PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Estimating GARCH models using support vector machines
Fernando Perez-Cruz, Julio Afonso-Rodriguez and Javier Giner
Estimating GARCH models using support vector machines Volume 3, Number 3, pp. 163-172, 2003.

Abstract

Support vector machines (SVMs) are a new nonparametric tool for regression estimation. We will use this tool to estimate the parameters of a GARCH model for predicting the conditional volatility of stock market returns. GARCH models are usually estimated using maximum likelihood (ML) procedures, assuming that the data are normally distributed. In this paper, we will show that GARCH models can be estimated using SVMs and that such estimates have a higher predicting ability than those obtained via common ML methods.

EPrint Type:Article
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
Theory & Algorithms
ID Code:528
Deposited By:Fernando Perez-Cruz
Deposited On:24 December 2004