Seminar: Professor Timo Teräsvirta -- Conditional Correlations Models of Autoregressive Conditional Heteroskedasticity with Nonstationary GARCH Equations

Conditional Correlations Models of Autoregressive Conditional Heteroskedasticity with
Nonstationary GARCH Equations 

Professor Timo Teräsvirta 

CREATES, University of Aarhus 

Abstract.  We investigate the effects of careful modelling the long-run dynamics of the
volatilities of stock market returns on the conditional correlation structure.  To this
end we allow the individual unconditional variances in Conditional Correlation GARCH
models to change smoothly over time by incorporating a nonstationary component in the
variance equations.  The modelling technique to determine the parametric structure of
this time-varying component is based on a sequence of specification Lagrange
multiplier-type tests derived in Amado and Teräsvirta (2009).  The variance equations
combine the long-run and the short-run dynamic behaviour of the volatilities.  The
structure of the conditional correlation matrix is assumed to be either time independent
or to vary over time.  We apply our model to seven pairs of daily returns of stocks
belonging to the S&P 500 stock index and traded at the New York Stock Exchange.  The
results suggest that accounting for deterministic changes in the unconditional variances
considerably improves the fit of the multivariate Conditional Correlation GARCH models
to the data.  The effect of careful specification of the variance equations on the
estimated correlations is variable: in some cases rather small, in others more
discernible.  

Location: Room 498, Level 4, Merewether Bldg University of Sydney (Corner of City Road
and Butlin Avenue) 

Date and time: Friday, 20 November 2009 11:00 am to 12:00 pm 

To register, please contact shelton@maths.usyd.edu.au.