Prediction of Shanghai Stock Exchange Composite Index Based on RBF Neural Network

After propose the improved BP neural network model, we then use the improved BP neural network and support vector machine to predict the SZZS and make a compare the results of two methods.

在提出改进的BP神经网络模型之后，本文分别使用改进的BP神经网络和支持向量机方法对上证指数做出预测，并对两种方法的预测结果做出比较。

Measurement on Hurst Exponent of SSE Index and Its Application

上证指数Hurst指数的测定及应用

Tail Dependence Analysis of SZI HSI Based on Copula Method

上证指数和恒生指数的copula尾部相关性分析

GARCH ( 1,1) was used to discuss the influence of Index of Shanghai Stock Market on the household saving.

利用GARCH（1,1）讨论了上证指数上涨对居民储蓄的影响。

In the course of the study, firstly, it uses HP filter trend decomposition method to investigate the relationship between economic growth cycle and fluctuations of the Shanghai Composite Index.

在研究过程中，首先，基于2002年1月至2011年12月的月度数据采用HP滤波趋势分解法探讨经济增长周期与上证指数波动的关系。

Then stationarity of time series for the data has been examined by adopting AD method, followed by cointegration test and Granger causal relation test against each macroeconomic variable and Shanghai Stock Exchange index respectively.

然后，运用AD方法检验数据时间序列的平稳性，接着对各宏观经济变量对上证指数进行协整检验和Granger因果关系检验。

The Comparison on Applications of GARCH and SV Models& Taking Shanghai Composite Index as An Example

GARCH模型和SV模型的应用比较研究&以上证指数的波动性为例

Forecast and Analysis of Shanghai Stock Index Based upon ARIMA model

基于ARIMA模型对上证指数的预测

However, neither Shanghai index nor any macroeconomic variable studied here has Granger causal relation, that means none of the macroeconomic variables can predict the Shanghai index tendency alone.

然而，上证指数和所研究的任何一个宏观经济变量都不存在双向的Granger因果关系，说明任何一个宏观经济变量都无法单独的预测上证指数的走势。

The Shanghai Composite Index ended up44 points.

上证指数收涨44点。

This paper discusses the application of neural network model in prediction of stock index and predicts the Shanghai exchange index by establishing BP network model.

本文探讨了神经网络模型在股票预测上的应用，通过建立BP网络模型对上证指数进行了预测分析。

In this paper, I shall apply uniform design sampling and combine EM arithmetic to find the optimal combination of dynamic indicator in stock market of shanghai.

本文将应用均匀设计抽样并结合EM算法，针对上海股票市场上证指数的历史行情，给出了技术分析指标&动态指标的最佳参数组合。

On the basis of complete study, the thesis argues that the stable distribution fits the ShangHai Stock Index better. And also, the VaR and the expected shortfall can be used as the risk measurement.

本文通过对上证指数进行全面系统研究，指出利用稳定分布能够更好地拟合我国股票市场的收益率，并且在稳定分布情况下，利用VaR和预期损失两个指标来度量风险。

GARCH model is used on the basis of the hypothesis of mixed normal distribution to analyze the Shanghai-securities index volatility.

因此，只有用正态混合分布假设下的GARCH模型才可以较好地解释上证指数的波动性。

The Application and Forecasting on Shanghai Index of The Class of ARCH Model

ARCH族模型在上证指数中的应用与预测

ARCH-Type Models and Its Application of the Return Volatility in Shanghai Stock Exchange Index

ARCH类模型及其在上证指数收益波动中的应用

The housing price, CPI and Shanghai Index are most influenced by economic development.

经济增长的变化对房地产价格、CPI、上证指数的影响也最大。

Making use of the two estimation methods of EVT the paper predicts the VaR of Shanghai Securities Composite Index. Then the reliability of the methods has been evaluated by back testing.

主要使用极值理论中的两种方法对上证指数进行VaR估计，并且进行后验测试以评价该方法的可靠性。

The Non-linear Structure of the Shanghai Stock Exchange Index

上证指数的非线性动态结构分析

The neural networks is adopted to forecast Shanghai stock indexes. The result of emulation indicates that the neural networks has better global convergence and higher training efficiency and forecasting precision.

将该神经网络用于上证指数的趋势预测，仿真结果表明：该神经网络比BP神经网络具有更好的全局收敛性、更高的学习效率和预测精度。