Monitoring the Intraday Volatility Pattern

Gabrys Robertas (), Hörmann Siegfried () and Kokoszka Piotr ()
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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

Journal of Time Series Econometrics, 2013, vol. 5, issue 2, 87-116

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.

Keywords: change point detection; intraday volatility; functional data analysis; sequential analysis (search for similar items in EconPapers)
Date: 2013
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DOI: 10.1515/jtse-2012-0006

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