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Title: Risk measure estimation for $\beta-$mixing time series and applications Authors:  Valerie Chavez-Demoulin - University of Lausanne (Switzerland)
Armelle Guillou - Strasbourg (France) [presenting]
Abstract: The application of extreme-value theory in the context of stationary $\beta-$mixing sequences that belong to the Frechet domain of attraction is discussed. In particular, we propose a methodology to construct bias-corrected tail estimators. Our approach is based on the combination of two estimators of the extreme-value index to cancel the bias. Then the resulting estimator is used to estimate an extreme quantile. In a simulation study, we outline the performance of our proposals that we compare to alternative estimators recently introduced in the literature. Also, we compute the asymptotic variance in specific examples when possible. Our methodology is applied to two datasets on finance and environment.