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Classifying the Markets Volatility with ARMA Distance Measures

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  • Edoardo Otranto

    (DEIR, Sassari)

Abstract

The financial time series are often characterized by similar volatility structures. The selection of series having a similar behavior could be important for the analysis of the transmission mechanisms of volatility and to forecast the time series, using the series with more similar structure. In this paper a metrics is developed in order to measure the distance between two GARCH models, extending well known results developed for the ARMA models. The statistic used to calculate it follows known distributions, so that it can be adopted as a test procedure. These tools can be used to develope an agglomerative algorithm in order to detect clusters of homogeneous series.

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File URL: http://128.118.178.162/eps/em/papers/0402/0402009.pdf
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Bibliographic Info

Paper provided by EconWPA in its series Econometrics with number 0402009.

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Length: 11 pages
Date of creation: 17 Feb 2004
Date of revision: 05 Mar 2004
Handle: RePEc:wpa:wuwpem:0402009

Note: Type of Document - pdf; prepared on WinXP; to print on Laser witer II NP; pages: 11; figures: 4 figures in the document. PDF document submitted via ftp
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Web page: http://128.118.178.162

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Keywords: GARCH models; clusters; agglomerative algorithm;

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References

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  1. Bollerslev, Tim, 1986. "Generalized autoregressive conditional heteroskedasticity," Journal of Econometrics, Elsevier, vol. 31(3), pages 307-327, April.
  2. Bollerslev, Tim & Engle, Robert F. & Nelson, Daniel B., 1986. "Arch models," Handbook of Econometrics, in: R. F. Engle & D. McFadden (ed.), Handbook of Econometrics, edition 1, volume 4, chapter 49, pages 2959-3038 Elsevier.
  3. Bollerslev, Tim & Chou, Ray Y. & Kroner, Kenneth F., 1992. "ARCH modeling in finance : A review of the theory and empirical evidence," Journal of Econometrics, Elsevier, vol. 52(1-2), pages 5-59.
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Cited by:
  1. Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
  2. Otranto, Edoardo, 2010. "Identifying financial time series with similar dynamic conditional correlation," Computational Statistics & Data Analysis, Elsevier, vol. 54(1), pages 1-15, January.
  3. F. Lisi & E. Otranto, 2008. "Clustering Mutual Funds by Return and Risk Levels," Working Paper CRENoS 200813, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.

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