Identifying Financial Time Series with Similar Dynamic Conditional Correlation

One of the main problems in modelling multivariate conditional covariance time series is
the parameterization of the correlation structure. If no constraints are imposed, it implies
a large number of unknown coefficients. The most popular models propose parsimonious
representations, imposing similar correlation structures to all the series or to groups of
time series, but the choice of these groups is quite subjective. A statistical approach is
proposed to detect groups of homogeneous time series in terms of correlation dynamics for
one of the widely used models: the Dynamic Conditional Correlation model. The approach
is based on a clustering algorithm, which uses the idea of distance between dynamic
conditional correlations, and the classical Wald test, to compare the coefficients of two
groups of dynamic conditional correlations. The proposed approach is evaluated in terms
of simulation experiments and applied to a set of financial time series.