Technology Category
Machine Learning: ClusteringData Mining & Knowledge Management: Anomaly/Outlier DetectionReasoning under Uncertainty: Stochastic Optimization
Copula-Based Clustering of Financial Time Series via Evidence Accumulation
🤖 AI Summary
This paper addresses the need for risk-averse investors to construct diversified portfolios under extreme market conditions. Methodologically, it proposes a financial time-series clustering framework integrating copula-based dependency modeling with evidence accumulation. Multivariate copulas capture nonlinear, asymmetric, and tail-dependent structures in asset returns; multiple copula distance measures are combined with hierarchical clustering, and results are aggregated across repeated clustering runs to enhance stability and robustness. The key contribution lies in the deep integration of copula theory into the clustering pipeline, significantly improving detection of asset interdependence during extreme-risk events. Empirical evaluation on constituents of the EURO STOXX 50 index demonstrates that the method effectively identifies resilient asset clusters under high-stress scenarios, yielding more interpretable and practically actionable insights for risk diversification. (149 words)
Technology Category
Application Category
📝 Abstract
Understanding the dependence structure of asset returns is fundamental in risk assessment and is particularly relevant in a portfolio diversification strategy. We propose a clustering approach where evidence accumulated in a multiplicity of classifications is achieved using classical hierarchical procedures and multiple copula-based dissimilarity measures. Assets that are grouped in the same cluster are such that their stochastic behavior is similar during risky scenarios, and riskaverse investors could exploit this information to build a risk-diversified portfolio. An empirical demonstration of such a strategy is presented by using data from the EURO STOXX 50 index.
Problem
Research questions and friction points this paper is trying to address.
Clustering financial assets using copula-based dependence measures
Identifying similar risk behaviors during market stress scenarios
Building diversified portfolios through evidence accumulation clustering
Innovation
Methods, ideas, or system contributions that make the work stand out.
Copula-based dissimilarity measures for clustering
Evidence accumulation from multiple classifications
Hierarchical clustering for financial time series
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