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Journal of Time Series Econometrics

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Monitoring the Intraday Volatility Pattern

Robertas Gabrys
  • Department of Information and Operations Management, Marshall School of Business, University of Southern California, Los Angeles, CA, USA
  • Email:
/ Siegfried Hörmann
  • Corresponding author
  • Department of Mathematics, Université Libre de Bruxelles, CP 210 Bd. du Triomphe, Brussels 1050, Belgium
  • Email:
/ Piotr Kokoszka
  • Department of Statistics, Colorado State University, Fort Collins, CO, USA
  • Email:
Published Online: 2013-07-26 | DOI: https://doi.org/10.1515/jtse-2012-0006


A functional time series consists of curves, typically one curve per day. The most important parameter of such a series is the mean curve. We propose two methods of detecting a change in the mean function of a functional time series. The change is detected on line, as new functional observations arrive. The general methodology is motivated by, and applied to, the detection of a change in the mean intraday volatility pattern. The methodology is asymptotically justified by applying a new notion of weak dependence for functional time series. It is calibrated and validated by simulations based on real intraday volatility curves.

Keywords: change point detection; intraday volatility; functional data analysis; sequential analysis


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About the article

Published Online: 2013-07-26

Citation Information: Journal of Time Series Econometrics, ISSN (Online) 1941-1928, ISSN (Print) 2194-6507, DOI: https://doi.org/10.1515/jtse-2012-0006. Export Citation

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