First, get the equity return's conditional standard volatility by GARCH model;
首先，利用GARCH模型求得权益收益的条件标准差；
The results show that the GARCH model can be a good fit to the weekly return series of Shenzhen Stock Index.
结果表明，深证成指周收益率序列的波动性可以用GARCH模型进行很好的拟合。
The results show the method of calculation VaR of GARCH model is effective in risk management of China's stock market.
实证研究表明，GARCH模型的V aR计算方法对我国股市风险的管理有较好的效果。
Based on this conclusion, the authors set up an AR-GARCH model of issuing scale of national debt. This model has high accuracy and strong capacity of prediction.
据此基本结论建立的国债发行规模的AR - GARCH模型精度高，有很强的预测功能。
Then the movement feature of the market is analyzed with the GARCH model, and whether the market return follows non-linear and leptokurtic heavy tail feature is validated.
文章还采用GARCH模型对市场的运行特征做了分析，对市场收益率是否服从非线性和尖峰胖尾的特征进行验证。
First of all, the author discusses the extension from univariate GARCH to multivariate GARCH model and the important role of the MGARCH model in the modern financial research.
笔者首先讨论了在金融时间序列的考察中从一元GARCH模型扩展到多元GARCH模型的必要性。分析了多元GARCH模型在金融建模中的重要作用。
In particular, it has been demonstrated that the conventional GARCH model can exaggerate volatility persistence compared to the (true) volatility process perceived by the market.
特别是，相对于被市场所发现的真实波动过程来说，传统的GARCH模型夸大了波动的持续性。
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。
The generalized autoregressive conditional heteroscedasticity (GARCH) model has the ability to describe the volatility of time series.
广义自回归条件异方差(GARCH)模型具有描述时间序列波动性的能力。
The turnover was used to measure the trading volume which was analyzed using the Autoregressive- Generalized Autoregressive Conditional Heteroskedasticity (AR- GARCH) model.
以市场换手率度量交易量，采用自回归广义自回归条件异方差（AR-GARCH）模型研究了中国股市交易量的时间系列。
The turnover was used to measure the trading volume which was analyzed using the Autoregressive- Generalized Autoregressive Conditional Heteroskedasticity (AR- GARCH) model.
以市场换手率度量交易量，采用自回归广义自回归条件异方差（AR-GARCH）模型研究了中国股市交易量的时间系列。
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