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

Antonio Peruzzi () and Roberto Casarin ()
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Antonio Peruzzi: Ca’ Foscari University of Venice
Roberto Casarin: Ca’ Foscari University of Venice

A chapter in Mathematical and Statistical Methods for Actuarial Sciences and Finance, 2022, pp 371-376 from Springer

Abstract: 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.

Keywords: Latent space models; Bayesian inference; Non-parametric methods (search for similar items in EconPapers)
Date: 2022
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-99638-3_60

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DOI: 10.1007/978-3-030-99638-3_60

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