自回归条件异方差模型(autoregressive conditional heteroscedasticity model，ARCH)适于描述时间序列 波动性。近年来，ARCH 已逐渐为电力系统领域学 者所关注 [1-2] ，但还未得...
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Engle 教授在1982 年创造性地引入自回归条件异方差(Autoregressive Conditional Heteroscedasticity Model，ARCH)模型来刻画金融资产的价格波动行为，该模型是特别用来建立条件方差模型并对其进行预测的。
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Generalized Autoregressive Conditional Heteroscedasticity model 而导出一般化自我回归条件异质变异模型
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。
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。
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