GARCH Processes with Non-parametric Innovations for Market Risk Estimation

  • José Miguel Hernández-Lobato
  • Daniel Hernández-Lobato
  • Alberto Suárez
Conference paper

DOI: 10.1007/978-3-540-74695-9_74

Part of the Lecture Notes in Computer Science book series (LNCS, volume 4669)
Cite this paper as:
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

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.

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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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