Block Bootstrap Prediction Intervals For Garch Processes

Abstract

In this paper, we propose a new resampling algorithm based on block bootstrap to obtain prediction intervals for future returns and volatilities of GARCH processes. The finite sample properties of the proposed methods are illustrated by an extensive simulation study and they are applied to Japan Yen (JPY) / U.S. dollar (USD) daily exchange rate data. Our results indicate that: (i) the proposed algorithm is a good competitor or even better and (ii) computationally more efficient than traditional method(s).