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State-of-the-art in modeling nonlinear dependence among many random variables with copulas and application to financial indexes

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EN
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EN
In this paper, we focus our attention on multi– dimensional copula models for returns of the indexes of selected prominent international financial markets. Our modeling results, based on elliptic copulas, 7‐ dimensional hierarchical Archimedean copulas, vine co‐ pulas and factor copulas demonstrate a dominant role of the SPX index among the considered major stock indexes (mainly at the first tree of the optimal vine copulas). Some interesting weaker conditional dependencies can be de‐ tected at it’s highest trees. Interestingly, while global op‐ timal model (for the whole period of 277 months) belong to the Factor FDG copulas class, the optimal local models can be found (with very minor differences in the values of GoF test statistic) in the classes of Factor FDG and hier‐ archical Archimedean copulas. The dominance of these models is most striking over the interval of the financial market crisis, where the quality of the best Student class model was providing a substantially poorer fit.
Twórcy
autor
  • Slovak University of Technology in Bratislava, Radlinského 11, Bratislava, Slovak republic
  • Slovak University of Technology in Bratislava, Radlinského 11, Bratislava, Slovak republic
  • Faculty of Management, Co‑ menius University, Odbojá rov 10, P.O.BOX 95, 820 05 Bratislava, Slovak republic
Bibliografia
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  • [2] T. Bacigál and M. Zdimalová , “Convergence of Linear Approximation of Archimedean Generator from Williamson’s Transform in Examples”, Tatra Mountains Mathematical Publications, vol. 69, no. 1, 2017, 1–18, 10.1515/tmmp‑2017‑0010.
  • [3] T. Bedford and R. M. Cooke, “Vines: A New Graphical Model for Dependent Random Variables”, The Annals of Statistics, vol. 30, no. 4, 2002, 1031–1068.
  • [4] C. Czado, “Pair‑Copula Constructions of Multivariate Copulas”. In: P. Jaworski, F. Durante, W. K. Hä rdle, and T. Rychlik, eds., Copula Theory and Its Applications, Berlin, Heidelberg, 2010, 93–109, 10.1007/978‑3‑642‑12465‑5_4.
  • [5] C. Czado. “Vine copulas and their applications to Financial data”. AFMathConf 2013, Brussels, Technische Universitä t München.
  • [6] J. Dissmann, E. C. Brechmann, C. Czado, and D. Kurowicka, “Selecting and estimating regular vine copulae and application to financial returns”, arXiv:1202.2002 [stat], 2012, arXiv:1202.2002.
  • [7] P. H. Franses and D. v. Dijk, Non‑Linear Time Series Models in Empirical Finance, Cambridge University Press, 2000.
  • [8] C. Genest, B. Remillard, and D. Beaudoin, “Goodness‑of‑fit tests for copulas: A review and a power study”, Insurance: Mathematics and Economics, vol. 44, no. 2, 2009, 199–213.
  • [9] M. Hofert, “Construction and Sampling of Nested Archimedean Copulas”. In: P. Jaworski, F. Durante, W. K. Härdle, and T. Rychlik, eds., Copula Theory and Its Applications, Berlin, Heidelberg, 2010, 147–160, 10.1007/978‑3‑642‑12465‑5_7.
  • [10] M. Hofert, I. Kojadinovic, M. Maechler, J. Yan, and J. G. Nešlehová . “copula: Multivariate Dependence with Copulas”. https://CRAN.Rproject.org/package=copula. Accessed on: 2019‑11‑08.
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  • [13] P. Krupskii and H. Joe, “Factor copula models for multivariate data”, Journal of Multivariate Analysis, vol. 120, 2013, 85–101, 10.1016/j.jmva.2013.05.001.
  • [14] G. Mazo and S. Girard. “FDGcopulas: Multivariate Dependence with FDG Copulas”. https://CRAN.R-project.org/package=FDGcopulas. Accessed on: 2019‑11‑08.
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  • [22] O. Okhrin and A. Ristig, “Hierarchical Archimedean Copulae: The HAC Package”, Journal of Statistical Software, vol. 58, no. 1, 2014, 1–20, 10.18637/jss.v058.i04.
  • [23] R Core Team. R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria, 2018.
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Uwagi
Opracowanie rekordu w ramach umowy 509/P-DUN/2018 ze środków MNiSW przeznaczonych na działalność upowszechniającą naukę (2019).
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-46986860-2190-43a3-bc9b-431187b871ba
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