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)

Download this article


    [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]


    1. Cheng, G. Food and Nutrition in China (中国食物与营养), (9) (2006).

    2. Cheng, G. 农村工作通讯, (22) (2011).

    3. Chen, X.

    4. Hua, R. & Zhang, Y. Journal of Nanjing University of Finance and Economics (南京财经大学学报), (4) (2011).

    5. Huang, H., Xiong, T. & Li, C. Journal of Agrotechnical Economics (农业技术经济), (1) (2018).

    6. Jian, Z. & Xiang, X. The Journal of Quantitative & Technical Economics (数量经济技术经济研究), (4) (2012).

    7. Li, J. & Li, C. China Rural Economy (中国农村经济), (5) (2017).

    8. Li. J., Chen, Y. & Li, C. Management World (管理世界), (8) (2018).

    9. Xiao, X., Li, C. & Li, J. China Rural Economy (中国农村经济), (2) (2014).

    10. Wang, J. & An, D. Journal of Huazhong Agricultural University (Social Sciences Edition) (华中农业大学学报 (社会科学版)), (3) (2010).

    11. Wang. Y., Wang, X. & Wu, L. Journal of Agrotechnical Economics (农业技术经济), (12) (2015).

    12. Wu, H., Shi, H. & Ge, Y. Journal of Agrotechnical Economics (农业技术经济), (6) (2018).

    13. Zhou, W. & He, J. Journal of Financial Research (金融研究), (9) (2011).

    14. Zhu, X., Han, L. & Zeng C. Management World (管理世界), (12) (2012).

    15. Etienne, X. L., S. H. Irwin, and P. Garcia, 2015, “Price Explosiveness, Speculation, and Grain Futures Prices,” American Journal of Agricultural Economics, 97(1): 65–87.

    16. Figuerola-Ferretti, I., C. L. Gilbert, and J. R. McCrorie, 2015, “Testing for Mild Explosivity and Bubbles in LME Non-ferrous Metals Prices,” Journal of Time Series Analysis, 36(5): 763–782.

    17. Li, J., C. Li and J. P. Chavas, 2017, “Food Price Bubbles and Government Intervention,” Canadian Journal of Agricultural Economics, 65(1): 135–157.

    18. Phillips, P. C. B., S. P. Shi, and J. Yu, 2015a, “Testing for Multiple Bubbles: Historical episodes of Exuberance and Collapse in the S&P 500,” International Economic Review, 56(4): 1043–1078.

    19. Phillips, P. C. B., S. P. Shi, and J. Yu, 2015b, “Testing for Multiple Bubbles: Limit Theory of Real Time Detectors,” International Economic Review, 56(4): 1079–1134.

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
  • Footnote