中国A、B股市场收益波动风险的度量及比较

本文运用 GARCH方法对 1 996年 1 2月以来深圳和上海两地的 A、B股市场收益的波动风险进行了度量。经比较发现 :中国 B股市场收益的波动风险在绝大部分时间要高于 A股市场。在此基础上 ,本文对中国 A、B股市场风险差异的成因进行了分析 ,认为有效监管、降低市场风险对证券市场的发展有重要意义。     

中国股市收益率与波动率跳跃性特征的实证分析

股票市场收益率通常小幅波动，但是当市场出现重大或者异常信息时，收益率会在短时间内发生大规模的运动．产生跳跃性变化，市场波动率也明显加剧。本文采用Jump—GARCH对沪深两市A股B股的这类跳跃性特征进行实证分析。根据该模型：当收益率小规模变化时，波动率由GARCH（1，1）平稳随机过程产生，但是当收益率发生跳跃性变化，波动率将背离GARCH（1，1）过程．调整到一个较高的水平。实证结果表明，该模型能够有效地估计出沪深两市收益率和波动率的跳跃性变化．比正态分布的GARCH模型更合理地反应了市场收益率和波动率过程。本文同时讨论了A股B股的跳跃性特征。     

上海B股市场系统性风险及投资机遇分析

运用单因素模型,选取上海证券交易所中同时兼具A、B股、且具有行业代表性的12家上市公司作为研究样本,对比分析上海A、B股市场系统性风险,结果显示:样本期内上海B股市场的系统性风险远高于A股市场,这为中小投资者投资B股提供了便利。同时,对目前关于上海B股市场前途的主要战略构想及其可能带来的投资机遇进行深入分析,结果发现:上海B股市场未来无论是选择改革还是选择发展,都将为投资者带来巨大的投资机遇。     

我国股市波动非对称性和混合分布假定的经验分析

混合分布假定(MDH)用信息流的自相关结构解释收益波动的GARCH效应和持续性。本文先运用EGARCH—GED模型考察沪深A、B股市场的波动非对称性,发现A股市场波动显著非对称、B股市场“杠杆效应”不显著。然后,我们根据“杠杆效应”的显著与否,把同期交易量作为信息替代指标分别引入EGARCH—GED和GARCH—GED模型的条件方差方程,实证检验混合分布假定。最后,我们比较预期和未预期交易量对条件方差的解释力。研究表明：在B股和上证A股市场上,同期交易量对收益波动的GARCH效应和持续性有显著的解释力;同期交易量不能作为市场信息的完全替代指标;未预期交易量对收益波动有显著的解释力、比预期交易量的解释力强。     

反转策略在中国A股市场的适用性分析

选取A股市场1999年至2008年的非金融股票数据,建立一维排序模型,分别持有1年、2年和3年,计算了价值股组合和绩优股组合的持有期收益率。实证结果表明:运用反转投资策略能够获取超额收益,B/M、C/P和E/P策略在大多数年份适用于我国市场,能够取得超额收益,而GS策略则完全不适用于中国市场。     

深圳A、B股双重上市公司半年报的比较研究

文章通过对2004年深圳证券交易所A、B股市场双重上市公司对半年股票价格反应的分析,进行市场有效性的半强有效性检验,发现在不同股票市场、在同一股票市场的不同组合之间存在不同的反应;从低收益组合到高收益组合、从B股市场到A股市场,信息的有效性存在着一个渐强趋势。     

A股与B股发展状况测评及探索

<正> 自1992年B股作为我国A股证券市场向海外延伸的一种人民币特种股推出以来,其市场发展可谓坎坷多阻。首先是正当B股成为海外金融机构关注的热点时,国家紧随B股又推出了H股,使得本跃跃欲试的海外投资者(尤其是港澳投资者)立马转舵,挥师近土。1993年下旬B股再度上扬,恰逢中央政府实行经济紧缩政策,A股与B股双双落马,境外投资者更对B股持悲观态度。1996年上旬A股市场捷报频传,B股也有所启动,偏巧去年东南亚金融危机爆发,B股自身劣性凸显,再次沉入海底,A、B股落差竟达6-14倍,其中18家跌破面值,引起各方人士的恐慌与关注。人们不仅担忧B股市态会拖累A股市场的健康运作,影响我国国际化开放型证券市场的良性互动,也对B股的发展提出了种种改革建议。针对以上情况,本文将对A、B股之间关系进行测评,并提出B股的发展看法。     

