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Tail dependence estimation based on smooth estimation of diagonal section

Yıl 2022, Cilt: 71 Sayı: 3, 650 - 665, 30.09.2022

Selim Orhun Susam

https://doi.org/10.31801/cfsuasmas.988076

Öz

This paper is mainly developed around the diagonal section which is strongly related to tail dependence coefficients as defined in Nelsen [19]. Hence, we propose a flexible method for estimating tail dependence coefficients based on the new smooth estimation of the diagonal section based on the Bernstein polynomial approximation. To assess the performance of the new estimators we conduct the Monte-Carlo simulation study. As a result of the simulation study, both estimators perform satisfactory performance. Also, the estimation methods are illustrated by real data examples.

Anahtar Kelimeler

Copula tail dependence Bernstein polynomial diagonal section

Kaynakça

  • Amblard, C., Girard, S., Estimation procedures for a semiparametric family of bivariate copulas, Journal of Computational and Graphical Statistics, 14(2) (2005), 363-377. DOI:10.1198/106186005X48722
  • Babu, G. J., Canty, A. J., Chaubey, Y. P., Application of Bernstein polynomials for smooth estimation of a distribution and density function, Journal of Statistical Planning and Inference, 105 (2002), 377-392. DOI:10.1016/S0378-3758(01)00265-8
  • Brown, B. M., Elliot, D., Paget, D. F., Lipschitz constants for the Bernstein polynomials of a Lipschitz continuous function, Journal of Approximation Theory, 49 (1987), 196-199. DOI:10.1016/0021-9045(87)90087-6
  • Caillault, C., Guegan, D., Empirical estimation of tail dependence using copulas: application to Asian markets, Quantitative Finance, 5 (2007), 489-501. DOI: 10.1080/14697680500147853
  • Deheuvels, P., La fonction de dependance empirique et ses proprietes un test non parametrique d independance, Acad. Roy. Belg. Bull. Cl. Sci., 65(5) (1979), 274-292.
  • Dimitrova, D., Kaishev, K., Penev, I., GeD spline estimation of multivariate Archimedean copulas, Computational Statistics and Data Analysis, 52 (2008), 3570-3582. DOI:10.1016/j.csda.2007.11.010
  • Duncan, M., Applied Geometry for Computer Graphics and CAD, Springer Verlag, London, 2005.
  • Durante, F., Okhrin, O., Estimation procedures for exchangeable Marshall copulas with hydrological application, Stoch. Environ. Res. Risk Assess, 29 (2015), 205-226. DOI:10.1007/s00477-014-0866-7
  • Durante, F., Fern´andez-S´anchez, J., Pappad´a, R., Copulas, diagonals, and tail dependence, Fuzzy Sets and Systems, 264 (2015), 22-41. DOI: 10.1016/j.fss.2014.03.014
  • Durante, F., Koles´arov´a, A., Mesiar, R., Sempi, C., Copulas with given diagonal sections: novel constructions and applications, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(4) (2007), 397-410. DOI: 10.1142/S0218488507004753
  • Embrechts, P., Mcneil, A., Straumann, D., Correlation and dependence in risk management: properties and pitfalls. In: Risk management: value at risk and beyond, Cambridge University Press, Cambridge, (2002), 176-223.
  • Erdely, A., Exact distribution under independence of the diagonal section of the empirical copula, Kybernetika, 44(6) (2008), 826-845.
  • Ferreira, M., Nonparametric estimation of the tail-dependence coefficient, REVSTAT Statistical Journal, 11(1) (2013), 1-16.
  • Ferreira, H., Ferreira, M., Tail dependence between order statistics, Journal of Multivariate Analysis, 105(1) (2012), 176-192. DOI:10.1016/j.jmva.2011.09.001
  • Feller, W., An Introduction to Probability Theory and its Applications, Wiley, New York, 1965.
  • Frahm, G., Junker, M., Schmidt, R., Estimating the tail-dependence coefficient: properties and pitfalls, Insur. Math. Econ., 37(1) (2005), 80-100. DOI:10.1016/j.insmatheco.2005.05.008
  • Goegebeur, Y., Guillou, A., Asymptotically unbiased estimation of the coefficient of tail dependence, Scandinavian Journal of Statistics, 40(1) (2012), 174-189. DOI:10.2307/23357259
  • Joe, H., Multivariate Models and Dependence Concepts, Chapman Hall, London, 1997.
  • Nelsen, R., An Introduction to Copulas, Springer Series in Statistics, Springer, New York, 2006.
  • Schmidt, R., Stadtmuller, U., Non-parametric estimation of tail dependence, Scand. J. Stat., 33(2) (2006), 307-335. DOI: 10.1111/j.1467-9469.2005.00483.x
  • Susam, S. O., Hudaverdi, B. U., Testing independence for Archimedean copula based on Bernstein estimate of Kendall distribution function, Journal of Statistical Computation and Simulation, 88(13) (2018), 2589-2599. DOI:10.1080/00949655.2018.1478978
  • Susam, S. O., Hudaverdi, B. U., A goodness-of-fit test based on Bezier curve estimation of Kendall distribution, Journal of Statistical Computation and Simulation, 90(13) (2020), 1194-1215. DOI:10.1080/00949655.2020.1720680
  • Susam, S. O., Erdogan, M., Plug-in estimation of dependence characteristics of Archimedean copula via Bezier curve, RevStat-Statistical Journal, (In Press).
  • Sklar, A., Fonctions de repartition a n dimensions et leurs marges, Publications de lInstitut de Statistique de lUniversite de Paris, 8 (1959), 229-231.

