上市公司“大小非”解禁的实证研究——对我国股票市场半强式有效性的检验

根据股票价格对信息反映范围的不同,市场被分为弱式有效市场、半强式有效市场和强式有效市场。本文选取2009年3月有“大小非”解禁行为的股票作为研究对象,运用事件研究方法,通过对样本股票在解禁前后一定时期的超额收益变化进行实证分析,得出结论：我国股票市场尚未达到半强式有效。     

基于GARCH-VaR模型的股票市场风险度量与预测分析——以新能源行业为例

一个有效的风险测度模型必须能够有效刻画金融市场的典型事实,本文将GARCH模型与VaR方法相结合,以新能源行业股票收益率为对象,比较研究了残差不同分布假定下股票收益率VaR值的精确程度,并做了向前一步的VaR失败率回测检测,结果表明残差正态分布下的GARCH-M(1,1)模型很好地刻画了新能源行业股票市场的风险特征,为预测风险提供了较好的方法.     

对我国证券市场"大小非"解禁问题的剖析

2006年是中国证券市场制度改革年,伴随股权分置改革问题的解决,证券市场进入了后股改时代.许多在股改时期的限售股已经达到解禁时间,在限售股解禁之后有些上市公司的股票成为全流通股.这也达到了股权分置改革的目的.但是,在获得股改成功的同时也存在诸多问题,"大小非"获得了全流通的权利后不计成本的抛售股票给A股市场造成了巨大的压力.在全球金融危机的影响下,"大小非"的集中抛售对股票市场的危害更为严重,造成我国上证指数暴跌73%左右.大量中小投资者损失惨重,对我国股票市场也都失去了信心.所以,在"大小非"解禁高峰即将到来的2009年和2010年我们必须对"大小非"问题进行探讨分析,以解决它给我国证券市场带来的问题,促进证券市场健康发展.本文分析了我国"大小非"的涵义和历史根源.了解到"大小非"存在的现状,探讨了"大小非"对我国证券市场的影响,重点提出了解决"大小非"问题的对策和建议.     

股票需求曲线是水平的吗?

文章从异质信念与卖空限制的理论出发对股票需求曲线的形状进行了研究,理论上证明了股票需求曲线倾斜这一命题,然后以股权分置改革为背景,采用事件研究法实证分析了4 413起股改限售股解禁事件的价格效应,研究发现,解禁事件发生的-20至20天的窗口期累积异常收益为-1.85%,解禁事件带来明显负的异常收益,支持了股票需求曲线向下倾斜的假说。     

大小非解禁的市场反应

本文利用事件研究方法,研究截止2010年3月31日解禁的大小非样本对二级市场股票价格的影响。结果表明,大小非解禁对市场产生了显著负面影响。在此基础上,对大小非样本进行了分类研究,发现大小非解禁后真正对二级市场产生的实质性冲击主要集中在高估值、非控股股东和自由减持三类样本,而在解禁前中低估值、控股股东和大宗交易减持三类大小非样本对二级市场存在一定的心理冲击,但幅度有限,且在解禁后市场对解禁前的心理冲击有一定的矫正。另外,值得注意的是,高估值和非控股股东两类大小非样本在解禁前存在明显人为拉高股价的操纵迹象。     

“大小非”解禁对股市的影响及对策研究

＂大小非＂问题对股市的影响不在于解禁,而在于解禁以后的减持,如果大部分不减持,那么解禁规模再大,对市场的影响也是有限的。尽管如此,面对＂大小非＂问题给股市带来的负面影响,监管当局仍应进一步出台政策进行规范管理,以维护市场正常秩序。     

股指期货推出前后沪深300指数风险统计特征研究

股指期货是现代资本市场发展的产物,股指期货与现货的关系是学术界的研究的热点问题之一。文章介绍了风险价值VaR方法、非参数核密度估计理论以及VaR模型的回测评价原理,以我国股指期货推出前后一年间每一小时交易的高频数据为研究对象,运用非参数核密度估计法以及风险价值方法,计算在不同置信水平下的VaR值。在90%、95%以及99%置信水平下,股指期货推出前后,沪深300指数的收益率最大跌幅分别为0.921%、1.33%以及2.28%,变化为最大跌幅分别为0.871%、1.15%以及1.76%。表明在我国股指期货推出后,沪深300指数收益率的风险降低了。最后文章从市场效率,套利机制以及套期保值等视角对实证结果进行了分析。     

