基于事件流模型的粮食期货极端风险测度计算研究

doi:10.16381/j.cnki.issn1003-207x.2022.0618

Abstract

Abstract:

Food plays a critical role in human survival and development， and has always been the global focus. Recently， natural and social events such as rising energy prices， frequent extreme weather， the severe COVID-19， and the Russian-Ukrainian conflict have jointly pushed up the food prices. Keeping food price staying in a reasonable range and preventing extreme fluctuations are crucial to ensure national food security. It is of strategic significance to pay attention to the stable operation of the grain futures market， extreme risk management and early warning.By utilizing the prices of grain futures in the US （CBOT rice， CBOT wheat， CBOT corn） and the Chinese markets （ZCE rice， ZCE wheat， DCE corn）， the data are described and visualized， and the clustering characteristics of extreme risks are uncovered.Then event flow models are employed to model the occurring process of extreme fluctuations by means of the ACD-POT model and Hawkes-POT model and derive the formula of extreme risk measures （VaR）.Finally， three accuracy test methods are used to evaluate the fitting and prediction effects of the benchmark models （POT， EGARCH-POT）， the event flow models （ACD-POT model， Hawkes-POT model）， and the ensemble model.The results show that event flow models are able to capture the fat-tailed and clustering characteristics of extreme price fluctuations and fits the price data very well. In addition， further comparison finds that the in-sample and out-of-sample accuracy of Hawkes-POT models is better than other models， and the power-law Hawkes-POT model performs the best. The results not only deepen our understanding of the occurring pattern of extreme price fluctuations in grain futures， but also provide a new avenue for risk management and market supervision in food markets.

Key words: extreme risk, extreme value theory, autoregressive conditional duration （ACD） model, Hawkes process, Value at Risk （VaR）

CLC Number:

D632.9

Zhiqiang Jiang,Haiyan Hu,Pengfei Dai, et al. Estimating Extreme Risk Measures of Grain Future Market Based on Event Flow Models[J]. Chinese Journal of Management Science, 2026, 34(3): 39-50.

