Subsampling the mean of heavy‐tailed dependent observations

Piotr S. Kokoszka · Michael Wolf

30 citations0/0 files storedJournal of Time Series Analysis

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Financial Risk and Volatility Modeling Probability and Statistical Research Statistical Methods and Inference

Abstract

Abstract. We establish the validity of subsampling confidence intervals for the mean of a dependent series with heavy‐tailed marginal distributions. Using point process theory, we focus on GARCH‐like time series models. We propose a data‐dependent method for the optimal block size selection and investigate its performance by means of a simulation study.

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1986Modelling the persistence of conditional variances 2002MIXING AND MOMENT PROPERTIES OF VARIOUS GARCH AND STOCHASTIC VOLATILITY MODELS 2000Limit theory for the sample autocorrelations and extremes of a GARCH (1,1) process 2002Regular variation of GARCH processes 1998The sample autocorrelations of heavy-tailed processes with applications to ARCH 2002A characterization of multivariate regular variation 2003ESTIMATION OF THE MAXIMAL MOMENT EXPONENT OF A GARCH(1,1) SEQUENCE

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