Cointegration models with non Gaussian GARCH innovations

Cointegration models with non Gaussian GARCH innovations This paper presents the estimation procedures for a bivariate cointegration model when the errors are generated by a constant conditional correlation model. In particular, the method of maximum likelihood is discussed when the errors follow Generalised Autoregressive Conditional Hetroskedastic (GARCH) models with Gaussian and some non Gaussian innovations. The method of estimation is illustrated using simulated observations. Data analysis is provided to highlight the applications of the proposed models. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png METRON Springer Journals

Cointegration models with non Gaussian GARCH innovations

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Publisher
Springer Milan
Copyright
Copyright © 2017 by Sapienza Università di Roma
Subject
Statistics; Statistics, general; Statistical Theory and Methods
ISSN
0026-1424
eISSN
2281-695X
D.O.I.
10.1007/s40300-017-0133-z
Publisher site
See Article on Publisher Site

Abstract

This paper presents the estimation procedures for a bivariate cointegration model when the errors are generated by a constant conditional correlation model. In particular, the method of maximum likelihood is discussed when the errors follow Generalised Autoregressive Conditional Hetroskedastic (GARCH) models with Gaussian and some non Gaussian innovations. The method of estimation is illustrated using simulated observations. Data analysis is provided to highlight the applications of the proposed models.

Journal

METRONSpringer Journals

Published: Nov 21, 2017

References

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