On a threshold autoregression with conditional heteroscedastic variances
type: Item
instance of: scholarly article
title: On a threshold autoregression with conditional heteroscedastic variances
zbMATH Open document ID: 0921.62113
DOI: 10.1016/S0378-3758(96)00196-6
author: Bernhard Rupp
published in: Journal of Statistical Planning and Inference
publication date: 1997-10-01T00:00:00Z
review text: This paper considers time series models with a piecewise linear conditional mean and a piecewise linear conditional variance function. These models combine elements of the traditional threshold autoregressive models capable of modeling time series with jumps and limit cycles, and the ARCH/GARCH models used to represent conditional heteroscedastic variances. These models appear well-suited for modeling financial time series as is illustrated by an analysis of daily returns of the Hong Kong Seng Index. The authors establish conditions for stationarity and ergodicity. They also derive maximum likelihood estimators of the underlying parameters and study their asymptotic properties. Residuals of the fitted model are used as a check on the adequacy of the model.
Mathematics Subject Classification ID: 62M10
Mathematics Subject Classification ID: 62P05
Mathematics Subject Classification ID: 91G70
zbMATH DE Number: 1069347
zbMATH Keywords: ARCH models
zbMATH Keywords: double-threshold autoregressive models
zbMATH Keywords: ergodicity
zbMATH Keywords: threshold autoregressive models
zbMATH Keywords: financial time series
reviewed by: Johannes Ledolter
MaRDI profile type: Publication
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full work available at URL: s0378-3758(96)00196-6
OpenAlex ID: W1990030964
description: scientific article; zbMATH DE number 1069347