股指期货对中国A股市场波动性影响的实证研究

本文对广泛使用的GARCH模型进行了修正,以此来检验股指对股市波动性的影响。本文研究采用的数据为沪深300指数,样本数据分为了短期(股指期货上市前后三个月共64个交易日)、中期(股指期货上市前后1年共242个交易日)和长期(股指期货上市前后近3年共690个交易日)三个时间段,并且中期和短期的沪深300指数数据采用了日和分钟两种频率。实证表明,无论是对股市整体还是中小盘市场,股指期货的推出在短期内加大了股市的波动,中期和长期看则没有显著的影响。     

股指期货对中国A股市场波动性影响的实证研究

本文对广泛使用的GARCH模型进行了修正,以此来检验股指对股市波动性的影响.本文研究采用的数据为沪深300指数,样本数据分为了短期(股指期货上市前后三个月共64个交易日)、中期(股指期货上市前后1年共242个交易日)和长期(股指期货上市前后近3年共690个交易日)三个时间段,并且中期和短期的沪深300指数数据采用了日和分钟两种频率.实证表明,无论是对股市整体还是中小盘市场,股指期货的推出在短期内加大了股市的波动,中期和长期看则没有显著的影响     

股指期货和沪深300指数联动关系实证研究

利用多元GARCH模型,本文评价了股指期货和沪深300数量联系。实证研究发现,股指期货和沪深300的存在密切的先行滞后关系,股指期货和沪深300之间的波动也存在显著的联系。并且本文对投资者的投资策略提出相关的建议。     

沪深300股指期货最优套期保值实证研究

我国股市近年波动剧烈,股票市场系统性风险可见一斑。为了可以使广大投资者在沪深300股指期货推出后能迅速正确地对其利用来规避系统性风险以及最大限度地实现套期保值．本文对沪深300股指期货套期保值进行了实证研究。在分析和比较常用的几种股指期货最优套期保值比率确定模型的基础上,基于方差最小化模型框架,利用沪深300股指期货合约模拟运行以来的样本数据,通过OLS法、VAR模型、ECM模型和GARCH模型四种估计方法,对其最优套期保值比率进行了实证测算和绩效比较．提出了相应的政策建议和投资策略。     

非同步交易时间段下股指期现货开收盘收益率间动态关系的实证分析

本文运用E-G协整、Granger因果检验和方差分解,基于5分钟高频数据,对沪深300股指期货及其股票指数的开盘价格、收盘价格之间的引导关系进行检验。结果表明:股指期货开盘收益率对于沪深300的开盘收益率具有强烈的指导作用;沪深300收盘收益率对于股指期货的收盘收益率的指导作用明显,这说明投资者可以在股市收盘后,根据股市反应的信息调整股指期货的投资策略;第一天股指期货收盘收益率对于沪深300的开盘收益率具有较强的引导作用,这说明投资者对隔夜信息的反应效率较强。     

股指期货对抑制股市波动的有效性研究

本文基于2006年到2012年的沪深300股指对数收益率日数据,采用引入虚拟变量的GARCH模型及TGARCH模型,实证分析股指期货制度对抑制中国股票现货市场波动的有效性及新信息引起的股市波动非对称性。结果表明:股指期货推出后,在减小现货市场波动性的同时,降低了投资者对利空消息的敏感性,一定程度上弱化了"追涨杀跌"等市场非理性行为。     

股指期货与现货指数的波动溢出效应

以沪深300现货指数与沪深300股指期货指数的月度数据作为研究对象,基于HP滤波分析、Granger因果性检验、向量自回归模型等方法研究了股指期货与股票现货市场间的波动溢出效应,结果表明沪深300股指期货风险与沪深300指数之间不仅存在长期的均衡关系,同时两市场具有双向的波动溢出效应,股指期货市场的波动溢出强于股票现货市场的波动溢出。     

股指期货对现货波动性和信息传递速度的影响——基于沪深300数据的实证

本文通过选取沪深300股指期货推出前后具有相似基本面、相同走势的区间数据进行分组比较,并利用GARCH模型进行拟合检验,发现沪深300股指期货的推出在短期内由于投资者结构不完善等原因导致现货波动增大,但很快又急速变小,开始显现稳定股市波动的作用.此外,股指期货的引入导致噪声交易增多,价格所包含信息减少,但同时也提高了信息的传递速度.     

股指期货是股灾的“幕后推手”吗——基于2015年股灾期间沪深300股指期货高频数据实证分析

选取2015年6月15日至8月26日股灾期间沪深300股指期货与沪深300指数5分钟高频数据,通过E-G两步协整检验、格兰杰因果检验、脉冲响应模型等,对股灾期间股指期货市场价格发现功能及波动溢出效应进行实证研究.结果表明:股灾期间沪深300股指期货仍具备价格发现功能,但存在对现货市场的单向波动溢出,具有一定的"助跌"效应.     

