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Estimating GARCH models using kernel machine learning  

Hwang, Chang-Ha (Department of Statistics, Dankook University)
Shin, Sa-Im (Department of Statistics, Dankook University)
Publication Information
Journal of the Korean Data and Information Science Society / v.21, no.3, 2010 , pp. 419-425 More about this Journal
Abstract
Kernel machine learning is gaining a lot of popularities in analyzing large or high dimensional nonlinear data. We use this technique to estimate 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 show that GARCH models can be estimated using kernel machine learning and that kernel machine has a higher predicting ability than ML methods and support vector machine, when estimating volatility of financial time series data with fat tail.
Keywords
GARCH; generalzed approximate cross validation; kernel machine; support vector machine;
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Times Cited By KSCI : 4  (Citation Analysis)
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