Abstract
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. © Springer-Verlag Berlin Heidelberg 2007.
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CITATION STYLE
Hernández-Lobato, J. M., Hernández-Lobato, D., & Suárez, A. (2007). GARCH processes with non-parametric innovations for market risk estimation. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (Vol. 4669 LNCS, pp. 718–727). Springer Verlag. https://doi.org/10.1007/978-3-540-74695-9_74
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