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...

Many financial decisions such as portfolio allocation, risk management, option pricing and hedge strategies are based on the forecast of the conditional variances, covariances and correlations of financial returns. Although the decisions are based on forecasts covariance matrix little is known...

The Autoregressive Conditionally Heteroscedastic (ARCH) model is useful for handling volatilities in economical time series phenomena that ARIMA models are unable to handle. The ARCH model has been adopted in many applications that contain time series data such as financial market prices,...