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A Conditional Value-at-Risk Based Portfolio Selection With Dynamic Tail Dependence Clustering

Author

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  • De Luca, Giovanni
  • Zuccolotto, Paola

Abstract

In this paper we propose a portfolio selection procedure specifically designed to protect investments during financial crisis periods. To this aim, we focus attention on the lower tails of the returns distributions and use a combination of statistical tools able to take into account the joint behavior of stocks in event of high losses. In detail, we propose to firstly cluster time series of stock returns on the basis of their lower tail dependence coefficients, estimated with copula functions, and secondly to use the obtained clustering solution to build an optimal minimum CVaR portfolio. In addition, the procedure is defined in a time-varying context, in order to model the possible contagion between stocks when volatility increases. This results in a dynamic portfolio selection procedure, which is shown to be able to outperform classical strategies.

Suggested Citation

  • De Luca, Giovanni & Zuccolotto, Paola, 2013. "A Conditional Value-at-Risk Based Portfolio Selection With Dynamic Tail Dependence Clustering," MPRA Paper 50129, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:50129
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    References listed on IDEAS

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    1. Giovanni De Luca & Paola Zuccolotto, 2011. "A tail dependence-based dissimilarity measure for financial time series clustering," 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. 5(4), pages 323-340, December.
    2. Marco Corazza & Florence Legros & Cira Perna & Marilena Sibillo, 2017. "Mathematical and Statistical Methods for Actuarial Sciences and Finance," Post-Print hal-01776135, HAL.
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    Cited by:

    1. Fabrizio Durante & Roberta Pappadà & Nicola Torelli, 2015. "Clustering of time series via non-parametric tail dependence estimation," Statistical Papers, Springer, vol. 56(3), pages 701-721, August.

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    More about this item

    Keywords

    Copula functions; Tail dependence; Time series clustering.;
    All these keywords.

    JEL classification:

    • C38 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables - - - Classification Methdos; Cluster Analysis; Principal Components; Factor Analysis
    • C58 - Mathematical and Quantitative Methods - - Econometric Modeling - - - Financial Econometrics
    • G11 - Financial Economics - - General Financial Markets - - - Portfolio Choice; Investment Decisions

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