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从表（5）及（6）我们可以得到在样品和价值观外的样品有关的银行收益和风险。这些替代值入式（23），我们可以知道，最好的风险对冲的Sharpe比率GARCH的银行模式。因此，夏普比率越高的价值，相对较好的成效模式带来的银行。表（7）：加拿大的夏普比率显示，在方面的开箱即样本的有效性，投资组合对冲催化裂解- GARCH模型（0.518）是银行最好，其次是投资组合对冲的MS -催化裂解- GARCH模型（0.515）探讨各测试法和投资组合套期保值- GARCH模型（0.466）是对冲性能方面最差。表（8）：香港夏普比率显示，鉴于外的样品的性能，投资组合对冲的MS -催化裂解- GARCH模型（4.220）方面是银行最好，其次是投资组合对冲催化裂解- GARCH模型（4.177 ）及投资组合避险探讨各测试法，GARCH模型（3.031）是最差的。因此，通过夏普比率，我们知道，银行有更好的性能，同时采用投资组合套期保值而不是单独的对冲和投资组合套期保值的MS -催化裂解- GARCH模型和投资组合对冲催化裂解- GARCH模型是最优的风险对冲银行模式。
From Table (5) and (6) we can gain values of in-sample and out-of-sample returns and risks concerning banks. Substitute these values into Equation (23) and we can know the best risk hedging GARCH model for banks through Sharpe Ratio. Therefore, the higher the value of Sharpe ratio, the relative better effectiveness model brings to banks. Table (7): Canadian Sharpe ratio shows that, in aspect of out-of-sample effectiveness, portfolio hedging DCC-GARCH model (0.518) is the best for banks, the next is portfolio hedging MS-DCC-GARCH model (0.515) and portfolio hedging BEKK-GARCH model (0.466) is the worst in terms of hedging performance. Table (8): Hong Kong Sharpe ratio shows that, in aspect of out-of-sample performance, portfolio hedging MS-DCC-GARCH model (4.220) is the best for banks, the next is portfolio hedging DCC-GARCH model (4.177) and portfolio hedging BEKK-GARCH model (3.031) is the worst. Therefore, through Sharpe ratio we know that bank has better performance while using portfolio hedging but not separate hedging and portfolio hedging MS-DCC-GARCH model and portfolio hedging DCC-GARCH model is optimal risk hedging models for banks.
由于动态避险需求频繁交易，其交易成本比固定避险比率的OLS高。考虑动态对冲有效性的经济意义，它是一个必须考虑到交易成本。实用的计算方法是表现为方程（29）和效用分析结果如表（9）显示。鉴于外的样品在加拿大方面，实用而银行是-106.633使用投资组合对冲的MS -催化裂解- GARCH模型而单独的避险OLS模型是-115.321，从中我们可以知道，银行的净效益，同时使用MS -催化裂解- GARCH模型为8.688％- C的比较，有独立的对冲母机。其中，C是交易成本，交易成本显示，如果低于8.688％，而考虑的是比银行更好地采取静态避险交易成本母机的MS -催化裂解- GARCH模型，此外，来自香港证明材料组合套期保值的MS -催化裂解- GARCH模型是独立的对冲更好地在性能方面母机（0.27％）模型。从上述我们可以知道，投资组合对冲的MS -催化裂解- GARCH模型，并享有比OLS更好的前提下，经济意义，美国银行的交易成本，使用投资组合对冲的MS -催化裂解- GARCH模型并不过高。由于美国银行资产规模庞大，有经济，交易成本极低。因此，形式表（9）我们可以知道，投资组合对冲的MS -催化裂解- GARCH模型所使用的银行不仅优于静态避险OLS模型，但也最实用的改进。
Since dynamic hedge demands frequent transactions, its transaction cost is higher than the OLS of fixed hedge ratio. To consider economic significance of dynamic hedging effectiveness, it is a must to take transaction cost into account. Method for calculating utility is showed as Equation (29) and results of utility analysis are showed in Table (9). In aspect of out-of-sample in Canada, utility is -106.633 while bank using portfolio hedging MS-DCC-GARCH model while separate hedging OLS model is -115.321, from which we can know that net benefit of bank while using MS-DCC-GARCH model is 8.688%-C compared with separate hedging OLS. Among them, C is transaction cost, showing that if transaction cost is lower than 8.688%, MS-DCC-GARCH model with transaction cost taken into account is better than static hedging OLS for banks; in addition, materials from Hong Kong show that portfolio hedging MS-DCC-GARCH model is better separate hedging OLS model in terms of performance (0.27%). From the above mentioned we can know that portfolio hedging MS-DCC-GARCH model is better than OLS and enjoys economic significance under the precondition that transaction cost of American bank using portfolio hedging MS-DCC-GARCH model is not excessively high. As assets of American bank are huge and have scale economy, transaction cost will be extremely low. Therefore, form Table (9) we can know that portfolio hedging MS-DCC-GARCH model used by bank is not only superior to static hedging OLS model but also has the most utility improvements.
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