Clustering Mutual Funds by Return and Risk Levels
AbstractMutual 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.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia in its series Working Paper CRENoS with number 200813.
Date of creation: 2008
Date of revision:
cluster; distance; garch models; risk;
Find related papers by JEL classification:
- C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models &bull Diffusion Processes
- G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions
- G23 - Financial Economics - - Financial Institutions and Services - - - Non-bank Financial Institutions; Financial Instruments; Institutional Investors
This paper has been announced in the following NEP Reports:
- NEP-ALL-2008-08-06 (All new papers)
- NEP-FMK-2008-08-06 (Financial Markets)
- NEP-RMG-2008-08-06 (Risk Management)
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.:
- 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.
- Edoardo Otranto, 2004. "Classifying the Markets Volatility with ARMA Distance Measures," Econometrics 0402009, EconWPA, revised 05 Mar 2004.
- 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.
- Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
- 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.
- Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer, vol. 32(3), pages 241-254, September.
- Newton Da Costa Jr & Jefferson Cunha & Sergio Da Silva, 2005.
"Stock Selection Based on Cluster Analysis,"
- 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.
- 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
- Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods and Applications, Springer, vol. 22(2), pages 227-242, June.
- R. Gargano & E. Otranto, 2013. "Financial Clustering in Presence of Dominant Markets," Working Paper CRENoS 201318, Centre for North South Economic Research, University of Cagliari and Sassari, Sardinia.
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.