条件异方差 ARCH or GARCH
广义自回归条件异方差 GARCH
广义自回归条件异方差模型 GARCH
自回归条件异方差 GARCH
建立考虑异方差的广义自回归条件异方差模型 GARCH
采用考虑外生变量的广义自回归条件异方差 GARCH
广义条件自回归异方差 GARCH
最常用的非线性时间序列模型是广义自回归条件异方差 GARCH
一般自回归条件异方差 GARCH
Therefore, evaluation could be carried out by means of Generalized Autoregressive Conditional Heteroscedasticity (GARCH), which could make hedge ratio vary with time.
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。
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。
High-level ARCH effect is certification in the BDI logarithm process by ARCH LM test, GARCH(1,1)model is used to eliminate the conditional heteroscedasticity.
通过ARCH LM检验认为BD I对数序列存在高阶ARCH效应，并用GARCH（1，1）模型消除残差序列的条件异方差性。
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