Figures/Tables 8

模型	ACD模型参数					Hawkes过程参数					POT模型参数				lnL	AIC	拟合优度检验
模型	ω	a	b	γ	κ	μ	n	δ	η	ρ	ξ	β₀	β₁	β₂	lnL	AIC	KS _W	KS _ζ
CBOT稻谷
线性	1.121	0.237	0.700	0.660	1.121						0.129	0.520	0.190	2.253	-1535.6	3089.2	0.292	0.367
ACD	(0.196)	(0.026)	(0.020)	(0.030)	(0.196)						(0.034)	(0.058)	(0.043)	(0.505)	-1535.6	3089.2	0.292	0.367
对数	0.436	0.205	0.671	0.666	1.500						0.124	0.512	0.192	2.330	-1534.0	3085.9	0.322	0.283
ACD	(0.023)	(0.013)	(0.010)	(0.031)	(0.143)						(0.034)	(0.058)	(0.044)	(0.498)	-1534.0	3085.9	0.322	0.283
指数						0.037	0.553	0.048	0.036		0.184	0.620		2.738	-1544.1	3102.1	0.492	0.306
衰减						(0.004)	(0.048)	(0.024)	(0.008)		(0.037)	(0.059)		(0.501)	-1544.1	3102.1	0.492	0.306
幂律						0.039	0.268	0.201	0.843	0.161	0.166	0.518		3.754	-1544.2	3104.3	0.445	0.337
衰减						(0.004)	(0.025)	(0.016)	(0.502)	(0.023)	(0.034)	(0.058)		(0.511)	-1544.2	3104.3	0.445	0.337
CBOT小麦
线性	0.723	0.158	0.789	0.888	1.126						-0.068	0.420	-0.014	5.697	-1543.3	3104.5	0.113	0.063
ACD	(0.098)	(0.012)	(0.010)	(0.032)	(0.054)						(0.025)	(0.048)	(0.034)	(0.445)	-1543.3	3104.5	0.113	0.063
对数	0.516	0.159	0.661	0.852	1.174						-0.055	0.526	0.020	4.407	-1550.2	3118.5	0.195	0.090
ACD	(0.018)	(0.009)	(0.008)	(0.033)	(0.062)						(0.027)	(0.049)	(0.036)	(0.459)	-1550.2	3118.5	0.195	0.090
指数						0.040	0.213	0.317	0.013		-0.062	0.334		6.163	-1538.3	3090.6	0.160	0.135
衰减						(0.004)	(0.017)	(0.022)	(0.004)		(0.026)	(0.046)		0.450	-1538.3	3090.6	0.160	0.135
幂律						0.046	0.404	0.380	0.014	1.128	-0.052	0.394		5.546	-1540.4	3096.7	0.175	0.096
衰减						(0.004)	(0.037)	(0.016)	(0.005)	(0.137)	(0.026)	(0.046)		0.455	-1540.4	3096.7	0.175	0.096
CBOT玉米
线性	3.247	0.241	0.505	0.659	1.517						-0.061	0.350	0.025	5.392	-1492.2	3002.3	0.980	0.139
ACD	(0.291)	(0.033)	(0.026)	(0.026)	(0.130)						(0.035)	(0.044)	(0.035)	(0.448)	-1492.2	3002.3	0.980	0.139
对数	0.909	0.180	0.493	0.645	1.551						-0.061	0.372	0.019	5.313	-1494.7	3007.4	0.967	0.183
ACD	(0.030)	(0.016)	(0.012)	(0.025)	(0.137)						(0.036)	(0.045)	(0.036)	(0.450)	-1494.7	3007.4	0.967	0.183
指数						0.051	0.199	0.314	0.051		-0.076	0.446		4.809	-1491.4	2996.8	0.982	0.174
衰减						(0.004)	(0.021)	(0.033)	(0.012)		(0.034)	(0.045)		(0.439)	-1491.4	2996.8	0.982	0.174
幂律						0.045	0.311	0.364	0.120	0.562	-0.066	0.476		4.444	-1491.3	2998.6	0.940	0.226
