我国股市波动性及受基金投资行为影响的研究

发布时间：2018-06-13 03:25

本文选题：股票市场 + 波动性　；参考：《杭州电子科技大学》2012年硕士论文

【摘要】：我国作为一个新兴的金融市场，，股票市场的发展自沪市和深市开始营业开始，经历了二十多年。在这二十多年里经历了两次大规模的金融危机，我国的股票市场依然在健康的发展，并成功推出了创业板。然而，毕竟我国是一个新兴的金融市场，其股票市场的波动显得异常剧烈，而且呈现出一些独有的特征。如熊市长，牛市短；波动幅度过大；对信息反应过于剧烈等。同时，基金作为机构投资者，其投资行为对我国股市波动的影响也异常明显。因此，研究我国股票市场的这些波动特征并进而确定基金的投资行为对我国股票市场波动的影响效应对于我国相关机构进行金融监管和投资者更好的选择投资策略，从而使得我国股票市场可以更好的发挥资源优化配置的作用。股票市场收益率的波动问题一直是学术界研究的重点，学者们从最初简单的通过图形来描绘波动特征，发展到后来用指标描绘波动特征。随后又出现了ARCH模型，进而出现了GARCH类模型，用来描绘波动的各种特征。后来，学者们又开始致力于研究影响这些波动的因素，包括政策、机构投资者、投资心理等。越来越多的学者开始将重点放在研究基金在投资行为中呈现出的羊群效应和回馈效应对我国股市波动的影响上。本文在总结之前相关学者研究方法和研究成果的基础上，通过描述性分析了我国上证综指、深证成指和创业板指数的收益波动特征，并对三者的波动特征进行了对比分析；采用GARCH类模型分别对三个指数的收益率波动性进行拟合，并对三个GARCH类模型的拟合效果进行了对比；建立股票池来模拟我国股票市场整体的收益波动；通过建立对数回归模型研究基金的投资行为对于我国股票市场收益波动的影响。通过本文的相关研究，我们得到了如下结论：（一）上证综指、深证成指和创业板指数的时间序列均呈现显著的尖峰厚尾现象，且创业板指数左偏程度最大，上证综指左偏程度最小。（二）沪市和深市的收益时间序列都呈现出显著的ARCH效应，而创业板市场的收益时间序列则没有呈现出显著的ARCH效应。在GARCH（1, 1），TGARCH（1, 1）和EGARCH（1, 1）模型中，EGARCH（1, 1）对上证综指和深证成指收益波动的拟合效果最好。两个市场的收益波动均呈现显著的杠杆性、聚集性、长记忆性和持续性、均值回复性。沪市相比深市而言，呈现出更强的非对称性，即更强的杠杆性。EGARCH（1, 1）可以很好的消除上证综指和深证成指收益时间序列的ARCH效应。（三）本文建立的股票池可以很好的模拟我国股票市场的收益波动性。我国股票的板块市也会受到个股不同而在个股上呈现出不同的走势，并且受基金投资行为的影响程度和方向也会不同，我国基金在投资过程中主要采用正回馈投资策略，一定程度上加剧了我国股市的波动，同时基金的关注度也会在一定程度上加大波动性，相比成交量等其他影响因素，基金的投资行为的影响程度显著较大。
[Abstract]:As a new financial market, the development of the stock market has begun to start in Shanghai and Shenzhen. It has experienced more than 20 years. In the more than 20 years, the stock market has experienced two large-scale financial crises. The stock market in China is still developing healthfully, and it has successfully launched the gem. However, China is a new finance after all. In the market, the volatility of its stock market appears unusual, and presents some unique features, such as the mayor of bear, short bull market, excessive volatility, excessive response to information and so on. At the same time, as an institutional investor, the investment behavior of the fund has a very obvious effect on the volatility of our stock market. Therefore, the study of the stock market in China Some volatility features and then determine the effect of the investment behavior of the fund on the volatility of the stock market in China, which makes the financial supervision and investors better choose the investment strategies for the relevant institutions of our country, thus making the stock market of our country better play the role of optimizing the allocation of resources.
The volatility of the stock market returns has always been the focus of academic research. Scholars have developed the volatility characteristics from the initial simple graphics to describe the volatility. Then, the ARCH model appeared, and then the GARCH model was developed to describe the various characteristics of the volatility. Later, scholars began to work again. In the study of the factors affecting these fluctuations, including policy, institutional investors, investment psychology, and so on, more and more scholars began to focus on the effect of herd and feedback effects on the stock market volatility in the investment behavior of the research fund.
On the basis of summarizing the previous scholars' research methods and research results, this paper makes a descriptive analysis of the volatility characteristics of the Shanghai Composite Index, Shenzhen stock index and gem index, and makes a comparative analysis on the volatility characteristics of the three parties, and uses the GARCH model to fit the volatility of the rate of return of the three indices respectively. The fitting effect of the three GARCH models is compared, the stock pool is established to simulate the volatility of the whole stock market in China, and the effect of the investment behavior of the fund on the volatility of the stock market in China is studied by establishing a logarithmic regression model. The following conclusions are obtained by the relevant research in this paper.
(1) the Shanghai Composite Index has a significant peak and thick tail phenomenon in the time series of the Shenzhen stock index and the gem index, and the left deviation of the gem index is the most, and the left deviation of the Shanghai composite index is the least.
(two) the time series of the Shanghai stock market and the Shenzhen stock market all present a significant ARCH effect, while the return time series of the GEM market does not show a significant ARCH effect. In GARCH (1, 1), TGARCH (1, 1) and EGARCH (1, 1) model, EGARCH (1, 1) has the best fitting effect on the Shanghai Composite Index and the deep proof index returns. The benefits of two markets are the best. The volatility shows significant leverage, aggregation, long memory and sustainability, and the mean reversibility. Compared with the Shenzhen market, the Shanghai stock market shows a stronger asymmetry, that is, the stronger leverage.EGARCH (1, 1) can well eliminate the ARCH effect of the Shanghai Composite Index and the time sequence of the Shenzhen Stock index income.
(three) the stock pool established in this paper can well simulate the volatility of income in the stock market of our country. The stock market in China will also be different in the stock market, and the influence degree and direction of the investment behavior of the fund will be different. In the process of investment, the fund of our country mainly adopts positive feedback investment policy. Slightly, to a certain extent, the volatility of China's stock market is aggravated, and the degree of concern of the fund will also increase volatility to a certain extent. Compared with other factors such as the volume of turnover, the investment behavior of the fund has a significant impact.
【学位授予单位】：杭州电子科技大学
【学位级别】：硕士
【学位授予年份】：2012
【分类号】：F224;F832.51

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