  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. We propose a method for consistent detection of
multiple change points in high-dimensional GARCH panel data set where both
individual GARCH processes and their correlations are allowed to change over
time. We prove its consistency in multiple change point estimation, and
demonstrate its good performance through simulation studies 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.
