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.
This work has been supported by Consejería de Educació n de la Comunidad Autónoma de Madrid, European Social Fund, Universidad Autónoma de Madrid and Dirección General de Investigació n under project TIN2004-07676-C02-02.
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Hernández-Lobato, J.M., Hernández-Lobato, D., Suárez, A. (2007). GARCH Processes with Non-parametric Innovations for Market Risk Estimation. In: de Sá, J.M., Alexandre, L.A., Duch, W., Mandic, D. (eds) Artificial Neural Networks – ICANN 2007. ICANN 2007. Lecture Notes in Computer Science, vol 4669. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-540-74695-9_74
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DOI: https://doi.org/10.1007/978-3-540-74695-9_74
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