Estimation for heavy tailed moving average process

Hakim Ouadjed; Tawfiq Fawzi Mami

Kybernetika (2018)

  • Volume: 54, Issue: 2, page 351-362
  • ISSN: 0023-5954

Abstract

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In this paper, we propose two estimators for a heavy tailed MA(1) process. The first is a semi parametric estimator designed for MA(1) driven by positive-value stable variables innovations. We study its asymptotic normality and finite sample performance. We compare the behavior of this estimator in which we use the Hill estimator for the extreme index and the estimator in which we use the t-Hill in order to examine its robustness. The second estimator is for MA(1) driven by stable variables innovations using the relationship between the extremal index and the moving average parameter. We analyze their performance through a simulation study.

How to cite

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Ouadjed, Hakim, and Mami, Tawfiq Fawzi. "Estimation for heavy tailed moving average process." Kybernetika 54.2 (2018): 351-362. <http://eudml.org/doc/294147>.

@article{Ouadjed2018,
abstract = {In this paper, we propose two estimators for a heavy tailed MA(1) process. The first is a semi parametric estimator designed for MA(1) driven by positive-value stable variables innovations. We study its asymptotic normality and finite sample performance. We compare the behavior of this estimator in which we use the Hill estimator for the extreme index and the estimator in which we use the t-Hill in order to examine its robustness. The second estimator is for MA(1) driven by stable variables innovations using the relationship between the extremal index and the moving average parameter. We analyze their performance through a simulation study.},
author = {Ouadjed, Hakim, Mami, Tawfiq Fawzi},
journal = {Kybernetika},
keywords = {extreme value theory; mixing processes; tail index estimation},
language = {eng},
number = {2},
pages = {351-362},
publisher = {Institute of Information Theory and Automation AS CR},
title = {Estimation for heavy tailed moving average process},
url = {http://eudml.org/doc/294147},
volume = {54},
year = {2018},
}

TY - JOUR
AU - Ouadjed, Hakim
AU - Mami, Tawfiq Fawzi
TI - Estimation for heavy tailed moving average process
JO - Kybernetika
PY - 2018
PB - Institute of Information Theory and Automation AS CR
VL - 54
IS - 2
SP - 351
EP - 362
AB - In this paper, we propose two estimators for a heavy tailed MA(1) process. The first is a semi parametric estimator designed for MA(1) driven by positive-value stable variables innovations. We study its asymptotic normality and finite sample performance. We compare the behavior of this estimator in which we use the Hill estimator for the extreme index and the estimator in which we use the t-Hill in order to examine its robustness. The second estimator is for MA(1) driven by stable variables innovations using the relationship between the extremal index and the moving average parameter. We analyze their performance through a simulation study.
LA - eng
KW - extreme value theory; mixing processes; tail index estimation
UR - http://eudml.org/doc/294147
ER -

References

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