本文通过采用理论分析与实证分析相结合的方法,研宄融资融券
余额时间序列、融资余额时间序列以及融券余额吋间序列与股市波动
率时间序列之间的关系
在理论层而,本文对融资融券业务进行了梳理介绍。首先界定相
关概念和相关理论,为下文定量分析打下理论基础,包括融资融券概
念、特征、功能、与普通证券交易的区别、风险影响以及股市波动性
的相关概念,借鉴了“股价高估”理论、市场有效性理论和理性预則
模型。之后进行国外文献纹述和国内文献综述,同时介绍我国融资融
券交易得发展现状,主要包括融资融券业务的发展历程、融资融券交
易现状以及存在的问题。之后对融资融券影响我国股市波动性进行分
析,介绍融资融券的模式、影响我国股票市场波动性的要素以及对股
市波动性的作用机制,为下文的实证分析提供理论基础和支撑m
在实证层面,探究融资融券与我国股市波动性之间的关系。利用
上海证券交易所第四次扩容以后的融资融券余额数据,以2010年03
月31日到2016年11月25日这一周期为研宄对象,基于2010年04
月02日到2016年11月25日沪市周融资融券余额数据,对融资融券
余额变动、融资余额变动和融券余额变动对我国股票市场波动性的影
响进行实证研究,首先进行变量选取和变量说明,随后确定研究方法
并介绍向量自回归模型VAR,之后进行股市波动率时间序列、融资融
券余额时间序列、融资余额时间序列和融券余额时间序列的平稳性检
验、向量自回归VAR模型最佳滞后阶数确定、格兰杰Granger因果关
系检验、脉冲响应函数分析以及方差分解,应用VAR模型对股市波动 性指标、融资融券余额的一阶差分、融资余额的一阶差分、融券余额
的一阶差分这四个时间序列数据进行回归分析,并得出相应的结论
通过实证分析,木文的研宄结论如下:
首先,通过构建股市波动率指标SZ与融资融券指标RR、股市波
动率SZ与融资指标RZ以及股市波动率SZ与融券指标RQ之间的VAR
模型,并分别确定各模型的最优滞后阶数,分别为2, 2, 0,从模型估
计可知融资融券余额RR、融资余额RZ、融券余额RQ以及股市波动性
SZ他们之间存在着某种相关性,至于具体的关系,接下来需要进行格
兰杰Granger因果关系检验、脉冲响应分析和方差分解
其次,通过格兰杰Granger因果关系检验判断融资融券是如何影
响股票市场的波动以及股票市场的波动是如何影响融资融券的交易,
检验发现:(1)RR不是导致SZ变化的格兰杰原因,SZ是导致RR变
化的格兰杰原因;(2) RZ不是导致SZ变化的格兰杰原因,SZ是导致
RZ变化的格兰杰原因;(3) RQ不是导致SZ变化的格兰杰原因,SZ不
是导致RQ变化的格兰杰原因。综合以上检验可知,融资融券交易与
股市波动性之间存在因果关系,股市波动性的变化能够引起融资融券
交易的变化w
再次,应用脉冲响应分析方法,进行Cholesky分解,分析融资
融券余额RR、融资余额RZ、融券余额RQ以及股市波动性SZ序列之
间冲击响应的情况,研究发现:(1)融资融券余额的增加会平抑股票
市场的波动性,股票市场的正向波动会促进融资融券余额的增加;(2)
融资余额的增加会平抑股票市场的波动性,股票市场的正向波动会促
进融资余额的增加;(3)融券余额的增加会平抑股票市场的波动性,
作用并不显著,由于融券规模在我国证券市场较小,故其对股市波动
性的影响较小,股票市场的正向波动开始会促使融券余额的增加,第
一期后便会形成负向冲击,促使融券余额的减少14]
最后,在构建的融资融券余额RR、融资余额RZ、融券余额RQ以
及股市波动性SZ之间的VAR模型基础上,进行方差分解,得出结论
如下:(1)股市波动性的预测误差方差主要来自于股市波动性自身的
影响,融资融券RR影响不是非常显著,贡献率较小;(2)股市波动
性的预测误差方差主要来自于股市波动性自身的影响,融资交易RZ
影响不是非常显著,贡献率较小;(3)股市波动性的预测误差方差
主要来自于股市波动性自身的影响,融券交易RQ影响不是非常显著,
贡献率较小。 关键词:融资融券股票市场波动性向量自回归模型VAR &39;
I
.
RESEARCH ON THE EFFECT OF SECURITIES
MARGIN ON VOLATILITY OF CHINA fS STOCK
MARKET
ABSTRACT
On March 30,2010, the Shanghai Stock Exchange announced that the
margin trading system would be launched a pilot project in the March 31,
2010. The declaration of the Member margin trading would begin. The
margin trading business began to officially start. In six years, The margin
trading business was expanded for four times in six years, which expanded
the scale of transactions, the subject of shares expanded from 700 to 900
until September 22,2014. The number of underlying shares would account
for one-third of the total number of A-share listed companies. The market
capitalization would account for 80% of the total market capitalization of
A-share market which would further enhance the subject of stock
popularity and representation and would have important significance to the
development of China&39;s capital market.
This paper studies the relationship of the time series of t securities
margin balance, time series of the financing balance and the securities
lending balance time series and time series of stock market volatility by
using the method of theoretical analysis and empirical analysis.
At the theoretical level, this paper introduces the business of securities
margin trading. Firstly, it defines the related concepts and related theories,
which lay the theoretical foundation for the following quantitative analysis,
including the concept, characteristics, function of of securities margin,
difference between ordinary securities trading and securities margin
trading, risk influence and the volatility of stock market. Then it analyzes
the influence of the volatility of China&39;s stock market, introduces the mode
of margin trading, the factors that affect the volatilily of China&39;s stock
market and the mechanism of stock market volatility, which provides the theoretical basis and support for the empirical analysis.
In the empirical level, we explore the relationship between securities
margin and the volatility of China&39;s stock market. We conduct empirical on
the influence of changes in balance of margin, changes in financing
balances, changes in securities lending on the volatility of China&39;s Stock
Market based on the data of the balance of securities margin after Shanghai
Stock Exchange expanded for the fourth time taking March 31, 2010 to
November 25, 2016 as the research object, based on the data from April 2,
2010 to November 20.Firstly, , the variable is selected and described, and
then the research method is detennined, and he vector autoregressive
model VAR is introduced. Then, we conduct stationary test, determination
of Optimal Lag Order of Vector Autoregressive VAR Mode, Granger
causality test, the analysis of Impulse Response Function and ariance
decomposition on the time series of stock market volatility, the time series
of the balance of securities margin trading, the time series of financing
balance and time series of securities margin. The time series data of stock
market volatility index, The first-order difference of the securities margin
trading balance, first - order difference of financing balance, first - order
difference of securities margin are studied on regression analysis by VAR
model.
Through the empirical analysis, the conclusions of this paper are as
follows:
Firstly, the VAR model is built among stock market volatility index-
SZ, securities margin index-RR, stock market volatility-SZ, securities
margin index- RQ. The optimal hysteresis order of each model is
determined respectively showing 2, 2, 0. From the model estimation, there
is a correlation what is determined Granger causality test, impulse response
analysis and variance decomposition in RR, RZ, RQ and RQ.
Secondly, how securities margin influences stock market fluctuation
and how the volatility of the stock market affects the margin trading are
judged by Granger causality test. The test shows: (1) RR is not the Granger
cause which cause SZ change, SZ is the Granger cause which cause RR
change; (2) RZ is not the
==>>