以改进后的MS-TGARCH模型研究股票市场的波动不对称性

本文引进成交量作为影响波动规律的时间因素,用多种状态转换模型对沪深300指数进行了实证研究,发现了中国股市目前有同西方市场相同的波动规律,并从这几年股市的发展角度作出了解释,最后提出几点对投资者和管理者的建议。此外,文中还提出了一个新的模型,此模型能明显提高估计精度。     

中国股市波动率的年线效应

本文以上证综合指数年线作为标准,把股市划分为牛市和熊市两种状态,并采用ARMA-EGARCH-M模型分别研究了牛市和熊市的股市波动率,称为年线效应。通过实证分析,得到以下主要结论：无论是牛市还是熊市,中国股市的日历效应都不显著;熊市的收益率序列几乎不存在任何的序列相关性;牛市波动率更容易受到坏消息的影响,而在熊市中,好消息的影响较大。如果不对消息内容进行区分,牛市会更容易受消息的冲击,加剧市场波动;而熊市中好消息和坏消息对市场波动的影响并不存在显著的差别。     

次贷危机背景下中美股市波动性研究——基于贝叶斯随机波动模型

该文基于贝叶斯随机波动模型的四种形武,对次贷危机期间中美股指收益率波动性进行分析,并且利用模型的DIC值比较其优劣.实证结果表明:次贷危机背景下,中美股市存在很明显的杠杆效应,SV-T和SV-MT分别很好地模拟了中美两个股市的波动性.     

金融危机对中国股市波动影响的实证研究——基于GARCH模型族的分析

针对2008年由美国次贷危机引起的一场全球金融危机,利用GARCH模型族方法对金融危机前后中国股票市场的波动特征进行比较研究。本文首先对GARCH模型误差项的选择进行了比较,然后采用GARCH模型族对上证综指对数日收益率波动性进行分析研究。结论显示:上证综指的日收益率序列在金融危机前后均表现出波动的集群特征和"杠杆效应";金融危机之前,中国股市符合高风险高收益的特征,而金融危机之后,高风险并不意味着高收益;金融危机发生后,股票市场波动的持续性和长期记忆性减弱,意味着股票市场短期波动加大,短期风险增加。     

随机波动率模型下的GMM估计对我国股市的实证分析

随机波动率模型(Stochastic Volatility Mode)是金融时间序列中一个十分重要的模型,广泛应用于验证波动率的随机性。GMM(general moment method)是一种十分有效的运用于线性和非线性模型的参数估计与假设检验中的方法。本文采用GMM方法,来验证我过2001年到2011年的上证指数时间序列是否符合随机波动率模型。     

基于SVAR模型的货币政策传导与股市波动

本文通过构建实际利率、货币供应量变动率和上证综指收益率的三元结构SVAR（结构向量自回归）模型,进行定量分析。主要创新结论有：我国利率已经由以往纯粹的外生变量逐步转化为经济系统的内生变量,应继续推进利率市场化改革;利率的调整往往对股市具有较大影响,如果频繁的运用存贷款利率货币调控政策,则对股票市场的发展产生阻碍作用;所以现阶段的调控措施,央行应该侧重于运用数量型货币工具存款准备金率,而非传统的价格型货币工具存贷款利率,才能维护股市稳定,保证经济持续健康发展。     

基于SEMIFAR模型的我国股市波动率的长记忆性研究

以深圳股票市场1997年1月1日至2011年10月10日深证成分指数行情数据为样本,采用SEMIFAR模型,研究中国股票市场波动率的长记忆特性。首先,对长记忆的统计检验进行计量分析,研究发现对数日波动率序列衰减缓慢并在滞后200阶的情况下依然显著,这表明我国股票市场波动率序列具有长记忆性。紧接着,尝试使用SEMIFAR模型对日波动率序列进行建模和预测,结果发现SEMIFAR模型在对数日波动率序列长记忆建模中效果很好。     

