Clustering of financial time series in risky scenarios

DOI10.1007/s11634-013-0160-4zbMath1414.62241OpenAlexW2148710478MaRDI QIDQ2418377

Nicola Torelli, Fabrizio Durante, Roberta Pappadà

Publication date: 3 June 2019

Published in: Advances in Data Analysis and Classification. ADAC (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s11634-013-0160-4

zbMATH Keywords

copulacluster analysistail dependenceSpearman's correlation

Mathematics Subject Classification ID

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

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Dissimilarity functions for rank-invariant hierarchical clustering of continuous variables, PATHS AND INDICES OF MAXIMAL TAIL DEPENDENCE, A double clustering algorithm for financial time series based on extreme events, The bootstrap for testing the equality of two multivariate time series with an application to financial markets, A testing approach to clustering scalar time series, Tail maximal dependence in bivariate models: estimation and applications, A Review of Two Decades of Correlations, Hierarchies, Networks and Clustering in Financial Markets, Trimmed fuzzy clustering of financial time series based on dynamic time warping, Robust fuzzy clustering based on quantile autocovariances, Regime dependent interconnectedness among fuzzy clusters of financial time series, Clustering of time series via non-parametric tail dependence estimation, Hierarchical time series clustering on tail dependence with linkage based on a multivariate copula approach, The construction of multilayer stock network model, Clustering of financial instruments using jump tail dependence coefficient, A tail-revisited Markowitz mean-variance approach and a portfolio network centrality, Conditional empirical copula processes and generalized measures of association

Uses Software

• QRM
• clusfind

Cites Work

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