Modeling and fitting of time series with heavy distribution tails and strong time dependence by Gaussian time series
Teoriâ veroâtnostej i ee primeneniâ, Tome 63 (2018) no. 1, pp. 186-190 Cet article a éte moissonné depuis la source Math-Net.Ru

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In the model of Gaussian copula time series with the tails of one-dimensional distributions belonging to the Fréchet maximum domain of attraction and the description of dependency based on Gaussian variables (see [A. E. Mazur and V. I. Piterbarg, Moscow Univ. Math. Bull., 70 (2015), pp. 197–201]), an estimator for the copula (which is a nonlinear function that takes Gaussian variables to variables from the Fréchet maximum domain of attraction) is built. This opens the way for statistical analysis of data time series with potentially heavy tails using the machinery of asymptotic analysis of Gaussian sequences. The consistency and asymptotic normality for this estimator are proved.
Keywords: Gaussian sequence, empirical quantile function.
Mots-clés : Fréchet maximum domain of attraction
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A. E. Mazur. Modeling and fitting of time series with heavy distribution tails and strong time dependence by Gaussian time series. Teoriâ veroâtnostej i ee primeneniâ, Tome 63 (2018) no. 1, pp. 186-190. http://geodesic.mathdoc.fr/item/TVP_2018_63_1_a8/

[1] A. E. Mazur, V. I. Piterbarg, “Gaussian copula time series with heavy tails and strong time dependence”, Moscow Univ. Math. Bull., 70:5 (2015), 197–201 | DOI | MR | Zbl

[2] H. Rootzén, “Weak convergence of the tail empirical process for dependent sequences”, Stochastic Process. Appl., 119:2 (2009), 468–490 | DOI | MR | Zbl

[3] M. R. Leadbetter, G. Lindgren, H. Rootzén, Extremes and related properties of random sequences and processes, Springer Ser. Statist., Springer-Verlag, New York–Berlin, 1983, xii+336 pp. | DOI | MR | MR | Zbl | Zbl

[4] H. Drees, “Tail empirical processes under mixing conditions”, Empirical process techniques for dependent data, Birkhäuser Boston, Boston, MA, 2002, 325–342 | MR | Zbl

[5] H. Drees, “Weighted approximations of tail processes for $\beta$-mixing random variables”, Ann. Appl. Probab., 10:4 (2000), 1274–1301 | DOI | MR | Zbl

[6] H. Drees, “On smooth statistical tail functionals”, Scand. J. Statist., 25:1 (1998), 187–210 | DOI | MR | Zbl

[7] H. Drees, “A general class of estimators of the extreme value index”, J. Statist. Plann. Inference, 66:1 (1998), 95–112 | DOI | MR | Zbl

[8] V. I. Piterbarg, Asymptotic methods in the theory of Gaussian processes and fields, Transl. Math. Monogr., 148, reprint ed., Amer. Math. Soc., Providence, RI, 2012, xii+206 pp. | MR | Zbl | Zbl

[9] V. I. Piterbarg, Twenty lectures about Gaussian processes, Atlantic Financial Press, London, 2015, xi+167 pp. | Zbl

[10] L. De Haan, A. Ferreira, Extreme value theory. An introduction, Springer Ser. Oper. Res. Financ. Eng., Springer, New York, 2006, xviii+417 pp. | DOI | MR | Zbl

[11] A. E. Mazur, Modelirovanie i podgonka vremennykh ryadov s tyazhelymi khvostami raspredelenii i silnoi vremennoi zavisimostyu posredstvom gaussovskikh ryadov, Dep. VINITI, 2017