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Modelling Australian Stock Market Volatility: A Multivariate GARCH Approach

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Abstract

This paper uses a multivariate generalized autoregressive conditional heteroskedasticity (MGARCH) model to provide an insight into the nature of interaction between stock market returns of four countries, namely, Australia, Singapore, the UK, and the US. Using weekly data spanning from January 1992 to December 2008 the results indicate that all markets (particularly Australia and Singapore) display significant positive mean-spillovers from the US stock market returns but not vice versa. We also found strong evidence for both own and cross ARCH and GARCH effects among all four markets, indicating the existence of significant volatility and cross volatility spillovers across all four markets. Given a high degree of common time-varying co-volatility among these four countries, investors will be highly unlikely to benefit a reduction of risk if they diversify their financial portfolio with stocks from these four countries only

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File URL: http://www.uow.edu.au/content/groups/public/@web/@commerce/@econ/documents/doc/uow065433.pdf
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Bibliographic Info

Paper provided by School of Economics, University of Wollongong, NSW, Australia in its series Economics Working Papers with number wp09-11.

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Length: 15 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:uow:depec1:wp09-11

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Postal: School of Economics, University of Wollongong, Northfields Avenue, Wollongong NSW 2522 Australia
Phone: +612 4221-3659
Fax: +612 4221-3725
Web page: http://business.uow.edu.au/econ/index.html
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Keywords: Multivariate GARCH; Stock returns; Volatility; Australia;

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Cited by:
  1. Villalba Padilla, Fátima Irina & Flores-Ortega, Miguel, 2014. "Análisis de la volatilidad del índice principal del mercado bursátil mexicano, del índice de riesgo país y de la mezcla mexicana de exportación mediante un modelo GARCH trivariado asimétrico ||," Revista de Métodos Cuantitativos para la Economía y la Empresa = Journal of Quantitative Methods for Economics and Business Administration, Universidad Pablo de Olavide, Department of Quantitative Methods for Economics and Business Administration, vol. 17(1), pages 3-22, June.

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