ARCH类模型在上证指数中的应用

论文编号:XXLW058 论文字数:12214,页数:31

摘     要

 ARCH模型是一种动态非线性的时间序列模型，它反映了经济变量之间的特殊的不确定形式：方差随时间变化而变化。作为一种全新的理论，ARCH模型在近年来取得了极为迅速的发展，已被广泛应用于经济金融领域。中国股票市场自建立起到现在取得了长足的进步，但市场表现出的波动幅度和风险性要大大高于国外成熟的资本市场，因此对其进行波动特征研究就显得尤为重要。
 本文以上证综指日收益率作为研究对象，采用EViews5.0统计软件对样本数据进行统计分析，将ARCH类模型应用于我国股票市场的实证研究,从实证结果中总结出中国股市的总体特征，主要得出以下结论：序列数据的尖峰厚尾特征；收益率与风险同向波动特征；序列数据波动的非对称特征。
关键词：  上证指数  ARCH  波动性  条件异方差 日收益率

Abstract
 ARCH model is a kind of dynamic non-linear time series model. It reflects a special feature of economic variables time-varying variances. As a new theory, ARCH model has caused extensive interests of economists and has been developed very fast since it came into being. Now it is being widely used in economic and financial fields. Chinese stock market grows fast and has made great progress since it was founded. However our country ’s stock market is very young, and the market risk and volatility is much larger than that of foreign markets, so it is very necessary to study the volatility character of it.
  In this text，Shanghai stock composite price index will be regarded as the main study object and describe the volatility character with the statistic software EViews5.0.
 Main characteristics, such as excess kurtosis, rate of return and risk fluctuate in the same direction, asymmetric effect of the series dates volatility will be founded through ARCH model on China’s stock market.

Keywords：Shanghai stock price index; ARCH; Volatility; conditional heteroskedasticity; daily rate of return

目  录

中文摘要 Ⅰ
英文摘要 Ⅱ
目  录 Ⅲ
第一章  绪论  1
 1.1  研究动机与目的 1
 1.2  研究背景 1
 1.3  研究方法 2
 1.4  论文內容概述 2
第二章  上证指数日收益率基本统计特征分析  3
 2.1  样本选取及数据处理 3
 2.2  样本数据的基本统计特征分析 3
 2.2.1 正态性分析 3
 2.2.2 平稳性检验 4
 2.2.3 均值方程的确定及残差序列自相关检验 6
 2.2.4 异方差性检验检验 9
第三章  ARCH类模型在上证指数波动分析中的应用 10
 3.1 模型简介 10
 3.1.1 ARCH模型 10
 3.1.2 广义ARCH模型——GARCH（p，q）模型 11
 3.1.3 ARCH-M及GARCH-M模型 11
 3.2 ARCH效应检验 12
 3.2.1 粗略检验 12
 3.2.2 ARCH-LM检验 13
 3.3 ARCH类模型的具体应用 14
 3.3.1 参数估计 14
 3.3.2 模型存在的缺陷 16
第四章  模型的进一步推广应用 18
    4.1 非对称性研究 18
 4.2 模型简介 19
 4.2.1 TARCH模型 19
 4.2.2 EGARCH模型 19
 4.3 非对称ARCH类模型的应用 20
 4.3.1 TARCH模型估计结果 20
 4.3.2 EARCH模型估计结果 21
第五章 结论 24
致  谢 25
参考文献 26

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