股指期货是否导致了2015年股灾的爆发?——基于股指期货与现货市场波动溢出效应分析

本文运用1分钟高频交易数据,通过建立DCC-MVGARCH模型和BEKK-MVGARCH模型,研究沪深300、上证50、中证500三种股票指数收益率分别与IF、IH、IC股指期货合约收益率之间的联动性和波动溢出效应,基于该视角分析论证2015年中国股市崩盘是否由股指期货交易导致。研究结果表明,三种股指期货与现货指数的联动性在股市运行平稳期和股灾爆发期间都没有发生较大程度的改变,股票市场的波动主要受自身上一期波动的影响,股指期货对现货市场的波动性影响较小,且这种波动溢出关系在股灾前后并无明显差异,因此,股指期货交易并不是导致此次股灾的主要原因。     

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。     

股指期货的波动溢出效应

我国沪深300股指期货自推行以来,市场运行稳定,交易活跃,达到了预期目标,是我国资本市场走向成熟的标志之一。本文主要从实证分析的角度对股指期货波动溢出效应进行研究。通过对股指期货运行四年来的交易数据建立双变量BEKK-GARCH模型进行研究。研究表明,我国股指期货和股票市场之间存在双向非对称的波动溢出效应,并且股指期货的溢出效应相对更加明显。     

A股股指期货的价格发现能力研究

该文以新加坡新华富时A50股指期货和内地仿真交易的沪深300股指期货为对象,研究A股股指期货的价格发现能力.本文利用Granger因果检验和误差修正模型等方法,实证检验发现新华富时A50股指期货和沪深300股指期货仿真交易均表现出现货价格领先于期货价格的现象,井未表现出价格发现能力.     

沪深300股指期货市场功能发挥的经验研究

2010年4月16日,沪深300股指期货正式启动.现选取沪深300股指期货上市前后各两年(2008年4月15日-2012年4月16日)的期货、现货价格数据和交易量数据,通过分解交易量的方法,运用EGARCH模型检验沪深300股指期货的市场稳定功能;运用VECM模型检验其价格发现功能.结果显示,推出两年后,沪深300股指期货确实在一定程度上发挥了市场稳定与价格发现的功能.总体而言,沪深300股指期货虽然仍有改善的空间,但它却是我国在金融衍生品交易领域的一次成功尝试.未来我国进一步推出其他类型的金融衍生品,应当充分借鉴沪深300股指期货的成功经验.     

沪深300股指期货与现货市场的非线性信息溢出与价格发现功能研究

采用线性与非线性Granger因果检验、协整检验和VECM模型,研究了沪深300股指期货和现货市场的线性与非线性信息溢出,并检验了期货市场的价格发现功能发挥情况。研究结果显示:线性信息溢出方面,沪深300股指期货市场对现货市场只有线性均值信息溢出,现货市场对期货市场只存在线性方差信息溢出;非线性信息溢出方面,两个市场之间不存在非线性均值信息溢出,不过二者之间存在显著的非线性方差信息溢出;沪深300股指期、现货市场之间存在着长期均衡的关系,不过不同于成熟市场中期货市场在价格发现方面居于主导地位的结论,我国股指现货市场在价格发现方面占主导地位,而期货市场处于从属地位。     

A股股指期货的价格发现能力研究

本文以新加坡新华富时A50股指期货和内地仿真交易的沪深300股指期货为对象。研究A股股指期货的价格发现能力。本文利用Granger因果检验和误差修正模型等方法,实证检验发现新华富时A50股指期货和沪深300股指期货仿真交易均表现出现货价格领先于期货价格的现象。并未表现出价格发现能力。     

沪深300股指期货与股指现货市场关联性研究

文章利用协整关系检验、Granger因果检验、误差修正模型等计量方法,对沪深300股指期货上市近2年来的交易数据进行分析,研究发现:我国沪深300股指期货价格与沪深300股指指数间存在长期稳定的均衡关系,且在一定滞后期范围内,存在着从沪深300股指指数到沪深300股指期货价格的单向因果关系,即沪深300股指指数是股指期货价格变化的原因,前者对后者有单向的价格引导作用。     

沪深300股指期货期现套利分析

我国于2010年4月16日正式推出沪深300股指期货,股指期货推出后,沪深300股指期货指数和沪深300现货指数都出现较大波动,随之股指期货套利成为投资者获利或者是规避风险的投资方法之一.而期现套利是应用比较广泛的.     

股指期货定价研究

本文以股票市场的价格波动形式为前提,提出了基于股票市场波动形式的定价模型。并依据有效市场假说得到了股指期货应遵循的微分方程,最终得到股指期货定价公式。而后又将此模型进一步扩展,提出了基于分形市场假说的股指期货价格应遵循的微分方程和定价公式。最后以沪深300指数期货合约为例对推导出的定价公式进行了实证检验。     

股指期货市场操纵与价格发现功能的关系研究

股指期货市场操纵会影响股指期货市场的价格发现功能,同样地,股指期货市场的价格发现功能也会影响股指期货市场的操纵行为。本文在理论探讨的基础上,利用股指期货主力合约及对应的沪深300指数高频数据对市场操纵行为进行实证分析。研究结果表明,当操纵嫌疑只存在于期货市场时,股指期货市场的价格发现功能将会减弱;当操纵嫌疑存在于期货、现货两个市场时,股指期货市场的价格发现功能相对会增强。而且,当股指期货市场价格发现功能较强时,市场操纵的难度和成本都将下降。当前中国股指期货市场的操纵行为可能主要局限于某些个别的、离散的交易日内,系统地通过操纵现货指数来操纵期货市场的可能性较低。     

