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Risk assessment of agricultural futures markets: a new analysis framework based on price bubble model

LI Jian 1 LI Chongguang1

(1.College of Economics & Management, Huazhong Agricultural University)

【Abstract】This paper introduced price bubble theory and its analysis method, suggested the assessment method and analysis framework of agricultural commodity futures market risk from the perspective of price bubble, set three risk assessment indexes including “bubble duration”, “bubble frequency” and “bubble intensity”, and conducted measurement and assessment of the historical risk levels of China’s agricultural commodity futures market during 2006-2014. According to the risk assessment results, this paper divided agricultural commodity futures products into high, middle and low levels in risk and summarized the risk features and control implications of the commodities at each risk level. The research results of this paper show that commodity function cannot completely reflect its risk attribute, so it is necessary to complete the risk regulating system of agricultural commodity futures market in the principle of “monitoring by level and preventing by priority.”

【Keywords】 agricultural futures market; risk assessment; price bubble; right-tail unit root test; Agricultural Futures Market; Risk Assessment; Price Bubble; Right-tail Unit Root Test;


【Funds】 National Natural Science Foundation of China (71673103)

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    [1]. ① Source: www.cfachina.org [^Back]

    [2]. ① Source: The data are processed by the authors according to those from http://www.dce.com.cn/, http://www.czce.com.cn/portal/index.htm) and http://www.shfe.com.cn/ [^Back]

    [3]. ② In this paper, the “risk assessment” of agricultural commodity futures market refers to risk measurement and assessment of the historical price series of commodities, including three parts: (1) bubble test of historical data; (2) risk rating of commodities according to risk assessment indexes; and (3) summary of risk features and regulating means of commodities at different risk levels. [^Back]

    [4]. ① Some concepts, quite new in this paper, have no unified translation yet in the domestic academic circle in China, so the authors translated them and noted them with original English versions. For any improperness, readers’ correction is welcome. Also, see Jian and Xiang (2012) and Deng (2013) for reference. [^Back]

    [5]. ② PSY is the initials of Phillips et al. (2015). [^Back]

    [6]. ① Window series refers to the observation value series adopted by each calculation of ADF values in recursive regression. It is noteworthy that PSY model needs to set initial window series, so recursive regression may have an initial sample size. The start of initial window series is the sample start, and its end is set as r0. [^Back]

    [7]. ② The CV adopted by this paper was obtained by Monte Carlo simulation for 2000 times. It is the same below without separate note. [^Back]

    [8]. ① Paddy rice (early indica rice) and canola oil contracts were listed on April 20, 2009 and July 2, 2007. By the end of 2014, there were 1386 and 1826 samples respectively. These two agricultural commodities were also included in the research due to their important research significance and relatively more sample days. [^Back]

    [9]. ② http://www.dce.com.cn [^Back]

    [10]. ③ http://www.czce.com.cn/portal/index.htm [^Back]

    [11]. ④ http://www.shfe.com.cn [^Back]

    [12]. ① In practice, the “comprehensive assessment” of market risk may also have different weights set over the three risk indexes, namely bubble duration, bubble frequency and bubble intensity, according to market experience and specific situations and then conduct finer analysis and assessment of the historical risk levels of agricultural commodity futures market. [^Back]


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This Article


CN: 11-1262/F

Vol , No. 05, Pages 73-87

May 2017


Article Outline


  • 1 Introduction
  • 2 Modeling
  • 3 Research data
  • 4 Risk assessment of agricultural commodity futures market
  • 5 Conclusion and discussion
  • Footnote