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Multivariate ARCH with spatial effects for stock sector and size

Author

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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.

Suggested Citation

  • Caporin Massimiliano & Paruolo Paolo, 2005. "Multivariate ARCH with spatial effects for stock sector and size," Economics and Quantitative Methods qf0509, Department of Economics, University of Insubria.
    • Handle: RePEc:ins:quaeco:qf0509
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    File URL: https://www.eco.uninsubria.it/RePEc/pdf/QF2005_13.pdf
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    References listed on IDEAS

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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.
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    5. 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 841-862, November.
    6. 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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    Cited by:

    1. Massimiliano Caporin & Paolo Paruolo, 2015. "Proximity-Structured Multivariate Volatility Models," Econometric Reviews, Taylor & Francis Journals, vol. 34(5), pages 559-593, May.

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