浅析我国推行股指期货的必要性和可行性

股指期货 (全称股票指数期货 )是一种以股票价格指数作为标的物的金融期货合约。它具有三个经济功能 :一是价格发现功能。二是稳定市场和增强流动性的功能。三是促进资本形成。由于股票指数基本上能代表整个市场股票价格变动的趋势和幅度 ,人们开始尝试着将股票指数改造成一种可交易的期货合约并利用它对所有股票进行套期保值 ,规避系统风险 ,于是股指期货应运而生。我国现阶段推行股票指数交易是否必要与可行 ,需要考虑各方面的因素。本文分别从以下几个方面来阐述我国现阶段推行股票指数交易的必要性与可行性。一、我国推行股指期货的必要…     

股指期货与现货价格引导关系研究——基于中国市场的实证分析

本文基于沪深300股指期货IF1108的1分钟高频交易数据,对中国股指期货与现货间价格引导关系进行了实证研究。结果发现:股指期货在价格发现中起主导作用;股指期货价格领先于现货股指2-30分钟,现货股指领先于股指期货价格的时长不超过13分钟;股指期货价格与现货股指间存在长期均衡关系。     

金鱼缸效应在防止股指期货内幕交易中的应用

一、股指期货的作用股指期货是一种以股票价格指数为标的物的金融期货合约,由交易双方订立的、约定在未来某一特定时间按约定价格进行股价指数交易的一种标准化合约。股指期货交易的实质是投资者将其对整个股票市场价格指数的预期风险转移至期货市场的过程,通过对股票趋势持不同判断的投资者对合约的买卖,来冲抵股票市场风险。股票指数期货不涉及股票本身的交割,其价格根据股票指数计算,合约以现金清算形式进行交割。股指期货为现货市场投     

基于高频数据的我国股指期货和现货价格互动关系实证分析

股指期货是以股票价格指数为标的物的金融期货品种。本文旨在应用高频数据分析股指与股指期货日内互动关系。通过协整检验、误差修正模型及Granger因果分析,结果表明:我国股指的期现价格存在长期协整关系,股指期货和现货市场存在相互引导关系,股指期货对股指现货价格发现机制较为显著。     

股指期货交易量与股指现货波动关系研究——来自香港恒生指数的实证

文章以香港恒生股票指数及其期货为样本,研究了股指波动性与指数期货交易量之间的关系。研究结果表明,它们之间存在单向因果关系,股指现货市场的日间价格波动并没有明显增加股指期货的交易,但股指期货的交易量却对指数现货的波动性产生延迟影响,这从一定程度上反映了香港市场股指期货主要被投资者用于套利而不是风险对冲的工具。     

股指期货风险测算及监管

股票指数能够对所选择的一组股票价格的变动指标进行衡量与反映,而股指期货与利率期货和外汇期货等其他期货相同,是专门为人们对股票市场价格风险进行管理而设计的,它作为金融期货市场新生代的品种,具有非常快速的发展历程。股指期货就是投资者转移风险的过程,将股票指数的预期风险转移到期货市场,风险的抵消主要是通过投资者对股市走势持不同判断的买卖操作。但是由于目前交易主体结构不健全,选取标的物以及现货的交易机制存在一定的缺陷,加上我国政府具有不合理的干预机制等多种原因造成股指期货风险。一旦没有加强对股指期货的管理与运用不当,则会给投资者造成巨大的财产损失,甚至会在一定程度上对国家的金融秩序造成扰乱。本文在对股指期货存在的风险进行讨论的基础上,对股指期货风险测算以及监管措施提出相应的对策,旨在促进股指期货更好的发展。     

模拟股指期货价格与现货价格关系研究

本文的研究对象是以沪深300指数为标的的股指期货与现货指数价格之间的关系。2005年正式发布的沪深300指数,并于2006年正式成立了中国金融交易所,推出了沪深300股指期货的模拟交易。本文主要利用Granger因果检验和协整理论实证分析股指期货和现货指数价格的相互引导关系,并用脉冲响应函数度量相互影响的大小。通过沪深300股指期货与指数的实证分析,得出结论:在2008年10月至2009年5月的这段时间内,模拟股指期货价格与现货价格之间互为Granger原因,但股指期货对现货的影响要大一些,股指期货的价格引导现货价格。另外,协整检验表明,模拟股指期货价格与现货价格之间存在长期的协整关系。     

