. More generally, results suggest that previous results reporting that the class of GARCH models provide relatively poor volatility forecasts may not be robust at higher frequencies, failing to hold here for the crash-adjusted FTSE100 index in particular.

Suggested Citation

  • David McMillan & Alan Speight & Owain Apgwilym, 2000. "Forecasting UK stock market volatility," Applied Financial Economics, Taylor & Francis Journals, vol. 10(4), pages 435-448.
  • Handle: RePEc:taf:apfiec:v:10:y:2000:i:4:p:435-448
    DOI: 10.1080/09603100050031561
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    References listed on IDEAS

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    Cited by:

    1. Liu, Hung-Chun & Chiang, Shu-Mei & Cheng, Nick Ying-Pin, 2012. "Forecasting the volatility of S&P depositary receipts using GARCH-type models under intraday range-based and return-based proxy measures," International Review of Economics & Finance, Elsevier, vol. 22(1), pages 78-91.
    2. Manh Ha Nguyen & Olivier Darné, 2018. "Forecasting and risk management in the Vietnam Stock Exchange," Working Papers halshs-01679456, HAL.
    3. Wei Liu & Bruce Morley, 2009. "Volatility Forecasting in the Hang Seng Index using the GARCH Approach," Asia-Pacific Financial Markets, Springer;Japanese Association of Financial Economics and Engineering, vol. 16(1), pages 51-63, March.
    4. Dima Alberg & Haim Shalit & Rami Yosef, 2008. "Estimating stock market volatility using asymmetric GARCH models," Applied Financial Economics, Taylor & Francis Journals, vol. 18(15), pages 1201-1208.
    5. Jorge Caiado, 2004. "Modelling And Forecasting The Volatility Of The Portuguese Stock Index Psi-20," Portuguese Journal of Management Studies, ISEG, Universidade de Lisboa, vol. 0(1), pages 3-21.
    6. Milton Abdul Thorlie & Lixin Song & Muhammad Amin & Xiaoguang Wang, 2015. "Modeling and forecasting of stock index volatility with APARCH models under ordered restriction," Statistica Neerlandica, Netherlands Society for Statistics and Operations Research, vol. 69(3), pages 329-356, August.
    7. Ulu, Yasemin, 2007. "Optimal prediction under LINLIN loss: Empirical evidence," International Journal of Forecasting, Elsevier, vol. 23(4), pages 707-715.
    8. Md. Zahangir Alam & Md. Noman Siddikee & Md. Masukujjaman, 2013. "Forecasting Volatility of Stock Indices with ARCH Model," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 4(2), pages 126-143, April.
    9. Imen Dakhlaoui & Chaker Aloui, 2016. "The Interactive Relationship Between the US Economic Policy Uncertainty and BRIC Stock Markets," International Economics, CEPII research center, issue 146, pages 141-157.
    10. Ercan Balaban & Asli Bayar & Robert Faff, 2006. "Forecasting stock market volatility: Further international evidence," The European Journal of Finance, Taylor & Francis Journals, vol. 12(2), pages 171-188.
    11. Heitham Al-Hajieh & Hashem AlNemer & Timothy Rodgers & Jacek Niklewski, 2015. "Forecasting the Jordanian stock index: modelling asymmetric volatility and distribution effects within a GARCH framework," Copernican Journal of Finance & Accounting, Uniwersytet Mikolaja Kopernika, vol. 4(2), pages 9-26.
    12. Shiyi Chen & Kiho Jeong & Wolfgang Härdle, 2008. "Support Vector Regression Based GARCH Model with Application to Forecasting Volatility of Financial Returns," SFB 649 Discussion Papers SFB649DP2008-014, Sonderforschungsbereich 649, Humboldt University, Berlin, Germany.
    13. Angelidis, Timotheos & Degiannakis, Stavros, 2008. "Volatility forecasting: Intra-day versus inter-day models," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 18(5), pages 449-465, December.
    14. McMillan, David G. & Kambouroudis, Dimos, 2009. "Are RiskMetrics forecasts good enough? Evidence from 31 stock markets," International Review of Financial Analysis, Elsevier, vol. 18(3), pages 117-124, June.
    15. Awartani, Basel M.A. & Corradi, Valentina, 2005. "Predicting the volatility of the S&P-500 stock index via GARCH models: the role of asymmetries," International Journal of Forecasting, Elsevier, vol. 21(1), pages 167-183.
    16. I.-Yuan Chuang & Jin-Ray Lu & Pei-Hsuan Lee, 2007. "Forecasting volatility in the financial markets: a comparison of alternative distributional assumptions," Applied Financial Economics, Taylor & Francis Journals, vol. 17(13), pages 1051-1060.
    17. Kambouroudis, Dimos S. & McMillan, David G., 2015. "Is there an ideal in-sample length for forecasting volatility?," Journal of International Financial Markets, Institutions and Money, Elsevier, vol. 37(C), pages 114-137.
    18. repec:nax:conyad:v:62:y:2017:i:4:p:1063-1080 is not listed on IDEAS
    19. David McMillan & Raquel Quiroga Garcia, 2009. "Intra-day volatility forecasts," Applied Financial Economics, Taylor & Francis Journals, vol. 19(8), pages 611-623.
    20. David Morelli, 2003. "Capital asset pricing model on UK securities using ARCH," Applied Financial Economics, Taylor & Francis Journals, vol. 13(3), pages 211-223.
    21. Yasemin Ulu, 2005. "Out-of-sample forecasting performance of the QGARCH model," Applied Financial Economics Letters, Taylor and Francis Journals, vol. 1(6), pages 387-392, November.

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