Straddle Option is a financial trading tool that explores volatility premiums in high-volatility markets without predicting price direction. Although deep reinforcement learning has emerged as a powerful approach to trading automation in financial markets, existing work mostly focused on predicting price trends and making trading decisions by combining multidimensional datasets like blogs and videos, which led to high computational costs and unstable performance in high-volatility markets. To tackle this challenge, we develop automated straddle option trading based on reinforcement learning and attention mechanisms to handle unpredictability in high-volatility markets. Firstly, we leverage the attention mechanisms in Transformer DDQN through both self-attention with time series data and channel attention with multi-cycle information. Secondly, a novel reward function considering excess earnings is designed to focus on long-term profits and neglect short-term losses over a stop line. Thirdly, we identify the resistance levels to provide reference information when great uncertainty in price movements occurs with intensified battle between the buyers and sellers. Through extensive experiments on the Chinese stock, Brent crude oil, and Bitcoin markets, our attention-based Transformer-DDQN model exhibits the lowest maximum drawdown across all markets, and outperforms other models by 92.5% in terms of the average return excluding the crude oil market due to relatively low fluctuation.

This study uses Shanghai Crude Oil Futures (SC) as a proxy for the upstream segment of China’s petrochemical industry and investigates how its market efficiency influences five key downstream product markets. Considering that markets differ in how they absorb information and in their structural features, we employ the Feasible Exact Local Whittle (FELW) estimator to construct a continuous market efficiency index. To capture efficiency dynamics across different time horizons, the study applies the Maximal Overlap Discrete Wavelet Transform (MODWT) to decompose the efficiency series into short-, medium-, and long-term components. These are then examined by Quantile-on-Quantile (QQ) regression to trace the varying marginal effects across different efficiency states. The results reveal strong state dependence and structural differences in the efficiency transmission from SC to downstream markets. Among the five markets, Low-Sulfur Fuel Oil and Asphalt exhibit the most stable transmission patterns, with the former showing a “saddle-shaped” structure and the latter following a “dual-path” pattern. In contrast, the links between SC and the markets for Linear Low-Density Polyethylene and Polypropylene are highly nonlinear and less predictable. Purified Terephthalic Acid demonstrates a dual mechanism of efficiency resonance and long-term anchoring. These findings deepen our understanding of information efficiency within industrial value chains. They also offer practical insights for managing market risk, guiding price policies, and designing regulatory frameworks in the energy sector.

We construct the China Crude Oil Volatility Index (CNOVX)—the first model-free, optionimplied measure of forward-looking oil price risk for China—using INE crude oil options from 2021 to 2024 and an adapted CBOE methodology that accounts for sparse strike availability via smooth interpolation and extrapolation. Our results show that CNOVX increases with trading activity in the futures market, declines with option volume, and is strongly predicted by the 30-day realized variance of the SC crude oil futures contract. External shocks, including the Russia–Ukraine conflict and the Geopolitical Risk Index, significantly elevate CNOVX levels. During the COVID-19 pandemic, mortality risk intensifies the volatility-amplifying role of futures trading and strengthens the volatility-dampening effect of options, while confirmed case counts have weaker influence. We further document a pronounced asymmetric leverage effect: negative futures returns raise CNOVX more than positive returns of equal size. However, volatility feedback effects are negligible, as changes in implied volatility respond primarily to contemporaneous market conditions. Overall, CNOVX serves as a timely and informative benchmark for monitoring risk in China’s evolving crude oil derivatives market, with valuable implications for investors, hedgers, and policymakers.

This paper is the first to examine the evolution of price discovery in agricultural commodity markets across the four distinct phases determined by trade friction and trade policy uncertainty. Using cointegrated vector autoregressive model and common factor weights, we report that corn, cotton, soybean meal, and sugar (palm oil, soybean, soybean oil, and wheat) futures (spot) play a dominant role in price discovery during the full sample period. Moreover, the leadership in price discovery evolves over time in conjunction with changes in trade friction phases. However, such results vary across commodities. We also report that most of the agricultural commodity markets are predominantly led by futures markets in price discovery during phase Ⅲ, except for the wheat market. Our results indicate that taking trade friction into consideration would benefit portfolio managements and diversifying agricultural trade partners holds significance.

The use of green finance to slow down global warming in support of sustainable development remains widely discussed. This study examines whether investment structure moderates the impact of green finance on the environment in China, one of the top carbon-emitting nations and the second-largest economy in the world. We primarily used the moments-quantile regression approach with fixed-effect models on panel data from 1992Q1 to 2020Q4. First, the results confirmed that green finance and public and private investments worked synergistically to lower CO2 emissions, especially in Central and Western China. However, there was no proof that green finance and foreign direct investment were complementary in reducing CO2 emissions in China, unlike the Central region. Second, green finance marginally lowered CO2 emissions in all provinces, mainly in Eastern and Western China; this reduction was largely dependent on private investment in the Western region’s most polluting areas and foreign direct investment in Eastern and Western China’s least polluting provinces. Third, the beneficial effect of green finance occurred at varying optimal thresholds and investment-related conditions across Chinese regions at different quantiles. Lastly, we showed that in contrast to the variable impacts of urbanization, oil prices, and economic growth across Chinese regions at different quantiles, renewable energy, and trade openness reduced CO2 emissions. In conclusion, the study makes some policy recommendations for China’s sustainable economic development, an important model from which other countries can tailor their investment strategies and environmentally friendly policies.

