Nonlinear models for autoregressive conditional heteroskedasticity

This paper contains a brief survey of nonlinear models of autoregressive conditional heteroskedasticity. The models in question are parametric nonlinear extensions of the original model by Engle (1982). After presenting the individual models, linearity testing and parameter estimation are discussed. Forecasting volatility with nonlinear models is considered. Finally, parametric nonlinear models based on multiplicative decomposition of the variance receive attention.

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Year of publication:	2011-01-05
Authors:	Teräsvirta, Timo
Institutions:	School of Economics and Management, University of Aarhus
Subject:	nonlinear ARCH \| nonlinear GARCH \| neural network \| nonlinear volatility \| smooth transition GARCH \| threshold GARCH

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Extent:	application/pdf
Series:	CREATES Research Papers.
Type of publication:	Book / Working Paper
Language:	English
Notes:	2 pages long
Classification:	C22 - Time-Series Models ; C52 - Model Evaluation and Testing
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10008784443