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

  • Han Lin Shang
  • Yang Yang
  • Fearghal Kearney
S.I.: Application of O. R. to Financial Markets
  • 15 Downloads

Abstract

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.

Keywords

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

Notes

Acknowledgements

The authors thank insightful comments and suggestions from three reviewers. The first author acknowledges a faculty research grant from the College of Business and Economics at the Australian National University. The second author would like to acknowledge financial support from the Australian Government Research Training Program Stipend Scholarship.

Supplementary material

10479_2018_3108_MOESM1_ESM.pdf (804 kb)
Supplementary material 1 (pdf 803 KB)

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Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2018

Authors and Affiliations

  1. 1.Research School of Finance, Actuarial Studies and StatisticsAustralian National UniversityCanberraAustralia
  2. 2.Queen’s Management SchoolQueen’s University BelfastBelfastUK

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