On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators

Elena Di Bernardino; Didier Rullière

Dependence Modeling (2013)

  • Volume: 1, page 1-36
  • ISSN: 2300-2298

Abstract

top
We study the impact of certain transformations within the class of Archimedean copulas. We give some admissibility conditions for these transformations, and define some equivalence classes for both transformations and generators of Archimedean copulas. We extend the r-fold composition of the diagonal section of a copula, from r ∈ N to r ∈ R. This extension, coupled with results on equivalence classes, gives us new expressions of transformations and generators. Estimators deriving directly from these expressions are proposed and their convergence is investigated. We provide confidence bands for the estimated generators. Numerical illustrations show the empirical performance of these estimators.

How to cite

top

Elena Di Bernardino, and Didier Rullière. "On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators." Dependence Modeling 1 (2013): 1-36. <http://eudml.org/doc/267249>.

@article{ElenaDiBernardino2013,
abstract = {We study the impact of certain transformations within the class of Archimedean copulas. We give some admissibility conditions for these transformations, and define some equivalence classes for both transformations and generators of Archimedean copulas. We extend the r-fold composition of the diagonal section of a copula, from r ∈ N to r ∈ R. This extension, coupled with results on equivalence classes, gives us new expressions of transformations and generators. Estimators deriving directly from these expressions are proposed and their convergence is investigated. We provide confidence bands for the estimated generators. Numerical illustrations show the empirical performance of these estimators.},
author = {Elena Di Bernardino, Didier Rullière},
journal = {Dependence Modeling},
keywords = {Transformations of Archimedean copulas; self-nested diagonal; non-parametric estimation; tail dependence; estimation},
language = {eng},
pages = {1-36},
title = {On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators},
url = {http://eudml.org/doc/267249},
volume = {1},
year = {2013},
}

TY - JOUR
AU - Elena Di Bernardino
AU - Didier Rullière
TI - On certain transformations of Archimedean copulas: Application to the non-parametric estimation of their generators
JO - Dependence Modeling
PY - 2013
VL - 1
SP - 1
EP - 36
AB - We study the impact of certain transformations within the class of Archimedean copulas. We give some admissibility conditions for these transformations, and define some equivalence classes for both transformations and generators of Archimedean copulas. We extend the r-fold composition of the diagonal section of a copula, from r ∈ N to r ∈ R. This extension, coupled with results on equivalence classes, gives us new expressions of transformations and generators. Estimators deriving directly from these expressions are proposed and their convergence is investigated. We provide confidence bands for the estimated generators. Numerical illustrations show the empirical performance of these estimators.
LA - eng
KW - Transformations of Archimedean copulas; self-nested diagonal; non-parametric estimation; tail dependence; estimation
UR - http://eudml.org/doc/267249
ER -

