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Adaptive Detection of Multiple Change-Points in Asset Price Volatility

  • Marc Lavielle
  • Gilles Teyssière

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 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Berlin · Heidelberg 2007

Authors and Affiliations

  • Marc Lavielle
    • 1
  • Gilles Teyssière
    • 2
  1. 1.Laboratoire de MathématiquesUniversité René Descartes and Université Paris-SudParis
  2. 2.Laboratoire de Statistique Appliquée et MOdélisation Stochastique (SAMOS)Université Paris 1Paris

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