Estimating GARCH models using support vector machines

Fernando Perez-Cruz, Julio Afonso-rodriguez and Javier Giner

Quantitative Finance, 2003, vol. 3, issue 3, 163-172

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.

Date: 2003
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DOI: 10.1088/1469-7688/3/3/302

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