Summary
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 conditional mean and volatility through the likelihood maximization; b) in regard to boosting, its simultaneous application to trend and volatility components of the likelihood, and the use of likelihood-based models (e.g. GARCH) as the base hypothesis rather than gradient fitting techniques using least squares. The behavior of the proposed algorithms is evaluated over simulated data, resulting in frequent and significant improvements in relation to the ARMA-GARCH models.
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Matías, J.M., Febrero, M., González-Manteiga, W., Reboredo, J.C. (2008). Gradient Boosting GARCH and Neural Networks for Time Series Prediction. In: Okun, O., Valentini, G. (eds) Supervised and Unsupervised Ensemble Methods and their Applications. Studies in Computational Intelligence, vol 126. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-78981-9_8
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DOI: https://doi.org/10.1007/978-3-540-78981-9_8
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