Corpus ID: 17185342

BaSTA : consistent multiscale multiple change-point detection for ARCH processes

  title={BaSTA : consistent multiscale multiple change-point detection for ARCH processes},
  author={P. Fryzlewicz and S. S. Rao},
  • P. Fryzlewicz, S. S. Rao
  • Published 2013
  • The emergence of the recent financial crisis, during which markets frequently underwent changes in their statistical structure over a short period of time, illustrates the importance of non-stationary modelling in financial time series. Motivated by this observation, we propose a fast, well-performing and theoretically tractable method for detecting multiple change-points in the structure of an ARCH model for financial returns with piecewise-constant parameter values. Our method, termed BaSTA… CONTINUE READING

    Figures and Tables from this paper.


    Publications referenced by this paper.
    Detecting multiple breaks in financial market volatility dynamics
    • 318
    • PDF
    Modelling Financial Time Series
    • 850
    • Highly Influential
    Nonparametric Methods in Change Point Problems
    • 590
    • Highly Influential
    Least-squares Estimation of an Unknown Number of Shifts in a Time Series
    • 249
    • PDF
    Adaptive Detection of Multiple Change-Points in Asset Price Volatility
    • 58
    • PDF
    Time dependent spectral analysis of nonstationary time series
    • 137
    • Highly Influential
    • PDF
    Change-point estimation in ARCH models
    • 250
    Recent Advances in ARCH Modelling
    • 80
    • Highly Influential
    • PDF
    Normalized least-squares estimation in time-varying ARCH models
    • 51
    • PDF