Managing distribution changes in time series prediction
Journal of Computational and Applied Mathematics - Special issue: The international conference on computational methods in sciences and engineering 2004
Minimum distance estimation of GARCH(1,1) models
Computational Statistics & Data Analysis
Implied volatility in oil markets
Computational Statistics & Data Analysis
Stochastic variance models in discrete time with feedforward neural networks
Neural Computation
Boosting GARCH and neural networks for the prediction of heteroskedastic time series
Mathematical and Computer Modelling: An International Journal
Hi-index | 0.03 |
A simple iterative algorithm for nonparametric first-order GARCH modelling is proposed. This method offers an alternative to fitting one of the many different parametric GARCH specifications that have been proposed in the literature. A theoretical justification for the algorithm is provided and examples of its application to simulated data from various stationary processes showing stochastic volatility, as well as empirical financial return data, are given. The nonparametric procedure is found to often give better estimates of the unobserved latent volatility process than parametric modelling with the standard GARCH(1,1) model, particularly in the presence of asymmetry and other departures from the standard GARCH specification. Extensions of the basic iterative idea to more complex time series models combining ARMA or GARCH features of possibly higher order are suggested.