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Basics of copula’s theory

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  • Blagoveschensky, Yury

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Abstract

The most important properties of p-variate distributions on the hypercube whose univariate marginals are uniformly distributed on [0; 1] are discussed. These distributions, also called p-copulas, have become a popular tool in order to study financial markets, macroeconomics and other fields. The study of Russian articles shows that in most cases these articles contain a list of several typical copulas and techniques of their using but they hold no discussions about meaning of acts over copulas. The review is an attempt to change for the better this situation, even if it were a little.

Suggested Citation

  • Blagoveschensky, Yury, 2012. "Basics of copula’s theory," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 26(2), pages 113-130.
  • Handle: RePEc:ris:apltrx:0174
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    File URL: http://pe.cemi.rssi.ru/pe_2012_2_113-130.pdf
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    References listed on IDEAS

    as
    1. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 22(2), pages 98-134.
    2. Penikas, Henry, 2011. "Copula-Based Price Risk Hedging Models," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 22(2), pages 3-21.
    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. Fantazzini, Dean, 2011. "Analysis of multidimensional probability distributions with copula functions. II," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 23(3), pages 98-132.
    Full references (including those not matched with items on IDEAS)

    Citations

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    Cited by:

    1. Travkin, Alexandr, 2013. "Pair copula constructions in portfolio optimization ploblem," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 32(4), pages 110-133.
    2. 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.
    3. 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.
    4. Penikas, Henry, 2014. "Investment portfolio risk modelling based on hierarchical copulas," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 35(3), pages 18-38.
    5. Aivazian, Sergei & Afanasiev, Mikhail & Rudenko, Victoria, 2014. "Analysis of dependence between the random components of a stochastic production function for the purpose of technical efficiency estimation," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 34(2), pages 3-18.
    6. Bologov , Yaroslav, 2013. "A copula-based approach to portfolio credit risk modeling," Applied Econometrics, Publishing House "SINERGIA PRESS", vol. 29(1), pages 45-66.

    More about this item

    Keywords

    copula; Sklar’s theorem; Lipschitz condition; product operation on copulas; contingency.;

    JEL classification:

    • C19 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Other
    • 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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