This research utilizes the GARCH-MIDAS-X (GM) and DCC-MIDAS-X (DM) models, including their asymmetric extensions (AGM and ADM), to conduct a comparative analysis of how various forms of U.S. economic policy uncertainty (EPU) affect the volatilities of futures and spot markets, as well as the correlations among three crude oil markets. The findings reveal the following: (1) Among the different types of EPU, only trade policy and government spending policy uncertainties significantly influence the volatilities of futures and spot returns across all petroleum markets. Trade policy uncertainty exerts a stronger negative effect, while government spending policy uncertainty has a milder positive impact. Furthermore, within the same market, there is heterogeneity in the response of futures and spot prices to different types of EPU. The WTI futures market is influenced by fewer types of EPU indices compared to the WTI spot. (2) Government spending, fiscal, regulation, and health care policy uncertainties primarily drive abnormal negative effects on crude oil return volatilities through positive shocks. Conversely, trade policy uncertainty tends to produce positive effects via negative shocks. (3) Except for government spending and financial regulation policy uncertainties, other forms of EPU significantly enhance the correlations between futures and spot prices in Brent and Oman. Additionally, most EPU indices display asymmetric effects on the correlations between futures and spot returns in WTI and Brent, though these asymmetries are less prominent in the Oman market.

In this study, we employ the term Adjusted Market Inefficiency Magnitude (AMIM) to quantify the efficiency of the Chinese and American stock markets and further use the conditional quantile-based connectedness approach to investigate the extreme risk spillover between the Chinese and American stock markets under different shock scales. Our results show that the stock markets in China and the United States are efficient for most periods, and the efficiency has time-varying characteristics. Extreme events have a certain impact on the efficiency. The risk spillover from the US stock market to the Chinese stock market dominates and exhibits time-varying characteristics. The Chinese stock market has stronger external spillover ability during extreme rises, while the US stock market has stronger external spillover ability during extreme declines. The total spillover index and directional spillover index exhibit a U-shaped characteristic under different quantiles. The spillover-out (spillover-in) level in extreme states is stronger than that in normal states, and the spillover effects in extreme rising and falling states are asymmetric, with a larger total spillover level in extreme falling states. The impact of the COVID-19 and the Russia-Ukraine war on extreme risk spillovers between the Chinese and American stock markets is very weak. These findings offer valuable insights for financial regulators in systemic risk mitigation and for investors in strategic asset allocation.

By taking Bitcoin, Ethereum, and Ripple as research objects, this paper applies the multifractal detrended partial cross-correlation analysis (MF-DPXA) to study the intrinsic cross-correlation between cryptocurrencies. Combining MF-DPXA and time-delay DCCA methods, we develop the removing factors time-delayed detrended cross-correlation analysis (R-TD-DCCA) to study the risk transmission direction between cryptocurrencies after removing the influence of common factors. The results show that after removing the influence of cryptocurrencies, the persistence of the cross-correlation between cryptocurrencies is enhanced, and the multifractal degrees of the cross-correlation between Bitcoin and Ethereum and between Ethereum and Ripple are increased, but the multifractal degree of the cross-correlation between Bitcoin and Ripple is weakened. However, after removing the influence of the S&P 500 index, the multifractal degree of the cross-correlation between cryptocurrencies has weakened. The Hurst exponent of local dynamic cross-correlation between cryptocurrencies is almost always greater than 0.5. With the increase in time delay, the risk of Bitcoin is mainly transmitted to Ethereum and Ripple, and the risk of Ripple is mainly transmitted to Ethereum. When removing the impact of the S&P 500 index, the short-term risk of Bitcoin is mainly transmitted to Ethereum and Ripple. The findings of this study have several implications for re-understanding the intrinsic interdependence structure and portfolios.

Weather change, as a physical risk factor of climate change, increasingly impacts the energy market. This paper investigates China's major energy futures using a QVAR framework to analyze spillover effects under different market conditions, addressing mean-model limitations. It also reveals state-dependent weather impacts on spillovers, providing physical climate risk evidence. The results show the following: (1) Spillover effects intensify under extreme conditions, with crude oil and fuel oil as main transmitters, and methanol and coking coal as key recipients. Coking coal shows a stronger spillover absorption capacity under extreme conditions. (2) The Total Spillover Index (TSI) displays significant time-varying feature and sensitivity to external shocks, with heightened asymmetry and complexity in extreme markets. (3) Weather change significantly affects spillovers of China's energy futures, with temperature, cooling and heating loads, and precipitation showing different impacts on TSI across market conditions. These findings provide references for energy finance regulation and risk early warning under climate change conditions.

