股指期货的诞生能够帮助投资者对冲持有股票组合的风险，当前我国股票市场上的投资者越来越多，进入股指期货市场进行套期保值的投资者也越来越多。而在我国资本市场上很大一部分投资者都是个人投资者，其相关的专业知识较为薄弱，也伴随着难以克服的心理弱点，对资本市场的有效性也产生较大影响。现有文献大多讨论传统套期保值模型，探究如何在理想状态下选取最优套期保值比率，而较少在模型中考虑投资者情绪对套期保值效果的影响。为此，文章研究基于投资者情绪的股指期货套期保值模型，分析在不同投资者情绪状态下的最小二乘法（OLS）模型、双变量向量自回归（B-VAR）模型、向量误差修正（VECM）模型以及误差修正条件异方差（ECM-GARCH）模型所计算的套期保值率能够实现的套期保值效果，以期寻找不同情绪状态下更合适的套期保值模型。

文章采取文献分析法、实证分析法以及比较分析法对上述问题进行研究，首先梳理文献发展脉络，选出上述四种主流套期保值模型；之后，以沪深300股指期货为例，使用中国个人投资者情绪指数将样本期分为投资者情绪高涨组和低落组，对沪深300现货指数和期货价格进行全样本以及不同投资者情绪状态的上述四种套期保值模型回归，计算出全样本以及不同投资者情绪状态下的不同模型的套期保值率；之后，还将套期保值模型进行改进，利用指数化之后的投资者情绪数据对期货价格进行加权处理，计算改进模型之后的套期保值率；最后，利用套期保值率评价方法，对上述计算的套期保值率进行比较分析。

文章研究发现，建立套期保值模型，计算套期保值比率，较传统套期保值方法能够显著降低套保成本；投资者情绪对套期保值效率的影响十分显著，不同投资者情绪状态下适用的最优套期保值模型不一样，在以上四种模型中，投资者情绪高涨状态下OLS模型的套期保值效果表现最优，而在投资者情绪低落状态下，VECM模型表现最优；在加入投资者情绪数据的模型中，B-VAR模型和VECM模型表现最佳；文章还发现投资者情绪高涨状态下所计算的套期保值比率都低于投资者情绪低落状态下的套期保值比率，这也说明出投资者情绪与套期保值效果之间的关系，能够帮助投资者在不同投资者情绪状态下做出合理决策。综上所述，文章为持有股票现货的投资者进行套期保值提供了新的思路，有利于帮助套期保值者更好地规避市场价格变动等风险，改善利用股指期货进行套期保值的效果。

The birth of stock index futures can help investors hedge the risk of holding stock portfolios. At present, there are more and more investors in China's stock market, and more and more investors enter the stock index futures market for hedging. In China's capital market, a large part of investors are individual investors, whose relevant professional knowledge is relatively weak, but also accompanied by insurmountable psychological weakness, which also has a greater impact on the effectiveness of the capital market. Most of the existing literatures discuss the traditional hedging model and explore how to select the optimal hedging ratio under the ideal condition, but rarely consider the influence of investor sentiment on the hedging effect in the model. Therefore, this paper studies the hedge model of stock index futures based on investor sentiment. The hedging effect of the hedging rate calculated by the least square method (OLS) model, vector autoregression (B-VAR) model, vector error correction (VECM) model and error-corrected conditional heteroscedasticity (ECM-GARCH) model under different investor mood states was analyzed. In order to find a more suitable hedging model under different emotional states.

In this paper, literature analysis, empirical analysis and comparative analysis are adopted to study the above issues. Firstly, the development of literature is sorted out, and the above four mainstream hedging models are selected. Then, taking CSI 300 stock index futures as an example, the sample period was divided into high investor sentiment group and low investor sentiment group by using China Individual investor sentiment Index. The whole sample of CSI 300 spot index and futures prices and the above four hedging models of different investor sentiment states were returned. The hedging rate of the whole sample and different models under different investor mood states is calculated. After that, the hedge model is improved, and the index investor sentiment data is used to weight the futures price, and the hedge rate after the improved model is calculated. Finally, the hedging rate is compared and analyzed by using the hedging rate evaluation method.

It is found that establishing hedging model and calculating hedging ratio can significantly reduce the cost of hedging compared with traditional hedging methods. Investor sentiment has a significant impact on hedging efficiency, and the optimal hedging models applicable to different investor sentiment states are different. Among the above four models, OLS model has the best hedging effect when investor sentiment is high, while VECM model has the best performance when investor sentiment is low. Among the models adding investor sentiment data, B-VAR model and VECM model perform best; It is also found that the hedging ratio calculated under the condition of high investor sentiment is lower than that under the condition of low investor sentiment, which also indicates the relationship between investor sentiment and hedging effect, and can help investors make reasonable decisions under different investor sentiment states. To sum up, this paper provides a new way for investors holding stock spot to hedge, which is conducive to helping hedgers to better avoid risks such as market price changes and improve the hedging effect by using stock index futures.