衰减						(0.004)	(0.031)	(0.024)	(0.045)	(0.058)	(0.032)	(0.044)		(0.443)	-1491.3	2998.6	0.940	0.226
郑商早籼
线性	3.028	0.230	0.536	0.671	1.491						0.158	0.160	-0.045	2.252	-434.2	886.4	0.731	0.098
ACD	(0.470)	(0.058)	(0.044)	(0.045)	(0.202)						(0.093)	(0.038)	(0.072)	(0.363)	-434.2	886.4	0.731	0.098
对数	1.083	0.155	0.438	0.636	1.572						0.158	0.198	-0.045	1.915	-435.0	888.1	0.969	0.131
ACD	(0.062)	(0.032)	(0.026)	(0.045)	(0.242)						(0.095)	(0.038)	(0.072)	(0.364)	-435.0	888.1	0.969	0.131
指数						0.065	0.232	0.412	0.096		0.175	0.211		1.573	-436.5	887.0	0.959	0.137
衰减						(0.008)	(0.068)	(0.193)	(0.046)		(0.096)	(0.038)		(0.362)	-436.5	887.0	0.959	0.137
幂律						0.051	0.183	0.352	9.999	2E-6	0.168	0.200		1.667	-435.3	886.5	0.978	0.141
衰减						(0.008)	(0.038)	(0.073)	(61.02)	(0.031)	(0.095)	(0.038)		(0.356)	-435.3	886.5	0.978	0.141
郑商强麦
线性	1.995	0.323	0.542	0.710	1.408						-0.100	0.505	0.173	0.774	-1001.9	2021.7	0.985	0.123
ACD	(0.316)	(0.043)	(0.034)	(0.041)	(0.149)						(0.038)	(0.036)	(0.060)	(0.309)	-1001.9	2021.7	0.985	0.123
对数	0.554	0.259	0.577	0.680	1.470						-0.099	0.494	0.167	0.891	-1004.0	2026.1	0.986	0.172
ACD	(0.034)	(0.017)	(0.014)	(0.040)	(0.169)						(0.037)	(0.036)	(0.060)	(0.315)	-1004.0	2026.1	0.986	0.172
指数						0.024	0.340	0.504	0.031		-0.093	0.481		1.743	-1003.3	2020.7	0.938	0.214
衰减						(0.005)	(0.027)	(0.041)	(0.008)		(0.036)	(0.035)		(0.322)	-1003.3	2020.7	0.938	0.214
幂律						0.021	0.427	0.736	0.082	0.658	-0.100	0.493		1.651	-1003.1	2022.3	0.991	0.256
衰减						(0.004)	(0.034)	(0.036)	(0.027)	(0.061)	(0.037)	(0.036)		(0.319)	-1003.1	2022.3	0.991	0.256
大商玉米
线性	0.158	0.028	0.669	0.599	4.199						-0.024	0.226	-0.016	1.997	-788.4	1594.8	0.605	0.144
ACD	(0.026)	(0.004)	(0.024)	(0.015)	(0.052)						(0.046)	(0.029)	(0.048)	(0.238)	-788.4	1594.8	0.605	0.144
对数	0.061	0.280	0.519	0.548	3.317						-0.036	0.237	-0.018	1.938	-787.2	1592.4	0.760	0.123
ACD	(0.036)	(0.019)	(0.033)	(0.019)	(0.067)						(0.047)	(0.029)	(0.049)	(0.233)	-787.2	1592.4	0.760	0.123
指数						0.025	0.701	0.069	0.033		-0.014	0.238		1.823	-793.7	1601.4	0.636	0.374
衰减						(0.005)	(0.056)	(0.066)	(0.007)		(0.048)	(0.029)		(0.241)	-793.7	1601.4	0.636	0.374
幂律						0.020	0.428	0.779	0.116	0.460	-0.014	0.275		1.507	-795.6	1607.2	0.655	0.380
衰减						(0.005)	(0.036)	(0.044)	(0.046)	(0.041)	(0.047)	(0.029)		(0.235)	-795.6	1607.2	0.655	0.380

