Clustering Heteroskedastic Time Series by Model-Based Procedures

Edoardo Otranto ()

Working Paper CRENoS from Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia

Abstract:

Financial time series are often characterized by similar volatility structures, often represented by GARCH processes. The detection of clusters of series displaying similar behavior could be important to understand the differences in the estimated processes, without having to study and compare the estimated parameters across all the series. This is particularly relevant dealing with many series, as in ?nancial applications. The volatility of a time series can be characterized in terms of the underlying GARCH process. Using Wald tests and the AR metrics to measure the distance between GARCH processes, it is possible to develop a clustering algorithm, which can provide three classi?cations (with increasing degree of deepness) based on the heteroskedastic patterns of the time series. The number of clusters is detected automatically and it is not ?xed a priori or a posteriori. The procedure is evaluated by simulations and applied to the sector indexes of the Italian market.

Keywords: Agglomerative algorithm; AR metrics; Cluster analysis; GARCH models; Wald test (search for similar items in EconPapers)
JEL-codes: C02 C19 C22 (search for similar items in EconPapers)
New Economics Papers: this item is included in nep-ecm, nep-ets and nep-ore
Date: 2008
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Journal Article: Clustering heteroskedastic time series by model-based procedures (2008)
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