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Abstract

Based on the Copula-CoVaR framework, the Beta-skew-t-EGARCH model is used to capture the leverage effect of financial assets, the thick tail distribution and the conditional skewness. Then, based on the standard deviation, this paper introduces the extreme value theory, combined with the Copula function and the CoVaR method which are used to measure the effect of China’s crude oil market on the risk spillover effects of domestic and international commodity markets, and reveals the impact of China’s crude oil market. The results show that, regardless of the domestic Crude oil market and international commodity market, domestic crude oil market and domestic commodity market, there is a two-way positive risk spillover effect, which shows asymmetric characteristics. The results provide some theoretical supports for the regulatory authorities to enhance the marketization level of China’s crude oil market, some suggestions for the investment institutions to assess the risk level of China’s crude oil market, and some reference value for investors to invest in China’s crude oil market. Finally, based on the conclusions of the study, this paper provides some targeted recommendations to the development of China’s crude oil market.

Keywords

Oil markets commodity markets risk spillover effect

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References

Balkema

A.A.

and de Haan

, Residual life time at great age, The Annals of Probability2 (1974), 792–804.

Harri

, Nalley

and Hudson

, The relationship between oil, exchange rates and commodity prices, Journal of Agricultural and Applied Economics2 (2009), 501–510.

Sklar

, Fonctions de répartition à n dimensions et leursmarges,(in French), Publ. Inst. Statist. Univ. Paris8 (1959), 229–231.

Sklar

, Random variables, distribution functions, and Copulas–A personal look backward and forward, Hayward, CA: Institute of Mathematical Statistics, 1996, 1–14.

Harvey

A.C.

and Chakravarty

, Beta-t-(E)GARCH, Cambridge Working paper in Economics, CWPE 0840, 2008.

Fernández

and Steel

, On Bayesian modelling of fat tails and skewness,Association, Journal of the American Statistical93 (1998), 359–371.

C.Q.

and She

S.X.

, Research on information spillover of fuel oil futures market in Shanghai,Science, Journal of Management3 (2009), 92–101.

Pan

H.F.

and Zhang

J.S.

, Analysis of domestic oil price fluctuation based on ARCH model,Study, Statistical4 (2005), 16–20.

Pan

H.F.

and Zhang

J.S.

, Extreme risk spillover effect test of crude oil market at home and abroad,’Science, s Management3 (2007), 25–30.

10.

Georg

and Eric

, On dependence consistency of CoVaR and some other systemic risk measures,&Modeling, Risk1 (2014), 49–77.

11.

Baffes

, Oil spills on other commodities,Policy, Resource8 (2007), 126–134.

12.

Reboredo

J.C.

and Ugolini

, The impact of downward/upward oil price movements on metal prices, Resource Policy9 (2016), 129–141.

13.

Reboredo

J.C.

, Is gold a hedge or safe haven against oil price movements,Policy, Resource5 (2013), 130–137.

14.

Jiao

J.L.

and Fan

, Research on the interactive relationship between China’s crude oil price and international crude oil price,Review, Management7 (2004), 48–53.

15.

Sieczka

and Holyst

J.A.

, Correlations in commodity markets,Economics, Journal of Agricultural and Applied2 (2009), 1621–1630.

16.

Gropp

, Lo Duca

and Vesala

, Cross-border bank contagion in Europe, International Journal of Central Banking1 (2009), 97–139.

17.

Pindyck

and Rotemberg

, The excess comovement of commodity prices, The Economic Journey11(1990), 1173–1189.

18.

Sari

and Hammoudeh

and Soytas

, Dynamics of oil price, precious metal prices, and exchange rate,Economics, Energy2 (2010), 351–362.

19.

Hammoudeh

and Yuan

, Metal volatility in presence of oil and interest rate shocks,Economics, Energy2 (2008), 606–620.

20.

Hosamani

S.-M.

, Kulkarni

B.-B.

, Boli

R.-G.

and Gadag

V.-M.

, QSPR analysis of certain graph theocratical matrices and their corresponding energy, Applied Mathematics & Nonlinear Sciences2(1) (2017), 131–150.

21.

Nazlioglu

and Soytas

, Oil price, agricultural commodity prices, and the dollar: a panel cointegration and causality analysis,Economics, Energy4 (2012), 1098–1104.

22.

Nazlioglu

and Soytas

, World oil prices and agricultural commodity prices: evidence from an emerging market, Energy Economics5 (2011), 488–496.

23.

Adrian

and Brunnermeier

M.K.

, CoVaR, American Economic Review7 (2016), 1705–1741.

24.

Chang

T.H.

and Su

H.M.

, The substitutive effect of biofuels on fossil fuels in the lower and higher crude oil price periods, Energy7 (2010), 2807–2813.

25.

Lokesha

, Shruti

and Deepika

, Reckoning of the dissimilar topological indices of human liver, Applied Mathematics & Nonlinear Sciences3(1) (2018), 265–276.

26.

Gao

and Wang

W.-F.

, The fifth geometric-arithmetic index of bridge graph and carbon nanocones, Journal of Difference Equations and Applications23(1-2SI) (2017), 100–109.

27.

Gao

, Zhu

, Guo

and Wang

, Ontology learning algorithm for similarity measuring and ontology mapping using linear programming, Journal of Intelligent & Fuzzy Systems33(5) (2017), 3153–3163.

28.

DuMouchel

W.H.

, Estimating the stable index a in order to measure tail thickness: a critique, The Annals of Statistics1 (1983), 1019–1031.

29.

Dong

X.L.

and Zhang

Q.S.

, Multivariate analysis of volatility spillover effects in domestic and foreign crude oil markets, Chinese Soft Science12 (2006), 120–125.

30.

Liu

X.X.

, Duan

and Xie

F.Z.

, Risk spillover effect in stock market: an analysis based on EVT-Copula-CoVaR model, World Economy11 (2011), 145–159.

31.

Ftiti

, Fatnassi

and Tiwari

A.K.

, Neoclassical finance, behavioral finance and noise traders: assessment of gold-oil markets, Finance Research Letter5 (2016), 33–40.

Based on Copula-CoVaR model of risk spillover effect of oil markets and other commodity markets

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