Boosting and Neural Networks for the Prediction of Heterokedastic Time Series

This work develops and evaluates new algorithms based on neural networks and boosting techniques, designed to model and predict heteroskedastic time series. The main novel elements of these new algorithms are as follows: a) in regard to neural networks, the simultaneous estimation of trend and volatility through the maximization of the likelihood; b) in regard to boosting, its simultaneous application to the likelihood trend and volatility components, and the use of likelihood-based models (e.g., GARCH) as the base hypothesis rather than gradient adjustment techniques using least squares. The behavior of the proposed algorithms is evaluated over simulated data and over the Standard & Poor's 500 Index returns series, resulting in frequent and signi cant improvements in relation to the ARMA-GARCH models.

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