De Luca, Giovanni and Zuccolotto, Paola (2013): A Conditional Value-at-Risk Based Portfolio Selection With Dynamic Tail Dependence Clustering.
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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.
Item Type: | MPRA Paper |
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Original Title: | A Conditional Value-at-Risk Based Portfolio Selection With Dynamic Tail Dependence Clustering |
English Title: | A Conditional Value-at-Risk Based Portfolio Selection With Dynamic Tail Dependence Clustering |
Language: | English |
Keywords: | Copula functions, Tail dependence, Time series clustering. |
Subjects: | C - Mathematical and Quantitative Methods > C3 - Multiple or Simultaneous Equation Models ; Multiple Variables > C38 - Classification Methods ; Cluster Analysis ; Principal Components ; Factor Models C - Mathematical and Quantitative Methods > C5 - Econometric Modeling > C58 - Financial Econometrics G - Financial Economics > G1 - General Financial Markets > G11 - Portfolio Choice ; Investment Decisions |
Item ID: | 50129 |
Depositing User: | Prof. Giovanni De Luca |
Date Deposited: | 24 Sep 2013 12:35 |
Last Modified: | 05 Oct 2019 16:43 |
References: | Davies DL, Bouldin DW (1979), ”A Cluster Separation Measure”. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1, 224-227. De Luca G, Zuccolotto P (2011), ”A Tail Dependence-based Dissimilarity Measure for Financial Time Series Clustering”. Advances in Classification and Data Analysis, 5, 323-340. De Luca G, Zuccolotto P (2013), ”Time Series Clustering on Lower Tail Dependence”, in Corazza M, and Pizzi C, Mathematical and Statistical Methods for Actuarial Sciences and Finance, Springer. Dolnicar S, Leisch F, Weingessel A, Buchta C, Dimitriadou EA (1998), ”Comparison of Several Cluster Algorithms on Artifcial Binary Data Scenarios from Tourism Marketing”. SFB Adaptive Information Systems and Modeling in Economics and Management Science, Working Paper 7. Dimitriadou E, Dolnicar S, Weingessel A (2002), ”An Examination of Indexes for Determining the Number of Clusters in Binary Data Sets”. Psychometrika, 67, 137-159. Friedman HP, Rubin J (1967), ”On Some Invariant Criteria for Grouping Data”. Journal of the American Statistical Association, 62, 1159-1178. Hartigan JA, Wong MA (1979), ”A K-means Clustering Algorithm”. Applied Statistics, 28, 100-108. Joe H (1997), Multivariate Models and Dependence Concept. Chapman & Hall, New York. Krokhmal P, Palmquist J, Uryasev S (2002), ”Portfolio Optimization with Conditional Value-at-Risk Objective and Constraints” Journal of Risk, 4, 43-68. Nelsen RB (2006), An Introduction to Copulas. Springer-Verlag, New York. Rand WM (1971), ”Objective Criteria for the Evaluation of Clustering Methods”. Journal of the American Statistical Association, 66, 846-850. Ratkowsky DA, Lance GN (1978), ”A Criterion for Determining the Number of Groups in a Classifcation”. Australian Computer Journal, 10, 115-117. Rockafellar RT, Uryasev S (2000), ”Optimization of Conditional Value-at-Risk”. Journal of Risk; 2, 21-41. Scott AJ, Symons MJ (1971), ”Clustering Methods Based on Likelihood Ratio Criteria”. Biometrics, 27, 387-397. Sokal RR, Michener CD (1958), ”A Statistical Method for Evaluating Systematic Relationships”. University of Kansas Science Bulletin, 38, 1409-1438. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/50129 |