Abstract
In this work we propose a procedure for time-varying clustering of financial time series. We use a dissimilarity measure based on the lower tail dependence coefficient, so that the resulting groups are homogeneous in the sense that the joint bivariate distributions of two series belonging to the same group are highly associated in the lower tail. In order to obtain a dynamic clustering, tail dependence coefficients are estimated by means of copula functions with a time-varying parameter. The basic assumption for the dynamic pattern of the copula parameter is the existence of an association between tail dependence and the volatility of the market. A case study with real data is examined.
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Acknowledgements
This research was funded by a grant from the Italian Ministry od Education, University and Research to the PRIN Project entitled “Multivariate statistical models for risks evaluation” (2010RHAHPL_005).
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De Luca, G., Zuccolotto, P. (2014). Dynamic Clustering of Financial Assets. In: Vicari, D., Okada, A., Ragozini, G., Weihs, C. (eds) Analysis and Modeling of Complex Data in Behavioral and Social Sciences. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Cham. https://doi.org/10.1007/978-3-319-06692-9_12
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DOI: https://doi.org/10.1007/978-3-319-06692-9_12
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