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Clustering heteroskedastic time series by model-based procedures

Otranto, Edoardo (2008) Clustering heteroskedastic time series by model-based procedures. Cagliari, CRENoS. p. 26 (Working Papers CRENoS, 2008, 01). Working Paper.

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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 financial 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 classifications (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 fixed a priori or a posteriori. The procedure is evaluated by simulations and applied to the sector indexes of the Italian market

Item Type:Working Paper
ID Code:42
Status:Published
Uncontrolled Keywords:Agglomerative algorithm, AR metrics, cluster analysis, GARCH models, Wald test
Subjects:Area 13 - Scienze economiche e statistiche > SECS-S/01 Statistica
Divisions:002 Altri enti e centri di ricerca del Nord Sardegna > CRENoS-Centro Ricerche Economiche Nord Sud, Università di Cagliari e Università di Sassari
001 Università di Sassari > 01 Dipartimenti > Economia, impresa, regolamentazione
Publisher:CRENoS
Publisher Policy:Depositato per gentile concessione del CRENoS
Deposited On:18 Aug 2009 10:01

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