Yıl 2022, Cilt: 71 Sayı: 3, 650 - 665, 30.09.2022

Selim Orhun Susam

https://doi.org/10.31801/cfsuasmas.988076

Öz

Kaynakça

  • Amblard, C., Girard, S., Estimation procedures for a semiparametric family of bivariate copulas, Journal of Computational and Graphical Statistics, 14(2) (2005), 363-377. DOI:10.1198/106186005X48722
  • Babu, G. J., Canty, A. J., Chaubey, Y. P., Application of Bernstein polynomials for smooth estimation of a distribution and density function, Journal of Statistical Planning and Inference, 105 (2002), 377-392. DOI:10.1016/S0378-3758(01)00265-8
  • Brown, B. M., Elliot, D., Paget, D. F., Lipschitz constants for the Bernstein polynomials of a Lipschitz continuous function, Journal of Approximation Theory, 49 (1987), 196-199. DOI:10.1016/0021-9045(87)90087-6
  • Caillault, C., Guegan, D., Empirical estimation of tail dependence using copulas: application to Asian markets, Quantitative Finance, 5 (2007), 489-501. DOI: 10.1080/14697680500147853
  • Deheuvels, P., La fonction de dependance empirique et ses proprietes un test non parametrique d independance, Acad. Roy. Belg. Bull. Cl. Sci., 65(5) (1979), 274-292.
  • Dimitrova, D., Kaishev, K., Penev, I., GeD spline estimation of multivariate Archimedean copulas, Computational Statistics and Data Analysis, 52 (2008), 3570-3582. DOI:10.1016/j.csda.2007.11.010
  • Duncan, M., Applied Geometry for Computer Graphics and CAD, Springer Verlag, London, 2005.
  • Durante, F., Okhrin, O., Estimation procedures for exchangeable Marshall copulas with hydrological application, Stoch. Environ. Res. Risk Assess, 29 (2015), 205-226. DOI:10.1007/s00477-014-0866-7
  • Durante, F., Fern´andez-S´anchez, J., Pappad´a, R., Copulas, diagonals, and tail dependence, Fuzzy Sets and Systems, 264 (2015), 22-41. DOI: 10.1016/j.fss.2014.03.014
  • Durante, F., Koles´arov´a, A., Mesiar, R., Sempi, C., Copulas with given diagonal sections: novel constructions and applications, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 15(4) (2007), 397-410. DOI: 10.1142/S0218488507004753
  • Embrechts, P., Mcneil, A., Straumann, D., Correlation and dependence in risk management: properties and pitfalls. In: Risk management: value at risk and beyond, Cambridge University Press, Cambridge, (2002), 176-223.
  • Erdely, A., Exact distribution under independence of the diagonal section of the empirical copula, Kybernetika, 44(6) (2008), 826-845.
  • Ferreira, M., Nonparametric estimation of the tail-dependence coefficient, REVSTAT Statistical Journal, 11(1) (2013), 1-16.
  • Ferreira, H., Ferreira, M., Tail dependence between order statistics, Journal of Multivariate Analysis, 105(1) (2012), 176-192. DOI:10.1016/j.jmva.2011.09.001
  • Feller, W., An Introduction to Probability Theory and its Applications, Wiley, New York, 1965.
  • Frahm, G., Junker, M., Schmidt, R., Estimating the tail-dependence coefficient: properties and pitfalls, Insur. Math. Econ., 37(1) (2005), 80-100. DOI:10.1016/j.insmatheco.2005.05.008
  • Goegebeur, Y., Guillou, A., Asymptotically unbiased estimation of the coefficient of tail dependence, Scandinavian Journal of Statistics, 40(1) (2012), 174-189. DOI:10.2307/23357259
  • Joe, H., Multivariate Models and Dependence Concepts, Chapman Hall, London, 1997.
  • Nelsen, R., An Introduction to Copulas, Springer Series in Statistics, Springer, New York, 2006.
  • Schmidt, R., Stadtmuller, U., Non-parametric estimation of tail dependence, Scand. J. Stat., 33(2) (2006), 307-335. DOI: 10.1111/j.1467-9469.2005.00483.x
  • Susam, S. O., Hudaverdi, B. U., Testing independence for Archimedean copula based on Bernstein estimate of Kendall distribution function, Journal of Statistical Computation and Simulation, 88(13) (2018), 2589-2599. DOI:10.1080/00949655.2018.1478978
  • Susam, S. O., Hudaverdi, B. U., A goodness-of-fit test based on Bezier curve estimation of Kendall distribution, Journal of Statistical Computation and Simulation, 90(13) (2020), 1194-1215. DOI:10.1080/00949655.2020.1720680
  • Susam, S. O., Erdogan, M., Plug-in estimation of dependence characteristics of Archimedean copula via Bezier curve, RevStat-Statistical Journal, (In Press).
  • Sklar, A., Fonctions de repartition a n dimensions et leurs marges, Publications de lInstitut de Statistique de lUniversite de Paris, 8 (1959), 229-231.
Toplam 24 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Uygulamalı Matematik
Bölüm Research Article
Yazarlar

