The Stochastic Volatility models (SV model) is a kind of time series model which can reflect fluctuation that can not be observed directly.
随机波动(SV)模型是一种重要的具有隐性波动的时间序列模型。
Based on this we simulate the volatility features of the two kinds of yield rate time series and analyze their fitting results using the stochastic volatility models.
在此基础上利用随机波动类模型对两种收益率的波动性特征进行拟合，并对拟合优度进行分析。
In this paper, we extended the basic stochastic volatility models to a stochastic volatility models with ARMA (1, 1) conditional heteroskedasticity and correlated errors.
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。
Based on reading document widely, we summarize the development of stochastic volatility modeling from viewpoint of how to unite discrete data and continuous models.
本文在广泛阅读文献的基础上，从如何把离散数据与连续模型相统一的角度，系统概括了随机波动建模问题的研究进展。
The results indicated that in these two types of models, the EGARCH-M model and the leverage stochastic volatility model had better fitting results.
拟合结果表明，在两类模型中egarch - M模型和杠杆随机波动模型具有较好的拟合效果。
Stochastic volatility model is one of the most important models in describing the volatility of financial market and its parameter estimation is a hot topic in this area.
随机波动模型作为金融市场波动量化研究的一种重要模型，其参数估计问题是近十余年来该领域的研究热点。
The volatility of load time series is analyzed, and the short-term load forecasting based on SV(Stochastic Volatility) models is presented with the consideration of the time-varying characteristics.
研究了负荷时间序列波动性，考虑方差时变特征，提出了基于随机波动（SV）模型的短期负荷预测方法。
The volatility of load time series is analyzed, and the short-term load forecasting based on SV(Stochastic Volatility) models is presented with the consideration of the time-varying characteristics.
研究了负荷时间序列波动性，考虑方差时变特征，提出了基于随机波动（SV）模型的短期负荷预测方法。
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