考虑多尺度多状态的原油市场与油轮市场动态相依性及风险传染研究

doi:10.16381/j.cnki.issn1003-207x.2022.2287

Abstract

Abstract:

In this paper， considering that market participants are concerned about market volatility in different time frames， the influence of tanker type on the dependence structure and the possibility of structural changes， a time-varying MRS copula model based on MODWT decomposition is constructed to describe the multi-state dynamic dependence relationship between crude oil market and tanker market， and the tail risk dependence coefficient is used to measure the contagion effect of multi-scale and multi-state tail risk. It reveals the risk contagion change rule between crude oil market and tanker market. Finally， the time-varying MRS copula model is compared with the traditional copula model in order to verify the validity of the time-varying MRS copula model in studying the dependence between crude oil price and tanker market. The results show that：（1） There are two states （high and low dependence states） of the dependence structure between crude oil market and tanker market at different scales， indicating structural changes in the dependence structure between crude oil market and tanker market at different scales. （2） As the time scale increases， the degree of tail risk volatility within the tanker market becomes less and less， and finally stabilizes at the long-term scale， while the risk contagion effect increases with the increase of the time scale. （3） The mean value of time-varying tail risk between the crude oil market and the overall tanker market at different time scales is greater than that between the crude oil market and the tanker segment， indicating that the overall tanker market is more vulnerable to the crude oil market. In addition， the time-varying tail risks in both states show asymmetry and the high-dependence state is larger than the low-dependence state. Among them， the risk contagion effect in the high-dependence state decreases with increasing time scale.

Key words: tanker market, crude oil market, risk contagion, MODWT decomposition, time-varying MRS copula model

CLC Number:

Kaili Xue, Haibo Kuang, Bin Meng. Study on Dynamic Dependence and Risk Contagion Effect of Crude Oil Market and Tanker Market Considering Multi-scale and Multi-state[J]. Chinese Journal of Management Science, 2025, 33(7): 54-67.