基于非对称Laplace分布的ES方法在我国沪深300股指期货市场的研究

股指期货具有价格发现,规避风险以及提高资金配置效率等功能。我国于2010年4月16日正式推出沪深300股指期货,这是我国资本市场发展的一大里程碑。股指期货的跨期性、杠杆性、联动性和多样性等特点很好地弥补金融市场的缺陷,不仅可以健全股票市场的价格机制还可以大大降低交易成本,并且为广大参与者提供了一个可以避险的投资工具。沪深300股指期货的推出彻底改变了我国金融市场风险管理工具缺乏的现状,但同时也面临股指期货高杠杆交易巨大风险的挑战。本文将运用ES模型度量股指期货的基差风险,最后实证研究预测沪深300股指期货的基差风险。本文将在国内外相关理论的研究基础上,运用非对称Laplace分布来拟合沪深300股指期货的基差;建立非对称Laplace分布下的Expected Shortfall模型来分析沪深300股指期货的波动情况;计算出该模型下的ES值和Va R值;分别将模型得出的Va R和ES值与实际的基差波动进行比较,考察ES和Va R对实际基差波动的拟合情况;对ES和Va R分别进行有效性检验;最后对我国目前使用Expected Shortfall模型还存在的问题进行简要的阐述,给出我国沪深300股指期货管理的相关经验。     

基于最小生成树的超度量聚类的若干金融案例分析

本文介绍了基于最小生成树的超度量聚类方法,并应用该方法做了两个案例：一是对2007-2008年全球金融危机中世界主要国家的股指进行了实证分析。揭示了金融危机在全球传递的特征和全球股市的内在结构;二是对股指期货上市前后的中国期货市场交易品种进行了实证分析,揭示了期货市场的内在结构。     

Implied Volatility in Options Markets and Conditional Heteroscedasticity in Stock Markets

This study examines whether or not the volatility of stock index returns forecasted by a GARCH-M specification is consistent with the implied volatility observed in options markets. Recent data for the New York Stock Exchange Composite Index and Standard & Poor's 500 Index and their options are employed. The patterns of the term structure of implied volatility are compared with those of volatility estimates obtained from the GARCH process. The results indicate that the GARCH process appears to partially explain the variation of implied volatilities and the term structure of implied volatilities.     

股指期货对市场波动性影响的比较——基于非对称GARCH模型的探讨及成因分析

在理论上,股指期货为投资者提供了不同于传统证券的投资工具及避险工具,为控制市场风险提供了可能。但在实践中,股指期货的推出并不必然地降低证券市场的波动性。为此,本文选取处于不同发展阶段的八个国家和地区的市场予以考察,试图比较在理论和实践层面,波动性产生差异的成因。其结果显示:一、股指期货推出对市场波动性的影响,除日本显著增大,印度显著减小外,其余市场均无显著变化;二、有6个市场存在非对称效应,即利空消息对市场的冲击显著强于利好消息;三、将股指期货推出的影响从总信息冲击中抽离后发现,其推出对于比利时、中国香港、中国台湾和中国大陆是利好消息,对日本是利空消息,其余市场没有显著变动;四、上述变动的差异是宏观经济、市场结构和交易者行为共同作用的结果;五、对于中国市场而言,应当着重改善市场结构,引导交易者理性投资。     

Stock market closure and intraday stock index futures market volatility: “contagion”, bid–ask bias or both?

Chang et al. [Journal of Business 68 (1) (1995) 61] examine the impact of the closure of the New York Stock Exchange (NYSE) on S&P500 stock index futures traded on the Chicago Mercantile Exchange. They document a decline in futures market volatility immediately after the close of the NYSE, and an increase 15 minutes later when the futures market closes. They attribute this to contagion–i.e. a decline in information transfer from equities to futures markets following the closure of the underlying market. This paper examines the impact of the extension of trading hours in Hang Seng Index futures traded on the Hong Kong Futures Exchange on the 20 November, 1998 to 15 minutes after the close of the underlying market (the Stock Exchange of Hong Kong). Using the unique natural experiment provided by this change, a pattern similar to US markets is documented for the Hang Seng Index Futures following the change in trading hours. This provides strong evidence that the intraday pattern in volatility is caused by market closure. Unlike US futures exchanges, price reporters on the floor of the Hong Kong Futures Exchange collect quote data in addition to trade data. This data facilitates a test of another plausible microstructure explanation for the observed behaviour–bid–ask bounce associated with trading activity. This paper provides evidence that bid–ask bounce also explains part of the observed intraday behaviour in price volatility.     

