The Strength of Weak Learnability
Machine Learning
Approximation and radial-basis-function networks
Neural Computation
A decision-theoretic generalization of on-line learning and an application to boosting
Journal of Computer and System Sciences - Special issue: 26th annual ACM symposium on the theory of computing & STOC'94, May 23–25, 1994, and second annual Europe an conference on computational learning theory (EuroCOLT'95), March 13–15, 1995
Parameter convergence and learning curves for neural networks
Neural Computation
Prediction games and arcing algorithms
Neural Computation
Improved Boosting Algorithms Using Confidence-rated Predictions
Machine Learning - The Eleventh Annual Conference on computational Learning Theory
Time Series Analysis: Forecasting and Control
Time Series Analysis: Forecasting and Control
An algorithm for nonparametric GARCH modelling
Computational Statistics & Data Analysis
Ensembles of Learning Machines
WIRN VIETRI 2002 Proceedings of the 13th Italian Workshop on Neural Nets-Revised Papers
An introduction to boosting and leveraging
Advanced lectures on machine learning
Stationary and Integrated Autoregressive Neural Network Processes
Neural Computation
Neural Networks: A Comprehensive Foundation (3rd Edition)
Neural Networks: A Comprehensive Foundation (3rd Edition)
Improved rates and asymptotic normality for nonparametric neural network estimators
IEEE Transactions on Information Theory
IEEE Transactions on Neural Networks
Neural networks in financial engineering: a study in methodology
IEEE Transactions on Neural Networks
Financial volatility trading using recurrent neural networks
IEEE Transactions on Neural Networks
Hi-index | 0.98 |
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.