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Clustering Mutual Funds by Return and Risk Levels

Author

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  • F. Lisi
  • E. Otranto

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Abstract

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.

Suggested Citation

  • 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.
  • Handle: RePEc:cns:cnscwp:200813
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    References listed on IDEAS

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    1. Sergio Da Silva & Jefferson Cunha & Newton Da Costa, Jr, 2005. "Stock selection based on cluster analysis," Economics Bulletin, AccessEcon, vol. 13(1), pages 1-9.
    2. 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.
    3. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    4. repec:ebl:ecbull:v:13:y:2005:i:1:p:1-9 is not listed on IDEAS
    5. 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.
    6. 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.
    7. Edoardo Otranto, 2004. "Classifying the Markets Volatility with ARMA Distance Measures," Econometrics 0402009, EconWPA, revised 05 Mar 2004.
    8. Otranto, Edoardo, 2008. "Clustering heteroskedastic time series by model-based procedures," Computational Statistics & Data Analysis, Elsevier, vol. 52(10), pages 4685-4698, June.
    9. 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.
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    Citations

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    Cited by:

    1. Edoardo Otranto & Romana Gargano, 2015. "Financial clustering in presence of dominant markets," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 9(3), pages 315-339, September.
    2. Luca De Angelis, 2013. "Latent class models for financial data analysis: some statistical developments," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 22(2), pages 227-242, June.

    More about this item

    Keywords

    cluster; distance; garch models; risk;

    JEL classification:

    • 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
    • C22 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Time-Series Models; Dynamic Quantile Regressions; Dynamic Treatment Effect Models; Diffusion Processes

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