Structural Breaks in Financial Time Series

DOI10.1007/978-3-540-71297-8_37zbMath1178.91217OpenAlexW1497363055MaRDI QIDQ3646984

Elena Andreou, Eric Ghysels

Publication date: 27 November 2009

Published in: Handbook of Financial Time Series (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/978-3-540-71297-8_37

zbMATH Keywords

long memoryGARCHfinancial time seriesstructural breakIGARCHchange-point testfunctional central limit theorem (FCLT)empirical volatilitygeometric volatility

Mathematics Subject Classification ID

Statistical methods; risk measures (91G70) Research exposition (monographs, survey articles) pertaining to game theory, economics, and finance (91-02)

Related Items (14)

Change points detection and parameter estimation for multivariate time series ⋮ Testing for parameter stability in nonlinear autoregressive models ⋮ Forecasting with GARCH models under structural breaks: An approach based on combinations across estimation windows ⋮ Monitoring the intraday volatility pattern ⋮ Multiple change point detection and validation in autoregressive time series data ⋮ A test for second-order stationarity of a time series based on the maximum of Anderson-Darling statistics ⋮ A tail adaptive approach for change point detection ⋮ Segmenting mean-nonstationary time series via trending regressions ⋮ Change points in heavy‐tailed multivariate time series: Methods using precision matrices ⋮ Some Nonparametric Tests for Change-Point Detection Based on the ℙ-ℙ and ℚ-ℚ Plot Processes ⋮ On detecting changes in the jumps of arbitrary size of a time-continuous stochastic process ⋮ Structural breaks in time series ⋮ Structural breaks in panel data: Large number of panels and short length time series ⋮ Testing for a Change of the Innovation Distribution in Nonparametric Autoregression: The Sequential Empirical Process Approach

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