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Multivariate ARCH with spatial effects for stock sector and size

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  • Caporin Massimiliano

    ()
    (Department of Economics, University of Padova, Italy)

  • Paruolo Paolo

    ()
    (Department of Economics, University of Insubria, Italy)

Abstract

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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File URL: http://eco.uninsubria.it/dipeco/Quaderni/files/QF2005_13.pdf
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Bibliographic Info

Paper provided by Department of Economics, University of Insubria in its series Economics and Quantitative Methods with number qf0509.

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Length: 41 pages
Date of creation: Dec 2005
Date of revision:
Handle: RePEc:ins:quaeco:qf0509

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Keywords: Spatial models; GARCH; Volatility; Large scale models; Portfolio allocation.;

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  1. 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.
  2. 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.
  3. 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.
  4. Jurgen A Doornik & Henrik Hansen, . "An omnibus test for univariate and multivariate normalit," Economics Papers W4&91., Economics Group, Nuffield College, University of Oxford.
  5. Y. K. Tse, 2002. "Residual-based diagnostics for conditional heteroscedasticity models," Econometrics Journal, Royal Economic Society, vol. 5(2), pages 358-374, 06.
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