Owing to the liberalisation of financial markets, the impact of international capital flows on the Chinese stock market has become substantial. This study investigates the effects of economic policy uncertainty (EPU), geopolitical risk (GPR), consumer sentiment (CCI), macroeconomic fundamentals (MECI), and money supply (M2) on the correlations between the stock and exchange rate markets. The negative correlation between these two markets has become more pronounced in recent years. Moreover, EPU, GPR, CCI, and MECI negatively impact long-term stock-exchange rate correlations, while M2 has a positive impact. Portfolios of stock-exchange rates effectively reduce risk, especially when considering structural breaks.

Recently, the Shanghai Futures Exchange (SHFE) introduced gold futures trading in China. This paper is the first to study the SHFE gold futures, and to evaluate the futures hedging effectiveness since the introduction. The results show that hedging with gold futures reduces the variance of a hedged gold spot position by about 88% in its first two years of existence. During the second half of 2008, however, when the global financial crisis escalated, the variance reduction dropped to about 70%. Overall, the new Chinese gold futures prove to be attractive and well-needed hedging vehicles for domestic Chinese gold producers, refiners, consumers and investors.

This paper investigates the hedging effectiveness of the Copper Futures contracts using daily settlement prices for the period from 23 July, 2008 to 3 July, 2009. Different econometric models are used to estimate the optimal hedging ratios of Copper Futures on the Shanghai Futures Market. The hedging performance is firstly analyzed by the OLS regression model, the Error Correction model (ECM) and the Bivariate-GARCH Model. Then the Minimum-Variance Hedge Strategy is adopted to evaluate the statistical models. Secondly this research uses a non-parametrical method, the Genetic Algorithms to predict the hedging ratio based on the historical data. Then finally whether the Genetic Programming could produce better hedging parameters than the standard hedging model will be revealed.

This study estimates optimal hedge ratios using various econometric models. These models are evaluated based on the in- and out-of-sample optimal hedge ratio forecasts. Using daily data of spot and futures 1-month, 3-month, 6-month prices of aluminum and copper in the Shanghai Futures Exchange, the optimal hedge ratios are calculated from the OLS regression model, the VAR with error correction model, the bivariate GARCH model and the Exponential GARCH (EGARCH) Model. Hedging performance in terms of variance reduction of returns from four different models are also conducted. It is found that the EGARCH hedge ratio provides the largest reduction in the variance of the return portfolio, but they do not perform better than the alternatives over the out-of-sample period.