中国股市过度波动的原因分析

股市所有制结构的国有化以及股民的经验、心理与风险承受能力等因素导致了股民信心的过度波动,而股民信心的过度波动与投机性机构投资者制造股票差价的倾向又导致了股市过度波动。     

期权定价模型中的波动率分析

期权定价模型是布莱克的一种典型的相对经济理论,它在金融实践过程中产生巨大的经济影响,这种期权的模型需要输入参数中在市场中无法直接观察取得的重要变量,即波动率数值,也就是说,基于历史数据来计量历史波动。人们通常在期权经济定价中,结合期权的价格,采用定量模型倒推出隐含的波动率,这种隐含的波动率对于投资者未来市场的预期有很重要的作用,对于期权市场和经济市场的避险和套期保值业务来说,为了能够进行更好的风险规避和管理,就必须要了解隐含波动率的波动规律。     

中外股市波动传染建模与实证研究——基于Contagion-DCC-MGARCH模型

在DCC-MGARCH模型的基础上加入传染方程式构建Contagion-DCC-MGARCH模型,然后将该模型运用到中国与港、日、印、英、法、德、俄、美、巴和澳大利亚这10个国家和地区股市的波动传染实证分析中。研究结果表明:波动传染存在"集聚效应",2008年金融危机期间的波动传染显著增大;来自香港、日本、印度和俄罗斯股市的传染较大,来自欧美股市的传染较小;中外股市的波动传染主要是由前期已经实现的波动传染引起的,国外单个股市已实现的波动对波动传染的影响强于国内股市已实现波动的影响。     

Stock Market Volatility and Economic Factors

This paper examines the ability of rational economic factors to explain stock market volatility. A simple model of the economy under uncertainty identifies four determinants of stock market volatility: uncertainty about the price level, the riskless rate of interest, the risk premium on equity and the ratio of expected profits to expected revenues. In initial tests these variables have significant explanatory power and account for over 50 per cent of the variation in market volatility from 1929 to 1989. When the regression coefficients are allowed to vary over time using cluster regression, the four factors explain over 90 per cent of the variation in market volatility. The results are useful in explaining the past behavior of stock market volatility and in forecasting future volatility.     

央行汇率沟通与股票市场波动

本文基于2005年7月至2017年2月的日度数据,检验中国央行汇率沟通对于股票市场波动率的影响,研究结果发现:(1)央行汇率沟通会对本国股票市场产生明显的溢出效应,并显著增加股票市场的波动率,B股市场波动率反应程度显著强于A股市场。(2)书面沟通的效果强于口头沟通,口头沟通中行长沟通效果强于非行长沟通;汇率贬值的沟通效果显著强于汇率升值沟通。(3)汇率沟通对于股票市场波动率的影响存在着明显的非线性。     

Market Structure and Return Volatility: Evidence from the Hong Kong Stock Market

There is no consensus about the cause for higher volatility at the market open than at the market close in the U.S. market. As an order–driven, nonspecialist market, the Hong Kong stock market provides a useful setting for an examination. If halt of trade were the major cause of higher open–to–open volatility, the open–to–open volatility in the Hong Kong market would be higher. However, this is not observed. The autocorrelation of the open–to–open return series also indicates that the temporary price deviation at the market opening is not significant. We view these findings as consistent with the specialist argument.     

The Effect of Market Transparency: Volatility and Liquidity in the Korean Stock Market

Faced with unprecedented competition, stock markets should have fairness and transparency. The effects of market transparency for the stock market volatility and liquidity will be investigated using the case of the Korean stock market. The evidence from this study indicates that increasing the market transparency makes the price discovery process more efficient than before from the viewpoint of stock market volatility, and increases the stock market liquidity compared with before.     