Figures/Tables 12

References 35

[1]	Choi K H， Yoon S M. Asymmetric dependence between oil prices and maritime freight rates： A time-varying Copula approach［J］.Sustainability，2020，12（24）： 10687.
[2]	Eslami P， Jung K， Lee D， et al. Predicting tanker freight rates using parsimonious variables and a hybrid artificial neural network with an adaptive genetic algorithm［J］. Maritime Economics & Logistics， 2017， 19（3）： 538-550.
[3]	Shi W M， Yang Z Z， Li K X. The impact of crude oil price on the tanker market［J］. Maritime Policy & Management， 2013， 40（4）： 309-322.
[4]	Abouarghoub W， Mariscal I B F， Howells P. A two-state Markov-switching distinctive conditional variance application for tanker freight returns［J］. International Journal of Financial Engineering and Risk Management， 2014， 1（3）： 239-263.
[5]	Alizadeh A H， Huang C Y， van Dellen S. A regime switching approach for hedging tanker shipping freight rates［J］. Energy Economics， 2015， 49： 44-59.
[6]	Yang Y Y， Liu C， Sun X L， et al. Spillover effect of international crude oil market on tanker market［J］. International Journal of Global Energy Issues， 2015， 38（41516）： 257-277.
[7]	Gavriilidis K， Kambouroudis D S， Tsakou K， et al. Volatility forecasting across tanker freight rates： The role of oil price shocks［J］. Transportation Research Part E： Logistics and Transportation Review， 2018， 118： 376-391.
[8]	Gong X， Lin B Q. Time-varying effects of oil supply and demand shocks on China's macro-economy［J］. Energy， 2018， 149： 424-437.
[9]	Maitra D， Chandra S， Dash S R. Liner shipping industry and oil price volatility： Dynamic connectedness and portfolio diversification［J］. Transportation Research Part E： Logistics and Transportation Review， 2020， 138： 101962.
[10]	Chen F E， Miao Y Q， Tian K， et al. Multifractal cross-correlations between crude oil and tanker freight rate［J］. Physica A： Statistical Mechanics and Its Applications， 2017， 474： 344-354.
[11]	冯钰瑶，刘畅，孙晓蕾. 不确定性与原油市场的交互影响测度：基于综合集成的多尺度方法论［J］. 管理评论， 2020， 32（7）： 29-40.
	Feng Y Y， Liu C， Sun X L. Measuring the interaction between uncertainty and crude oil market： A multiscale methodology based on synthetic integration［J］. Management Review， 2020， 32（7）： 29-40.
[12]	杨坤，于文华，魏宇. 基于R-vine copula的原油市场极端风险动态测度研究［J］. 中国管理科学， 2017， 25（8）： 19-29.
	Yang K， Yu W H， Wei Y. Dynamic measurement of extreme risk among various crude oil markets based on R-vine copula［J］. Chinese Journal of Management Science， 2017， 25（8）： 19-29.
[13]	余乐安，查锐，贺凯健，等. 国际油价与中美股价的相依关系研究——基于不同行业数据的分析［J］. 中国管理科学， 2018， 26（11）： 74-82.
	Yu L A， Zha R， He K J， et al. The analysis of dependence relationship between oil and stock prices： Evidence from China and American industrial sector indices［J］. Chinese Journal of Management Science， 2018， 26（11）： 74-82.
[14]	Aloui R， Hammoudeh S， Nguyen D K. A time-varying copula approach to oil and stock market dependence： The case of transition economies［J］. Energy Economics， 2013， 39： 208-221.
[15]	Liu B Y， Ji Q， Fan Y. Dynamic return-volatility dependence and risk measure of CoVaR in the oil market： A time-varying Mixed-Clayton copula model［J］. Energy Economics， 2017， 68： 53-65.
[16]	赵林海，陈名智. 金融机构系统性风险溢出和系统性风险贡献——基于滚动窗口动态Copula模型双时变相依视角［J］. 中国管理科学， 2021， 29（7）： 71-83.
	Zhao L H， Chen M Z. Systemic risk spillovers and systemic risk contributions of financial institutions in China： A perspective of dual time-varying dependence of rolling window dynamic copula model［J］. Chinese Journal of Management Science， 2021， 29（7）： 71-83.
[17]	林娟，吴春晓，张明. 上海黄金是人民币汇率风险的对冲工具和安全港吗？——基于常量和动态Copula模型［J］. 中国管理科学， 2023， 31（5）： 104-115.
	Lin J， Wu C X， Zhang M. Is Shanghai gold a hedge and a safe haven for the RMB？Based on the constant and time-varying copula models［J］. Chinese Journal of Management Science，2023，31（5）： 104-115.
[18]	Alizadeh A H， Nomikos N K. Cost of carry， causality and arbitrage between oil futures and tanker freight markets［J］. Transportation Research Part E： Logistics and Transportation Review， 2004， 40（4）： 297-316.
[19]	Zhang Y. Investigating dependencies among oil price and tanker market variables by copula-based multivariate models［J］. Energy， 2018， 161： 435-446.
[20]	Bai X W， Lam J S L. A Copula-GARCH approach for analyzing dynamic conditional dependency structure between liquefied petroleum gas freight rate， product price arbitrage and crude oil price［J］. Energy Economics， 2019， 78： 412-427.
[21]	Sun X L， Liu C， Wang J， et al. Assessing the extreme risk spillovers of international commodities on maritime markets： A GARCH-Copula-CoVaR approach［J］. International Review of Financial Analysis， 2020， 68： 101453.
[22]	Fei F， Fuertes A， Kalotychou E. Dependence in credit default swap and equity markets： Dynamic copula with Markov-switching［J］. International Journal of Forecasting， 2017， 33（3）： 662-678.
[23]	BenSaïda A. The contagion effect in European sovereign debt markets： A regime-switching vine copula approach［J］. International Review of Financial Analysis， 2018， 58： 153-165.
[24]	Garcia R， Tsafack G. Dependence structure and extreme comovements in international equity and bond markets［J］. Journal of Banking & Finance， 2011， 35（8）： 1954-1970.
[25]	Wang Y C， Wu J L， Lai Y H. A revisit to the dependence structure between the stock and foreign exchange markets： A dependence-switching copula approach［J］.Journal of Banking & Finance，2013，37（5）： 1706-1719.
[26]	Ji Q， Bouri E， Roubaud D， et al. Risk spillover between energy and agricultural commodity markets： A dependence-switching CoVaR-copula model［J］. Energy Economics， 2018， 75： 14-27.
[27]	王未卿，刘祥东，李慧忠. 房地产股票投资能对冲通货膨胀吗？——基于Markov转换GRG copula的相关性测度研究［J］. 中国管理科学，2020，28（12）： 23-34.
	Wang W Q， Liu X D， Li H Z. Does real estate stock investment hedge inflation？Research on correlation measurement based on Markov-switching GRG copula［J］. Chinese Journal of Management Science， 2020， 28（12）： 23-34.
[28]	Ding X X， Dai S Y， Chen F E， et al. Long memory and scaling behavior study of bulk freight rate volatility with structural breaks［J］. Transportation Letters， 2018， 10（6）： 343-353.
[29]	Leonov Y， Nikolov V. A wavelet and neural network model for the prediction of dry bulk shipping indices［J］. Maritime Economics & Logistics，2012，14（3）： 319-333.
[30]	Pal D， Mitra S K. Time-frequency contained co-movement of crude oil and world food prices： A wavelet-based analysis［J］. Energy Economics， 2017， 62： 230-239.
[31]	He K J， Yu L， Lai K K. Crude oil price analysis and forecasting using wavelet decomposed ensemble model［J］. Energy， 2012， 46（1）： 564-574.
[32]	Nelson D B. Conditional heteroskedasticity in asset returns： A new approach［J］. Econometrica， 1991， 59（2）： 347-370.
[33]	Percival D B， Walden A T. Wavelet methods for time series analysis［M］. Cambridge： Cambridge University Press， 2000.
[34]	Sklar A. Fonctions de repartition à n dimensions et leurs marges［J］. Publication de l’Institut de Statistique de l’ Université de Paris， 1959（8）： 229-231.
[35]	Joe H. Multivariate models and dependence concepts［M］. London： Chapman & Hall， 1997.

