We develop some asymptotic theory for applications of block bootstrap resampling schemes to multivariate integrated and cointegrated time series. It is proved that a multivariate, continuous-path block bootstrap scheme applied to a full rank integrated process, succeeds in estimating...

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...

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...

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...

We analyze fast procedures for conducting Monte Carlo experiments involving bootstrap estimators, providing formal results establishing the properties of these methods under general conditions.

An algorithm for robust fitting of AR models is given, based on a linear regression idea. The new method appears to outperform the Yule-Walker estimator in a setting of data contaminated with outliers.

The problem of nonparametric estimation of a multivariate density function is addressed. In particular, a general class of estimators with favorable asymptotic performance (bias, variance, rate of convergence) is proposed. The proposed estimators are characterized by the flatness near the origin...