Bootstrap prediction intervals for autoregressive conditional heteroskedasticity

In this paper, we construct prediction intervals for autoregressive conditional heteroskedasticity (ARCH) models using the bootstrap. We use both a parametric and non-parametric bootstrap, which take account of parameter uncertainty. We compare our prediction intervals to traditional asymptotic prediction intervals and find that the bootstrap leads to improved accuracy. The accuracy of the bootstrap is empirically demonstrated with the Yen/US$ exchange rate.