Adaptive Detection of Multiple Change-Points in Asset Price Volatility

Marc Lavielle () and Gilles Teyssière ()
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Marc Lavielle: Université René Descartes and Université Paris-Sud
Gilles Teyssière: Université Paris 1

A chapter in Long Memory in Economics, 2007, pp 129-156 from Springer

Abstract: Summary This chapter considers the multiple change-point problem for time series, including strongly dependent processes, with an unknown number of change-points. We propose an adaptive method for finding the segmentation, i.e., the sequence of change-points τ with the optimal level of resolution. This optimal segmentation $$ \hat \tau $$ is obtained by minimizing a penalized contrast function J(τ, y)+ßpen(τ). For a given contrast function J(τ, y) and a given penalty function pen(τ), the adaptive procedure for automatically choosing the penalization parameter β is such that the segmentation $$ \hat \tau $$ does not strongly depend on β. This algorithm is applied to the problem of detection of change-points in the volatility of financial time series, and compared with Vostrikova’s (1981) binary segmentation procedure.

Keywords: GARCH Model; Multiple Change; Brownian Bridge; Contrast Function; Volatility Process (search for similar items in EconPapers)
Date: 2007
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-540-34625-8_5

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DOI: 10.1007/978-3-540-34625-8_5

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