Boosting GARCH and neural networks for the prediction of heteroskedastic time series

Boosting GARCH and neural networks for the prediction of heteroskedastic time series

This work develops and evaluates new algorithms based on GARCH models, 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...

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Bibliographic Details
Published in:Mathematical and computer modelling Vol. 51; no. 3; pp. 256 - 271
Main Authors: Matías, J.M., Febrero-Bande, M., González-Manteiga, W., Reboredo, J.C.
Format: Journal Article Conference Proceeding
Language:English
Published: Kidlington Elsevier Ltd 01-02-2010
Elsevier
Subjects:
Boosting
Combinatorics
Combinatorics. Ordered structures
Designs and configurations
Exact sciences and technology
GARCH
Heteroskedasticity
Mathematical analysis
Mathematics
Methods of scientific computing (including symbolic computation, algebraic computation)
Neural networks
Numerical analysis. Scientific computation
Sciences and techniques of general use
Volatility
Boosting
GARCH
Neural networks
Volatility
Heteroskedasticity
ARMA model
Fitting
Time series
Neural network
Algorithm
Variance
Applied mathematics
Least squares method
Mathematical model
Computer aided analysis
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Summary:This work develops and evaluates new algorithms based on GARCH models, 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 the conditional mean and volatility through the maximization of likelihood; (b) in regard to boosting, its simultaneous application to mean and variance 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 and over the Standard & Poor’s 500 Index returns series, resulting in frequent and significant improvements in relation to the ARMA-GARCH models.
ISSN:0895-7177
1872-9479
DOI:10.1016/j.mcm.2009.08.013