Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution
Pesaran, M. Hashem
Faculty of Economics, University of Cambridge, UK
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Pesaran, B., & Pesaran, M. H. (2007). Modelling Volatilities and Conditional Correlations in Futures Markets with a Multivariate t Distribution.
This paper considers a multivariate t version of the Gaussian dynamic conditional correlation (DCC) model proposed by Engle (2002), and suggests the use of devolatized returns computed as returns standardized by realized volatilities rather than by GARCH type volatility estimates. The t-DCC estimation procedure is applied to a portfolio of daily returns on currency futures, government bonds and equity index futures. The results strongly reject the normal-DCC model in favour of a t-DCC specification. The t-DCC model also passes a number of VaR diagnostic tests over an evaluation sample. The estimation results suggest a general trend towards a lower level of return volatility, accompanied by a rising trend in conditional cross correlations in most markets; possibly reflecting the advent of euro in 1999 and increased interdependence of financial markets.
Volatilities and Correlations, Futures Market, Multivariate t, Financial Interdependence, VaR diagnostics
This record's URL: http://www.dspace.cam.ac.uk/handle/1810/194714