市场类型		市场名称	指标	数据来源	类型
原油市场		Brent原油市场	Brent原油期货	Wind	月度
原油市场		WTI原油市场	WTI原油期货	Wind	月度
油轮市场	油轮整体市场	原油油轮市场	BDTI (波罗的海原油运价指数)	Clarksons	月度
	油轮整体市场	成品油油轮市场	BCTI (波罗的海成品油运价指数)	Clarksons	月度
	油轮细分市场	VLCC型油轮市场	VLCC Tanker (选取航线TD15: 260000t West Africa - China)	Clarksons	月度
		阿芙拉型油轮市场	Aframax Tanker (选取航线TD9: 70000mt, Caribbean to US Gulf)	Clarksons	月度
		巴拿马型油轮市场	Panamax Tanker (选取航线TC5:55000mt, CPP/UNL naphtha condensate, Middle East/Japan)	Clarksons	月度
		苏伊士型油轮市场	Suezmax Tanker (选取航线TD6:135000mt, Black Sea/Mediterranean)	Clarksons	月度

统计量	r_Brent	r_WTI	r_BDTI	r_BCTI	r_VLCC	r_Aframax	r_Panamax	r_Suezmax
均值	0.3303	0.3094	-0.0226	0.2188	-0.1477	-0.0327	0.2524	-0.0784
中值	1.2798	1.7945	-0.5129	-1.0773	0.3306	-1.5025	0.3863	1.2144
最大值	35.6977	63.3269	56.9910	50.1292	90.6575	70.9432	68.5807	92.3002
最小值	-63.3933	-78.1866	-57.8084	-70.1012	-91.4878	-87.7757	-113.6044	-77.0168
标准差	10.2021	11.8259	16.4442	15.5255	20.3595	25.5776	20.8838	24.5681
偏度	-1.4549	-1.0037	0.1349	0.0152	0.2674	-0.1471	-0.4852	0.0439
峰度	11.0151	15.3572	4.9196	5.5277	7.6859	3.6109	7.2853	4.7488
J-B检验	618.0228	1332.2060	31.9401	54.3161	189.0685	3.9079	164.0962	26.0603
ADF	-11.0802^***	-11.5743^***	-11.3384^***	-11.6977^***	-14.4317^***	-13.3355^***	-12.8128^***	-12.3066^***
ARCH	8.0660	26.1399	5.8752	13.1681	5.4215	12.5833	11.4763	3.1792
	[0.0004]	[0.0000]	[0.0162]	[0.0004]	[0.0209]	[0.0005]	[0.0000]	[0.0437]
样本量	204	204	204	204	204	204	204	204

