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This study proposes a new range-based Markov-switching dynamic conditional correlation (MSDCC) model for estimating the minimum-variance hedging ratio and comparing its hedging performance with that of alternative conventional hedging models, including the naive, OLS regression, return-based DCC, range-based DCC and return-based MS-DCC models. The empirical results show that the embedded Markov-switching adjustment in the range-based DCC model can clearly delineate uncertain exogenous shocks and make the estimated correlation process more in line with reality. Overall, in-sample and out-of sample tests indicate that the range-based MS-DCC model outperforms other static and dynamic hedging models.