An algorithm for nonparametric GARCH modelling.
type: Item
instance of: scholarly article
title: An algorithm for nonparametric GARCH modelling.
zbMATH Open document ID: 1043.68108
DOI: 10.1016/S0167-9473(02)00080-4
author: Alexander J. McNeil
author: Peter Bühlmann
published in: Computational Statistics and Data Analysis
publication date: 2003-01-21T00:00:00Z
review text: A simple iterative algorithm for nonparametric first-order GARCH modelling is proposed. This method offers an alternative to fitting one of the many different parametric GARCH specifications that have been proposed in the literature. A theoretical justification for the algorithm is provided and examples of its application to simulated data from various stationary processes showing stochastic volatility, as well as empirical financial return data, are given. The nonparametric procedure is found to often give better estimates of the unobserved latent volatility process than parametric modelling with the standard GARCH(1,1) model, particularly in the presence of asymmetry and other departures from the standard GARCH specification. Extensions of the basic iterative idea to more complex time series models combining ARMA or GARCH features of possibly higher order are suggested.
Mathematics Subject Classification ID: 68W05
Mathematics Subject Classification ID: 68U20
zbMATH DE Number: 1856182
zbMATH Keywords: GARCH modelling
zbMATH Keywords: Nonparametric methods
zbMATH Keywords: Volatility estimation
MaRDI profile type: Publication
cites work: Generalized autoregressive conditional heteroscedasticity
cites work: ARCH modeling in finance. A review of the theory and empirical evidence
cites work: Autoregressive Conditional Heteroscedasticity with Estimates of the Variance of United Kingdom Inflation
cites work: Modelling the persistence of conditional variances
cites work: Weak convergence and distributional assumptions for a general class of nonliner arch models
cites work: Q3998409
cites work: State space and hidden Markov models
cites work: Limit theory for the sample autocorrelations and extremes of a GARCH \((1,1)\) process.
cites work: Conditional Heteroskedasticity in Asset Returns: A New Approach
cites work: Optimal global rates of convergence for nonparametric regression
cites work: Nonparametric Autoregression with Multiplicative Volatility and Additive mean
full work available at URL: s0167-9473(02)00080-4
OpenAlex ID: W2005908614
description: scientific article; zbMATH DE number 1856182