参数	r_Brent	r_WTI	r_BDTI	r_BCTI	r_VLCC	r_Aframax	r_Panamax	r_Suezmax
a	-0.2359	-0.3240	-0.5798	-0.5680	-0.6887	-0.5162	0.6657	0.6654
a	(0. 3705)	(0.5273)	(0.1824)	(0.1829)	(0.1779)	(0.1897)	(0.0737)	(0.0599)
b	0.4124	0.4324	0.7557	0.7326	0.7899	0.3899	-0.9310	-0.9288
b	(0.3468)	(0.5076)	(0.1336)	(0.1399)	(0.1561)	(0.2392)	(0.0370)	(0.0314)
α	0.6751	0.5756	0.2462	0.3579	0.6065	0.8157	0.6857	0.6432
α	(0.1309)	(0.1754)	(0.1634)	(0.1941)	(0.2342)	(0.1710)	(0.1722)	(0.1952)
β	0.1980	0.2018	0.6028	0.4969	0.1625	0.1092	0.2112	0.2055
β	(0.1087)	(0.1157)	(0.2031)	(0.2420)	(0.1108)	(0.0920)	(0.1071)	(0.1253)
Dof	5.8101	5.3430	5.5581	4.9535	3.6322	15.5380	5.1129	4.7100
ARCH	1.7865	1.0988	0.5673	0.4544	0.2249	2.3687	1.1265	0.1958
ARCH	[0.1092]	[0.3679]	[0.6653]	[0.7115]	[0.5083]	[0.0871]	[0.1479]	[0.6096]

时间尺度	收益对	最优模型	时变参数	最小值	最大值	均值	标准差	时变参数	最小值	最大值	均值	标准差
d₁时间尺度(短期)	(u_Brent, v_Aframax)	时变MRS Clayton	θ⁰	0.0046	6.1223	1.9650	1.1090	θ¹	0.0001	0.6303	0.0796	0.1041
	(u_Brent, v_Panamax)	时变MRS Clayton	θ⁰	0.0001	36.8231	12.3186	10.5259	θ¹	0.0001	1.1028	0.1690	0.2193
	(u_Brent, v_Suezmax)	时变MRS Clayton	θ⁰	0.0001	10.0100	4.5858	4.5921	θ¹	0.0001	0.0296	0.0039	0.0046
	(u_Brent, v_VLCC)	时变MRS Clayton	θ⁰	0.0001	37.1588	12.8850	10.6466	θ¹	0.0001	2.4148	1.1209	0.5045
d₂时间尺度(中期)	(u_Brent, v_Aframax)	时变MRS SJC Copula	τ⁰_L	0.0000	0.8781	0.2572	0.2356	τ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_Brent, v_Aframax)	时变MRS SJC Copula	τ⁰_U	0.0000	0.9858	0.6024	0.3275	τ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_Brent, v_Panamax)	时变MRS SJC Copula	τ⁰_L	0.0000	0.9493	0.7944	0.1079	τ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_Brent, v_Panamax)	时变MRS SJC Copula	τ⁰_U	0.0002	0.0012	0.0010	0.0001	τ¹_U	0.0002	0.0010	0.0010	0.0001
	(u_Brent, v_Suezmax)	时变MRS Clayton	θ⁰	0.0001	55.9061	17.2179	15.6236	θ¹	0.0001	1.8828	0.1193	0.1937
	(u_Brent, v_VLCC)	时变MRS Clayton	θ⁰	0.0001	45.1563	12.9884	11.2706	θ¹	0.0001	2.3159	0.2912	0.3899
d₄时间尺度(长期)	(u_Brent, v_Aframax)	时变MRS SJC Copula	τ⁰_L	0.0005	0.9867	0.6066	0.3562	τ¹_L	0.0005	0.0011	0.0010	0.0000
	(u_Brent, v_Aframax)	时变MRS SJC Copula	τ⁰_U	0.0000	0.2897	0.1186	0.0640	τ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_Brent, v_Panamax)	时变MRS Clayton	θ⁰	0.0001	49.4121	14.2155	12.9225	θ¹	0.0001	4.2503	1.5005	0.9502
	(u_Brent, v_Suezmax)	时变MRS SJC Copula	τ⁰_L	0.0000	0.9758	0.9090	0.0735	τ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_Brent, v_Suezmax)	时变MRS SJC Copula	τ⁰_U	0.0009	0.0010	0.0010	0.0000	τ¹_U	0.0009	0.0010	0.0010	0.0000
	(u_Brent, v_VLCC)	时变MRS SJC Copula	τ⁰_L	0.0010	0.9047	0.1204	0.2116	τ¹_L	0.0010	0.0654	0.0013	0.0045
	(u_Brent, v_VLCC)	时变MRS SJC Copula	τ⁰_U	0.0032	0.3824	0.1296	0.0967	τ¹_U	0.0010	0.0912	0.0014	0.0063

