Multivariate ARCH with spatial effects for stock sector and size
This paper applies a new spatial approach for the specfication of multivariate GARCH models, called Spatial Effects in ARCH, SEARCH. We consider spatial dependence associated with industrial sectors and capitalization size. This parametrization extends current feasible specifications for large scale GARCH models, keeping the numbers of parameters linear as a function of the number of assets. An application to daily returns on 150 stocks from the NYSE for the period January 1994 to June 2001 shows the benefits of the present specification when compared to alternative specifications.
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- Won Koh & Badi H. Baltagi & Seuck Heun Song, 2004.
"Testing for Serial Correlation, Spatial Autocorrelation and Random Effects,"
Econometric Society 2004 Far Eastern Meetings
415, Econometric Society.
- Won Koh & Byoung Cheol Jung & Badi H. Baltagi & Seuck Heun Song, 2004. "Testing for Serial Correlation, Spatial Autocorrelation and Random Effects," Econometric Society 2004 Australasian Meetings 338, Econometric Society.
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- Jurgen A. Doornik & Henrik Hansen, 2008.
"An Omnibus Test for Univariate and Multivariate Normality,"
Oxford Bulletin of Economics and Statistics,
Department of Economics, University of Oxford, vol. 70(s1), pages 927-939, December.
- Jurgen A Doornik & Henrik Hansen, . "An omnibus test for univariate and multivariate normalit," Economics Papers W4&91., Economics Group, Nuffield College, University of Oxford.
- Y. K. Tse, 2002. "Residual-based diagnostics for conditional heteroscedasticity models," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 358-374, 06.
- Baltagi, Badi H. & Heun Song, Seuck & Cheol Jung, Byoung & Koh, Won, 2007. "Testing for serial correlation, spatial autocorrelation and random effects using panel data," Journal of Econometrics, Elsevier, vol. 140(1), pages 5-51, September.
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