我国期货市场期货价格发现功能的实证研究——以上海期货交易所黄金期货为例

本文通过建立带有滞后项的线性回归模型,来研究上海期货交易所黄金期货合约交易价格与上海黄金交易所黄金现货价格之间的引导关系,以此来研究我国期货市场的价格发现功能。主要结果表明黄金期货交易价格受到自身过去交易价格和黄金现货过去交易价格的双重影响,而黄金现货价格则仅受黄金期货过去价格的影响,表明了黄金期货价格对黄金现货价格有更强的引导作用,表明黄金期货价格发现功能的存在。     

IF1006与沪深300:谁引导谁?

本文首先介绍了期货价格与现货价格关系的主要理论及研究现状,然后利用协整检验、Granger因果检验、GS模型以及误差修正模型对上海、深圳期货交易所沪深300指数与IF1006合约价格进行了实证分析,发现期货滞后价格对现货价格有引导关系,而现货滞后价格对期货价格没有引导关系,利用GS模型发现期货价格在价格发现功能中起主要作用.     

我国期货市场期货价格发现功能的实证研究——以上海期货交易所燃料油期货为例

<正>一、摘要本文通过建立带有滞后项的线性回归模型,来研究上海期货交易所燃料油期货货合约交易价格与黄埔现货价格之间的引导关系,以此来研究我国期货市场的价格发现功能。主要结果表明燃料油期货交易价格受到自身过去交易价格和燃油现货当前交易价格的双重影响,而燃油现货价格则仅受当前期货价格的影响,表明了燃油期货价格对燃油现货价格有更强的引导作用,表明了燃油期货价格发现功能的存在。所谓价格发现(price discovery),是指期货市场通过公开公正和高效竞争的期货交易运行机制,形成具有真实性、预期性、连续性和权威性价格的过程。     

沪深300股指期货价格发现功能实证研究——基于1分钟高频数据

采用Granger因果检验、脉冲响应函数和向量误差修正模型对沪深300股指期货的价格发现功能,以及期货和现货指数之间的领先滞后关系进行研究和分析.研究结果表明,沪深300股指期货在价格发现中起主导作用,期货价格和现货价格之间存在着长期协整关系、双向的Granger因果关系.期货领先现货7分钟     

股指期货与现货市场价格的互动、引导关系研究——基于沪深300股指期货的实证分析

股指期货功能的发挥建立在股指期货与现货市场价格形成有效互动、引导关系的基础之上。本文通过相关性检验和基差序列单位根检验得出沪深300股指期货与现货市场实现了有效互动;通过Granger因果关系检验、协整检验、向量误差修正模型和方差分解结果发现,前一期现货价格引导期货价格,而股指期货价格在价格发现中贡献度较低,在偏离均衡的动态调整过程中对现货价格的引导作用不明显,其价格发现功能未得到充分发挥。最后,根据所得结论给出提高我国股指期货市场信息效率的建议。     

我国股指期货价格发现功能的研究

本文根据沪深300股指期货高频数据,运用协整检验、Granger因果检验来分析股指期货同现货价格之间的领先滞后关系,借助VECM模型和GS模型方法揭示股指期货与现货在价格发现和信息传导中各自的重要程度,得出股指期货价格和现货价格间存在长期稳定的协整关系,股指期货具有价格发现的功能。     

不同到期日期货价格有效性的实证检验

随着期货市场对经济的稳定作用越来越明显,期货市场上现货价格与期货价格之间的动态关系以及我国期货市场的运行效率等一系列问题越来越受到监管者和投资者的关注。本文借助协整检验、误差修正模型(ECM)和脉冲响应等方法,以上海期货交易所金属铜期货品种为例,研究了不同到期日期货的期货价格与现货价格之间的动态关系,刻画出期货市场在价格发现中作用的大小。研究表明金属铜的期货价格与现货价格之间存在长期均衡关系,期货价格与现货价格相互作用、相互影响且互为因果关系,并且期货市场在价格发现功能中处于主导地位。     

股指期货、股市波动性与价格引导——基于沪深300数据的研究

2010年4月16日,股指期货在国内正式上市交易,至今为止,我国股指期货上市已有三年的时间,股指期货对股票市场波动性的影响如何,股指期货与现货的价格引导作用怎样？本文借助TARCH模型以及VAR脉冲响应模型,探究了沪深300股指期货对股市波动性的影响,并且对于股指期货与现货互相之间的价格引导作用进行了研究分析,结果表明：股指期货的上市减弱了股市对于信息的非对称反应程度;股指期货价格的波动对于现货价格的影响大于现货价格波动对于股指期货价格的影响。     

