2025/10/07 by Andrea Mecchina, Mecchina, Andrea, Roberta Pappadà +3
Computer Science · Decision Sciences · Economics, Econometrics and Finance · #Applications (stat.AP) #Complex Systems and Time Series Analysis #FOS: Computer and information sciences #Stock Market Forecasting Methods #Time Series Analysis and Forecasting
paper · pdf · doi:10.48550/arxiv.2510.05960
openalex publication_date 2025/10/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.