Local Likelihood for Non-Parametric Arch(1) Models
25 Pages Posted: 15 Jan 2003
Date Written: May 2002
Abstract
We propose a local likelihood estimation for the log-transformed ARCH(1) model in the financial field. Our nonparametric estimator is constructed within the likelihood framework for non-Gaussian observations: It is different from standard kernel regression smoothing, where the innovations are assumed to be normally distributed. We derive consistency and asymptotic normality for our estimators and conclude from simulation and real data analysis that the local likelihood estimator has better predictive potential than classical local regression.
Keywords: Conditional variance, Return time series, Volatility, Autoregressive Conditional heteroscedastic model, Local likelihood, Kernel regression smoothing
JEL Classification: C5, C16
Suggested Citation: Suggested Citation
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