Title: Risk measure estimation for $β$-mixing time series and applications,
Abstract: In this paper, we discuss the application of extreme value theory in the
context of stationary $\beta$-mixing sequences that belong to the Fréchet
domain of attraction. In particular, we propose a methodology to construct
bias-corrected tail estimators. Our approach is based on the combination of two
estimators for the extreme value index to cancel the bias. 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.