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Keywords:
extreme value theory; mixing processes; tail index estimation
extreme value theory; mixing processes; tail index estimation
Summary:
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.
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