Price Discovery in Chinese Stock Index Futures Market: New Evidence Based on Intraday Data

Using high-frequency data, this study investigates price discovery in the newly established stock index (CSI300) futures market in China. Our empirical results reveal new evidence that the CSI300 index futures market play a dominant role in the price discovery process about one year after its inception and new information is disseminated more rapidly in the stock index futures market than the stock market. This is different from findings in the previous literature. Our results also imply that the index futures market has evolved and can be used as a price discovery vehicle. Thus the CSI300 stock index futures market plays an important role in the capital markets in China.     

基于波动效应与价格发现的期指仿真交易研究

沪深300股指期货仿真交易的推出,对我国现货市场的影响如何以及这种影响是否有利于现货效率的改进。首次采用修正的GARCH模型和向量误差修正模型（VEC）将股指期货推出后现货市场波动性的变化和股指期货与现货市场的价格发现功能结合起来进行对比研究。结果表明,期指仿真交易的推出对于现货市场效率的改进确实存在正面的影响。其引入在短期内加大了现货市场的波动,但这一波动正是市场信息流动加速的反映,因而提高了市场信息的传递效率。同时期货价格领先于现货价格,存在由期货市场到现货市场长期的单向因果关系,说明期货价格具有引导现货价格向均衡方向调整的功能,从而在经验上支持了股指期货市场的开放政策。     

大陆与台湾股指期货价格发现功能比较研究

本文利用日内15分钟交易数据,对大陆与台湾股指期货的价格发现功能进行了比较,发现沪深300股指期货和现货间存在双向价格引导关系,但在信息传导效率上,期货领先现货,对台湾市场而言,仅存在期货对现货的单向引导关系;期货市场在长期价格发现功能中占主导地位,但台指期货的主导作用要强于沪深300股指期货。文章从投资者结构、合约设计、交易制度等影响因素分析了两岸股指期货价格发现功能的差异,并提出改善大陆股指期货价格发现功能的建议。     

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.     

Modelling International Price Relationships and Interdependencies Between the Stock Index and Stock Index Futures Markets of Three EU Countries: A Multivariate Analysis

This paper addresses the important relationship between stock index and stock index futures markets in an international context. By simply examining the spot‐futures relationship within a single country as most of the extant literature does and thus ignoring possible market interdependencies between countries, the dynamics of price adjustments may be misspecified and thus findings misleading. The main contribution of the paper is to improve our understanding of the pricing relationship between spot and futures markets in the light of international market interdependencies. Using a multivariate VAR‐EGARCH methodology, the paper investigates stock index and stock index futures market interdependence, that is lead‐lag relationships and volatility interactions between the stock and futures markets of three main European countries, namely France, Germany and the UK. In addition, the paper explicitly accounts for potential asymmetries that may exist in the volatility transmission mechanism between these markets. The main conclusions of the paper imply that investors need to account for market interactions across countries to fully and correctly exploit the potential for hedging and diversification.     

Index Futures Trading and Stock Return Volatility: Evidence from the Introduction of Mid Cap 400 Index Futures

The MidCap 400 stock index is used to provide new evidence on the relation between stock index futures trading and stock return volatility. The study documents a significant decrease in return volatility and systematic risk, and a significant increase in trading volume for the MidCap 400 stocks after the introduction of the MidCap index. A control sample of medium-capitalization stocks, however, exhibits similar contemporaneous changes in these measures. The MidCap stocks and the control stocks also experience a significant decrease in volatility and an increase in volume after the introduction of MidCap 400 index futures. Thus, the study finds no difference in the behavior of the MidCap 400 stocks and the control stocks and no evidence of a relation between index futures trading and volatility in the stock market.     

Forecasting Stock Index Futures Price Volatility: Linear vs. Nonlinear Models

The study examines the relative ability of various models to forecast daily stock index futures volatility. The forecasting models that are employed range from naïve models to the relatively complex ARCH-class models. It is found that among linear models of stock index futures volatility, the autoregressive model ranks first using the RMSE and MAPE criteria. We also examine three nonlinear models. These models are GARCH-M, EGARCH, and ESTAR. We find that nonlinear GARCH models dominate linear models utilizing the RMSE and the MAPE error statistics and EGARCH appears to be the best model for forecasting stock index futures price volatility.     

A Tale of Two Index Futures: The Intraday Price Discovery and Volatility Transmission Processes Between the China Financial Futures Exchange and the Singapore Exchange

This is the first study to examine the intraday price discovery and volatility transmission processes between the Singapore Exchange and the China Financial Futures Exchange. Using one- and five-minute high-frequency data from May to November 2011, the authors find that the Chinese Securities Index 300 index futures dominate Singapore's A50 index futures in both intraday price discovery and intraday volatility transmission. However, A50 futures contracts also make a substantial contribution (26-37 percent) to the price discovery process. These results have important implications for both traders and policymakers.