References

top
  1. [1] Alsina, C., Schweizer, B., and Frank, M. J. (2006). Associative functions: triangular norms and copulas. World Scientific. Zbl1100.39023
  2. [2] Autin, F., Le Pennec, E., and Tribouley, K. (2010). Thresholding methods to estimate copula density. J. Multivariate Anal., 101(1):200–222. Zbl1177.62075
  3. [3] Bienvenüe, A. and Rullière, D. (2011). Iterative adjustment of survival functions by composed probability distortions. Geneve Risk Ins. Rev., 37(2):156–179. 
  4. [4] Bienvenüe, A. and Rullière, D. (2012). On hyperbolic iterated distortions for the adjustment of survival functions. In Perna, C. and Sibillo, M., editors, Mathematical and Statistical Methods for Actuarial Sciences and Finance, pages 35–42. Springer Milan. Zbl1238.91080
  5. [5] Brechmann, E. (2013). Sampling from Hierarchical Kendall Copulas. J. SFdS, 154(1):192–209. Zbl1316.62015
  6. [6] Charpentier, A. and Segers, J. (2007). Lower tail dependence for Archimedean copulas: characterizations and pitfalls. Insurance Math. Econom., 40(3):525–532. Zbl1183.62086
  7. [7] Chomette, T. (2003). Arbres et dérivée d’une fonction composée. draft paper, ENS, http: // www. math. ens. fr/ culturemath/ maths/ pdf/ analyse/ derivation. pdf . 
  8. [8] Crane, G. and van der Hoek, J. (2008). Using distortions of copulas to price synthetic CDOs. Insurance Math. Econom., 42(3):903 – 908. Zbl1141.91500
  9. [9] Curtright, T. and Zachos, C. (2009). Evolution profiles and functional equations. J. Phys. A., 42(48):485208. Zbl1183.37165
  10. [10] Deheuvels, P. (1979). La fonction de dépendance empirique et ses propriétés. Acad. Roy. Belg. Bull. Cl. Sci., 65(5):274–292. Zbl0422.62037
  11. [11] Deheuvels, P. (1980). Non parametric tests of independence. In Raoult, J.-P., editor, Statistique non Paramétrique Asymptotique, volume 821 of Lecture Notes in Math., pages 95–107. Springer Berlin Heidelberg. Zbl0455.62031
  12. [12] Di Bernardino, E. and Rullière, D. (2013). Distortions of multivariate distribution functions and associated level curves: Applications in multivariate risk theory. Insurance Math. Econom., 53(1):190 – 205. Zbl1284.62149
  13. [13] Durante, F., Foschi, R., and Sarkoci, P. (2010). Distorted copulas: Constructions and tail dependence. Comm. Statist. Theory Methods, 39(12):2288–2301. [Crossref] Zbl1194.62075
  14. [14] Durante, F. and Jaworski, P. (2008). Absolutely continuous copulas with given diagonal sections. Comm. Statist. Theory Methods, 37(18):2924–2942. [Crossref] Zbl1292.60019
  15. [15] Durante, F. and Sempi, C. (2005). Copula and semicopula transforms. Int. J. Math. Math. Sci., 2005(4):645–655. Zbl1078.62055
  16. [16] Durrleman, V., Nikeghbali, A., and Roncalli, T. (2000). A simple transformation of copulas. Technical report, Groupe de Research Operationnelle Credit Lyonnais. 
  17. [17] Embrechts, P. and Hofert, M. (2011). Comments on: Inference in multivariate Archimedean copula models. TEST, 20(2):263–270. [Crossref] Zbl06106915
  18. [18] Embrechts, P. and Hofert, M. (2013). Statistical inference for copulas in high dimensions: A simulation study. ASTIN Bull., 43:81–95. Zbl1282.62146
  19. [19] Erdely, A., González-Barrios, J. M., and Hernández-Cedillo, M. M. (2013). Frank’s condition for multivariate Archimedean copulas. Fuzzy Sets and Systems, In press(Available online). Zbl1315.62048
  20. [20] Fermanian, J.-D., Radulovic, D., andWegkamp, M. (2004). Weak convergence of empirical copula processes. Bernoulli, 10(5):847–860. [Crossref] Zbl1068.62059
  21. [21] Fischer, M. and Köck, C. (2012). Constructing and generalizing given multivariate copulas: A unifying approach. Statistics, 46(1):1–12. [Crossref] Zbl1314.62124
  22. [22] Frees, E. W. and Valdez, E. A. (1998). Understanding relationships using copulas. N. Am. Actuar. J., 2(1):1–25. Zbl1081.62564
  23. [23] Genest, C., Ghoudi, K., and Rivest, L.-P. (1998). Discussion of “understanding relationships using copulas,” by edward frees and emiliano valdez, january 1998. N. Am. Actuar. J., 2(3):143–149. 
  24. [24] Genest, C., Masiello, E., and Tribouley, K. (2009). Estimating copula densities through wavelets. Insurance Math. Econom., 44(2):170–181. Zbl1167.91015
  25. [25] Genest, C., Nešlehová, J., and Ziegel, J. (2011). Inference in multivariate Archimedean copula models. TEST, 20(2):223–256. [Crossref] Zbl1274.62399
  26. [26] Genest, C. and Rivest, L.-P. (1993). Statistical inference procedures for bivariate Archimedean copulas. J. Amer. Statist. Assoc., 88(423):1034–1043. [Crossref] Zbl0785.62032
  27. [27] Hardy, M. (2006). Combinatorics of partial derivatives. Electron. J. Combin., 13(1) Zbl1080.05006
  28. [28] Hernández-Lobato, J. M. and Suárez, A. (2011). Semiparametric bivariate Archimedean copulas. Comput. Statist. Data Anal., 55(6):2038–2058. [Crossref] Zbl1328.62367
  29. [29] Hofert, M. (2008). Sampling Archimedean copulas. Comput. Statist. Data Anal., 52(12):5163 – 5174. [Crossref] Zbl05565089
  30. [30] Hofert, M. (2011). Efficiently sampling nested Archimedean copulas. Comput. Statist. Data Anal., 55(1):57 – 70. [Crossref] Zbl1247.62132
  31. [31] Hofert, M., Mächler, M., and McNeil, A. J. (2012). Likelihood inference for Archimedean copulas in high dimensions under known margins. J. Multivariate Anal., 110(0):133 – 150. Zbl1244.62073
