Research on Time-Varying Two-Way Spillover Effects Between Carbon and Energy Markets: Empirical Evidence From China

With the improvement of China’s carbon emission trading system, the spillover effect between carbon and energy markets is becoming more and more prominent. This paper selects four representative pilot carbon markets, including Beijing (BEA), Guangdong (GDEA), Hubei (HBEA) and Shanghai (SHEA). And three representative energy markets, including Crude Oil Futures (SC), power index (L11655) and China Securities new energy index (NEI). Combining the rolling window technology with DY spillover index, set a 50-weeks rolling window to measure the spillover index, and deeply analyze the time-varying two-way spillover effect between China’s carbon and energy markets. The results show that the spillover effect between China’s carbon and energy markets has significant time variability and two-way asymmetry. The time-varying spillover effect of different carbon pilot markets on the energy market has regional heterogeneity. The volatility spillover effect of Beijing and Shanghai carbon markets mainly comes from the crude oil futures market, Guangdong carbon market mainly comes from the new energy market, and Hubei carbon market mainly comes from crude oil and electricity market. The above research results contribute to the prevention of potential risk spillover between carbon and energy markets, which can promote the establishment of China’s unified carbon market and the prevention of systemic financial risks in energy market.

Crude Oil Price Forecast Based on Deep Transfer Learning: Shanghai Crude Oil as an Example

Crude oil is an important fuel resource for all countries. Accurate predictions of oil prices have important economic and social values. However, the price of crude oil is highly nonlinear under the influence of many factors, so it is very difficult to predict accurately. Shanghai crude oil futures were officially listed in March 2018. It is of great significance to accurately predict the price of Shanghai crude oil futures for guiding China’s domestic production practice. Forecasting the price of Shanghai crude oil futures is even more difficult because of the lack of price data due to the short listing time. In order to solve this problem, this paper proposes using Long Short-Term Memory Network (LSTM) based on transfer learning to predict the price of crude oil in Shanghai. The basic idea is to take advantage of the correlation between Brent crude oil and Shanghai crude oil, use Brent crude oil for training in the early stage, and then use Shanghai crude oil to fine-tune the network. The empirical results show that the LSTM model based on transfer learning has strong generalization ability and high prediction accuracy.

The Oil Futures and Options Markets in 2020: The “Message from Markets”

This paper considers the response of the equity and oil markets to the onset of crisis conditions after February 15, 2020. Based on derivative markets for equities and WTI (West Texas Intermediate) crude-oil futures contracts, implied equity and oil volatilities quantify the depth of the crisis and contrast it with the previous ones. The estimated Black [(1976) Journal of Financial Economics, 3, 167–179] vol skew and Merton [(1976) Journal of Financial Economics, 3, 125–144] option model parameters are able to discern between demand- and supply-side facets. The time when the futures curve is in contango identifies the beginning and, to date, conclusion of the crisis. Using the CAPM, co-movement of oil and equity prices permits computing forecasts of spot oil prices. In considering these events, we recognize the essential role of prices in financial markets: They are conveyors of information, the “Message from Markets,” in which financial theory proves useful, practical and applicable.