This article applies the dynamic conditional correlation model of Engle (2002) 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.

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This study compares efficiencies of five Generalised Autoregressive Conditional Heteroskedasticity (GARCH) models in terms of value at risk (VaR) backtesting on the number of prediction failures and the average deviation between VaR and realized return series. Unlike the previous literature which presumes constant correlation coefficients, a new model proposed by Engle (2002, the DCC model) is applied to highlight time-varying conditional correlations amongst positions, which is essential for portfolio risk management. From the empirical studies of exchange rates data including the US Dollar to British Pound, Japanese Yen and Euro Dollar, we find that the DCC model produces least prediction failures.

This paper explores optimal international asset allocation policies subjected to the equity holding constraint within an intertemporal framework. To deal with the co-existent realities of agents' heterogeneous preferences and international market friction, the perturbation method is employed to derive approximate analytic solutions.