This paper deals with the statistical inference of an autoregressive conditional heteroscedasticity (ARCH) model under restriction.
研究序约束条件下自回归条件异方差（ARCH）模型的统计推断。
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。
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。
This paper proves the elasticity of China's exchange market under interference after the Asian financial crisis by adopting Autoregressive Conditional Heteroscedasticity (ARCH) model.
本文通过自回归条件异方差(ARCH)模型证明了亚洲金融危机后中国汇市在干预下的弹性。
Frequent volatility is a feature of stock market. Autoregressive Conditional Heteroscedasticity (ARCH) model is often used to forecast the variance of the benefit of financial capitals.
股票价格的频繁波动是股票市场最明显的特征之一，自回归条件异方差类模型可以很好地预测金融资产收益率的方差。
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.
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。
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