Clustering Mutual Funds by Return and Risk Levels
Mutual funds classifications, often made by rating agencies, are very common and sometimes criticized. In this work, a three-step statistical procedure for mutual funds classification is proposed. In the first step time series funds are characterized in terms of returns. In the second step, a clustering analysis is performed in order to obtain classes of homogeneous funds with respect to the risk levels. In particular, the risk is defined starting from an Asymmetric Threshold-GARCH model aimed to describe minimum, normal and turmoil risk. The third step merges the previous two. An application to 75 European funds belonging to 5 different categories is given.
|Date of creation:||2008|
|Contact details of provider:|| Postal: Via S. Giorgio 12, I-09124 Cagliari|
Web page: http://www.crenos.unica.it/
More information through EDIRC
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Otranto, Edoardo, 2008.
"Clustering heteroskedastic time series by model-based procedures,"
Computational Statistics & Data Analysis,
Elsevier, vol. 52(10), pages 4685-4698, June.
- E. Otranto, 2008. "Clustering Heteroskedastic Time Series by Model-Based Procedures," Working Paper CRENoS 200801, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
- Sergio Da Silva & Jefferson Cunha & Newton Da Costa, Jr, 2005.
"Stock selection based on cluster analysis,"
AccessEcon, vol. 13(1), pages 1-9.
- T. Kalantzis & D. Papanastassiou, 2008. "Classification of GARCH time series: an empirical investigation," Applied Financial Economics, Taylor & Francis Journals, vol. 18(9), pages 759-764.
- Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
- Caiado, Jorge & Crato, Nuno, 2007. "A GARCH-based method for clustering of financial time series: International stock markets evidence," MPRA Paper 2074, University Library of Munich, Germany.
- Edoardo Otranto, 2004. "Classifying the Markets Volatility with ARMA Distance Measures," Econometrics 0402009, EconWPA, revised 05 Mar 2004.
- Andersen, Torben G & Bollerslev, Tim, 1998. "Answering the Skeptics: Yes, Standard Volatility Models Do Provide Accurate Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 885-905, November.
- Pattarin, Francesco & Paterlini, Sandra & Minerva, Tommaso, 2004. "Clustering financial time series: an application to mutual funds style analysis," Computational Statistics & Data Analysis, Elsevier, vol. 47(2), pages 353-372, September.
- repec:ebl:ecbull:v:13:y:2005:i:1:p:1-9 is not listed on IDEAS
When requesting a correction, please mention this item's handle: RePEc:cns:cnscwp:200813. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Antonello Pau)
If references are entirely missing, you can add them using this form.