Clustering of financial time series

D’Urso, Pierpaolo, Carmela Cappelli, Dario Di Lallo and Riccardo Massari
Authors registered in the RePEc Author Service: Pierpaolo D'Urso

Physica A: Statistical Mechanics and its Applications, 2013, vol. 392, issue 9, 2114-2129

Abstract: This paper addresses the topic of classifying financial time series in a fuzzy framework proposing two fuzzy clustering models both based on GARCH models. In general clustering of financial time series, due to their peculiar features, needs the definition of suitable distance measures. At this aim, the first fuzzy clustering model exploits the autoregressive representation of GARCH models and employs, in the framework of a partitioning around medoids algorithm, the classical autoregressive metric. The second fuzzy clustering model, also based on partitioning around medoids algorithm, uses the Caiado distance, a Mahalanobis-like distance, based on estimated GARCH parameters and covariances that takes into account the information about the volatility structure of time series. In order to illustrate the merits of the proposed fuzzy approaches an application to the problem of classifying 29 time series of Euro exchange rates against international currencies is presented and discussed, also comparing the fuzzy models with their crisp version.

Keywords: Financial time series; GARCH models; Partitioning around medoids; Dailies returns of Euro exchange rates; Econophysics (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:phsmap:v:392:y:2013:i:9:p:2114-2129

DOI: 10.1016/j.physa.2013.01.027

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