美国不确定性冲击对全球股市波动的影响研究

选取2000—2021年美国经济、金融、经济政策和地缘风险四类不确定性指数以及全球GDP排行前15国家股市收益率数据,基于多维不确定性冲击框架,运用单因子、双因子和多因子混频波动率GARCH-MIDAS模型,从样本内拟合与样本外预测两个方面实证考察美国不确定性冲击对全球主要国家股市波动的差异化影响。研究表明：美国经济不确定性和金融不确定性对多数国家股市长期波动均有正向推动作用,其中,美国金融不确定性的影响最为广泛;美国金融不确定性是影响中国股市长期波动的主要因素,且中美贸易摩擦主要通过美国金融不确定性传导;美国经济政策不确定性上升会增加俄罗斯和墨西哥股市长期波动,美国地缘风险对意大利股市长期波动存在显著正向影响。     

Concentration of the UK Stock Market

Abstract:  During 1999 and 2000 a large number of articles appeared in the financial press which argued that the concentration of the FTSE 100 had increased. Many of these reports suggested that stock market volatility in the UK had risen, because the concentration of its stock markets had increased. This study undertakes a comprehensive measurement of stock market concentration using the FTSE 100 index. We find that during 1999, 2000 and 2001 stock market concentration was noticeably higher than at any other time since the index was introduced. When we measure the volatility of the FTSE 100 index we do not find an association between concentration and its volatility. When we examine the variances and covariance's of the FTSE 100 constituents we find that security volatility appears to be positively related to concentration changes but concentration and the size of security covariances appear to be negatively related. We simulate the variance of four versions of the FTSE 100 index; in each version of the index the weighting structure reflects either an equally weighted index, or one with levels of low, intermediate or high concentration. We find that moving from low to high concentration has very little impact on the volatility of the index. To complete the study we estimate the minimum variance portfolio for the FTSE 100, we then compare concentration levels of this index to those formed on the basis of market weighting. We find that realised FTSE index weightings are higher than for the minimum variance index.     

Macroeconomic Determinants of European Stock Market Volatility

In this paper we investigate whether macroeconomic variability can explain time variation in European stock market volatility. We find that unlike the documented case of the USA, in many cases, the time variation in stock market volatility is found to be significantly affected by the past variability of either monetary or real macroeconomic factors. Our findings have important implications for capital and portfolio allocations.     

投资者情绪与中国股票市场过度波动

本文分析中国投资者分类情绪及信心变化与中国股票市场波动的同期及动态影响。实证结果表明:就同期而言,中国投资者对国内经济基本面的信心变化以及中国股票市场波动,将影响投资者对国际经济金融环境的信心;就中长期而言,中国投资者情绪中关于国内经济基本面和国内经济政策的信心变化是影响中国股票市场过度波动的重要原因;中国投资者关于国际经济金融环境的信心深受国内经济基本面的信心变化的影响,中国投资者对A股市场估值的判断,受投资者对A股市场的股票估值信心以及国内经济基本面信心变化影响较大。     

Expectations,Behavior, and Stock Market Volatility

Stock market volatility is caused by investors’ expectations and behavior. To study the implication relationship, on the one hand, we present an investor’s expectation-forming and decision-making model to summarize the key features of individual behavior. We think the individual expectation is determined mainly by the number of differences between positive signals and negative signals in the information flow. The behavior is determined by both the expectations of investors around him (her) and the expected returns from a potential action. On the other hand, we simulate an investor community to verify if the model is able to replicate the related stylized facts. Mainly, three conclusions are drawn from the simulation: (1) A relationship of asymmetrical conditional dependence exists between expectation consistency and behavior consistency. (2) Market volatility is caused mainly by the difference between expectation consistency and behavior consistency. As the density of connections in the investor community network increases, the difference between them grows. (3) Influential investors have profound impacts on the formation of normal investors’ expectations and behavior. Thus influential investors play an important role in determining the degree of market volatility.     

融资融券对我国股市波动性的影响

本文采集股价指数月收益率和融资融券交易额每月日平均变化率的数据,针对融资融券对我国股市波动性的影响,进行实证检验,并对结果做进一步的解释。