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Estimating tail decay for stationary sequences via extreme values

  • Assaf Zeevi (a1) and Peter W. Glynn (a2)


We study estimation of the tail-decay parameter of the marginal distribution corresponding to a discrete-time, real-valued stationary stochastic process. Assuming that the underlying process is short-range dependent, we investigate properties of estimators of the tail-decay parameter which are based on the maximal extreme value of the process observed over a sampled time interval. These estimators only assume that the tail of the marginal distribution is roughly exponential, plus some modest ‘mixing’ conditions. Consistency properties of these estimators are established, as well as minimax convergence rates. We also provide some discussion on estimating the pre-exponent, when a more refined tail asymptotic is assumed. Properties of a certain moving-average variant of the extremal-based estimator are investigated as well. In passing, we also characterize the precise dependence (mixing) assumptions that support almost-sure limit theory for normalized extreme values and related first-passage times in stationary sequences.


Corresponding author

Postal address: Graduate School of Business, Columbia University, 3022 Broadway, New York, NY 10027, USA. Email address:
∗∗ Postal address: Management Science and Engineering, Terman Engineering Center, Stanford University, Stanford, CA 94305, USA.


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Estimating tail decay for stationary sequences via extreme values

  • Assaf Zeevi (a1) and Peter W. Glynn (a2)


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