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Time-Varying Assets Clustering via Identity-Link Latent-Space Infinite Mixture: An Application on DAX Components

In: Mathematical and Statistical Methods for Actuarial Sciences and Finance

Author

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  • Antonio Peruzzi

    (Ca’ Foscari University of Venice)

  • Roberto Casarin

    (Ca’ Foscari University of Venice)

Abstract

Finance literature suggests that cross-correlations among assets increase during periods of financial distress, and that cross-correlation’s very own clustering structure varies over time. This work proposes an Identity-Link Latent-Space Infinite-Mixture model to analyze the clustering structure of cross-correlation over time. The model allows for the representation of stocks on a d-dimensional Euclidean space and the clustering of assets into groups. Model estimation is carried out within a Bayesian framework, which allows including prior extra-sample information in the inference and accounting for parameter uncertainty. We apply the model to time-varying correlations among the DAX components. We find evidence of clustering effects and positive dependence between the number of clusters and both annualized volatility and average cross-correlation.

Suggested Citation

  • Antonio Peruzzi & Roberto Casarin, 2022. "Time-Varying Assets Clustering via Identity-Link Latent-Space Infinite Mixture: An Application on DAX Components," Springer Books, in: Marco Corazza & Cira Perna & Claudio Pizzi & Marilena Sibillo (ed.), Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 371-376, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-99638-3_60
    DOI: 10.1007/978-3-030-99638-3_60
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