High-dimensional GARCH process segmentation with an application to Value-at-Risk

Haeran Cho*, Karolos K. Korkas

*Corresponding author for this work

Research output: Contribution to journalArticle (Academic Journal)peer-review

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An assumption in modelling financial risk is that the underlying asset returns are stationary. However, there is now strong evidence that multivariate financial time series entail changes not only in their within-series dependence structure, but also in the correlations among them. For this reason, we propose a method for consistent detection of multiple change-points in (possibly) high-dimensional GARCH panel data set, where both individual GARCH processes and their correlations are allowed to change. We prove its consistency in multiple change-point estimation, and demonstrate its good performance through an extensive simulation study and an application to the Value-at-Risk problem on a real dataset. Our methodology is implemented in the R package segMGarch, available from CRAN.
Original languageEnglish
JournalEconometrics and Statistics
Early online date10 Aug 2021
Publication statusE-pub ahead of print - 10 Aug 2021


  • multiple change-point detection
  • multivariate GARCH
  • stress period selection
  • Double CUSUM Binary Segmentation
  • high dimensionality
  • nonstationarity


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