中国股票市场ES和VaR的实证比较分析

以我国股票收益率为研究对象,分别在正态分布和非正态稳定分布条件下对ES和VaR的凸性、次可加性和有效性进行了实证比较分析,发现:在非正态稳定分布条件下VaR不满足凸性和次可加性,ES满足凸性和次可加性,在正态分布条件下VaR和ES都满足凸性和次可加性;在两种分布条件下ES的有效性都高于VaR的有效性,而在非正态稳定分布条件下ES的优势更加明显。由于本文的收益率分布拟合检验表明我国的股票收益率服从非正态稳定分布,所以在我国股票市场上ES是比VaR更好的风险度量。     

基于VaR的沪深300股指期货风险管理实证研究

我国以沪深300为标的指数的股指期货即将推出。股指期货在具有控制风险功能的同时,也与其他金融衍生产品一样,具有风险性,且其风险远远大于股票现货市场。因此,必须采用积极的风险管理技术,加强对股指期货的风险防范。在GARCH模型的基础上,采用VaR方法对我国的沪深300股指期货仿真交易进行定量研究,计算出它们的VaR值,并将其与期望值进行比较。经过对比分析可以得出:基于GARCH模型的VaR方法适合我国的股指期货风险管理。     

基于VaR的股票风险管理模型比较研究

在险价值穴ValueAtRisk,简称VaR雪是度量市场风险的一种普遍使用的工具,可看作是市场风险度量的基石。本文分别以资本资产定价模型(CAPM模型)和套利定价理论(APT理论)为基础,以VaR为风险管理工具,通过对单个股票或股票组合的资产回报进行风险管理建模,并利用我国股票市场的数据对两个不同的风险管理模型进行比较研究,分析了它们的有效性和局限性。     

我国主要证券市场VaR模型变动性研究

对证券市场风险度量模型的探索,一直是国内外金融风险管理者关注和研究的热点之一。VaR(Value-at-Risk)风险度量模型,目前已成为金融机构、非金融企业、金融监管部门测量和监控市场风险的主流工具。然而VaR模型能否有效正确地度量证券市场风险,不但取决于估计的精度,还取决于选用VaR模型本身的变动性。因此,探索我国主要证券市场VaR模型的变动性,有一定的现实意义。针对我国主要证券市场指数,本文首先通过图形展示了三类(参数、半参数和非参数)VaR估计方法在不同的窗口设定下控制风险的表现;其次在平均相对偏差(MRB)和平方根相对偏差(RMSRB)的双重标准下,对三类VaR估计模型的变动性进行了比较研究,结果表明:在我国主要证券市场上,参数类VaR估计模型本身的变动性和偏离程度较小,半参数类VaR估计模型次之,而非参数类VaR估计模型本身的变动性和偏离程度较大,这在一定程度上符合新兴国家证券市场存在较大投机收益的特点。     

Assessing downside and upside risk spillovers across conventional and socially responsible stock markets

Since the aftermath of the recent global financial crisis, socially responsible (SR) investments have become an alternative form of conventional finance, giving rise to further systemic risk between conventional and SR stock markets. In this paper, we assess this risk transmission using Value at Risk (VaR) modeling for the US, Europe and the Asia-Pacific region, over the period covering January 2004–December 2016. We find that socially responsible stock markets exhibit less risk than do conventional markets in terms of the risk hedging properties induced by the SR screening. Second, contributions to systemic risk vary across market phases and return distribution levels, with a larger contribution and spillover effect during the recent global financial crisis. For example, at the downside of the distribution (CoVaR at 5%), the conventional European index shows the highest contribution to the world market’s systemic risk, while the US stock market shows the highest contribution at the upside of the distribution (CoVaR at 95%). This finding is justified by the difference in the risk aversion of investors that varies with the market state as well as the disparities in the development of SR markets.     

Tail risk in emerging markets of Southeastern Europe

This article examines the left-tail behaviour of returns on stocks in Southeastern Europe (SEE). We apply conditional extreme value theory (EVT) approach on daily returns of six stock market indices from SEE between 2004 and 2013. Predictive performance of value-at-risk (VaR) and expected shortfall (ES) based on EVT is compared against several alternatives, such as historical simulation and analytical approach based on GARCH with a single conditional distribution. Model backtesting with daily returns shows that EVT-based models provide more reliable VaR and ES forecasts than the alternative models in all six markets. Unlike the alternatives, the EVT-based models cannot be rejected as VaR confidence level is increased. This emphasizes the importance of extreme events in SEE markets and indicates that the ability of a model to capture volatility clustering accurately is not sufficient for a correct assessment of risk in these markets.     

