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Spatial effects in multivariate ARCH

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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 proposes a new approach for the specification of multivariate GARCH models for data sets with a potentially large cross-section dimension. The approach exploits the spatial dependence structure associated with asset characteristics, like industrial sectors and capitalization size. We use the acronym SEARCH for this model, short for Spatial Effects in ARCH. This parametrization extends current feasible specifications for large scale GARCH models, while keeping the numbers of parameters linear with respect to the number of assets. An application to daily returns on 20 stocks from the NYSE for the period January 1994 to June 2001 shows the benefits of the present specification.

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Bibliographic Info

Paper provided by Department of Economics, University of Insubria in its series Economics and Quantitative Methods with number qf0501.

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Length: 47 pages
Date of creation: May 2005
Date of revision:
Handle: RePEc:ins:quaeco:qf0501

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Cited by:
  1. Matteo Bonato & Massimiliano Caporin & Angelo Ranaldo, 2009. "Forecasting realized (co)variances with a block structure Wishart autoregressive model," Working Papers 2009-03, Swiss National Bank.
  2. Bonato, Matteo & Caporin, Massimiliano & Ranaldo, Angelo, 2013. "Risk spillovers in international equity portfolios," Journal of Empirical Finance, Elsevier, vol. 24(C), pages 121-137.

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