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 firstname.lastname@example.org.