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Artificial Neural Networks – ICANN 2007

Volume 4669 of the series Lecture Notes in Computer Science pp 718-727

GARCH Processes with Non-parametric Innovations for Market Risk Estimation

  • José Miguel Hernández-LobatoAffiliated withEscuela Politécnica Superior, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 11, Madrid 28049
  • , Daniel Hernández-LobatoAffiliated withEscuela Politécnica Superior, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 11, Madrid 28049
  • , Alberto SuárezAffiliated withEscuela Politécnica Superior, Universidad Autónoma de Madrid, C/ Francisco Tomás y Valiente, 11, Madrid 28049

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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.