| 引用本文: | 殷秀莉, 杨 凯, 董小刚, 丁嘉雯, 李竺遥.棉花期货价格波动率分析(J/M/D/N,J:杂志，M：书，D：论文，N：报纸).期刊名称,2022，39（5）：85-92 |
| CHEN X. Adap tive slidingmode contr ol for discrete2ti me multi2inputmulti2 out put systems[ J ]. Aut omatica, 2006, 42(6): 4272-435 |
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| 摘要: |
| 针对棉花已成为制约“十四五”规划中纺织行业发展不可或缺的关键因素这一事实,提出通过研究棉花期货价格的波动来体现纺织业成本变化。 基于 2008 年 1 月 2 日到 2021 年 9 月 6 日棉花 CF999 期货价格数据时序图,得到原序列不平稳的结论,处理后得棉花期货价格收益率序列,由 J-B 统计量、ADF 检验
统计量,得到棉花期货价格收益率序列是平稳时间序列,但不服从正态分布,有尖峰厚尾和高阶异方差性的统计特征;然后考虑收益率序列服从 T 分布和广义误差(GED)分布,分别构建 GARCH 类模型,具体利用GARCH、GARCH-M、EGARCH 和 MS-GARCH 4 种模型对棉花价格波动特征进行研究,结果表明:基于 T 分布
的模型比 GED 分布更能描述棉花期货价格收益率序列的波动特征;棉花市场不是“高风险、高回报”产业;收益率序列存在反杠杆效应;MS(3)-GARCH(1,1)模型的拟合效果最优,收益率序列有体制变换现象,各种波动的持续期不同,高波动状态最短,为 2. 12 d;最后提出相关建议。 |
| 关键词: 价格波动 GARCH-M 模型 EGARCH 模型 MS-GARCH 模型 |
| DOI： |
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| Analysis of Cotton Futures Price Volatility |
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YIN Xiu-li, YANG Kai, DONG Xiao-gang, DING Jia-wen, LI Zhu-yao
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School of Mathematics and Statistics, Changchun University of Technology, Changchun 130012, China
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| Abstract: |
| During the “ 14th Five-Year Plan ” period, cotton has become a key factor restricting the
development of the textile industry, so it is proposed to study the volatility of cotton futures price to reflect the
change of textile industry cost. Using the time series plot of the cotton CF999 futures price data from January 2,
2008, to September 6, 2021, the conclusion is that the original time series is unstable, and the original time series
is processed to obtain the cotton futures price return, which is tested by J-B statistical test and ADF test. It is
concluded that the cotton futures price return is a stable time series and does not obey the normal distribution. It
has the statistical characteristics of peak fat tail and high-order heteroscedasticity. Therefore, considering that the
cotton futures price return obeys the T distribution and the generalized error distribution (GED), GARCH models
are constructed and the characteristics of cotton price fluctuation are studied by using GARCH, GARCH-M,
EGARCH and MS-GARCH models. The results show that the model based on T distribution is better than GED
distribution to describe the volatility characteristics of cotton futures price return series; the cotton market is not a
“high risk and high return” industry; the return has an anti-leverage effect; the MS(3)-GARCH(1,1) model best
fits the data, and the return has the phenomenon of system conversion. The duration of various volatility is
different, and the shortest high volatility state is 2. 12 days. Finally, relevant suggestions are put forward. |
| Key words: price volatility GARCH-M model EGARCH model MS-GARCH model |
