Multi-Time Scale Spillover Effect of International Oil Price Fluctuation on China’s Stock Markets

Abstract

With the continuous increase of China’s foreign-trade dependence on crude oil and the accelerating integration of the international crude oil market and the Chinese finance market, the spillover effect of international oil price fluctuation on China’s stock markets increasingly attracts the attention of the public. In order to explore the impact of international oil price fluctuation on China’s stock markets and the time-varying spillover differences of industry sectors, this study proposes three research hypotheses and constructs a multi-time scale analysis framework based on wavelet analysis and a time-varying t-Copula model. In this paper, we use the Shanghai Composite Index as the representative of a general trend of the stock market, and we use the stock index of the China Securities Industry as the counterpart of industrial sectors. Based on the data from 5 January 2005 to 31 May 2020, this paper measures and analyzes the spillover effect of international oil price fluctuation on China’s stock markets, under different volatility periods. The results show that, firstly, the spillover effect of international oil price fluctuation on the Chinese stock markets is different. In the short and medium volatility period, the changes in international oil price are ahead of the changes in the Chinese stock markets, while the latter is ahead of the former under long-term fluctuations. Secondly, the spillover effect of international oil price fluctuation on China’s industry stock indexes is persistent. As the time scale increases, the tail dependency will increase. Finally, the impact of risk events aggravates the volatility of the stock markets in the short-term, while the mid- to long-term impact mainly affects the volatility trend. Investment risk control can make overall arrangement on the basis of the characteristics of oil price impact under different fluctuation stages.

Effectiveness of Artificial Neural Networks in Hedging against WTI Crude Oil Price Risk

“…Literature has also identified the impact of oil price shocks on macroeconomic aggregates such as the level of investment, stock prices and returns [12][13][14][15][16][17], inflation rate [18], industrial production and exchange rates [19][20][21][22][23], as well as financial and monetary policy [24]. There has also been a large number of studies concerning the impact of crude oil prices on various groups of commodities such as: gold [25,26], silver, platinum and palladium [27], zinc, copper and molybdenum [28], agricultural [29][30][31][32], and energy commodities [33][34][35][36][37].…”

Using Artificial Neural Networks to Support the Decision-Making Process of Buying Call Options Considering Risk Appetite

“…Issues related to the consequences of the risk of changes in oil prices were a popular topic of scientific research even before the coronavirus pandemic. Researchers analyzed the impact of oil price fluctuations on macroeconomic aggregates such as the level of investment, stock prices and returns [8][9][10][11][12][13], inflation rate [14], industrial production and exchange rates [15][16][17][18][19], as well as financial and monetary policy [20]. There has also been a large number of studies concerning the impact of crude oil prices on commodities such as: energy [21][22][23][24][25] and agricultural commodities [26][27][28][29], copper, gold [30,31], silver, zinc, platinum and palladium [32] and molybdenum [33].…”