Fast clustering of GARCH processes via Gaussian mixture models

Gian Piero Aielli and Massimiliano Caporin

Mathematics and Computers in Simulation (MATCOM), 2013, vol. 94, issue C, 205-222

Abstract: The financial econometrics literature includes several Multivariate GARCH models where the model parameter matrices depend on a clustering of financial assets. Those classes might be defined a priori or data-driven. When the latter approach is followed, one method for deriving asset groups is given by the use of clustering methods. In this paper, we analyze in detail one of those clustering approaches, the Gaussian mixture GARCH. This method is designed to identify groups based on the conditional variance dynamic parameters. The clustering algorithm, based on a Gaussian mixture model, has been recently proposed and is here generalized with the introduction of a correction for the presence of correlation across assets. Finally, we introduce a benchmark estimator used to assess the performances of simpler and faster estimators. Simulation experiments show evidence of the improvements given by the correction for asset correlation.

Keywords: Gaussian mixtures; Financial time series clustering; Multivariate GARCH; Block structures (search for similar items in EconPapers)
JEL-codes: C13 C32 C38 C53 C58 (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:matcom:v:94:y:2013:i:c:p:205-222

DOI: 10.1016/j.matcom.2012.09.015

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