Large once-off events cause large changes in prices but may not affect volatility and correlation dynamics as much as smaller events. Standard volatility models may deliver biased covariance forecasts in this case. We propose a multivariate volatility forecasting model that is accurate in the presence of large once-off events. The model is an extension of the dynamic conditional correlation model (DCC) model. Compared to the DCC model, our method produces more precise out-of-sample covariance forecasts and, when used in portfolio allocation, it leads to portfolios with similar return characteristics but lower turnover and hence higher profits.