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Monitoring the Intraday Volatility Pattern

Author

Listed:
  • Gabrys Robertas

    (Department of Information and Operations Management, Marshall School of Business, University of Southern California, Los Angeles, CA, USA)

  • Hörmann Siegfried

    (Department of Mathematics, Université Libre de Bruxelles, CP 210 Bd. du Triomphe, Brussels 1050, Belgium)

  • Kokoszka Piotr

    (Department of Statistics, Colorado State University, Fort Collins, CO, USA)

Abstract

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.

Suggested Citation

  • Gabrys Robertas & Hörmann Siegfried & Kokoszka Piotr, 2013. "Monitoring the Intraday Volatility Pattern," Journal of Time Series Econometrics, De Gruyter, vol. 5(2), pages 87-116, July.
    • Handle: RePEc:bpj:jtsmet:v:5:y:2013:i:2:p:87-116:n:1
    DOI: 10.1515/jtse-2012-0006
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    References listed on IDEAS

    as
    1. Torben G. Andersen & Tim Bollerslev & Ashish Das, 2001. "Variance‐ratio Statistics and High‐frequency Data: Testing for Changes in Intraday Volatility Patterns," Journal of Finance, American Finance Association, vol. 56(1), pages 305-327, February.
    2. Kargin, V. & Onatski, A., 2008. "Curve forecasting by functional autoregression," Journal of Multivariate Analysis, Elsevier, vol. 99(10), pages 2508-2526, November.
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