Therefore, from Table 11 we know that bank enjoys better hedging performance while using portfolio hedging MS-DCC-GARCH model but not others.
因此，从表11我们知道，银行享有更好的性能，同时利用组合套期保值对冲的MS -催化裂解-GARCH模型而不是其他人。
Out-of-sample returns and risks in other sections show that portfolio hedging MS-DCC-GARCH model used by bank has better performance.
外显示样品返回和在其他章节的风险投资组合对冲的MS -催化裂解-GARCH模型的银行具有更好的性能使用。
This article USES data from Shanghai a type stock market and originally measures the conditional expectation of correlation risk and idiosyncratic volatility by DCC-MV GARCH model.
本文采用上海A股市场的月收益率数据，率先使用DCC - MVGARCH模型，刻画了时变的个股间预期条件相关性和个股的预期条件特质波动率。
This article USES data from Shanghai a type stock market and originally measures the conditional expectation of correlation risk and idiosyncratic volatility by DCC-MV GARCH model.
本文采用上海A股市场的月收益率数据，率先使用DCC - MVGARCH模型，刻画了时变的个股间预期条件相关性和个股的预期条件特质波动率。
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