证券市场指标对股灾优化预警仿真研究

发布时间：2018-01-07 17:22

  本文关键词：证券市场指标对股灾优化预警仿真研究　出处：《计算机仿真》2017年05期 　论文类型：期刊论文

  更多相关文章： 牛熊指标 极端事件 分布 股灾预警

【摘要】：对证券市场指标中股灾进行准确预警,可以提高证券市场的安全系数。进行券市场指标中股灾预警时,需要对证券市场指标进行分类,构建极限定理,根据极限定理对金融市场极端情形之下"牛熊性"进行判断,完成股灾预警,但是传统方法是通过构建GM(1,1)灰色模型,根据选取的指标完成股灾优化预警,但缺少证券市场指标分类环节,不能构建相对应的极限定理,在大幅波动的条件下,存在股灾预警准确性差,可操作性差的问题。提出一种新的证券市场指标对股灾优化预警方法,建立了一个反映金融市场极端情形之下"牛熊性"的新指标,首先获取Quantile第一类分布族,利用第一类分布族中的rlp1,rlp2构建一个极限定理,利用极限定理构建证券市场牛熊性的指标,之后还将这一指标应用于2015年6月15日开始的股灾的预警分析,仿真结果表明,提出的新指标较好的反映了随着股灾爆发的临近,预估准确性强。
[Abstract]:The crash of stock market index accurately warning, can improve the safety coefficient of the securities market. Securities market index in stock market crash warning, it is necessary to classify the stock market index, construct the limit theorem under "limit theorem for financial market extremes of bull and bear" to judge, to complete the crash warning, but the traditional method is through the construction of GM (1,1) grey model, according to early warning indicators to optimize the stock market crash, but the lack of link classification of securities market index, cannot build corresponding limit theorem, a sharp fluctuations in the stock market crash, there are early warning accuracy, poor operability problems. Put forward a new index of securities market optimization of crash warning method, set up a financial market reflect the extreme situation of "bull" of the new index, first get Quantile first class of distributions, the first distribution group The rlp1 and rlp2 to construct a limit theorem, constructing CBBC securities market by using the limit theorem index, after analysis of the index will be used in June 15, 2015 start of the crash warning, the simulation results show that the new index is proposed to reflect with the stock market crash is approaching, prediction accuracy.

【作者单位】： 海南师范大学数学与统计学院;
【分类号】：F830.91;O213
【正文快照】： 1引言 牛市,熊市及盘整是股市的三种基本状态+3],寻找一种状态向另外一种状态转换的拐点是投资者最为关心的问题。与“牛”“熊”直接相关的是股市投资的收益和损失,作为度量未来可能损失和收益的两个常用指标,VaR(Value atRisk)和VaB(Valueat Best)在金融机构的风险管理之中,，

本文编号：1393528