中国商品期货市场风险度量的动态分析

摘要/Abstract

摘要： 近些年，中国商品期货市场高成长性和高波动性并存，甚至与国际大宗商品市场同步出现“过山车”式市场行情，市场风险问题引起广泛的关注。对商品期货市场风险的识别和测量已经成为值得深入探讨的研究问题。本文选取了基于得分函数的观察驱动模型，采用动态半参数模型架构对期望损失和在险价值进行估计，从而实现对中国商品期货市场风险的度量和预测。本文研究表明：基于学生-t分布、偏斜学生-t分布以及非对称学生-t分布下的广义自回归得分模型具有较好的应用性。单因素广义自回归得分模型相比于所构造的双因素模型，在应用于风险度量时平均损失更小，能够动态性地观测出中国商品期货市场的风险变化。特别地，化工期货的平均期望损失相对突出，但是波动范围较小；相比之下，能源期货的期望损失极端值较多，期望损失值达到-13.03%；农副产品类商品期货市场的风险程度较低。自2021年1月起，中国商品期货市场各自呈现出不同程度的风险聚集。能源、化工、谷物、软商品以及油脂油料期货集中于2021年3月22日—4月14日之间出现较大的期望损失，贵金属和有色金属期货的期望损失风险则集中于2021年1月12日—1月15日之间。在全球经济复苏具备有利因素的背景下，有效应对和规避商品市场风险已经引起市场参与者、政策制定者以及研究者们的关注。在风险监控过程中不仅需要关注外生事件的冲击，还要注重对期货市场风险的动态测量。

关键词: 大宗商品期货, GAS模型, ES-VaR, 动态半参数模型, 单因素估计

Abstract: The coexistence of high growth and high volatility over recent years in China’s commodity futures market where even “roller coaster” market conditions emerged in parallel with the international bulk commodity market have aroused wide concern over potential risks. Therefore, identifying and measuring commodity futures market risks is worth an in-depth exploration. In this paper, an observation-driven model based on score function is used to construct a dynamic semi-parametric model to estimate the expected shortfall and value at risk to measure and predict the risk of China’s commodity futures market. The results show that the generalized autoregressive scoring models based on student-T distribution, skewed student-T distribution, and asymmetric Student-t distribution have a good application. In this paper, the single-factor generalized autoregressive score model applied to risk measurement shows that the average loss of the single-factor generalized autoregressive score model is relatively smaller than that of the two-factor model, and the risk change of commodity futures can be observed more dynamically. The average Expected Shortfall of chemical futures is rather prominent, but the fluctuation range is small. In contrast, the Expected Shortfall extreme value of energy futures is higher, and the expected loss is as low as -13.03%. Comparatively speaking, the risk degree of the commodity futures market of agricultural and sideline products is relatively low. Commodity futures have exhibited varying degrees of risk accumulation in the near term since January 2021. Energy, chemicals, grains, soft commodities and grease oils futures showed significant expected losses from March 22 to April 14, 2021, while precious metals and non-ferrous metals futures showed significant expected losses from January 12 to January 15, 2021. Under the background of global economic recovery, the effective avoidance of commodity market risks has attracted the attention of market participants, policymakers and researchers. For the risk monitoring of the commodity market, we should pay attention to the impact of exogenous events and the persistence of risk events and carry out dynamic measurement of futures market risks.

Key words: commodities futures, GAS model, ES-VaR, dynamic semi-parametric model, single-factor estimation

张天顶, 曾松. 中国商品期货市场风险度量的动态分析[J]. 管理评论, 2023, 35(4): 12-26.

Zhang Tianding, Zeng Song. Dynamic Analysis of the Risk Measurement in China’s Commodity Futures Market[J]. Management Review, 2023, 35(4): 12-26.

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