Fighting global warming has become a vital requirement for environmental sustainability. Green finance has gained popularity as a promising mechanism for transitioning to a lowcarbon economy. Thus, this paper investigates whether excess green financing increases renewable energy capacities and enhances environmental quality from 1992Q1 to 2020Q4 in China, one of the major CO2 emitters. We primarily used the method of moments-quantile regression with fixed-effect models. First, we found nonlinear U-shaped impacts of green finance on wind power capacities in all Chinese regions, thermal power capacities in the Western and Central areas, and hydropower capacities in Eastern China, respectively. Second, we confirmed an inverted U-shaped impact of green finance on CO2 emissions in the Eastern region but U-shaped effects in the Western and Central regions. The impacts of green finance were asymmetrical due to the heterogeneous distributions of renewable energy sources and environmental quality within and between regions. Green finance mostly improved environmental quality when certain conditions and thresholds were met. Third, green finance had substantial marginal effects on environmental quality in the least polluted provinces (Q.20) in Western China and the most polluted provinces (Q.80) in Eastern China. Finally, there were heterogeneous effects of oil prices, urbanization, foreign direct investments, and trade openness on renewable energy consumption and environmental quality across Chinese provinces. Accordingly, this study provides some policy recommendations for China’s sustainable development, a key example from which the international community can adjust its green policies.

China’s energy stocks are confronted with significant climate-related challenges. This paper aims to measure the daily climate transition risk in China by assessing the intensity of climate policies. The daily climate physical risk encountered by China’s renewable energy stocks is also measured based on the perspective of temperature change. Then, the partial linear function coefficient model is adopted to empirically investigate the non-linear impacts of climate transition risk and climate physical risk on the return and volatility of renewable energy stocks. The nonlinear moderating effect of climate transition risk is also involved. It is found that: (1) Between 2017 and 2022, the climate transition risk in China exhibited a persistent upward trend, while the climate policies during this period particularly emphasized energy conservation, atmospheric improvements, and carbon emissions reduction. Additionally, the climate physical risk level demonstrated a pattern consistent with a normal distribution. (2) There is a U-shaped nonlinear impact of climate physical risk on the return and volatility of renewable energy stocks. High climate physical risk could not only increase the return of renewable energy stocks but also lead to stock market volatility. (3) Climate transition risk exhibits a U-shaped effect on the return of renewable energy stocks, alongside an inverted U-shaped effect on their volatility. Notably, a high level of climate transition risk not only increases the return of renewable energy stocks but also serves to stabilize the renewable energy stock market. Moreover, the heightened risk associated with climate transition enhances the negative impact of oil price volatility on the yield of renewable energy stocks and, concurrently, leads to an increase in volatility.The strength of this moderating effect is directly correlated with the level of climate risk.

We investigate the dynamic extremal connectedness of sectors within the Chinese stock market conditional on exogenous extreme risk through multivariate extreme value regression. To proxy the exogenous extreme risk, we independently consider market volatility-based measures and policy uncertainty-based measures. We discover that market volatility-based measures have a stronger influence than policy uncertainty-based measures on the extremal connectedness of sectors. The oil volatility index is the most influential on extremal connectedness, and the energy sector plays a direct role in transmitting exogenous extreme risk. Our findings provide new insights into understanding the drivers of systematic and idiosyncratic contagion.

This study investigates and compares the effects of the Coronavirus Disease 2019 (COVID-19) pandemic, the Chicago Mercantile Exchange (CME)'s negative price suggestion on prices and trading activities in the crude oil futures market to discuss the cause of negative crude oil futures prices. Through event studies, our results show that the COVID-19 pandemic no longer impacts crude oil futures prices in April after controlled market risk, while the CME’s negative prices suggestion can explain the crude oil futures price changes around and around even after April 8 to some degree. Moreover, our study uncovers anomalies in prices and trading activities by analyzing returns, trading volume, open interest, and illiquidity measures using vector autoregressive (VAR) models. The results imply that CME’s allowing negative prices strengthens the price impact on trading volume and makes illiquidity risk matter. Our results coincide with the following lawsuit evidence of market manipulation.

This study is the first to examine China’s crude oil options market. Using high-frequency data and three different price discovery measures, we conduct a rigorous analysis and find that after its first 8 months of operation, China’s crude oil options market has already played an important role in price discovery. Factors such as volume, volatility, and speculation can impact its price discovery ability. We also find a unique phenomenon in China’s crude oil derivatives market, namely that speculative activity mainly occurs in the futures market and adds to the price discovery of the futures market rather than to the options