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

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  • Cho, Haeran
  • Korkas, Karolos K.

Abstract

Models for financial risk often assume that underlying asset returns are stationary. However, there is strong evidence that multivariate financial time series entail changes not only in their within-series dependence structure, but also in the cross-sectional dependence among them. In particular, the stressed Value-at-Risk of a portfolio, a popularly adopted measure of market risk, cannot be gauged adequately unless such structural breaks are taken into account in its estimation. A method for consistent detection of multiple change points in high-dimensional panel data set is proposed where both conditional variance of individual time series and their correlations are allowed to change over time. The consistency of the proposed method in multiple change point estimation is proved, and its good performance is demonstrated through simulation studies and an application to the Value-at-Risk problem on a real dataset. The change point detection methodology is implemented in the R package segMGarch, available from CRAN.

Suggested Citation

  • Cho, Haeran & Korkas, Karolos K., 2022. "High-dimensional GARCH process segmentation with an application to Value-at-Risk," Econometrics and Statistics, Elsevier, vol. 23(C), pages 187-203.
    • Handle: RePEc:eee:ecosta:v:23:y:2022:i:c:p:187-203
    DOI: 10.1016/j.ecosta.2021.07.009
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