Selim Orhun Susam 0000-0001-7896-9055

Yayımlanma Tarihi 30 Eylül 2022
Gönderilme Tarihi 28 Ağustos 2021
Kabul Tarihi 27 Ocak 2022
Yayımlandığı Sayı Yıl 2022 Cilt: 71 Sayı: 3

Kaynak Göster

APA Susam, S. O. (2022). Tail dependence estimation based on smooth estimation of diagonal section. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, 71(3), 650-665. https://doi.org/10.31801/cfsuasmas.988076
AMA Susam SO. Tail dependence estimation based on smooth estimation of diagonal section. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. Eylül 2022;71(3):650-665. doi:10.31801/cfsuasmas.988076
Chicago Susam, Selim Orhun. “Tail Dependence Estimation Based on Smooth Estimation of Diagonal Section”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 71, sy. 3 (Eylül 2022): 650-65. https://doi.org/10.31801/cfsuasmas.988076.
EndNote Susam SO (01 Eylül 2022) Tail dependence estimation based on smooth estimation of diagonal section. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 71 3 650–665.
IEEE S. O. Susam, “Tail dependence estimation based on smooth estimation of diagonal section”, Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat., c. 71, sy. 3, ss. 650–665, 2022, doi: 10.31801/cfsuasmas.988076.
ISNAD Susam, Selim Orhun. “Tail Dependence Estimation Based on Smooth Estimation of Diagonal Section”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics 71/3 (Eylül 2022), 650-665. https://doi.org/10.31801/cfsuasmas.988076.
JAMA Susam SO. Tail dependence estimation based on smooth estimation of diagonal section. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2022;71:650–665.
MLA Susam, Selim Orhun. “Tail Dependence Estimation Based on Smooth Estimation of Diagonal Section”. Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics, c. 71, sy. 3, 2022, ss. 650-65, doi:10.31801/cfsuasmas.988076.
Vancouver Susam SO. Tail dependence estimation based on smooth estimation of diagonal section. Commun. Fac. Sci. Univ. Ank. Ser. A1 Math. Stat. 2022;71(3):650-65.

Communications Faculty of Sciences University of Ankara Series A1 Mathematics and Statistics.

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