Abstract
Employing the dataset of WTI oil spot price and stock price index in China，Brazil, India, US, German, France, UK and Japan, this paper obtains five subintervals of whole sample range through a nonparametric multiple change point algorithms. Furthermore, it analyzes dependence between oil spot price and stock price index through copula model and computes the value of VaR and ES based on simulation for every subinterval. It reveals that dependence between oil spot price and stock price index during financial crisis is an asymmetric tail dependence. The value of VaR and ES of the oil spot price and stock price index shows irregular fluctuation.
Highlights
Over the years, researches have been conducted on the connection between price changes in the oil and the stock markets
Apergis and Miller analyzed the impact of oil price shocks on the stock markets of 8 developed countries [1], and found there is no significant impact in this respect
Killian and Park discovered that the impact of oil price shocks on the stock market may vary depending on the causes of such shocks [4]
Summary
Researches have been conducted on the connection between price changes in the oil and the stock markets. Using Bivariate GARCH and VAR models, Jin Hongfei and Jin Luo studied oil prices and returns of the stock markets in China and United States [7], as well as their spillover effect. They found that oil price shocks have negative spillover effect on the return of US stock market and there is a two-way. Using Granger Causality Analysis, the VAR model, Impulse Response Function (IRF), Forecast Error Variance Decomposition (FEVD) and the MGARCH-BEKK (1,1) model, Liu Xiangyun and Zhu Chunming analyzed the volatility spillover and mean spillover effects of log returns of NYSE as of WTI spot date and Shanghai Stock Exchange as of its index date [8]. They found that oil price changes do provide useful information to forecast the returns of China's stock market, which is significantly subject to the spillover effect of oil prices
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