时间尺度	收益对	尾部风险(S₀)	最小值	最大值	均值	标准差	尾部风险(S₁)	最小值	最大值	均值	标准差
d₁ 时间尺度 (短期)	(u_BTI, v_Aframax)	λ⁰_L	0.1474	0.5381	0.2767	0.0842	λ¹_L	0.4801	0.9148	0.6878	0.0737
	(u_BTI, v_Aframax)	λ⁰_U	0.0479	0.4080	0.0603	0.0266	λ¹_U	0.4080	0.8226	0.7124	0.0872
	(u_BTI,v_Panamax)	λ⁰_L	0.1588	0.4048	0.1829	0.0209	λ¹_L	0.4048	0.8643	0.8415	0.0340
	(u_BTI,v_Panamax)	λ⁰_U	0.0614	0.7607	0.3006	0.1766	λ¹_U	0.3134	0.9475	0.7595	0.1572
	(u_BTI,v_Suezmax)	λ⁰_L	0.4355	0.7290	0.6310	0.0421	λ¹_L	0.1646	0.5118	0.2971	0.0383
	(u_BTI,v_Suezmax)	λ⁰_U	0.0650	0.9313	0.6428	0.2026	λ¹_U	0.0103	0.6882	0.2644	0.1613
d₂ 时间尺度 (中期)	(u_BTI, v_Aframax)	λ⁰_L	0.0612	0.8925	0.5219	0.1613	λ¹_L	0.0010	0.3657	0.0028	0.0255
	(u_BTI, v_Aframax)	λ⁰_U	0.0800	0.9283	0.5947	0.1572	λ¹_U	0.0049	0.4725	0.0906	0.0631
	(u_BTI,v_Panamax)	λ⁰_L	0.2187	0.8101	0.7138	0.0483	λ¹_L	0.0010	0.2187	0.0021	0.0152
	(u_BTI,v_Panamax)	λ⁰_U	0.0257	0.8695	0.5679	0.1731	λ¹_U	0.0010	0.0257	0.0011	0.0017
	(u_BTI,v_Suezmax)	λ⁰_L	0.0677	0.9567	0.7401	0.1414	λ¹_L	0.0294	0.8354	0.3636	0.2219
	(u_BTI,v_Suezmax)	λ⁰_U	0.5389	0.9549	0.8411	0.0544	λ¹_U	0.1897	0.6584	0.4050	0.0645
d₄ 时间尺度 (长期)	(u_BTI, v_Aframax)	λ⁰_L	0.3616	0.8997	0.8823	0.0376	λ¹_L	0.1416	0.3616	0.1728	0.0181
	(u_BTI, v_Aframax)	λ⁰_U	0.5455	0.8834	0.8632	0.0248	λ¹_U	0.2161	0.5455	0.2641	0.0274
	(u_BTI,v_Panamax)	λ⁰_L	0.4798	0.6927	0.5931	0.0366	λ¹_L	0.6927	0.9345	0.8935	0.0226
	(u_BTI,v_Panamax)	λ⁰_U	0.3181	0.9064	0.8049	0.1199	λ¹_U	0.0385	0.7510	0.1136	0.0667
	(u_BTI,v_Suezmax)	λ⁰_L	0.6304	0.8601	0.8306	0.0313	λ¹_L	0.0010	0.7937	0.0050	0.0555
	(u_BTI,v_Suezmax)	λ⁰_U	0.1206	0.8839	0.7892	0.1146	λ¹_U	0.0010	0.7112	0.0047	0.0497

Study on Dynamic Dependence and Risk Contagion Effect of Crude Oil Market and Tanker Market Considering Multi-scale and Multi-state

