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Analysis of multidimensional probability distributions with copula functions. II

Author

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  • Fantazzini, Dean

    () (Moscow School of Economics, Moscow State University, HSE)

Abstract

This article contains the second part of the consultation series on copula functions and their use in modeling multidimensional probability distributions. It describes pair-copula functions (including the concept of canonical and D-vines), alternative measures of dependence useful to summarize the dependence structure of the analyzed variables (including measures of tail dependence, particularly relevant in the case of asymmetric distributions), as well as parametric, semi-parametric and nonparametric methods of statistical estimation of copula functions.

Suggested Citation

  • Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions. II," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 23(3), pages 98-132.
  • Handle: RePEc:ris:apltrx:0094
    as

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    File URL: http://pe.cemi.rssi.ru/pe_2011_3_98-132.pdf
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    References listed on IDEAS

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    1. Aas, Kjersti & Czado, Claudia & Frigessi, Arnoldo & Bakken, Henrik, 2009. "Pair-copula constructions of multiple dependence," Insurance: Mathematics and Economics, Elsevier, vol. 44(2), pages 182-198, April.
    2. Yan, Jun, 2007. "Enjoy the Joy of Copulas: With a Package copula," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 21(i04).
    3. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions. III," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 24(4), pages 100-130.
    4. Gourieroux,Christian & Monfort,Alain, 1995. "Statistics and Econometric Models," Cambridge Books, Cambridge University Press, number 9780521471626, October.
    5. Ruud, Paul A., 2000. "An Introduction to Classical Econometric Theory," OUP Catalogue, Oxford University Press, number 9780195111644.
    6. Marc Hallin & Thomas S. Ferguson & Christian Genest, 2000. "Kendall's tau for serial dependence," ULB Institutional Repository 2013/2093, ULB -- Universite Libre de Bruxelles.
    7. Chen, Xiaohong & Fan, Yanqin, 2006. "Estimation and model selection of semiparametric copula-based multivariate dynamic models under copula misspecification," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 125-154.
    8. White,Halbert, 1996. "Estimation, Inference and Specification Analysis," Cambridge Books, Cambridge University Press, number 9780521574464, Fall.
    9. Fantazzini, Dean, 2010. "Three-stage semi-parametric estimation of T-copulas: Asymptotics, finite-sample properties and computational aspects," Computational Statistics & Data Analysis, Elsevier, vol. 54(11), pages 2562-2579, November.
    10. Jean-David FERMANIAN & Olivier SCAILLET, 2003. "Nonparametric Estimation of Copulas for Time Series," FAME Research Paper Series rp57, International Center for Financial Asset Management and Engineering.
    11. Gunky Kim & Mervyn J. Silvapulle & Paramsothy Silvapulle, 2007. "Estimating the Error Distribution in the Multivariate Heteroscedastic Time Series Models," Monash Econometrics and Business Statistics Working Papers 8/07, Monash University, Department of Econometrics and Business Statistics.
    12. Bouye, Eric & Durlleman, Valdo & Nikeghbali, Ashkan & Riboulet, Gaël & Roncalli, Thierry, 2000. "Copulas for finance," MPRA Paper 37359, University Library of Munich, Germany.
    13. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 22(2), pages 98-134.
    14. Kim, Gunky & Silvapulle, Mervyn J. & Silvapulle, Paramsothy, 2007. "Comparison of semiparametric and parametric methods for estimating copulas," Computational Statistics & Data Analysis, Elsevier, vol. 51(6), pages 2836-2850, March.
    15. Andrew J. Patton, 2006. "Modelling Asymmetric Exchange Rate Dependence," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 47(2), pages 527-556, May.
    16. Ser-Huang Poon, 2004. "Extreme Value Dependence in Financial Markets: Diagnostics, Models, and Financial Implications," Review of Financial Studies, Society for Financial Studies, vol. 17(2), pages 581-610.
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    Citations

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    Cited by:

    1. Balaev, Alexey, 2014. "The copula based on multivariate t-distribution with vector of degrees of freedom," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 33(1), pages 90-110.
    2. Travkin, Alexandr, 2013. "Pair copula constructions in portfolio optimization ploblem," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 32(4), pages 110-133.
    3. repec:nea:journl:y:2017:i:35:p:33-52 is not listed on IDEAS
    4. Knyazev, Alexander & Lepekhin, Oleg & Shemyakin, Arkady, 2016. "Joint distribution of stock indices: Methodological aspects of construction and selection of copula models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 42, pages 30-53.
    5. Penikas, Henry, 2014. "Investment portfolio risk modelling based on hierarchical copulas," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 35(3), pages 18-38.
    6. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions. III," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 24(4), pages 100-130.
    7. Travkin, A., 2015. "Estimating Pair-Copula Constructions Using Empirical Tail Dependence Functions: an Application to Russian Stock Market," Journal of the New Economic Association, New Economic Association, vol. 25(1), pages 39-55.
    8. Blagoveschensky, Yury, 2012. "Basics of copula’s theory," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 26(2), pages 113-130.

    More about this item

    Keywords

    pair copula; D-vines; canonical vines; measure of dependence; tail dependence; rank correlation; maximum likelihood method; one-step ML; two-step ML; canonical ML; three-stage KME–CML method; semi-parametric and nonparametric methods of statistical estimation;

    JEL classification:

    • C49 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics - - - Other
    • C69 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Other

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