# Statistics for Heteroscedastic Time Series Extremes

@inproceedings{Bucher2022StatisticsFH, title={Statistics for Heteroscedastic Time Series Extremes}, author={Axel Bucher and Tobias Jennessen}, year={2022} }

. Einmahl, de Haan and Zhou (2016, Journal of the Royal Statistical Society: Series B, 78(1), 31– 51) recently introduced a stochastic model that allows for heteroscedasticity of extremes. The model is extended to the situation where the observations are serially dependent, which is crucial for many practical applications. We prove a local limit theorem for a kernel estimator for the scedasis function, and a functional limit theorem for an estimator for the integrated scedasis function. We…

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