Dynamic tail dependence clustering of financial time series

DOI10.1007/s00362-015-0718-7zbMath1416.62581OpenAlexW1886358209MaRDI QIDQ1685205

Paola Zuccolotto, Giovanni De Luca

Publication date: 13 December 2017

Published in: Statistical Papers (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00362-015-0718-7

zbMATH Keywords

tail dependenceconditional value-at-risktime series clusteringtime-varying copula functions

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Classification and discrimination; cluster analysis (statistical aspects) (62H30) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70)

Related Items (7)

Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables ⋮ A double clustering algorithm for financial time series based on extreme events ⋮ Quantile correlation coefficient: a new tail dependence measure ⋮ Robust fuzzy clustering based on quantile autocovariances ⋮ Regime dependent interconnectedness among fuzzy clusters of financial time series ⋮ Hierarchical time series clustering on tail dependence with linkage based on a multivariate copula approach ⋮ Goodness-of-fit test of copula functions for semi-parametric univariate time series models

Uses Software

• AS 136

Cites Work

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• Algorithm AS 136: A K-Means Clustering Algorithm
• Comparing several parametric and nonparametric approaches to time series clustering: a simulation study
• An examination of indexes for determining the number of clusters in binary data sets
• An introduction to copulas.
• Clustering heteroskedastic time series by model-based procedures
• Clustering of time series via non-parametric tail dependence estimation

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