棉花作为中国重要的经济农作物，在农业经济方面做出了巨大贡献。尤其在我国新疆地区，棉花作为主要种植的农产品之一，对新疆地区经济发展、农民创收的贡献尤为显著。棉花期货品种上市后，交易量逐年上升，为我国建立健全农产品期货市场提供了宝贵经验。但纵观历史数据，发现我国棉花期货价格运行并不稳定，经常在极短的时间内出现大涨大跌的态势，给期货市场参与者带来了很大的风险。本文则旨在研究中国棉花期货价格的影响因素，并从棉花期货的基本面属性和金融属性两方面来讨论相关因素对其影响机制；此外，本文还对中国棉花期货价格进行了预测。
       实验方法上，本文选取了2004年6月至2021年12月期间各影响因素的月度数据，并以此为基础，构建了向量自回归模型。研究发现，基本面因素对于中国棉花期货价格波动的影响程度非常有限，这一结果对于期货市场上热衷于基本面分析的参与者来说或许能起到提醒作用。方差分解显示供需因素中贡献率最大的是下游棉纱产量，因此维持纺织业稳定发展或对棉花期货平稳运行具有重要意义。此外，在对金融因素的梳理分析中，笔者发现金融因素对棉花期货价格影响非常显著，方差分解显示贡献率最大的是我国棉花现货价格，这也从一定程度上说明了我国棉花期现货市场的紧密联系。
       在对我国棉花期货价格进行预测时，本文得出中国棉花现货价格、国际棉花期货价格与中国棉花期货价格可以相互进行预测的结论，最终建立的VAR、VECM模型的良好预测效果也证实了我国棉花期货市场与现货市场及国际期货市场存在紧密联系。此外，对比发现VAR、VECM模型整体预测效果要优于多元线性回归模型，也说明了加入历史观测值对预测效果的重要性。
       本文认为，对于棉花期货市场的参与者来说，不仅需要关注基本面因素，更多的要关注相关金融因素，尤其是现货价格及国际期货价格。而且需要更多的参考历史信息才能最大程度的准确把握棉花期货价格的波动。

        Cotton is a crop in China related to the economy, which is so important that had made great contribution to agricultural economy. Especially in Xinjiang, cotton, as one of the main agricultural products, contributes a lot to Xinjiang's economic development and farmers' income. With the development of futures market in China, the trading volume of cotton futures varieties increases year by year, which provides valuable experience for establishing and improving the agricultural futures market in China. But historical data show that China's cotton futures prices are not stable, It often rises and falls in a flash, which leads to a great risk to futures market participants. This paper aims to study the influencing factors of China's cotton futures price and discuss the influencing mechanism of relevant factors from the fundamental and financial attributes of cotton futures. In addition, this paper also includes the forecast of China's cotton futures price.
        In terms of experimental methods, monthly time series data of influencing factors from June 2004 to December 2021 are used in this paper. After testing the data, a vector autoregressive model is established in this paper. The results indicate that fundamentals have a very limited impact on the volatility of Chinese cotton futures, which may serve as a reminder to participants who are keen on fundamental analysis in the futures market. The variance decomposition shows that the downstream cotton yarn output contributes the most to supply and demand factors, so it is of great significance to maintain the stable development of textile industry or the smooth operation of cotton futures. In addition, the author finds that financial factors have a very significant impact on cotton futures prices in the sorting and analysis of financial factors, and the variance decomposition shows that the largest contribution rate is China's cotton spot price, which also explains to some extent the close relationship of China's cotton spot market in the period.
        In the prediction of China's cotton futures price, this paper finds China's cotton spot price, international cotton futures price and China's cotton futures price, which can be predicted mutually. The good prediction result of VAR and VECM models finally established also confirmed the close relationship between China's cotton futures market and spot market and international futures market. In addition, the comparison shows that the overall prediction effect of VAR and VECM model is better than that of multiple linear regression model, which also indicates the importance of adding historical observations to the prediction effect.  
        This paper argues that participants in the cotton futures market need to pay attention not only to fundamental factors, but also to relevant financial factors, especially spot prices and international futures prices. And more historical information is needed to accurately grasp the fluctuation of cotton futures prices.