模型	α（%）	线性ACD-POT						对数ACD-POT
模型	α（%）	失败	LRuc	LRind	LRcc	DQhit	DQVaR	失败	LRuc	LRind	LRcc	DQhit	DQVaR
样本内检验
CBOT 稻谷	5	190	0.30	0.00	0.00	0.00	0.00	190	0.30	0.00	0.00	0.00	0.00
	2.5	87	0.89	0.03	0.08	0.01	0.00	91	0.77	0.04	0.11	0.03	0.01
	1	39	0.54	0.08	0.17	0.00	0.00	40	0.44	0.09	0.17	0.00	0.00
CBOT 小麦	5	199	0.21	0.03	0.05	0.28	0.28	202	0.14	0.09	0.08	0.27	0.25
	2.5	90	0.90	0.27	0.54	0.80	0.75	92	0.93	0.12	0.29	0.36	0.33
	1	35	0.81	0.35	0.63	0.92	0.92	36	0.94	0.37	0.67	0.85	0.84
CBOT 玉米	5	191	0.36	0.00	0.00	0.00	0.00	197	0.17	0.00	0.00	0.00	0.00
	2.5	93	0.71	0.00	0.00	0.00	0.00	91	0.87	0.00	0.00	0.00	0.00
	1	37	0.84	0.40	0.69	0.78	0.78	36	0.97	0.38	0.68	0.84	0.79
郑商早籼	5	69	0.35	0.12	0.20	0.08	0.03	69	0.36	0.12	0.20	0.16	0.10
	2.5	34	0.58	0.08	0.18	0.01	0.00	33	0.71	0.01	0.04	0.00	0.00
	1	16	0.32	0.02	0.03	0.00	0.00	16	0.32	0.02	0.03	0.00	0.00
郑商强麦	5	211	0.00	0.00	0.00	0.00	0.00	183	0.00	0.00	0.00	0.00	0.00
	2.5	71	0.60	0.00	0.00	0.00	0.00	68	0.88	0.00	0.00	0.00	0.00
	1	27	0.96	0.00	0.01	0.00	0.00	26	0.89	0.00	0.01	0.00	0.00
大商玉米	5	127	0.28	1.00	0.56	0.89	0.78	125	0.37	0.38	0.46	0.90	0.88
	2.5	55	0.71	0.57	0.79	1.00	0.99	57	0.92	0.63	0.89	1.00	1.00
	1	23	0.98	0.23	0.48	0.54	0.53	23	0.98	0.50	0.79	0.83	0.81
样本外检验
CBOT 稻谷	5	84	0.75	0.00	0.00	0.00	0.00	77	0.63	0.00	0.00	0.00	0.00
	2.5	39	0.80	0.00	0.00	0.00	0.00	40	0.92	0.00	0.00	0.00	0.00
	1	20	0.37	0.00	0.00	0.00	0.00	17	0.85	0.01	0.04	0.00	0.00
CBOT 小麦	5	96	0.43	0.41	0.52	0.74	0.46	90	0.88	0.79	0.96	0.73	0.63
	2.5	48	0.58	0.78	0.83	0.95	0.78	45	0.92	0.89	0.99	0.99	0.98
	1	20	0.60	0.50	0.69	0.71	0.14	15	0.50	0.61	0.70	0.49	0.28
CBOT 玉米	5	83	0.56	0.01	0.02	0.14	0.10	80	0.36	0.00	0.01	0.06	0.06
	2.5	48	0.57	0.00	0.01	0.00	0.00	47	0.67	0.00	0.01	0.00	0.00
	1	34	0.00	0.00	0.00	0.00	0.00	31	0.00	0.00	0.00	0.00	0.00
郑商早籼	5	82	0.00	0.59	0.00	0.00	0.00	83	0.00	0.40	0.00	0.00	0.00
	2.5	56	0.00	0.44	0.00	0.00	0.00	55	0.00	0.17	0.00	0.00	0.00
	1	24	0.00	0.68	0.00	0.00	0.00	26	0.00	0.79	0.00	0.00	0.00
郑商强麦	5	69	0.81	0.00	0.01	0.01	0.00	68	0.71	0.01	0.02	0.03	0.00
	2.5	31	0.43	0.00	0.01	0.00	0.00	32	0.55	0.01	0.02	0.01	0.00
	1	14	0.96	0.00	0.00	0.00	0.00	16	0.64	0.00	0.00	0.00	0.00
大商玉米	5	106	0.02	0.20	0.03	0.23	0.10	107	0.01	0.11	0.01	0.11	0.05
	2.5	62	0.00	0.10	0.00	0.03	0.02	61	0.01	0.09	0.01	0.04	0.04
	1	31	0.00	0.13	0.00	0.00	0.00	29	0.01	0.10	0.01	0.00	0.00

