New and Fast Block Bootstrap-Based Prediction Intervals for GARCH (1,1) Process with Application to Exchange Rates

Abstract

In this paper, we propose a new bootstrap algorithm to obtain prediction intervals for generalized autoregressive conditionally heteroscedastic (GARCH(1,1)) process which can be applied to construct prediction intervals for future returns and volatilities. The advantages of the proposed method are twofold: it (a) often exhibits improved performance and (b) is computationally more efficient compared to other available resampling methods. The superiority of this method over the other resampling method-based prediction intervals is explained with Spearman's rank correlation coefficient. The finite sample properties of the proposed method are also illustrated by an extensive simulation study and a real-world example.