中国股市收益和交易量动态引导关系的实证分析

实证检验沪深A、B股市场日收益和交易量之间的线性和非线性Granger因果关系。由于序列存在非线性结构可能使检验发现的仅是一种伪Granger因果。我们着重考察运用APGARCH模型过滤后股市收益和交易量之间的线性和非线性Granger因果。研究表明,上证A、B和深证A股市场收益和交易量之间互为线性Granger因果,深证B股仅存在从收益到交易量的线性Granger因果;上证A股市场交易量是收益的非线性Granger引导,深证B股市场收益和交易量之间互为非线性Granger引导。研究发现沪深A、B股市场收益和交易量之间具有相互的动态引导关系。     

股改即将完成 B股何去何从

A股市场股权分置改革即将完成，B股市场的改革问题又摆到了桌面上。面对因为A、B股市场合并传闻而不时出现刺激性上涨的B股市场，证券市场不得不思考一个B股市场到底该何去何从的问题，是继续发展，还是注销，或是与A（H）股合并，本文试图在结合B股市场功能、发展及现状的基础上探讨B股市场的走向。     

股改即将完成B股何去何从

A股市场股权分置改革即将完成,B股市场的改革问题又摆到了桌面上.面对因为A、B股市场合并传闻而不时出现刺激性上涨的B股市场,证券市场不得不思考一个B股市场到底该何去何从的问题,是继续发展,还是注销,或是与 A (H) 股合并,本文试图在结合B股市场功能、发展及现状的基础上探讨B股市场的走向.     

基于GARCH模型的上证指数与GDP波动性研究

中国的证券市场与宏观经济关系已进入弱相关阶段,本文通过使用ADF检验、ARCH效应检验、GARCH模型分析以及Granger检验对两者的波动性进行分析和研究。实证结果发现,中国的证券市场与宏观经济都具有非正态分布的特性。其次,证券市场不存在自回归条件异方差效应波动,宏观经济存在自回归条件异方差效应波动,并且GARCH(1,1)最适合描述宏观经济的自回归条件异方差效应波动,上证指数的预测与宏观经济的预测两者之间具有互相印证的关系。     

Volatility Spillovers between the US and China Stock Markets: Structural Break Test with Symmetric and Asymmetric GARCH Approaches

Abstract

The paper examines the short-run spillover effects of daily stock returns and volatilities between the Standard & Poor's (S&P) 500 stock index in the US and the Shanghai Stock Exchange (SSE) index in China. First, we find that a structural break occurred in the SSE stock return mean in December 2005. Second, by analyzing modified general autoregressive conditional heteroscedasticity (GARCH)(1,1)-M models, we find evidence of a symmetric and asymmetric volatility spillover effect from the US to the China stock market in the post-break period. Third, we observe the symmetric volatility spillover effect from China to the US in the post-break period.     

Volatility and Volume in Chinese Stock Markets

The fact that stock market returns in Europe and the USA are characterised by conditional heteroscedasticity is by now well documented in a large literature. We address the question of whether the same is true of the four Chinese stock markets (Shanghai and Shenzhen A and B) over the period from 25 November 1994 to 27 April 2001. Using daily index data, we make two departures from the standard GARCH(1,1) model. First, we use exponential GARCH (EGARCH) to allow for asymmetry in the volatility, which may be present as a result of leverage effects. Second, we respond to evidence of two-way causality between volume and return (and return volatility) by introducing a simultaneous equation model of the relationship. The results of estimating the model indicate that asymmetry does not seem to be present to a significant degree, possibly as a result of lack of information or concern among Chinese investors. We find that volume appears to play a significant part in determining index volatility, which may reflect information arrival effects or may alternatively result from the direct impact of trading on volatility. At the same time, we also find that both the level of returns and their conditional variance have an impact on trade volume, probably because positive (negative) returns tend to attract (deter) investors into the markets.     

