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李剑1 吕捷2 李崇光1


【摘要】近年来, 农产品期货市场价格波动剧烈、泡沫风险频现, 已经成为中国农业产业安全的潜在威胁。本文构建了农产品期货市场泡沫风险实时检测模型, 论证了运用价格泡沫实时检测模型进行农产品期货市场泡沫风险实时预警的可行性和有效性。通过构建模拟试验, 论证了模型在农产品期货市场泡沫风险实时预警中具有的4个优良特性:普适性、及时性、稳健性和简便性。在此基础上, 本文设计了农产品期货市场泡沫风险警情标准、预警响应机制和预警处置步骤, 构建了农产品期货市场泡沫风险实时预警系统并进行实例应用。

【关键词】 农产品期货市场;泡沫风险;实时预警;


【基金资助】 国家自然科学基金项目“‘金融化’背景下我国农产品期货与现货市场风险评价与传导研究” (项目编号:71673103) ; “农产品期货市场泡沫风险的测度、形成机理与预警研究” (项目编号:71803058) ; 华中农业大学自主科技创新基金项目“农产品期货市场价格风险实时预警研究” (项目编号:2662017QD023) ;

Real-time early warning system against bubble risk in agricultural futures market

LI Jian1 LYU Jie2 LI Chongguang

(1.College of Economics & Management, Huazhong Agricultural University)
(2.School of Agricultural Economics and Rural Development, Renmin University of China)

【Abstract】In recent years, China’s agricultural futures market has witnessed severe price volatility and bubble risk, which has become a potential threat to the development of agricultural market in China. This paper proposed a real-time bubble risk detecting model and analyzed its effectiveness in building the bubble risk real-time early warning system for the agricultural futures market. Based on a series of simulations, this research found that the model has four main merits for real-time bubble detection, namely, general applicability, immediacy, the robustness, and convenience. We further proposed alarming criteria, warning response mechanism, and a five-step early warning management procedure. Finally, this research applied the early warning system to the cotton futures market for validation and verification.

【Keywords】 agricultural futures market; bubble risk; real-time early warning system;


【Funds】 National Natural Science Foundation of China (71673103); National Natural Science Foundation of China (71803058); Independent Technology Innovation Foundation of Huazhong Agricultural University (2662017QD023);

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    [1]. ① In Equation (1), indicates the critical value of τ%. [^Back]

    [2]. ① The PSY model used in this paper can identify bubbles at the end of sample mainly by a key model design: double recursive regression. Theoretically, double recursive regression model calculates BSADF value at each time point in the sample independently and repeatedly, which makes GSADF statistics always find the maximum ADF value between the initial value and the observation, so the explosiveness at even the last time point of sample can also be detected for effectively identifying bubbles at the end of sample. [^Back]

    [3]. ② Traditional bubble detection methods often have poor detection results, when continuous bubbles exist in the sample interval (Gürkaynak, 2008; Phillips et al., 2015a, b). Therefore, the simulation test in this paper focused on the early warning effect of continuous bubbles at the end of sample. [^Back]

    [4]. ① By other commodities and data truncation methods for simulation, we reached a consistent conclusion. [^Back]

    [5]. ① For further representativeness, the authors selected bubbles of soybean, sugar, and early indica rice at different intervals for early warning test. Among them, soybean price bubble, at the early stage of the sample, lasts for 216 days; sugar price bubble, at the middle stage of the sample, lasts for 24 days; and the early indica rice price bubble, at the late stage of the sample, lasts for five days. In the test, we truncated samples of three commodities by the first, the fifth, and the tenth days of the bubble period to test the early warning effect of model. [^Back]

    [6]. ② After the authors tested all the products, this model was proved well robust in bubble detection. Due to space limitations, we reported just the test results of cotton sequence. [^Back]


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This Article


CN: 11-1262/F

Vol , No. 03, Pages 53-64

March 2019


Article Outline


  • 1 Introduction
  • 2 Modeling
  • 3 Excellent characteristics and simulation test of real-time early warning model of bubble risk in agricultural product futures market
  • 4 Design approach and application examples of real-time early warning system for bubble risk in agricultural products futures market
  • 5 Conclusion and discussion
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