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时间尺度	收益对	尾部风险(S₀)	最小值	最大值	均值	标准差	尾部风险(S₁)	最小值	最大值	均值	标准差
d₁时间尺度(短期)	(u_Brent,v_BDTI)	λ⁰_L	0.0003	0.9713	0.9363	0.0681	λ¹_L	0.0003	0.0010	0.0010	0.0000
	(u_Brent,v_BDTI)	λ⁰_U	0.0000	0.9462	0.8492	0.0718	λ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_WTI, v_BDTI)	λ⁰_L	0.0004	0.9703	0.9369	0.0681	λ¹_L	0.0004	0.0010	0.0010	0.0000
	(u_WTI, v_BDTI)	λ⁰_U	0.0000	0.9240	0.7946	0.0765	λ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_Brent, v_BCTI)	λ⁰_L	0.0000	0.9198	0.8231	0.0930	λ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_Brent, v_BCTI)	λ⁰_U	0.0000	0.9644	0.9142	0.0763	λ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_WTI, v_BCTI)	η⁰_L	0.0000	0.9825	0.7749	0.3115	η¹_L	0.0000	0.6826	0.0723	0.1341
d₂时间尺度(中期)	(u_Brent,v_BDTI)	λ⁰_L	0.0000	0.0012	0.0010	0.0001	λ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_Brent,v_BDTI)	λ⁰_U	0.0000	0.8199	0.6837	0.0896	λ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_WTI, v_BDTI)	λ⁰_L	0.0000	0.0010	0.0010	0.0001	λ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_WTI, v_BDTI)	λ⁰_U	0.0000	0.7718	0.6100	0.0967	λ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_Brent, v_BCTI)	η⁰_L	0.0000	0.8685	0.7981	0.0716	η¹_L	0.0000	0.0000	0.0000	0.0000
	(u_WTI, v_BCTI)	η⁰_L	0.0000	0.8314	0.7921	0.0564	η¹_L	0.0000	0.0000	0.0000	0.0000
d₄时间尺度(长期)	(u_Brent,v_BDTI)	λ⁰_L	0.1263	0.6167	0.4411	0.0908	λ¹_L	0.0056	0.1263	0.0138	0.0090
	(u_Brent,v_BDTI)	λ⁰_U	0.1584	0.9480	0.8680	0.0781	λ¹_U	0.0413	0.2446	0.1224	0.0457
	(u_WTI, v_BDTI)	η⁰_L	0.0000	0.9825	0.7749	0.3115	η¹_L	0.0000	0.6826	0.0723	0.1341
	(u_Brent, v_BCTI)	η⁰_L	0.0000	0.3480	0.0369	0.0723	η¹_L	0.0000	0.6146	0.1171	0.1677
	(u_WTI, v_BCTI)	λ⁰_L	0.0001	0.8732	0.1863	0.2294	λ¹_L	0.0001	0.0010	0.0010	0.0001
	(u_WTI, v_BCTI)	λ⁰_U	0.0000	0.0010	0.0010	0.0001	λ¹_U	0.0000	0.0010	0.0010	0.0001

