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

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.