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Forecasting UK stock market volatility

Listed author(s):
  • David McMillan
  • Alan Speight
  • Owain Apgwilym
Registered author(s):

    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.

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    Article provided by Taylor & Francis Journals in its journal Applied Financial Economics.

    Volume (Year): 10 (2000)
    Issue (Month): 4 ()
    Pages: 435-448

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    Handle: RePEc:taf:apfiec:v:10:y:2000:i:4:p:435-448
    DOI: 10.1080/09603100050031561
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