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중국 CSI 300 선물을 이용한 심천펀드 투자 위험관리에 관한 실증적 연구
이가연 ( Ka Youn Yi ),임병진 ( Byung Jin Yim ) 아시아.유럽미래학회 2012 유라시아연구 Vol.9 No.4
Risk management has become increasingly important in the future as investors recognize their exposure to a greater degree of uncertainty in stock markets. Hedging concerned with the management of risk is the most important function of futures markets. The basic motivation for hedging is to reduce the variability of profits and firm value that arises from market changes. Among the many hedging strategies designed for investors include financial derivatives, especially stock index futures which greatly differing from the traditional trading. Techniques like OLS, VAR, and VECM estimate constant hedge ratio and bivariate GARCH models estimates dynamic hedge ratios which factor in conditional distribution of spot and futures returns. However, there has been extensive debate on which model generates the best hedging performance. Exactly how many futures positions investors should use hedging and selection of an optimal hedging ratio has received considerable interest. Most studies adopt the ordinary least squares (OLS) model to estimate the optimal hedging ratio, a model referred to as static hedging. However, the practical use of these hedge ratios is to establish positions for the future, and the use of constant hedge ratios as forecasts may not be optimal if the joint distribution of cash and future prices is time-varying, resulting consequently in suboptimal out-of-sample performance. As volatility clustering always occurs in financial data, time-variant volatility is often identified using GARCH family models including the autoregressive conditional heteroscedasticity (ARCH) model developed by Engle and the generalized autoregressive conditional heteroscedasticity (GARCH) model developed by Bollerslev. ARCH and GARCH models may afford better prediction of changes in the basis by internalizing the temporal variability of the covariance matrix of spot and futures price changes and by allowing shocks to volatility to persist. This study investigates direct hedging performance of CSI 300 futures with respect to SSE portfolio of investing China to risk management using VECM, Bivariate GARCH (1,1) and OLS regression models. Daily hedging performance is evaluated. The sample period covers from April 16, 2010 to September 23, 2011. We found the following results. Firstly, unit roots are found in CSI 300 futures and SSE index. There exists at least one cointegrating relationship among them. Secondly, we can not find statistical differences among hedge ratios estimated from VECM, Bivariate GARCH (1,1) and OLS regression models. Thirdly, there are no significant differences in hedging performance among various models. Finally, overall hedging performance and hedge ratios estimated from OLS, VECM, and Bivariate GARCH (1,1) is relatively good.