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Clustering of financial time series in risky scenarios. (English) Zbl 1414.62241

Summary: A methodology is presented for clustering financial time series according to the association in the tail of their distribution. The procedure is based on the calculation of suitable pairwise conditional Spearman’s correlation coefficients extracted from the series. The performance of the method has been tested via a simulation study. As an illustration, an analysis of the components of the Italian FTSE-MIB is presented. The results could be applied to construct financial portfolios that can manage to reduce the risk in case of simultaneous large losses in several markets.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62H20 Measures of association (correlation, canonical correlation, etc.)
62M10 Time series, auto-correlation, regression, etc. in statistics (GARCH)

Software:

QRM; clusfind
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References:

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