Cointegration models with non Gaussian GARCH innovations

被引：3
|
作者：
John N. [1 ]
Narayana B. [1 ]
机构：
[1] Department of Statisics, Cochin University of Science and Technology, Cochin
来源：
METRON | 2018年 / 76卷 / 1期
关键词：
Cointegration; Fisher scoring algorithm; Generalised autoregressive conditional heterosedasticity; Volatility Models;
D O I：
10.1007/s40300-017-0133-z
中图分类号：
学科分类号：
摘要：
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. © 2017, Sapienza Università di Roma.
引用
收藏
页码：83 / 98
页数：15
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