In this study, the impact of volatility regime shifts on volatility persistence and hedge ratio estimation is determined for four major currencies using an iterated cumulative sums of squares (ICSS)-GARCH model. Employing a standard GARCH (1,1) model as the benchmark, within-sample results demonstrate that the inclusion of volatility shifts substantially reduces volatility persistence and the significance of the ARCH and GARCH coefficients. In terms of hedging effectiveness, the ICSS-GARCH model outperforms the standard GARCH model for all four currencies. In comparison to two constant volatility models, the standard GARCH model yields the lowest performance, whereas the ICSS-GARCH model performs at least as well as these models. In out-of-sample analysis, the GARCH model provides substantial variance reductions relative to the constant volatility models. Moreover, the ICSS-GARCH model yields positive variance reductions relative to all competing models, including the standard GARCH model. The results suggest that in cases where dynamic hedging is important, sudden shifts in volatility should not be ignored.1 There may be instances in which the objective is not to minimize the variance of a hedged portfolio. Regardless of the objective of the hedger, however, estimating hedged positions will usually involve estimation of variances and covariances. Motives for hedging can be found in Smith and Stulz (1985) and Froot et al. (1993). 2 If two variables are cointegrated, they will not drift "too far apart" and movements away from "equilibrium" will be corrected through a stationary or mean-reverting process. Ignoring cointegration if it exists implies that information relevant to the estimation is not being considered. Many studies subsequent to Engle and Granger (1987) have examined various issues associated with cointegration. See Hamilton (1994) for a lengthy literature survey on cointegration and error-correction and Watson (1994) for a survey paper on VARs and cointegration.Rev. Pac. Basin Finan. Mark. Pol.