模型	α（%）	指数Hawkes-POT						幂律Hawkes-POT
模型	α（%）	失败	LRuc	LRind	LRcc	DQhit	DQVaR	失败	LRuc	LRind	LRcc	DQhit	DQVaR
样本内检验
CBOT 稻谷	5	173	0.83	0.01	0.02	0.00	0.00	183	0.58	0.26	0.45	0.35	0.35
	2.5	85	0.76	0.22	0.44	0.00	0.00	85	0.76	0.22	0.44	0.32	0.24
	1	30	0.37	0.26	0.35	0.00	0.00	31	0.47	0.28	0.43	0.04	0.03
CBOT 小麦	5	183	0.92	0.22	0.46	0.87	0.86	184	0.86	0.23	0.48	0.79	0.79
	2.5	81	0.29	0.41	0.40	0.66	0.64	82	0.34	0.91	0.63	0.66	0.65
	1	28	0.15	0.21	0.16	0.68	0.57	29	0.21	0.23	0.22	0.75	0.61
CBOT 玉米	5	177	0.93	0.02	0.06	0.38	0.38	184	0.66	0.04	0.11	0.47	0.44
	2.5	79	0.27	0.85	0.53	0.03	0.02	80	0.32	0.88	0.60	0.28	0.26
	1	37	0.82	0.40	0.69	0.91	0.91	32	0.53	0.45	0.62	0.97	0.97
郑商早籼	5	67	0.44	0.47	0.57	0.67	0.64	66	0.52	0.79	0.79	0.55	0.48
	2.5	30	0.92	0.04	0.11	0.06	0.06	31	0.93	0.79	0.96	0.70	0.69
	1	11	0.72	0.66	0.85	1.00	1.00	13	0.82	0.60	0.85	1.00	1.00
郑商强麦	5	133	0.99	0.01	0.03	0.04	0.03	131	0.87	0.09	0.22	0.55	0.46
	2.5	76	0.25	0.00	0.00	0.00	0.00	67	0.94	0.12	0.30	0.68	0.58
	1	19	0.12	0.01	0.01	0.00	0.00	23	0.48	0.20	0.33	0.25	0.25
大商玉米	5	117	0.83	0.37	0.65	0.40	0.40	118	0.76	0.15	0.34	0.58	0.38
	2.5	56	0.85	0.74	0.93	0.96	0.95	60	0.73	0.62	0.83	0.99	0.96
	1	20	0.53	0.55	0.69	0.73	0.73	22	0.84	0.51	0.79	0.99	0.91
样本外检验
CBOT 稻谷	5	74	0.41	0.00	0.00	0.00	0.00	63	0.03	0.00	0.00	0.01	0.00
	2.5	40	0.92	0.00	0.00	0.00	0.00	30	0.08	0.02	0.01	0.08	0.05
	1	20	0.37	0.00	0.00	0.00	0.00	14	0.57	0.62	0.75	0.50	0.29
CBOT 小麦	5	92	0.72	0.92	0.93	0.77	0.76	89	0.97	0.80	0.97	0.79	0.73
	2.5	48	0.58	0.78	0.83	0.95	0.95	47	0.69	0.81	0.90	0.97	0.97
	1	19	0.76	0.52	0.78	0.71	0.71	19	0.76	0.52	0.78	0.71	0.71
CBOT 玉米	5	70	0.04	0.03	0.01	0.19	0.19	66	0.01	0.01	0.00	0.07	0.06
	2.5	47	0.67	0.01	0.03	0.05	0.04	47	0.67	0.04	0.12	0.21	0.18
	1	24	0.15	0.00	0.01	0.00	0.00	22	0.32	0.28	0.34	0.68	0.68
郑商早籼	5	68	0.00	0.72	0.00	0.03	0.03	64	0.00	0.86	0.02	0.10	0.10
	2.5	40	0.00	0.49	0.00	0.01	0.01	33	0.03	0.11	0.03	0.05	0.03
	1	15	0.06	0.47	0.13	0.20	0.09	11	0.48	0.60	0.68	0.98	0.83
郑商强麦	5	77	0.47	0.08	0.16	0.20	0.03	81	0.23	0.05	0.08	0.10	0.02
	2.5	35	0.93	0.06	0.18	0.60	0.10	37	0.80	0.09	0.22	0.69	0.40
	1	16	0.64	0.01	0.04	0.00	0.00	19	0.22	0.02	0.04	0.00	0.00
大商玉米	5	101	0.07	0.23	0.09	0.37	0.32	99	0.10	0.62	0.23	0.64	0.52
	2.5	58	0.02	0.06	0.01	0.06	0.06	48	0.36	0.21	0.30	0.62	0.56
	1	27	0.02	0.45	0.05	0.14	0.12	20	0.45	0.49	0.59	0.95	0.90

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粮食期货	起始时间	均值	标准差	偏度	峰度	JB统计量	ADF统计量	LB统计量
CBOT稻谷	2000.01.01	0.021	1.907	0.189	47.261	424891.125^***	-68.039^***	51.461^***
CBOT小麦	2000.01.01	0.024	1.894	0.026	8.240	6259.317^***	-72.974^***	10.634
CBOT玉米	2000.01.01	0.040	1.616	0.202	4.783	751.940^***	-69.853^***	27.100^***
郑商早籼	2009.04.20	0.008	0.905	0.696	8.016	2451.313^***	-49.254^***	20.073^**
郑商强麦	2003.03.28	-0.013	0.768	0.054	7.033	2809.193^***	-64.060^***	15.026
大商玉米	2004.09.22	0.024	0.756	0.240	6.665	2301.564^***	-66.919^***	27.097^***

Estimating Extreme Risk Measures of Grain Future Market Based on Event Flow Models

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