股指期货与股票现货市场竞争关系研究——来自中国的经验证据

本文利用模拟生态学中种群间动态关系的Lotka-Volterra模型,对沪深300股指期货同股票现货市场在交易规模方面的竞争关系进行实证分析。研究结果表明,沪深300股指期货推出初期,股指期货市场与股票现货市场在交易规模方面存在竞争性的交易转移效应;随着股指期货市场相关规则的不断健全和完善,股指期货市场与股票现货市场在交易规模方面由竞争关系转变为共存关系,出现交易引资效应。同时,研究还发现,股指期货市场与股票现货市场之间关系由竞争性转变为共存性的重要原因是股指期货市场监管力度的加大,股指期货市场投资者结构的优化,以及股指期货市场期现套利交易的盛行。     

The macroeconomic determinants of technology stock price volatility

Stock prices reflect the value of anticipated future profits of companies. Since business cycle conditions impact the future profitability of firms, expectations about the business cycle will affect the current value of firms. This paper uses daily and monthly data from July 1986 to December 2000 to investigate the macroeconomic determinants of US technology stock price conditional volatility. Technology share prices are measured using the Pacific Stock Exchange Technology 100 Index. One of the novel features of this paper is to incorporate a link between technology stock price movements and oil price movements. The empirical results indicate that the conditional volatilities of oil prices, the term premium, and the consumer price index each have a significant impact on the conditional volatility of technology stock prices. Conditional volatilities calculated using daily stock return data display more persistence than conditional volatilities calculated using monthly data. These results further our understanding of the interaction between oil prices and technology share prices and should be of use to investors, hedgers, managers, and policymakers.     

The impacts of climate policy uncertainty on stock markets: Comparison between China and the US

The interplay between climate policy uncertainty and stock market performance has emerged as a pressing research question in light of the challenges posed by climate change to financial markets. This paper measures China's daily and monthly climate policy uncertainty (CPU) from Jan 2000 to Mar 2022 based on Chinese news data for the first time. Then, the nonlinear and lag impacts of the US CPU and China's CPU on the return, volatility, correlation and tail dependence of China's and US stock markets are investigated and compared by adopting copula function and the distribution lag nonlinear model (DLNM). The data of stock markets includes the Shanghai Composite Index (SSCI) and NASDAQ from Jan 2000 to Mar 2022 from the Choice database, and the Shenzhen Composite Index (SCI) and S&P 500 are used for the robustness test. The empirical results indicate that (1) the growth trend of China’s CPU index is similar to that of the US. However, there are significant differences between the impacts of these two CPUs on stock markets. (2) For China, high CPU decreases current stock market return and increases volatility but decreases it in the future. It could also increase the upper tail dependence between China’s and the US stock markets’ volatilities in current period. (3) For the US, CPU decreases stock market return in the short term but increases it in the long term. High CPU increases volatility in short term, decreases volatility in 5 months and increases it again after 6 months. Both low and high CPU could increase the correlation between China's and US stock markets' volatilities.     

股指期货推出对现货市场价格影响的理论分析

本文构建一个由策略交易者、趋势跟随者、流动性提供者以及套利者组成的多期多市场决策模型,分析在股指期货推出时间给定的条件下,推出前后大盘价格的变动。我们发现,股指期货的引入对股票现货市场的短期效应受市场跟风行为的强弱,即羊群效应大小的影响。当跟风程度较弱时,股指在期货推出前短期下跌,推出后继续下跌;而当跟风程度较强时,股指在期货推出前短期上涨,推出后则下跌。由于国内投资者具有很强的跟风倾向,该模型预测股指期货在推出前短期内将抬高大盘,推出后则压低大盘。另外,股指期货的推出也可能降低市场的波动性。     

Studies of the 1987 stock market crash: Review and appraisal

Within four months of the stock market crash on October 19, 1987, there were six studies of what happened. The Brady Commission, the Commodity Futures Trading Commission, the Securities and Exchange Commission, the General Accounting Office, the New York Stock Exchange, and the Chicago Mercantile Exchange all produced reports that described and analyzed the Crash, and in some cases made recommendations for additional regulation. This paper examines the conclusions and analyses contained in these reports and provides a summary of their recommendations. Particular attention is given to the allegation that stock index futures trading was a significant factor in the Crash. In addition, the recommendations that higher margins be imposed on futures transactions and that formal trading halts be instituted in both the futures and stock markets are discussed in depth. A major conclusion of this review is that new market-making procedures are needed to cope with the growing institutionalization of trading in equity and equity-derivative markets. Columbia University     

A Likelihood Ratio Test of Price Volatilities: Comparing Stock Index Spot and Futures

This paper uses three methods to estimate the price volatility of two stock market indexes and their corresponding futures contracts. The classic variance measure of volatility is supplemented with two newer measures, derived from the Garman-Klass and Ball-Torous estimators. A likelihood ratio test is used to compare the classic variance measure of price volatilities of two stock market indexes and their corresponding futures contracts during the bull market of the 1980s. The stock market volatilities of the Standard & Poor's 500 (S&P 500) and New York Stock Exchange (NYSE) indexes were found to be significantly lower than their respective futures price volatilities. Since information may flow faster in the futures markets than in the corresponding stock market, our results support Ross's information-volatility hypothesis. It was also noted that the NYSE spot volatility was lower than the S&P 500 spot volatility. If the rate of information flow and firm size are positively related, then the lower NYSE spot volatility is explained by the size effect. The futures price volatilities for the two indexes were insignificantly different from each other. With stock index spot-futures price correlations approaching unity, one implication of our results for index futures activity is that smaller positions in futures contracts may suffice to achieve hedging or arbitrage goals.