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

DOI10.1016/j.mcm.2009.08.013zbMath1190.91126OpenAlexW2043726821MaRDI QIDQ984159

J. M. Matías, Wenceslao González Manteiga, Manuel Febrero-Bande, Juan C. Reboredo

Publication date: 16 July 2010

Published in: Mathematical and Computer Modelling (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.mcm.2009.08.013

zbMATH Keywords

neural networksGARCHvolatilityboostingheteroskedasticity

Mathematics Subject Classification ID

Time series, auto-correlation, regression, etc. in statistics (GARCH) (62M10) Applications of statistics to actuarial sciences and financial mathematics (62P05) Statistical methods; risk measures (91G70) Economic time series analysis (91B84) Neural nets and related approaches to inference from stochastic processes (62M45)

Related Items (4)

Boosting techniques for nonlinear time series models ⋮ Ai algorithms for fitting GARCH parameters to empirical financial data ⋮ Forecasting energy commodity prices using neural networks ⋮ Prediction and identification of physical systems by means of physically-guided neural networks with meaningful internal layers

Uses Software

• AdaBoost.MH

Cites Work

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• Prediction Games and Arcing Algorithms

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