GARCH Processes with Non-parametric Innovations for Market Risk Estimation

  • José Miguel Hernández-Lobato
  • Daniel Hernández-Lobato
  • Alberto Suárez
Part of the Lecture Notes in Computer Science book series (LNCS, volume 4669)


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.


Market Risk GARCH Model Stable Distribution Expect Shortfall Stable Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.


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Copyright information

© Springer-Verlag Berlin Heidelberg 2007

Authors and Affiliations

  • José Miguel Hernández-Lobato
    • 1
  • Daniel Hernández-Lobato
    • 1
  • Alberto Suárez
    • 1
  1. 1.Escuela Politécnica Superior, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 11, Madrid 28049Spain

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