The topic about the nexus of economic fluctuation and financial development in China is being on cutting-edge research. Using monthly time series data from 2001 to 2012 in China, the present paper examines the nexus of fluctuation of economic growth and financial development. Based on an exponential generalized autoregressive conditional heteroskedasticity (EGARCH) model with exogenous variables, the present paper suggests that financial development has statistically significantly reduced fluctuation of economic growth, which is in line with theoretical expectation that financial development as a shock absorber to mitigate economic volatility.

This paper examines the impact of idiosyncratic risk on the cross-section of weekly stock returns from 1963 to 2006. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size e§ect, value premium, return momentum and short-term reversal to measure relative mispricing. I ?nd that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or ?rm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these ?ndings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost.

This paper examines the impact of idiosyncratic risk on the cross-section of weekly stock returns from 1963 to 2006. I use an exponential GARCH model to forecast expected idiosyncratic volatility and employ a combination of the size effect, value premium, return momentum and short-term reversal to measure relative mispricing. I ?find that stock returns monotonically increase in idiosyncratic risk for relatively undervalued stocks and monotonically decrease in idiosyncratic risk for relatively overvalued stocks. This phenomenon is robust to various subsamples and industries, and cannot be explained by risk factors or ?rm characteristics. Further, transaction costs, short-sale constraints and information uncertainty cannot account for the role of idiosyncratic risk. Overall, these ?findings are consistent with the limits of arbitrage arguments and demonstrate the importance of idiosyncratic risk as an arbitrage cost.

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.