不同到期日期货价格有效性的实证检验

随着期货市场对经济的稳定作用越来越明显,期货市场上现货价格与期货价格之间的动态关系以及我国期货市场的运行效率等一系列问题越来越受到监管者和投资者的关注。本文借助协整检验、误差修正模型(ECM)和脉冲响应等方法,以上海期货交易所金属铜期货品种为例,研究了不同到期日期货的期货价格与现货价格之间的动态关系,刻画出期货市场在价格发现中作用的大小。研究表明金属铜的期货价格与现货价格之间存在长期均衡关系,期货价格与现货价格相互作用、相互影响且互为因果关系,并且期货市场在价格发现功能中处于主导地位。     

A50股指期货与中国内地股市价格发现实证研究

股指期货异地上市不仅会对本土股票市场和衍生品市场产生多方面的影响,而且涉及到本土市场金融定价权等一系列问题。理论上期货价格与现货价格应存在长期关系,并且期货价格具有价格发现功能,先导于现货价格。通过实证研究发现,我国大陆股票市场先导于A50股指期货市场,虽然期货价格和现货价格存在显著的长期均衡关系,但A50股指期货价格发现功能并不显著;其作为一种金融投资产品,没有股指期货的功能,但它对大陆股市的影响仍需引起重视。     

Impacts of Economic Integration on Stock Market Dependence Without Jump Effects

This article investigates the impacts of the Closer Economic Partnership Arrangement (CEPA) on stock market dependence between Hong Kong and China. To avoid the influence of unusual events on stock market dependence, the mixed generalized autoregressive conditional heteroscedastic with the autoregressive jump intensity (GARJI) margin model was modified to exclude jump innovations. The t copula was chosen to estimate the unknown dependence break and measure the average dependence level change. The stock market dependence break occurred about one and a half years after CEPA became effective, and the CEPA increased stock market dependence between Hong Kong and China. Moreover, this article shows the influence of stock market jump effects in the case of CEPA.     

On the relationship between conditional jump intensity and diffusive volatility

In standard options pricing models that include jump components to capture large price changes, the conditional jump intensity is typically specified as an increasing function of the diffusive volatility. We conduct model-free estimation and tests of the relationship between jump intensity and diffusive volatility. Simulation analysis confirms that the tests have power to reject the null hypothesis of no relationship if data are generated with the relationship. Applying the method to a few stock indexes and individual stocks, however, we find little evidence that jump intensity positively depends on diffusive volatility as a general property of the jump intensity. The findings of the paper give impetus to improving the specification of jump dynamics in options pricing models.     

News Arrival, Jump Dynamics, and Volatility Components for Individual Stock Returns

This paper models components of the return distribution, which are assumed to be directed by a latent news process. The conditional variance of returns is a combination of jumps and smoothly changing components. A heterogeneous Poisson process with a time‐varying conditional intensity parameter governs the likelihood of jumps. Unlike typical jump models with stochastic volatility, previous realizations of both jump and normal innovations can feed back asymmetrically into expected volatility. This model improves forecasts of volatility, particularly after large changes in stock returns. We provide empirical evidence of the impact and feedback effects of jump versus normal return innovations, leverage effects, and the time‐series dynamics of jump clustering.     

Evidence of an asymmetry in the relationship between volatility and autocorrelation

This paper focuses on the general determinants of autocorrelation and the relationship between autocorrelation and volatility in particular. Using UK stock market index and individual stock price data, a multivariate generalized autoregressive conditional heteroskedasticity (M-GARCH) model is used to generate estimates of conditional autocorrelation. The covariance equation of this model is modified to include the potential determinants of autocorrelation including volatility, which is proxied using the time series of filtered probabilities of a Markov regime switching model. Consistent with the previous literature, this paper documents a negative relationship between volatility and autocorrelation. The results suggest that an asymmetry exists in this relationship which is attributed to the constraints placed on short selling.     

