Multivariate ARCH with spatial effects for stock sector and size
AbstractThis 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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Bibliographic InfoPaper provided by Department of Economics, University of Insubria in its series Economics and Quantitative Methods with number qf0509.
Length: 41 pages
Date of creation: Dec 2005
Date of revision:
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Spatial models; GARCH; Volatility; Large scale models; Portfolio allocation.;
This paper has been announced in the following NEP Reports:
- NEP-ALL-2007-01-23 (All new papers)
- NEP-ECM-2007-01-23 (Econometrics)
- NEP-ETS-2007-01-23 (Econometric Time Series)
- NEP-FMK-2007-01-23 (Financial Markets)
- NEP-GEO-2007-01-23 (Economic Geography)
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- Christoffersen, Peter F, 1998. "Evaluating Interval Forecasts," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 39(4), pages 841-62, November.
- Y. K. Tse, 2002. "Residual-based diagnostics for conditional heteroscedasticity models," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 358-374, 06.
- Jurgen A Doornik & Henrik Hansen, .
"An omnibus test for univariate and multivariate normalit,"
W4&91., Economics Group, Nuffield College, University of Oxford.
- 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.
- 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.
- 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.
- 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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