Intraday forecasts of a volatility index: functional time series methods with dynamic updating
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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.
KeywordsFunctional principal component regression Functional linear regression Ordinary least squares Penalised least squares High-frequency financial data
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.
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