时间序列模型的误差分析与研究

【作者】 涂晔；

【导师】 车文刚；

【作者基本信息】 昆明理工大学 ， 测试计量技术及仪器， 2009， 硕士

【摘要】 随着经济的发展和人们生活水平的提高及投资意识的转变,人们对证券股票投资的热情越来越高,股票投资已成为经济投资的一个重要组成部分,成为一个国家的经济生活和社会生活中所不可缺少的一部分。面对瞬息万变的股票市场,投资者在进行投资活动时将面临许多不确定性因素。为了追求投资收益的最大化和投资风险的最小化,不断地探索股票时间序列的内在规律,寻找其有效的分析方法和工具就显得尤为重要了。因此,对金融时间序列模型误差的分析具有重要的理论意义和应用价值。在时间序列分析中,一般采用建模的方法对数据进行拟合。在传统时间序列分析方法中,尽管可以得到趋势明朗后模型与实际数据的较好拟合效果,但是还是有一些时序点的拟合误差较大。本文利用时间序列分析方法对两支股票进行建模,围绕股票时间序列分析模型的拟合误差及异常误差值进行了深入的分析研究,得出结论：一方面,两个时间序列模型的异常误差值出现所对应的时间序列点基本一致；另一方面,两个时间序列模型的误差波动情况具有一定的同步性。这说明在运用时间序列模型分析股价时间序列时,模型的误差大小、误差变化以及异常误差的产生与分析对象的大小无关。本文采用金钼股份(601958)和出版传媒(601999)的120个交易日收盘价数据,分析时间序列模型的误差值和异常误差,并对异常时序点的价格变化率进行分析,分析结果表明,股票时间序列模型误差的影响主要来自于外界因素对分析对象属性变化程度的影响。模型拟合效果与被拟合对象之间的关键点不在于对象本身的属性特征,而是在于外界因素对被拟合对象属性的刺激影响上。来自股票外界的因素影响了股票属性的变化方向和变化程度,表现为价格的上涨与下跌和涨幅与跌幅的大小。这一结论在今后时间序列模型的理论分析和实际应用中具有指导性和实用性。此外本文研究了适用于时间序列模型误差的异常误差值检测方法,较传统的离群值分析检测方法有所改进。更多还原

【Abstract】 With the development of economy and the conversion of people’s investment consciousness, the people’s investing enthusiasm of stock is higher and higher.Stock investment becomes an important part of economy investment, and also an important part of people’s life in modern times. In the face of the fast changing stock market, investors will face a lot of uncertain factors while carrying on investing in stock market. Through researches into its internal disciplinarian, effective analytic methods and tools are being looked for more interest with lower risk. Therefore the study of disciplinarian in the model error of financial time series has great theoretical significant and applicable value.Modeling methods are generally used in fitting security time series. In the traditional time series analyzing methods, the fitting results are effective in clear tendency, but some fitting errors are relatively great. This paper uses the time series analyzing methods to build the model for two different stocks, deeply analyze and research on the stock fitting error and unusual error. The analysis conclusions as follows:on one hand, the unusual errors of two different time series model almost have the same time point; on another hand, the errors fluctuate with almost a same step. It shows when we analyze the stock price series with the time series model, the model error size, the model error changing situation, and the production of unusual error are irrelevant with the size of analytic target.In this paper, the analyze data come from the close-price in one hundred and twenty trading days of Jinmu stock (601958) and Chubanchuanmei stock (601999).After analyze the model error, model unusual error and the price changing rate, the result shows that the influence to stock’s time series model error mainly produced by the influence of external factors act on the intensity of the attribute of analytic target.The key point of model fitting result doesn’t lie in the target’s own attributive character, but in the influence that external factors act on. The external factors influence the attributive character’s changing direction and intensity, it is shown as the direction of price rising or reducing, and amount of price rising or reducing. This conclusion will be guidance and practicality in theory analyzing for time series model in future. In this paper, a suitable method for detecting the unusual error in time series model is researched, and it’s an improved method than the traditional analysis of the outlier detection.更多还原