Modeling the Euro overnight rate

This paper describes the evolution of the daily Euro overnight interest rate (EONIA) by using several models containing the jump component, such as a single-regime ARCH-Poisson–Gaussian process, with either a piecewise function or an autoregressive conditional specification (ARJI) for the jump intensity, and a two-regime-switching process with jumps and time-varying transition probabilities. To model the jump intensity, we include the following effects which are significant for the occurrence of jumps: (1) the end of maintenance period effect because of reserve requirements, (2) the end of month effect, also known as the calendar day effect, caused mainly by accounting adjustments and finally, (3) the meeting effect caused by the meetings of the Governing Council of the European Central Bank (ECB). These effects lead to better performance and several of them are also included for the behavior of the transition probabilities. Since the target of the ECB is to maintain the EONIA rate close to the policy rate, we model the conditional mean of the overnight rate series as a reversion process to this policy rate, distinguishing two alternative speeds of reversion, specifically, a different speed if EONIA is higher or lower than the policy rate. We also study the jumps of the EONIA rate around the ECB's meetings by using the ex-post probabilities of the ARJI model. Finally, we develop a volatility forecasting analysis to measure the performance of the different candidate models.     

Modeling conditional return autocorrelation

Empirical estimates of conditional return autocorrelation are generated over the period 1973 to 2000 for S&P500 index data, as well as for a small selection of individual U.S. stocks. We find that conditional autocorrelation is highly variable, and these dynamics are consistent with changes in point autocorrelation estimates generated in various subperiods. The conditional autocorrelation estimates for some stocks exhibited a pattern of mean reversion, while for others, evidence of long-term trends and structural breaks was found. While we were unable to uncover what characteristics drive the nature of these autocorrelation patterns, our analysis ruled out industry, investor type or degree of internationalisation as explanations.     

The Determinants of Conditional Autocorrelation in Stock Returns

We investigate whether return volatility, trading volume, return asymmetry, business cycles, and day‐of‐the‐week are potential determinants of conditional autocorrelation in stock returns. Our primary focus is on the role of feedback trading and the interplay of return volatility. We present empirical evidence using conditional autocorrelation estimates generated from multivariate generalized autoregressive conditional heteroskedasticity (M‐GARCH) models for individual U.S. stock and index data. In addition to return volatility, we find that trading volume and market returns are important in explaining the time‐varying patterns of return autocorrelation.     

Modelling conditional heteroscedasticity and jumps in Australian short-term interest rates

The present paper explores a class of jump–diffusion models for the Australian short‐term interest rate. The proposed general model incorporates linear mean‐reverting drift, time‐varying volatility in the form of LEVELS (sensitivity of the volatility to the levels of the short‐rates) and generalized autoregressive conditional heteroscedasticity (GARCH), as well as jumps, to match the salient features of the short‐rate dynamics. Maximum likelihood estimation reveals that pure diffusion models that ignore the jump factor are mis‐specified in the sense that they imply a spuriously high speed of mean‐reversion in the level of short‐rate changes as well as a spuriously high degree of persistence in volatility. Once the jump factor is incorporated, the jump models that can also capture the GARCH‐induced volatility produce reasonable estimates of the speed of mean reversion. The introduction of the jump factor also yields reasonable estimates of the GARCH parameters. Overall, the LEVELS–GARCH–JUMP model fits the data best.     

On the Relation between the Expected Value and the Volatility of the Nominal Excess Return on Stocks

We find support for a negative relation between conditional expected monthly return and conditional variance of monthly return, using a GARCH-M model modified by allowing (1) seasonal patterns in volatility, (2) positive and negative innovations to returns having different impacts on conditional volatility, and (3) nominal interest rates to predict conditional variance. Using the modified GARCH-M model, we also show that monthly conditional volatility may not be as persistent as was thought. Positive unanticipated returns appear to result in a downward revision of the conditional volatility whereas negative unanticipated returns result in an upward revision of conditional volatility.     

The impact of co-jumps in the oil sector

We study the dynamics of the oil sector using a new multivariate stochastic volatility model with a structure of common factors subjected to jumps in mean and conditional variance. This model contributes to the literature allowing the estimation of spillover effects between assets in a multivariate framework through joint jumps (co-jumps), identifying the permanent and transitory effects through a structure defined by Bernoulli processes. The jump structure introduced in the article can be interpreted as a regime-switching model with an endogenous number of states, avoiding the difficulties associated with models with a fixed number of regimes. We apply the model to oil prices and stock prices of integrated oil companies. The jump structure allows dating the relevant events in the oil sector in the period 2000–2019. The period analyzed encompasses important events in the oil market such as the price escalation in 2008 and the falling prices in 2014. We also apply the model to estimate risk management measures and portfolio allocation and perform a comparison with other multivariate models of conditional volatility, showing the good properties of the model in these applications.