沪深300股指期货统计套利策略研究

发布时间：2018-05-14 01:12

本文选题：统计套利 + 协整　；参考：《河北金融学院》2017年硕士论文

【摘要】：股指期货是指将股票价格指数作为交易的标的资产,形成的标准化合约,是一种金融衍生产品。沪深300股票指数期货以沪深300指数为标的,具有较高的市场占有率,在同类产品中具有良好的代表性和稳定的市场表现,对于市场的周期性波动有较好的反应。本论文的目的在于发现投资机会,寻找并改进能够在实现投资者保值增值的同时稳定市场的投资策略,因此将其作为研究对象,同时预测在未来期货市场交易中定量投资策略将成为主流。沪深300股指期货交易具有使用保证金交易制度、当日无负债结算制度、合约有到期日、交易对象是标准化的期货合约等特点。本文针对其特点研究利用股指期货合约之间价差变动的套利方法。基本的跨期套利通过构建和对冲相同数量、相反头寸、不同月份、相同的标的合约来获益。具体分为牛市套利、熊市套利和蝶式套利。统计套利是按照历史数据,运用统计分析工具来研讨价格变动趋向是否稳定及价差分布是否具有规律性。由价差中心化序列变动情况得到套利区间出现的时机及概率,建立正确的止损边界,以相对更小的风险博取更多收益的套利方式。在本论文中选取了沪深300指数期货IF1007和IF1008合约1分钟的高频数据,并经过协整检验构建统计套利模型。具体的步骤是根据趋同性原则确定研究对象,由价差分布建立交易规则,制定进出场信号和止损信号。股票指数期货合约时间序列的价差序列的标准差随时间变化而改变,具有时变方差的特征,因此利用GARCH模型描述残差的条件异方差性,通过TARCH分析波动冲击的影响,建立价差中心化序列,确定止损阈值。本论文研究发现沪深300股指期货存在统计套利机会,统计套利是帮助投资人在金融市场上寻求套利机会的一种分析方法,其基本原理是选取具有稳定关系的价差序列,用统计分析方法估量出价差的均衡范围及偏离概率分布,进而对偏离价差进行投资套利。券商和投资者能够以适当的风险得到比无风险套利更多的收益。
[Abstract]:Stock index futures is a kind of financial derivative product, which takes the stock price index as the underlying asset and forms a standardized contract. Shanghai and Shenzhen 300 stock index futures have a high market share, and have a good representative and stable market performance in the same kind of products, and have a good response to the periodic fluctuations of the market. The purpose of this paper is to find out the investment opportunities, to find and improve the investment strategy that can stabilize the market while maintaining and increasing the value of investors, so we take it as the object of study. At the same time forecast in the future futures market trading quantitative investment strategy will become the mainstream. Shanghai and Shenzhen 300 stock index futures trading has the characteristics of using margin trading system, no debt settlement system on that day, and the contract has maturity date, and the trading object is the standardized futures contract, and so on. According to its characteristics, this paper studies the arbitrage method of the variation of price difference between stock index futures contracts. Basic intertemporal arbitrage benefits by building and hedging the same number of positions, different months, the same underlying contracts. Specifically divided into bull arbitrage, bear arbitrage and butterfly arbitrage. Statistical arbitrage is based on historical data, using statistical analysis tools to study whether the price trend is stable and whether the spread distribution is regular. The timing and probability of arbitrage interval are obtained from the variation of the spread centralization sequence, and the correct stop loss boundary is established, and the arbitrage way of obtaining more income with a relatively small risk is established. In this paper, the high frequency data of IF1007 and IF1008 contracts of CSI 300 index futures are selected, and the statistical arbitrage model is constructed by cointegration test. The specific steps are to determine the object of study according to the principle of convergence, to establish the trading rules by the spread of the spread, and to formulate the signals of exit and exit field and stop loss. The standard deviation of time series of stock index futures contracts varies with time and has the characteristic of time-varying variance. Therefore, the conditional heteroscedasticity of residual is described by GARCH model, and the influence of fluctuation shock is analyzed by TARCH. The central sequence of the price difference was established to determine the stop loss threshold. This paper finds that there are statistical arbitrage opportunities in Shanghai and Shenzhen 300 stock index futures. Statistical arbitrage is an analytical method to help investors to seek arbitrage opportunities in financial markets. Its basic principle is to select a stable spread sequence. The equilibrium range and deviation probability distribution of the spread are estimated by the statistical analysis method, and then the investment arbitrage of the deviation price difference is carried out. Brokers and investors can earn more than risk-free arbitrage at the right risk.
【学位授予单位】：河北金融学院
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：F724.5

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