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Published in:
2007 | OriginalPaper | Chapter
GARCH Processes with Non-parametric Innovations for Market Risk Estimation
Authors : José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez
Published in: Artificial Neural Networks – ICANN 2007
Publisher: Springer Berlin Heidelberg
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A procedure to estimate the parameters of GARCH processes with non-parametric innovations is proposed. We also design an improved technique to estimate the density of heavy-tailed distributions with real support from empirical data. The performance of GARCH processes with non-parametric innovations is evaluated in a series of experiments on the daily log-returns of IBM stocks. These experiments demonstrate the capacity of the improved estimator to yield a precise quantification of market risk.