On the tail index estimation of an autoregressive Pareto process
Marta Ferreira
Discussiones Mathematicae Probability and Statistics, Tome 33 (2013), p. 65-77 / Harvested from The Polish Digital Mathematics Library

In this paper we consider an autoregressive Pareto process which can be used as an alternative to heavy tailed MARMA. We focus on the tail behavior and prove that the tail empirical quantile function can be approximated by a Gaussian process. This result allows to derive a class of consistent and asymptotically normal estimators for the shape parameter. We will see through simulation that the usual estimation procedure based on an i.i.d. setting may fall short of the desired precision.

Publié le : 2013-01-01
EUDML-ID : urn:eudml:doc:270855
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Marta Ferreira. On the tail index estimation of an autoregressive Pareto process. Discussiones Mathematicae Probability and Statistics, Tome 33 (2013) pp. 65-77. http://gdmltest.u-ga.fr/item/bwmeta1.element.bwnjournal-article-doi-10_7151_dmps_1149/

[000] [1] B.C. Arnold, Pareto Distributions (International Cooperative Publishing House, Fairland, 1983).

[001] [2] B.C. Arnold, Pareto processes, in: Handbook of Statistics, D.N. Shanbhag and C.R. Rao, eds., Vol. 19, (Elsevier Science B.V., 2001).

[002] [3] R. Davis and S. Resnick, Basic properties and prediction of max-ARMA processes, Adv. Appl. Probab. 21 (1989) 781-803. doi: 10.1214/aos/1176347397. | Zbl 0716.62098

[003] [4] A.L.M. Dekkers, J.H.J. Einmahl and L. de Haan, A moment estimator for the index of an extreme value distribution, Ann. Statist. 17 (1989) 1833-1855. doi: 10.1214/aos/1176347397. | Zbl 0701.62029

[004] [5] H. Drees, On smooth statistical tail functionals, Scand. J. Statist. 25 (1998a) 187-210. doi: 10.1111/1467-9469.00097.

[005] [6] H. Drees, A general class of estimators of the extreme value index, J. Statist. Plann. Inference 66) 1998b (95-112. doi: 10.1016/S0378-3758(97)00076-1. | Zbl 0929.62034

[006] [7] H. Drees, Extreme quantile estimation for dependent data with applications to finance, Bernoulli 9 (2003) 617-657. doi: 0.3150/bj/1066223272. | Zbl 1040.62077

[007] [8] M. Ferreira, On the extremal behavior of a pareto process: an alternative for armax modeling, Kybernetika 48 (2012) 31-49. | Zbl 1263.62109

[008] [9] M. Ferreira, Tail dependence of a Pareto process, accepted for publication in Studies in Theoretical and Applied Statistics - Selected Papers of the Statistical Societies, Springer.

[009] [10] H. Ferreira and M. Ferreira, Tail dependence between order statistics, J. Multivariate Anal. 105 (2012) 176-192. doi: 10.1016/j.jmva.2011.09.001. | Zbl 1234.60057

[010] [11] H. Ferreira and M. Ferreira, Fragility Index of block tailed vectors, J. Statist. Plann. Inference 142 (2012) 1837-1848. doi: 10.1016/j.jspi.2012.01.021. | Zbl 06036341

[011] [12] J.L. Geluk, L. De Haan and C.G. De Vries, Weak and strong financial fragility. Tinbergen Institute Discussion Paper, TI 2007-023/2, 2007.

[012] [13] B.M. Hill, A simple general approach to inference about the tail of a distribution, Ann. Statist. 3 (1975) 1163-1174. doi: 10.1214/aos/1176343247. | Zbl 0323.62033

[013] [14] J.R.M. Hosking and J.R. Wallis, Parameter and quantile estimation for the generalized Pareto distribution, Technometrics 29 (1987) 339-349. doi: 10.1080/00401706.1987.10488243. | Zbl 0628.62019

[014] [15] T. Hsing, On tail estimation using dependent data, Ann. Statist. 19 (1991) 1574-1569. doi: 10.1214/aos/1176348261. | Zbl 0738.62026

[015] [16] H. Joe, Multivariate Models and Dependence Concepts (Chapman & Hall, London, 1997). doi: 10.1201/b13150. | Zbl 0990.62517

[016] [17] S.D. Krishnarani and K. Jayakumar, A class of autoregressive processes, Statist. Probab. Lett. 78 (2008) 1355-1361. doi: 10.1016/j.spl.2007.12.019. | Zbl 1152.62368

[017] [18] A.V. Lebedev, Statistical analysis of first-order MARMA processes, Mathematical Notes 83 (2008) 506-511. doi: 10.1134/S0001434608030243. | Zbl 1152.62059

[018] [19] J. Pickands III, Statistical inference using extreme order statistics, Ann. Statist. 3 (1975) 119-131. doi: 10.1214/aos/1176343003. | Zbl 0312.62038

[019] [20] S. Resnick and C. Stărică, Consistency of Hill's estimator for dependent data, J. Appl. Probab. 32 (1995) 139-167. doi: 10.2307/3214926. | Zbl 0836.60020

[020] [21] S. Resnick and C. Stărică, Tail index estimation for dependent data, Ann. Appl. Probab. 8 (1998) 1156-1183. doi: 10.1214/aoap/1028903376 . | Zbl 0942.60037

[021] [22] H. Rootzén, M.R. Leadbetter and L. de Haan, Tail and Quantile Estimation for Strongly Mixing Stationary Sequences. Technical Report, UNC Center for Stochastic Processes, 1990. | Zbl 0939.60007

[022] [23] M. Sibuya, Bivariate extreme statistics, Ann. Inst. Statist. Math. 11 (1960) 195-210. doi: 10.1007/BF01682329. | Zbl 0095.33703

[023] [24] R.L. Smith, Estimating tails of probability distributions, Ann. Statist. 15 (1987) 1174-1207. doi: 10.1214/aos/1176350499. | Zbl 0642.62022

[024] [25] H.C. Yeh, B.C. Arnold and C.A. Robertson, Pareto processes, J. Appl. Probab. 25 (1988) 291-301. doi: 10.2307/3214437.