A1326
Title: Dynamic identity-link latent space infinite-mixture: An application on DAX components
Authors: Antonio Peruzzi - Ca' Foscari University of Venice (Italy) [presenting]
Roberto Casarin - University Ca' Foscari of Venice (Italy)
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. An Identity-Link Latent-Space Infinite-Mixture model with random-walk intercept is proposed 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.
