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 generalized autoregressive conditional heteroscedasticity (GARCH) model has the ability to describe the volatility of time series.
因此，评估可由广义自回归条件异方差(GARCH模型)，这可能使避险比率意味着出随时间变化。
Therefore, evaluation could be carried out by means of Generalized Autoregressive Conditional Heteroscedasticity (GARCH), which could make hedge ratio vary with time.
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.
本文分两节对门限自回归模型中自回归条件异方差的广义谱密度检验进行了讨论。在第一节中，我们介绍了广义谱密度检验。
This thesis is composed of two sections in which we discuss generalized spectral density test of conditional autoregressive heteroscedasticity for threshold autoregressive model.
本文分两节对门限自回归模型中自回归条件异方差的广义谱密度检验进行了讨论。在第一节中，我们介绍了广义谱密度检验。
This thesis is composed of two sections in which we discuss generalized spectral density test of conditional autoregressive heteroscedasticity for threshold autoregressive model.
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