GARCH processes with non-parametric innovations for market risk estimation

Authors:
José Miguel Hernández-Lobato;Daniel Hernández-Lobato;Alberto Suárez
Affiliations:
Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain;Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain;Escuela Politécnica Superior, Universidad Autónoma de Madrid, Madrid, Spain
Venue:
ICANN'07 Proceedings of the 17th international conference on Artificial neural networks
Year:
2007

Citing 2
Cited 1

Neural Networks for Pattern Recognition

Neural Networks for Pattern Recognition
Computing the probability density function of the stable Paretian distribution

Mathematical and Computer Modelling: An International Journal

Semiparametric bivariate Archimedean copulas

Computational Statistics & Data Analysis

Quantified Score

Hi-index	0.00

Visualization

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.