中国股指期货与现货关系的实证研究——基于沪深300股指期货

股指期货与现货的关系一直是一个研究热点。但是这些研究主要基于国外股指期货或沪深300股指期货仿真交易,而对正式推出后的沪深300股指期货的研究很匮乏。文章利用相关性分析和Granger因果检验分析了沪深300股指期货对现货之间的价格发现功能,利用GARCH类模型分析了沪深300股指期货推出对现货波动性的影响。结论显示,沪深300股指期货与现货之间有着很强的相关性,但不构成相互决定关系;沪深300股指期货的推出降低了现货的波动性;同时,该股指期货的推出使现货收益的非对称性显著增强。上述结论与目前已有的对仿真交易沪深300股指期货的研究结论存在着一些不一致。     

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。     

股指期货交易中的策略运用

基于股指期货推出后极强的（做空）心理暗示，A股市场从二季度开始相继进入中期调整，使看空的投资者可以借股指期货来获利。由于股指期货初期的参与者以散户为主，资金出于对新市场的敏感和谨慎态度，敢于隔夜持仓的交易并不多见，交易者大多选择盘中了结和当天收盘前出局。所以，即便行情出现较大幅度的连续下跌，     

Forecasting value at risk with intra-day return curves

Methods for incorporating high resolution intra-day asset price data into risk forecasts are being developed at an increasing pace. Existing methods such as those based on realized volatility depend primarily on reducing the observed intra-day price fluctuations to simple scalar summaries. In this study, we propose several methods that incorporate full intra-day price information as functional data objects in order to forecast value at risk (VaR). Our methods are based on the recently proposed functional generalized autoregressive conditionally heteroscedastic (GARCH) models and a new functional linear quantile regression model. In addition to providing daily VaR forecasts, these methods can be used to forecast intra-day VaR curves, which we considered and studied with companion backtests to evaluate the quality of these intra-day risk measures. Using high-frequency trading data from equity and foreign exchange markets, we forecast the one-day-ahead daily and intra-day VaR with the proposed methods and various benchmark models. The empirical results suggested that the functional GARCH models estimated based on the overnight cumulative intra-day return curves exhibited competitive performance with benchmark models for daily risk management, and they produced valid intra-day VaR curves.     

What We can Learn About the Behaviour of Firms from the Average Monthly Frequency of Price‐Changes: An Application to the UK CPI Data

The monthly frequency of price‐changes is a prominent feature of many studies of the CPI micro‐data. In this paper, we see what the frequency implies for the behaviour of price‐setters in terms of the cross‐sectional distribution average of price‐spell durations across firms. We derive a lower bound for the mean duration of price‐spells averaged across firms. We use the UK CPI data at the aggregate and sectoral level and find that the actual mean is about twice the theoretical minimum consistent with the observed frequency. We construct hypothetical Bernoulli–Calvo distributions from the frequency data which we find can result in similar impulse responses to the estimated hazards when used in the Smets–Wouters (2003) model.     

Autocorrelation of the trade process: Evidence from the Helsinki Stock Exchange

This study investigates how duration-based trading intensity modifies the first-order autocorrelation and the transitory variance of the trade process. Because prices are conditional expected values, a structural model in which the trade duration represents the rate at which prices incorporate new information is developed. This refined model is an extension of the one developed by Madhavan, Richardson, and Roomans (1997) and allows parameters characterizing the arrival rate of new information to be derived. Testing this model with data from the Helsinki Stock Exchange, I was able to determine that a model ignoring trading intensity effects on price changes would underestimate the transitory effects of the trade process. This finding suggests that trade duration captures neglected elements of implicit trading costs that increase with market microstructure effects.     

Traditional beta, downside risk beta and market risk premiums

The article develops a downside risk asset-pricing model, which is based on Conditional-VaR (Mean-shortfall) risk measure. As in the traditional model the model leads to a monetary separation and yields a CVaR beta analogous to the traditional beta. An empirical study indicates that CVaR beta, which considers also downside risk, has greater explanatory power than the traditional beta. This is especially true in the case of a bearish market. Moreover, a combined model, which uses both betas, outperforms both the traditional and the CVaR models.The results indicate that in a bullish economy, risk premiums may be partially explained by the traditional beta. However, in a depressed economy investors are most likely more concerned about downside risk, which is poorly captured by the traditional beta. This downside risk can best be captured by CVaR beta, which is based on historical data and avoids assuming any prior distribution.     

