In this paper we consider a nonlinear model based on neural networks as well as linear models to forecast the daily volatility of the S&P 500 and FTSE 100 indexes. As a proxy for daily volatility, we consider a consistent and unbiased estimator of the integrated volatility that is computed from...

This paper applies linear and neural network-based "thick" models for forecasting inflation based on Phillips-curve formulations in the USA, Japan and the euro area. Thick models represent "trimmed mean" forecasts from several neural network models. They outperform the best performing linear...

We define the nagging predictor, which, instead of using bootstrapping to produce a series of i.i.d. predictors, exploits the randomness of neural network calibrations to provide a more stable and accurate predictor than is available from a single neural network run. Convergence results for the...

This paper presents an optimization approach-residual-based bootstrap averaging (RBBA)-for different types of forecast ensembles. Unlike traditional residual-mean-square-error-based ensemble forecast averaging approaches, the RBBA method attempts to find optimal forecast weights in an ensemble...