Co-movements of GCC emerging stock markets: New evidence from wavelet coherence analysis

This paper examines the short term and long term dependencies between stock market returns for the Gulf Cooperation Council (GCC) Countries (Bahrain, Kuwait, Oman, Qatar, Saudi Arabia, and the United Arab Emirates) during the period 2005–2010. Our empirical investigation is based on the wavelet squared coherence which allows us to assess the co-movement in both time-frequency spaces. Our results reveal frequent changes in the pattern of the co-movements especially after 2007 for all the selected GCC markets at relatively higher frequencies. We further note an increasing strength of dependence among the GCC stock markets during the last financial crisis signifying enhanced portfolio benefits for investors in the short term relative to the long term. On the financial side, we uncover that the strength of co-movement between GCC markets may impact the multi-country portfolio's value at risk (VaR) levels. These findings provide potential implications for portfolio managers operating in the GCC region who are invited to consider co-movement through both frequencies and time when designing their portfolios.     

Markets liquidity risk under extremal dependence: Analysis with VaRs methods

Value-at-Risk (VaR) is a widely used tool for assessing financial market risk. In practice, the estimation of liquidity extreme risk by VaR generally uses models assuming independence of bid–ask spreads. However, bid–ask spreads tend to occur in clusters with time dependency, particularly during crisis period. Our paper attempts to fill this gap by studying the impact of negligence of dependency in liquidity extreme risk assessment of Tunisian stock market. The main methods which take into account returns dependency to assess market risk is Time series–Extreme Value Theory combination. Therefore we compare VaRs estimated under independency (Variance–Covariance Approach, Historical Simulation and the VaR adjusted to extreme values) relatively to the VaR when dependence is considered. The efficiency of those methods was tested and compared using the backtesting tests. The results confirm the adequacy of the recent extensions of liquidity risk in the VaR estimation. Therefore, we prove a performance improvement of VaR estimates under the assumption of dependency across a significant reduction of the estimation error, particularly with AR (1)-GARCH (1,1)-GPD model.     

Modeling the dependence structure between default risk premium,equity return volatility and the jump risk: Evidence from a financial crisis

This paper investigates the dependence structure between default risk premium, equity return volatility and jump risk in the equity market before and during the subprime crisis. Using iTraxx CDS index spreads from Japanese and Australian markets, the paper models the different relationships that can exist in different ranges of behavior. We consider several Archimedean copula models with different tail dependence structures, namely, Gumbel, Clayton, Frank, AMH and Joe copulas. Although the dramatic change in the levels of the iTraxx CDS index, we find strong evidence that the dependence structure between CDS and stock market conditions is asymmetric and orienting toward the upper side. In addition, we find that the Japanese CDS market is more sensitive to the stock return volatility than the jump risk and the magnitude of this sensitivity is related to the market circumstances. However, Australian CDS market is more sensitive to the jump risk than stock return volatility before and during the financial crisis. This result has important implications for both global financial stability and default risk management. Specifically, the heterogeneity of markets, coupled with the diversity in the risk exposures cause the default risk premium and equity markets to exhibit different levels of sensitivity.     

VaR模型在套期保值比率计算中的实证分析

提出了考虑套期保值期内不同期限价格风险的最小平均VaR套期保值比率计算模型。基于我国外汇市场及股票市场数据,用最小平均VaR套期保值模型进行了实证分析,并同常用的最小方差及最小VaR套期保值模型进行了对比,得出了最小平均VaR模型在套期保值过程中的效果要优于其他两种模型,并能更有效地降低投资者提前终止套期保值可能面临额外风险的结论。     

最小平均VaR套期保值比率计算模型及实证研究

针对传统的套期保值模型只考虑最小化套期保值组合在到期日的价格风险,而且没有充分利用资产历史价格样本数据所提供的收益率信息的特点,本文提出了考虑套期保值期内不同期限价格风险的最小平均VaR套期保值比率计算模型。基于我国外汇市场及股票市场数据,本文对最小平均VaR套期保值模型进行了实证分析,并与常用的最小方差及最小VaR套期保值模型进行了对比,得出了最小平均VaR模型在套期保值过程中的效果要优于其他两种模型,能更有效地降低投资者提前终止套期保值可能面临额外风险的结论。     

Early warning signals using AVaRs of infinitely divisible GARCH models — evidence from stock index markets

Classical time series models have failed to properly assess the risks that are associated with large adverse stock price behaviour. This article contributes to autoregressive moving average model–GARCH (ARMA–GARCH) models with standard infinitely divisible innovations and assesses the performance of these models by comparing them with other time series models that have normal innovation. We discuss the limitations of value at risk (VaR) and aim to develop early warning signal models using average value at risk (AVaRs) based on the ARMA–GARCH model with standard infinitely divisible innovations. Empirical results for the daily Dow Jones Industrial Average Index, the England Financial Times Stock Exchange 100 Index and the Japan Nikkei 225 Index reveal that estimating AVaRs for the ARMA–GARCH model with standard infinitely divisible innovations offers an improvement over prevailing models for evaluating stock market risk exposure during periods of distress in financial markets and provides a suitable early warning signal in both extreme events and highly volatile markets.