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Structured Multivariate Volatility Models

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Author Info

  • Massimiliano Caporin

    () (University of Padua)

  • Paolo Paruolo

    (Università dell'Insubria)

Abstract

This paper proposes structured parametrizations for multivariate volatility models, which use spatial weight matrices induced by economic proximity. These structured specifications aim at solving the curse of dimensionality problem, which limits feasibility of model-estimation to small cross-sections for unstructured models. Structured parametrizations possess the following four desirable properties: i) they are flexible, allowing for covariance spill-over; ii) they are parsimonious, being characterized by a number of parameters that grows only linearly with the cross-section dimension; iii) model parameters have a direct economic interpretation that reflects the chosen notion of economic classification; iv) model-estimation computations are faster than for unstructured specifications. We give examples of structured specifications for multivariate GARCH models as well as for Stochastic- and Realized-Volatility models. The paper also discusses how to construct spatial weight matrices that are time-varying and possibly derived from a set of covariates.

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Bibliographic Info

Paper provided by Dipartimento di Scienze Economiche "Marco Fanno" in its series "Marco Fanno" Working Papers with number 0091.

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Length: 40 pages
Date of creation: Feb 2009
Date of revision:
Handle: RePEc:pad:wpaper:0091

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Related research

Keywords: MGARCH; Stochastic Volatility; Realized Volatility; Spatial models; ANOVA;

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Cited by:
  1. Caporin, M. & McAleer, M.J., 2010. "Ranking multivariate GARCH models by problem dimension," Econometric Institute Report EI 2010-34, Erasmus University Rotterdam, Econometric Institute.
  2. Caporin, M. & McAleer, M.J., 2012. "Robust Ranking of Multivariate GARCH Models by Problem Dimension," Econometric Institute Report EI2012-13, Erasmus University Rotterdam, Econometric Institute.
  3. Michael McAleer & Massimiliano Caporin, 2011. "Ranking Multivariate GARCH Models by Problem Dimension:An Empirical Evaluation," KIER Working Papers 778, Kyoto University, Institute of Economic Research.

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