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Forecasting UK stock market volatility


  • David McMillan
  • Alan Speight
  • Owain Apgwilym


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

    1. Andrew Clare & Raymond O'Brien & Stephen Thomas & Michael Wickens, "undated". "Macroeconomic Shocks and the Domestic CAPM: Evidence from the UK Stock Market," Discussion Papers 94/10, Department of Economics, University of York.
    2. Thoms, S. H., 1993. "An international CAPM for bonds and equities," Journal of International Money and Finance, Elsevier, vol. 12(4), pages 390-412, August.
    3. J. Tobin, 1958. "Liquidity Preference as Behavior Towards Risk," Review of Economic Studies, Oxford University Press, vol. 25(2), pages 65-86.
    4. Frankel, Jeffrey A. & MacArthur, Alan T., 1988. "Political vs. currency premia in international real interest differentials : A study of forward rates for 24 countries," European Economic Review, Elsevier, vol. 32(5), pages 1083-1114, June.
    5. Engel, Charles & Rodrigues, Anthony P, 1989. "Tests of International CAPM with Time-Varying Covariances," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 4(2), pages 119-138, April-Jun.
    6. Tobin, James, 1982. "Money and Finance in the Macroeconomic Process," Journal of Money, Credit and Banking, Blackwell Publishing, vol. 14(2), pages 171-204, May.
    7. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
    8. Engel, Charles M & Rodrigues, Anthony P, 1993. "Tests of Mean-Variance Efficiency of International Equity Markets," Oxford Economic Papers, Oxford University Press, vol. 45(3), pages 403-421, July.
    9. Harry Markowitz, 1952. "Portfolio Selection," Journal of Finance, American Finance Association, vol. 7(1), pages 77-91, March.
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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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Dakhlaoui, Imen & Aloui, Chaker, 2016. "The interactive relationship between the US economic policy uncertainty and BRIC stock markets," International Economics, Elsevier, vol. 146(C), pages 141-157.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. Ulu, Yasemin, 2007. "Optimal prediction under LINLIN loss: Empirical evidence," International Journal of Forecasting, Elsevier, vol. 23(4), pages 707-715.
    13. 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.
    14. 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.
    15. 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.
    16. repec:nax:conyad:v:62:y:2017:i:4:p:1063-1080 is not listed on IDEAS
    17. David Morelli, 2003. "Capital asset pricing model on UK securities using ARCH," Applied Financial Economics, Taylor & Francis Journals, vol. 13(3), pages 211-223.

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