Dependency And Risk Spillover Analysis Between International Crude Oil Futures Market And Chinese New Energy-Related Stock Markets

The scarcity and necessity of crude oil and the futures system in which it is traded dictate that changes in its supply and demand are closely linked to global economic developments.As the largest energy consumer and importer of crude oil,China has made every effort in recent years to coordinate green and low-carbon energy development with energy supply and has made new energy an essential part of its multiwheel drive energy supply system.As Chinese new energy development is comprehensive and its overall scale is among the highest in the world,the analysis of the dependency and risk transmission mechanism between the international crude oil futures market and the stock market of China’s new energy-related industries is of great significance for the formulation of energy reform policies,the production and operation decisions of new energy-related enterprises and energy-consuming enterprises,as well as investors’ investment in new energy-related emerging sectors.In this article,to investigate the risk spillover effects of the international crude oil futures market on the stock markets of various new energy-related industries in China,this paper uses two models,the two-dimensional copula and the R-vine copula,to portray the non-linear interdependence structure between the two and the overall of the international crude oil futures market and the stock markets of the Chinese photovoltaic,new energy vehicle,energy storage,wind power,and nuclear power industries,respectively.Combining the ideas of Reboredo and Ugolini(2015),the Copula function and the vine copula model are combined with the conditional value at risk indicator(Co Va R)to calculate four indicators and to measure the direction and intensity of the risk spillover effect of the crude oil futures market on the stock market of each new energy-related industry in China from the individual and overall perspectives,separately.Measure the direction and intensity of the upside and downside risk spillover effects of the international crude oil futures market on the equity markets of new energy industries.Finally,the backtesting test improved by Girardi and Ergun(2013)is extended to multidimensional and applied to test the risk spillover results calculated by both Copula-Co Va R and R-vine copula-Co Va R models.The results of the empirical analysis show that the information spillover from the sharp rise and fall of international crude oil futures prices exacerbates the risk exposure of the stock markets of various Chinese new energy-related industries.There are direct asymmetric positive and negative spillover effects of the international crude oil futures market on the stock markets of various Chinese new energy-related industries,and the risk information of the international crude oil futures market can significantly affect the stock markets of various Chinese new energy-related industries through the transmission of the stock markets of these industries.The intensity and asymmetry of the risk spillover effects of the international crude oil futures market on the stock markets of different Chinese new energy-related industries also differ.The wind power and nuclear power generation industries and the downstream industry of new energy vehicles are more sensitive to large fluctuations in crude oil prices;the photovoltaic,energy storage,and wind power industries are more sensitive to adverse shocks in the crude oil futures market;the new energy vehicle and nuclear power industries are more sensitive to positive shocks.With the Copula and Vine copula models,the values of Co Va R calculated in this paper effectively measure the downside risk spillover effects across financial markets.However,the single Copula model is limited by the distribution characteristics of the function itself,which may have the problem of adequately capturing the characteristics of one side of the tail.The vine copula model,however,considers the transmission of risk information across multiple markets and compensates for this by introducing multiple,different copula functions that greatly enhance the model’s applicability,making it more effective in measuring risk spillovers across multidimensional markets.