In this paper we propose a subsampling estimator for the distribution of statistics diverging at either known rates when the underlying time series in strictly stationary and strong mixing. Based on our results we provide a detailed discussion how to estimate extreme order statistics with...

This paper gives a computer-intensive approach to multi-step-ahead prediction of volatility in financial returns series under an ARCH/GARCH model and also under a model-free setting, namely employing the NoVaS transformation. Our model-based approach only assumes i..id innovations without...

The well-known ARCH/GARCH models for financial time series have been criticized of late for their poor performance in volatility prediction, that is, prediction of squared returns. Focusing on three representative data series, namely a foreign exchange series (Yen vs. Dollar), a stock index...

A new resampling procedure, the continuous-path block bootstrap, is proposed in the context of testing for integrated (unit root) time series. The continuous-path block bootstrap (CBB) is a nonparametric procedure that successfully generates unit root integrated pseudo time series retaining the...

Multivariate time varying volatility models have attracted a lot of attention in modern finance theory. We provide an empirical study of some multivariate ARCH and GARCH models that already exist in the literature and have attracted a lot of practical interest. Bayesian and classical techniques...