研究序约束条件下自回归条件异方差（ARCH）模型的统计推断。
This paper deals with the statistical inference of an autoregressive conditional heteroscedasticity (ARCH) model under restriction.
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.
模型的跨度为一年的样本外条件异方差预测，显示出该年末汇率的震荡，与实际情况一致。
Out-of-sample volatility prediction performance of one year confirms the actual higher volatility in the end of the year.
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。
This paper proves the elasticity of China's exchange market under interference after the Asian financial crisis by adopting Autoregressive Conditional Heteroscedasticity (ARCH) model.
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.
通过对我国股价指数的统计描述，表明我国金融资产收益率存在自回归条件异方差特征，并表现出非正态性。
Statistic descriptions indicate that the benefit of financial capitals in China has the characteristic of autoregressive conditional heteroskedasticity and abnormality.
通过对上证指数的统计分析表明，上证指数的收益率分布表现出非正态性，并存在自回归条件异方差的特征。
According to statistical analysis on Shanghai stock index, the distribution of the rate of return is non-positive skewed, and there exists an autoregressive heteroskedasticity in the rate of return.
然后，利用自回归条件异方差模型系统研究了我国封闭式基金市场的价格波动特性，分析了基金市场的风险特征。
Then, it studies the characteristic of price volatility and risk in closed-end securities investment fund market by use of ARCH models.
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。
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.
我们首先提出了一个带arma(1,1)条件异方差相关的随机波动模型，它是基本的随机波动模型的一个自然的推广。
In this paper, we extended the basic stochastic volatility models to a stochastic volatility models with ARMA (1, 1) conditional heteroskedasticity and correlated errors.
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.
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.
通过ARCHLM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。
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.
本文对残差具有异方差性的传递函数模型提出了一种新的模型——条件异方差传递函数模型，并对建立此类模型的条件和拟合及预测效果进行了研究。
In this paper, a new model-transmissibility function model with conditional heteroscedasticity is proposed, and its conditions as well as the fitting and forecasting effect have been studied.
本文对残差具有异方差性的传递函数模型提出了一种新的模型——条件异方差传递函数模型，并对建立此类模型的条件和拟合及预测效果进行了研究。
In this paper, a new model-transmissibility function model with conditional heteroscedasticity is proposed, and its conditions as well as the fitting and forecasting effect have been studied.
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