The focus of this article is to compare dynamic correlation models for the calculation of minimum variance hedge ratios between pairs of assets. Finding an optimal hedge requires not only knowledge of the variability of both assets, but also of the co-movement between the two assets. For this purpose, use is made of industry standard methods, like the naive hedging or the CAPM approach, more advanced GARCH techniques includ-ing estimating BEKK or DCC models and alternatively through the use of unobserved components models. This last set comprises models with stochastically varying variances and/or correlations, and an approximation to these with a single-source-of-error setup. In order to compare the performance of different models in producing attractive hedg-ing schemes, the reduction in portfolio variance is examined. The data suggests that the most important factor in reducing portfolio variance is the use of a flexible model for time varying volatility, rather than capturing time variation in correlations as closely as possible. Both in simulated and real data series, models incorporating stochastic volatility result in lower risk of the hedged portfolio than GARCH-based variants
