Carbon dioxide emissions represent a new traded asset that, in addition to reducing carbon dioxide emissions through cap-and-trade initiatives, can offer financial risk diversification benefits. In this paper, multivariate generalized autoregressive conditional heteroscedasticity (GARCH) models are used to model conditional correlations between carbon prices, oil prices, natural gas prices and stock prices. Compared with the diagonal or dynamic conditional correlation model, the constant conditional correlation model is found to fit the data the best and is used to generate hedge ratios and optimal portfolios. Carbon does not appear to be useful for hedging oil or the S&P 500 index but does seem to be useful for hedging natural gas. The average weight for the carbon/natural gas portfolio indicates that for a US$1 portfolio, 29 cents should be invested in carbon and 71 cents invested in natural gas. Hedge ratios and optimal portfolio weights vary considerably over the sample period, indicating that financial positions should be monitored frequently.