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Sensitivity of the causality in variance tests to GARCH(1,1) parameters

Javed, Farrukh LU and Mantalos, Panagiotis LU (2012)
Abstract
In this paper we discuss the sensitivity of causality of variance tests to the GARCH\((1,1)\) parameters. The idea of study is to investigate the impact of a number of volatile data sets on volatility spillover tests. We investigate a type of data generating process, AR(1)-GARCH(1,1), with an extensive set of Monte Carlo simulations. It is found that causation pattern, due to causality between two series, is influenced by the intensity of volatility clustering. Two testing procedures are applied for testing causality in the variance. We notice a severe size and power distortion when the clustering parameter is high and when the process is near integration. Furthermore, whenever there is a severe size distortion, there is a serial... (More)
In this paper we discuss the sensitivity of causality of variance tests to the GARCH\((1,1)\) parameters. The idea of study is to investigate the impact of a number of volatile data sets on volatility spillover tests. We investigate a type of data generating process, AR(1)-GARCH(1,1), with an extensive set of Monte Carlo simulations. It is found that causation pattern, due to causality between two series, is influenced by the intensity of volatility clustering. Two testing procedures are applied for testing causality in the variance. We notice a severe size and power distortion when the clustering parameter is high and when the process is near integration. Furthermore, whenever there is a severe size distortion, there is a serial autocorrelation in the standardized residuals. This is seen when the asymptotic distribution of the statistics is used to define a critical region. So, instead of relying on the asymptotic distribution, we calculate the percentiles of the test statistic with the null hypothesis of no spillover effect and use them as a critical region for both size and power. We observe a significant improvement in the results. (Less)
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author
organization
publishing date
type
Working Paper
publication status
submitted
subject
keywords
Causality, GARCH, Spillover, Volatility.
pages
20 pages
language
English
LU publication?
yes
id
30f4cee5-192e-456c-8991-d5ae91405647 (old id 3172527)
date added to LUP
2012-11-19 09:07:39
date last changed
2016-04-16 11:15:36
@misc{30f4cee5-192e-456c-8991-d5ae91405647,
  abstract     = {In this paper we discuss the sensitivity of causality of variance tests to the GARCH\((1,1)\) parameters. The idea of study is to investigate the impact of a number of volatile data sets on volatility spillover tests. We investigate a type of data generating process, AR(1)-GARCH(1,1), with an extensive set of Monte Carlo simulations. It is found that causation pattern, due to causality between two series, is influenced by the intensity of volatility clustering. Two testing procedures are applied for testing causality in the variance. We notice a severe size and power distortion when the clustering parameter is high and when the process is near integration. Furthermore, whenever there is a severe size distortion, there is a serial autocorrelation in the standardized residuals. This is seen when the asymptotic distribution of the statistics is used to define a critical region. So, instead of relying on the asymptotic distribution, we calculate the percentiles of the test statistic with the null hypothesis of no spillover effect and use them as a critical region for both size and power. We observe a significant improvement in the results.},
  author       = {Javed, Farrukh and Mantalos, Panagiotis},
  keyword      = {Causality,GARCH,Spillover,Volatility.},
  language     = {eng},
  pages        = {20},
  title        = {Sensitivity of the causality in variance tests to GARCH(1,1) parameters},
  year         = {2012},
}