时间尺度	收益对	尾部风险(S₀)	最小值	最大值	均值	标准差	尾部风险(S₁)	最小值	最大值	均值	标准差
d₁时间尺度(短期)	(u_Brent,v_Aframax)	η⁰_L	0.0000	0.8930	0.6300	0.1968	η¹_L	0.0000	0.3329	0.0139	0.0438
	(u_Brent,v_Panamax)	η⁰_L	0.0000	0.9814	0.7837	0.3026	η¹_L	0.0000	0.5334	0.0521	0.1136
	(u_Brent,v_Suezmax)	η⁰_L	0.0000	0.9331	0.4635	0.4646	η¹_L	0.0000	0.0000	0.0000	0.0000
	(u_Brent, v_VLCC)	η⁰_L	0.0000	0.9815	0.7968	0.2789	η¹_L	0.0000	0.7505	0.4915	0.1708
d₂时间尺度(中期)	(u_Brent,v_Aframax)	λ⁰_L	0.0000	0.8781	0.2572	0.2356	λ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_Brent,v_Aframax)	λ⁰_U	0.0000	0.9858	0.6024	0.3275	λ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_Brent,v_Panamax)	λ⁰_L	0.0000	0.9493	0.7944	0.1079	λ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_Brent,v_Panamax)	λ⁰_U	0.0002	0.0012	0.0010	0.0001	λ¹_U	0.0002	0.0010	0.0010	0.0001
	(u_Brent,v_Suezmax)	η⁰_L	0.0000	0.9877	0.7398	0.3546	η¹_L	0.0000	0.6920	0.0301	0.0870
	(u_Brent, v_VLCC)	η⁰_L	0.0000	0.9848	0.7836	0.3055	η¹_L	0.0000	0.7413	0.1160	0.1844
d₄时间尺度(长期)	(u_Brent,v_Aframax)	λ⁰_L	0.0005	0.9867	0.6066	0.3562	λ¹_L	0.0005	0.0011	0.0010	0.0000
	(u_Brent,v_Aframax)	λ⁰_U	0.0000	0.2897	0.1186	0.0640	λ¹_U	0.0000	0.0010	0.0010	0.0001
	(u_Brent,v_Panamax)	η⁰_L	0.0000	0.9861	0.7447	0.3255	η¹_L	0.0000	0.8495	0.5345	0.2274
	(u_Brent,v_Suezmax)	λ⁰_L	0.0000	0.9758	0.9090	0.0735	λ¹_L	0.0000	0.0010	0.0010	0.0001
	(u_Brent,v_Suezmax)	λ⁰_U	0.0009	0.0010	0.0010	0.0000	λ¹_U	0.0009	0.0010	0.0010	0.0000
	(u_Brent, v_VLCC)	λ⁰_L	0.0010	0.9047	0.1204	0.2116	λ¹_L	0.0010	0.0654	0.0013	0.0045
	(u_Brent, v_VLCC)	λ⁰_U	0.0032	0.3824	0.1296	0.0967	λ¹_U	0.0010	0.0912	0.0014	0.0063

[1]	Lu Wen, Ling Feng. Interbank Risk Contagion and Effective Bailout Frontier under Dynamic Government Intervention [J]. Chinese Journal of Management Science, 2025, 33(6): 37-48.
[2]	Yulei Rao, Hongbing Ouyang, Minchun Han, Zihong Wang. Research on the Contagion Mechanism of Bank Liquidity Risk from the Perspective of Macro Liquidity Tightening [J]. Chinese Journal of Management Science, 2025, 33(4): 24-35.
[3]	Changqing Luo,Lan Liu,Huiming Zhu,Min Zhang. An Empirical Study on the Dynamic Evolution of Crude Oil Market Pricing Power Based on Multi-scale Information Share Model [J]. Chinese Journal of Management Science, 2024, 32(6): 1-12.
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[5]	Xuetong Zhang,Weiguo Zhang,Chao Wang. Tail Risks in Developed and Emerging Markets——Test of Spillover, Contagion and Contagion Determinants [J]. Chinese Journal of Management Science, 2024, 32(4): 14-25.
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[10]	WANG Lei, LI Shou-wei, CHEN Ting-qiang, HE Jian-min. Inter-firm Credit Guarantee Network and Risk Contagion Based on Enterprise Behavior Preference [J]. Chinese Journal of Management Science, 2022, 30(2): 80-93.
[11]	ZHANG Yue-jun, ZHANG Han, WANG Jin-li. Volatility Forecasting of Crude Oil Market Based on Structural Changes and Long Memory [J]. Chinese Journal of Management Science, 2021, 29(9): 54-64.
[12]	ZHU Xiao-neng, WU Jie-nan. The “Ripple Effect” in Stock Market Co-movement [J]. Chinese Journal of Management Science, 2021, 29(8): 1-12.
[13]	CHEN Zhan, HUANG Xun, YAN Xiao-feng. Dynamic Portfolio Forecasting of High-dimensions Exchange Rate Assets Under Structure Break [J]. Chinese Journal of Management Science, 2021, 29(11): 13-22.
[14]	ZHU Peng-fei, TANG Yong, ZHONG Li. Portfolio Strategy Based on Wavelet-High Order Moments model-Take the International Crude Oil Markets as An Research Objects [J]. Chinese Journal of Management Science, 2020, 28(10): 24-35.
[15]	ZHOU Li-guo, HE Zhuo-jing, MENG Tian-cheng. Credit Risk Contagion in an Enterprise Group with Dynamic Copula Models [J]. Chinese Journal of Management Science, 2019, 27(2): 71-82.