  32. [32] Hofert, M. and Pham, D. (2013). Densities of nested Archimedean copulas. J. Multivariate Anal., 118(0):37 – 52. Zbl1277.62138
  33. [33] Jaworski, P. (2009). On copulas and their diagonals. Inform. Sci., 179(17):2863 – 2871. [Crossref] Zbl1171.62332
  34. [34] Jaworski, P. and Rychlik, T. (2008). On distributions of order statistics for absolutely continuous copulas with applications to reliability. Kybernetika (Prague), 44(6):757–776. Zbl1180.60013
  35. [35] Joe, H. (2005). Asymptotic efficiency of the two-stage estimation method for copula-based models. J. Multivariate Anal., 94(2):401–419. Zbl1066.62061
  36. [36] Juri, A. and Wüthrich, M. V. (2002). Copula convergence theorems for tail events. Insurance Math. Econom., 30(3):405–420. Zbl1039.62043
  37. [37] Juri, A. and Wüthrich, M. V. (2003). Tail dependence from a distributional point of view. Extremes, 6(3):213–246. [Crossref] Zbl1049.62055
  38. [38] Kim, G., Silvapulle, M. J., and Silvapulle, P. (2007). Comparison of semiparametric and parametric methods for estimating copulas. Comput. Statist. Data Anal., 51(6):2836–2850. [Crossref] Zbl1161.62364
  39. [39] Klement, E. P., Mesiar, R., and Pap, E. (2005a). Archimax copulas and invariance under transformations. C. R. Math. Acad. Sci. Paris, 340(10):755 – 758. Zbl1126.62040
  40. [40] Klement, E. P., Mesiar, R., and Pap, E. (2005b). Transformations of copulas. Kybernetika (Prague), 41(4):425 –434. Zbl1243.62019
  41. [41] Kojadinovic, I. and Yan, J. (2010). Modeling multivariate distributions with continuous margins using the copula r package. J. Stat. Softw., 34(9):1–20. 
  42. [42] Lambert, P. (2007). Archimedean copula estimation using Bayesian splines smoothing techniques. Comput. Statist. Data Anal., 51(12):6307 – 6320. [Crossref] Zbl05560110
  43. [43] Ledford, A. W. and Tawn, J. A. (1996). Statistics for near independence in multivariate extreme values. Biometrika, 83(1):169–187. [Crossref] Zbl0865.62040
  44. [44] McNeil, A. and Nešlehová, J. (2009). Multivariate Archimedean copulas, d-monotone functions and l1−norm symmetric distributions. Ann. Statist., 37(5B):3059–3097. [Crossref] Zbl1173.62044
  45. [45] Michiels, F. and De Schepper, A. (2012). How to improve the fit of Archimedean copulas by means of transforms. Statist. Papers, 53(2):345–355. [Crossref] Zbl06084557
  46. [46] Morillas, P. M. (2005). A method to obtain new copulas from a given one. Metrika, 61(2):169–184. [Crossref] Zbl1079.62056
  47. [47] Nelsen, R. B. (1999). An introduction to copulas, volume 139 of Lecture Notes in Statistics. Springer-Verlag, New York. Zbl0909.62052
  48. [48] Nelsen, R. B. and Fredricks, G. A. (1997). Diagonal copulas. In Beneš, V. and Štepán, J., editors, Distributions with given Marginals and Moment Problems, pages 121–128. Springer Netherlands. Zbl0906.60021
  49. [49] Nelsen, R. B., Quesada-Molina, J., Rodriguez-Lallena, J., and Úbeda-Flores, M. (2009). Kendall distribution functions and associative copulas. Fuzzy Sets and Systems, 160(1):52–57. Zbl1183.60006
  50. [50] Nelsen, R. B., Quesada-Molina, J. J., Rodríguez-Lallena, J. A., and Úbeda-Flores, M. (2008). On the construction of copulas and quasi-copulas with given diagonal sections. Insurance Math. Econom., 42(2):473–483. Zbl1152.60311
  51. [51] Omelka, M., Gijbels, I., and Veraverbeke, N. (2009). Improved kernel estimation of copulas: weak convergence and goodness-of-fit testing. Ann. Statist., 37(5B):3023–3058. [Crossref] Zbl05596928
  52. [52] Qu, L. and Yin, W. (2012). Copula density estimation by total variation penalized likelihood with linear equality constraints. Comput. Statist. Data Anal., 56(2):384 – 398. [Crossref] Zbl1239.62038
  53. [53] Rüschendorf, L. (1976). Asymptotic distributions of multivariate rank order statistics. Ann. Statist., 4:912–923. [Crossref] Zbl0359.62040
  54. [54] Segers, J. (2011). Diagonal sections of bivariate Archimedean copulas. Discussion of “Inference in multivariate Archimedean copula models” by Christian Genest, Johanna Nešlehová, and Johanna Ziegel. TEST, 20:281–283. [Crossref] Zbl06106918
  55. [55] Segers, J. (2012). Asymptotics of empirical copula processes under non-restrictive smoothness assumptions. Bernoulli, 18(3):764–782. [Crossref] Zbl1243.62066
  56. [56] Valdez, E. and Xiao, Y. (2011). On the distortion of a copula and its margins. Scand. Actuar. J., 4:292–317. [Crossref] Zbl1277.62140
  57. [57] Wysocki, W. (2012). Constructing Archimedean copulas from diagonal sections. Statist. Probab. Lett., 82(4):818 – 826.[Crossref] Zbl1242.62041

NotesEmbed ?

top

You must be logged in to post comments.

To embed these notes on your page include the following JavaScript code on your page where you want the notes to appear.

Only the controls for the widget will be shown in your chosen language. Notes will be shown in their authored language.

Tells the widget how many notes to show per page. You can cycle through additional notes using the next and previous controls.

    
                

Note: Best practice suggests putting the JavaScript code just before the closing </body> tag.