On the application of the dynamic conditional correlation model in estimating optimal time-varying hedge ratios

This article applies the dynamic conditional correlation model of Engle (2002 Engle, RF. 2002. Dynamic conditional correlation: a simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics, 20: 339–50. Taylor & Francis Online, Web of Science ® , Google Scholar) with error correction terms in order to investigate the optimal hedge ratios of British and Japanese currency futures markets. For a comparison, the estimates of three other models -- traditional generalized autoregressive conditional heteroskedasticity (GARCH), ordinary least square (OLS) and error correction model (ECM) -- are also reported. Results show that the dynamic conditional correlation model yields the best hedging performance in both futures markets. Nonetheless, the traditional multivariate GARCH model (which exhibits constant conditional correlations and time-varying hedge ratios) performs the worst hedging effectiveness, even inferior to the time-invariant hedging methods (OLS and ECM). The inclusion of dynamic conditional correlations in the GARCH model can therefore better capture the frequent fluctuations in futures markets.