GARCH Processes with Non-parametric Innovations for Market Risk Estimation

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.

• Type: Conference
• Year: 2007
• Where: ICANN
• Authors: José Miguel Hernández-Lobato, Daniel Hernández-Lobato, Alberto Suárez
• Keywords: GARCH Processes, ICANN 2007, Improved Technique, Neural Networks, Non-parametric Innovations