Bivariate FIGARCH and Fractional Cointegration
Queen Mary & Westfield College, Department of Economics Working Paper No. 408
21 Pages Posted: 1 Mar 2000
Date Written: December 1999
Abstract
We consider the modelling of volatility on closely related markets. Univariate fractional volatility (FIGARCH) models are now standard, as are multivariate GARCH models. In this paper we adopt a combination of the two methodologies. There is as yet little consensus on the methodology for testing for fractional cointegration. The contribution of this paper is to demonstrate the feasibility of estimating and testing cointegrated bivariate FIGARCH models. We apply these methods to volatility on the NYMEX and IPE crude oil markets. We find a common order of fractional integration for the two volatility processes and confirm that they are fractionally cointegrated. An estimated error correction FIGARCH model indicates that the preponderant adjustment is of the IPE towards NYMEX.
JEL Classification: C22, C32, G13
Suggested Citation: Suggested Citation
Do you have a job opening that you would like to promote on SSRN?
Recommended Papers
-
Measuring and Testing the Impact of News on Volatility
By Robert F. Engle and Victor K. Ng
-
Caviar: Conditional Value at Risk by Quantile Regression
By Simone Manganelli and Robert F. Engle
-
Dynamic Conditional Correlation - a Simple Class of Multivariate GARCH Models
-
Dynamic Conditional Correlation a Simple Class of Multivariate GARCH Models
-
Dynamic Conditional Correlation - a Simple Class of Multivariate GARCH Models
-
Dynamic Conditional Correlation : A Simple Class of Multivariate GARCH Models
-
Asset Pricing with a Factor Arch Covariance Structure: Empirical Estimates for Treasury Bills
By Robert F. Engle, Victor Ng, ...
-
Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH
By Kevin Sheppard and Robert F. Engle
-
Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH
By Robert F. Engle and Kevin Sheppard
-
Theoretical and Empirical Properties of Dynamic Conditional Correlation Multivariate GARCH
By Robert F. Engle and Kevin Sheppard