基于分形理论的原油期货高频交易风险测度研究

为了捕捉原油期货高频波动规律,采用WTI原油期货五分钟数据,基于分形理论分别构建GED分布和Skew-t分布的FIGARCH、FIAPARCH和HYGARCH模型,分析其波动特征并对风险进行测度。结果显示：三种模型均较好地刻画出WTI原油期货波动的长记忆特征;基于Skew-t分布的HYGARCH模型在度量原油期货高频交易风险时尤为精确;多头与空头头寸的VaR呈现非对称性;套期保值者或高频交易者可依据模型预测波动率,防止短期波动率过大导致保证金不足而被强制平仓。高频交易在提高市场流动性和拓宽市场深度方面具有一定的作用,因此,在风险可控的条件下,政府应该鼓励高频交易,促进我国衍生品市场繁荣发展,并增强衍生品市场稳定性和国际竞争力。     

基于注意力机制的LSTM股价趋势预测研究

针对中国股票市场,提出了一种基于注意力机制的LSTM股价趋势预测模型。选取42只中国上证50从2009年到2017年的股票数据为实验对象,根据股票市场普遍认可的经验规则,分别对每个技术指标进行量化处理得到股票涨跌的趋势数据,并和交易数据混合作为预测模型的输入,然后使用基于注意力机制的LSTM模型提取股价趋势特征进行预测。实验结果表明:引入股票离散型趋势数据到预测模型中,能够在已有交易数据和技术指标的基础上提升预测精确度,与传统的机器学习模型SVM和单一的LSTM模型相比,基于注意力机制的LSTM模型具有更好的预测能力。     

A comparison of financial duration models via density forecasts

Using density forecast evaluation techniques, we compare the predictive performance of econometric specifications that have been developed for modeling duration processes in intra-day financial markets. The model portfolio encompasses various variants of the Autoregressive Conditional Duration (ACD) model and recently proposed dynamic factor models. The evaluation is conducted on time series of trade, price and volume durations computed from transaction data of NYSE listed stocks. The results show that simpler approaches perform at least as well as more complex methods. With respect to modeling trade duration processes, standard ACD models successfully account for duration dynamics while none of the models provides an acceptable specification for the conditional duration distribution. We find that the Logarithmic ACD, if based on a flexible innovation distribution, provides a quite robust and useful framework for the modeling of price and volume duration processes.     

Prospect theory and risk appetite: an application to traders’ strategies in the financial market

According to behavioral finance theories, in this article we develop a dynamic model with heterogeneous traders, where the asset price is determined by the interaction among four different groups of agents: trend reversers, trend followers, risk averters and risk seekers. The main purpose of the study is centered on modeling and testing how the market efficiency changes along with the changes of agent’s behavior preference without exogenous influence. Combining with the assumption of risk appetite and prospect theory, focusing on analyzing the rules for selecting strategies, we establish a more reliable and comprehensive dynamic mechanism. In particular, our study suggests that diversified trading strategies will help to realize market efficiency.     

Intraday portfolio risk management using VaR and CVaR:A CGARCH-EVT-Copula approach

The study forecast intraday portfolio VaR and CVaR using high frequency data of three pairs of stock price indices taken from three different markets. For each pair we specify both the marginal models for the individual return series and a joint model for the dependence between the paired series. We have used CGARCH-EVT-Copula model, and compared its forecasting performance with three other competing models. Backtesting evidence shows that the CGARCH-EVT-Copula type model performs relatively better than other models. Once the best performing model is identified for each pair, we develop an optimal portfolio selection model for each market, separately.     

An inflated multivariate integer count hurdle model: an application to bid and ask quote dynamics

In this paper we develop a model for the conditional inflated multivariate density of integer count variables with domain ?ⁿ, n ∈ ?. Our modelling framework is based on a copula approach and can be used for a broad set of applications where the primary characteristics of the data are: (i) discrete domain; (ii) the tendency to cluster at certain outcome values; and (iii) contemporaneous dependence. These kinds of properties can be found for high‐ or ultra‐high‐frequency data describing the trading process on financial markets. We present a straightforward sampling method for such an inflated multivariate density through the application of an independence Metropolis–Hastings sampling algorithm. We demonstrate the power of our approach by modelling the conditional bivariate density of bid and ask quote changes in a high‐frequency setup. We show how to derive the implied conditional discrete density of the bid–ask spread, taking quote clusterings (at multiples of 5 ticks) into account. Copyright © 2009 John Wiley & Sons, Ltd.