GARCH Processes with Non-parametric Innovations for Market Risk Estimation

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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.

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.