Clustering Dynamics and Persistence for Financial Multivariate Panel Data

Igor Custodio João, Andre Lucas and Julia Schaumburg
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Igor Custodio João: Vrije Universiteit Amsterdam

No 21-040/III, Tinbergen Institute Discussion Papers from Tinbergen Institute

Abstract: We introduce a new method for dynamic clustering of panel data with dynamics for cluster location and shape, cluster composition, and for the number of clusters. Whereas current techniques typically result in (economically) too many switches, our method results in economically more meaningful dynamic clustering patterns. It does so by extending standard cross-sectional clustering techniques using shrinkage towards previous cluster means. In this way, the different cross-sections in the panel are tied together, substantially reducing short-lived switches of units between clusters (flickering) and the birth and death of incidental, economically less meaningful clusters. In a Monte Carlo simulation, we study how to set the penalty parameter in a data-driven way. A systemic risk surveillance example for business model classification in the global insurance industry illustrates how the new method works empirically.

Keywords: dynamic clustering; shrinkage; cluster membership persistence; Silhouette index; insurance (search for similar items in EconPapers)
JEL-codes: C33 C38 G22 (search for similar items in EconPapers)
Date: 2021-05-10
New Economics Papers: this item is included in nep-ecm, nep-ias and nep-ore
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