Intraday forecasts of a volatility index: functional time series methods with dynamic updating

Han Lin Shang*, Yang Yang, Fearghal Kearney

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


As a forward-looking measure of future equity market volatility, the VIX index has gained immense popularity in recent years to become a key measure of risk for market analysts and academics. We consider discrete reported intraday VIX tick values as realisations of a collection of curves observed sequentially on equally spaced and dense grids over time and utilise functional data analysis techniques to produce 1-day-ahead forecasts of these curves. The proposed method facilitates the investigation of dynamic changes in the index over very short time intervals as showcased using the 15-s high-frequency VIX index values. With the help of dynamic updating techniques, our point and interval forecasts are shown to enjoy improved accuracy over conventional time series models.
Original languageEnglish
Pages (from-to)331–354
Number of pages24
JournalAnnals of Operations Research
Issue number1-2
Publication statusPublished - 7 Nov 2019
Externally publishedYes


  • Functional principal component regression
  • Functional linear regression
  • Ordinary least squares
  • Penalised least squares
  • High-frequency financial data


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