A simple multivariate ARCH model specified by random coefficients
Computational Statistics & Data Analysis
Time series clustering and classification by the autoregressive metric
Computational Statistics & Data Analysis
Volatility spillovers, interdependence and comovements: A Markov Switching approach
Computational Statistics & Data Analysis
Clustering heteroskedastic time series by model-based procedures
Computational Statistics & Data Analysis
Asymmetric multivariate normal mixture GARCH
Computational Statistics & Data Analysis
Detecting unexpected correlation between a current topic and products from buzz marketing sites
DNIS'11 Proceedings of the 7th international conference on Databases in Networked Information Systems
On the estimation of dynamic conditional correlation models
Computational Statistics & Data Analysis
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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.