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数据回归-基于时变系数回归的中美股市收益率联动效应研究.pdf
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数据回归-基于时变系数回归的中美股市收益率联动效应研究.pdf
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基于时变系数回归的中美股市收益率联动效应研究
1
摘要
本篇论文的目的是研究自 2005 年 1 月至 2016 年 4 月期间,中国股市与美国
股市的收益率联动性。选取上海证券交易所(SSE)指数和纽约证券交易所
(NYSE)指数作为中国总体股价和美国总体股价的代表,为了分别检验总体收
益率联动与行业收益率联动,收集总体综合指数和三个典型行业综合指数(金融,
医疗,自然资源)。在对原始数据进行收益率计算、平稳性检验和描述性分析之
后,利用时变参数回归进行实证分析。在时变参数模型中,假设一国的股票收益
率是另一国股票收益率的解释变量,但回归系数即相互影响力是随时间改变的。
通过拟合回归系数的时变曲线,不仅能定量地描述两国股市的收益率联动,更能
直观地展现这种联动性的动态变化。而且,利用双向的回归,比较两个市场收益
率对彼此的影响力,能够有效验证可能存在的不对称性。
实证研究得到了四个主要的结论:(1)中美股市之间存在着收益率的联动,
两个市场的当期收益率会对彼此产生影响,但联动水平随时间变化显著。(2)中
美股市收益率相互影响是不对称的,美国股市对中国股市的影响更大,且这一影
响更平滑、波动更小。(3)中美股市总体收益率联动和行业收益率联动呈现相似
的周期性变化,这种周期变化与 2007-2008 年的次贷危机和 2011 年的欧债危机
有密切关系。( 4)中国股市和美国股市总体收益率联动反映了行业收益率联动的
平均水平,不同行业之间的股票收益率联动有很大差异,金融行业股和自然资源
行业股的收益率相关性显著,而两个市场的医疗行业股收益率几乎没有相关性。
中美股市收益率联动机制,对政府的监管决策、投资者的投资决策有重要影
响。对于政府,应当认清联动的特点和规律,由于高水平的收益率联动确实对金
融危机的传播起到推动作用,因此当检测到中美股市的总体收益率联动水平过高
时,就应该警惕和预防国际金融危机传导到国内;对于收益率联动性较高的典型
行业,要针对行业特性调整政策,防范行业板块股票的收益率异常波动。对于投
资者,一方面可以利用中外市场联动规律,预测国内股票尤其是联动水平较高行
业股票的短期收益率变动,实现套利投机;另一方面,在跨国配置股票资产时,
应当选择收益率联动较低的行业板块,实现风险的分散。
关键词:股票市场;收益率联动;时变系数回归
万方数据
基于时变系数回归的中美股市收益率联动效应研究
I
ABSTRACT
The goal of this dissertation is to investigate the stock return co-movements
between China and the US from January 2005 to April 2016. SSE (Shanghai Stock
Exchange) Composite Index and NYSE (New York Stock Exchange) Composite
Index are selected as the representatives of the overall stock prices in China and the
US. In order to test both the whole market co-movements and specific sector
co-movements, collect total composite index and sector composite index (in Finance,
Health Care, Nature Resource). After calculating rates of return, conducting stationary
tests and doing descriptive analyses, use time-varying coefficient regression to
produce the empirical research. In time-varying models, assume stock return in one
country is an explanatory variable of the return in the other, while the parameter i.e.
the two-way effect is changing all the time. The fluctuations of the parameter would
clearly show the dynamic interdependency of stock return between two markets over
time. Besides, by running regressions in both directions, it is possible to compare and
test the possible asymmetric effect.
The empirical results indicate four main conclusions. (1) Stock return linkages
do exist between China and the US. Current returns in two markets affect each other.
However the interrelationship would change over time. (2)The interdependency
between two markets is asymmetric. US stock market has stronger impacts on China
than the opposite direction. And US impacts on China is smooth and less volatile.
(3)Co-movements between China and the US, both in total and in sectors, have
experienced similar cycles in the last ten years. These changing cycles are closely
concerned with financial crises. The 2008 subprime crisis and 2011 financial crisis
dramatically weakened the stock correlations. (4) Total co-movement reflects the
average level of sector co-movements. The dependency levels of different sectors
vary greatly. Significant linkage effects of stock returns exist in finance and nature
resource, while health care sector in SSE and NYSE have few correlation.
The co-movement mechanism between Chinese and the US stock market would
significantly influence the regulatory decision making of our government and the
investment decision making of our investors. For the government, it is necessary to
recognize the characteristics and patterns of the stock linkage. Since high levels of
co-movement do play a catalytic role in the spread of the financial crisis, when the
overall co-movement level between China and the US is detected to be too high, the
government should take measures to guard against abnormal volatility and prevent the
万方数据
基于时变系数回归的中美股市收益联动效应研究
II
international financial crisis from spreading to China. For those typical industries that
have high stock interrelationship, adjust the policy for the characteristics of the
industry to prevent abnormal stock market ups and downs. For investors, on the one
hand, are able to take advantage of the stock co-movement laws to make profits. By
predicting the short term movements of stock prices using linkage effect especially in
typical industries or sectors, investors are able to achieve arbitrage. On the other hand,
when making decisions about transnational allocation stock assets, in order to scatter
the total risk of the portfolio, it is wiser for investors to choose sectors that have least
level of return co-movements.
KEYWORDS: stock markets; stock return co-movements; time-varying coefficient
regression
万方数据
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