Market Risk in Demutualized Self-Listed Stock Exchanges: An International Analysis of Selected Time-Varying Betas

Abstract

This article examines market risk in four demutualized and self-listed stock exchanges: the Australian Stock Exchange, the Deutsche Börse, the London Stock Exchange and the Singapore Stock Exchange. Daily company and the Morgan Stanley Capital International (MSCI) Index returns provide the respective asset and market portfolio data. A bivariate GARCH model is used to estimate time-varying betas for each exchange from listing until 7 June 2005. While the results indicate significant beta volatility, unit root tests show the betas to be mean-reverting. These findings are used to suggest that despite concerns that demutualized and self-listed exchanges entail new market risks that merit regulatory intervention, the betas of the exchange companies have not changed significantly since listing. However, market risk does vary considerable across the exchanges, with mean time-varying betas of 0.56 for the Deutsche Börse, 0.66 for the London Stock Exchange, 0.78 for the Singapore Stock Exchange, and 0.95 for the Australian Stock Exchange.     

Intraday return and volatility spillover mechanism from Chinese to Japanese stock market

We analyze the mechanism of return and volatility spillover effects from the Chinese to the Japanese stock market. We construct a stock price index comprised of those companies that have substantial operations in China. This China-related index responds to changes in the Shanghai Composite Index more strongly than does the TOPIX (the market index of the Tokyo Stock Exchange). This result suggests that China has a large impact on Japanese stocks via China-related firms in Japan. Furthermore, we find evidence that this response has become stronger as the Chinese economy has gained importance in recent years.     

基于日间高频交易数据的股市波动研究

本文利用上证综指在2005～2009年内的日间高频数据,通过已实现波动率这一概念对我国股市在这5年间的波动特性做了研究。进一步地,根据已实现波动率序列的统计特征,对其进行长记忆建模,并对模型的波动率预测效果与常规GARCH模型的预测效果做了对比分析。基于上证综指的研究结果表明,利用了日间高频信息的波动率模型在波动率预测上,比仅利用了收盘信息的GARCH模型更有优势。     

基于不同分布假设GARCH族模型对上证指数波动性分析的比较研究

股票市场的波动性研究已经成为众多研究者和投资者广泛关注的焦点。以上证股票收益率为研究对象,在三种不同的分布假设下,利用GARCH族模型对上证指数波动性进行了比较研究,分析表明:上证股票收益率具有显著的条件异方差性,且基于GED分布的GARCH(1,1)模型是消除该条件异方差性的最佳模型;上证股票收益率具有正的风险溢价,且基于GED分布的GARCH(1,1)-M模型是反映风险溢价情况的最优模型;上证股票收益率存在着明显的不对称性(杠杆效应),利空消息比利好消息更容易引起大的波动,且基于标准正态分布的EGRCH(1,1)模型是揭示该不对称性的最佳模型。     

Market microstructure and price discovery on the Tokyo Stock Exchange

This paper compares the price discovery processes at the opening and closing transactions for the fifty largest stocks trading on the Tokyo Stock Exchange. Open-to-open returns are found to have a greater volatility and a more negative autocorrelation pattern than close-to-close returns, similar to the pattern we found on the New York Stock Exchange. The results are consistent with pricing over-reaction at the opening and partial price-adjustment at the close. These patterns persist over time and prevail when estimated for returns conditional on the contemporaneous market effect. Our analysis of daytime and overnight returns suggest that pricing errors at the opening are corrected over the trading day. We present a new measure of volatility — the relative dispersion of stock returns around the market return — and find that it is greater at the opening, consistent with a more noisy price discovery process.