To meet carbon peak and neutrality targets, accurate carbon trading price forecasting is very important for enterprises making emission reduction decisions. By fusing convolutional neural network (CNN) and long short-term memory network (LSTM), the CNN–LSTM model is constructed. After variational mode decomposition (VMD), several intrinsic mode functions (IMFs) components are obtained and input into the CNN–LSTM model, thus constructing the combined sooty tern optimization algorithm (STOA)–VMD–CNN–LSTM forecasting model. To test this model, the carbon trading prices of the carbon emission trading markets of Hubei, Guangdong and Shenzhen were forecast. The prediction performance of the STOA–VMD–CNN–LSTM model is compared with ARIMA, BP, CNN and LSTM benchmark models and models combining different decomposition technologies. The international carbon trading price (EUR and CER) is used for prediction. Compared with other methods, the developed model makes fewer errors and achieves superior performance. Several important implications are provided for investors and risk managers involved in carbon financial products.

Understanding the risk transmission mechanism between cryptocurrencies and global stock markets is crucial for investors' risk management strategies. This study delves into this relationship, drawing on the Fractal Market Hypothesis and acknowledging the nonlinearity and asymmetry present in financial market correlations. We introduce a combined approach, integrating Multifractal Detrended Partial Cross-Correlation Analysis (MFDPCCA) and Asymmetric Multifractal Cross-Correlation Analysis (MF-ACCA), resulting in the Asymmetric Multifractal Partial Cross-Correlation Analysis (MF-APCCA) method. By applying this method, we aim to uncover the dynamics between cryptocurrencies and global stock markets. We focus on stock indices from E7 and G7 countries and consider the Bitcoin market for our analysis. Our initial findings, using the MF-ACCA method, reveal pronounced asymmetric cross-correlations between cryptocurrencies and these stock markets. Notably, the correlation strength between Bitcoin and the G7 stock markets surpasses that of Bitcoin and the E7 markets. Further, when we account for and remove the influence of the common factor, gold, our analysis with the MFAPCCA method indicates an enhanced long-memory cross-correlation between Bitcoin and these stock markets. This cross-correlation tends to amplify during periods of positive returns but shows anti-persistence during negative returns. Remarkably, these asymmetries become more pronounced during significant market shifts. When comparing Bitcoin's relationship with the G7 and E7 indices, the latter displays heightened asymmetric risk correlations in both upward and downward market phases. In conclusion, gold, recognized as a safe-haven asset, can serve as a buffer, diminishing the portfolio risk between Bitcoin and the stock market. These empirical findings bear significant weight, urging investors to reassess the intricate relationship between stock and cryptocurrency markets. It also underscores the importance of well-informed cross-market portfolio investments and the need for regulatory vigilance to prevent systemic financial pitfalls.

This study employs the cross-sectional absolute deviation model and Carhart pricing model to examine the existence and authenticity of various market sizes and liquidity levels within cryptocurrency markets. Additionally, we introduce a herding effect measurement index tailored for the cryptocurrency market and predict cryptocurrency prices by integrating the long short-term memory (LSTM) neural network model. Empirical results reveal the presence of both genuine and pseudo herding phenomena in cryptocurrency markets, with information acquisition asymmetry identified as a significant driver of herding behavior. Specifically, during market downturns in the overall market, only pseudo herding is observed in the upward market, whereas during periods of market prosperity, both genuine and pseudo herding are evident in the downward market. In markets of different sizes, herding is absent in cryptocurrency markets with small market value, while in large market value cryptocurrency markets, pseudo herding is not statistically significant. Genuine herding occurs in both upward and downward markets during non-downturn periods. Regarding cryptocurrency markets with different liquidity levels, herding behavior is not observed in markets with small trading volume. Conversely, in markets with large trading volume, pseudo herding is observed in both upward and downward markets during non-downturn periods, with genuine herding occurring in both markets during boom periods. Additionally, the LSTM model demonstrates superior capability in fitting the price trends of different cryptocurrencies, and considering the herding effect index significantly enhances the accuracy of cryptocurrency price prediction.

We employ a time-varying parameter vector autoregression (TVP-VAR) joint connectedness approach to study the dynamic risk spillover effects between cryptocurrencies and China’s financial market, further exploring the impact of cryptocurrencies on China’s financial market. Our results show that there is asymmetric risk transmission between cryptocurrencies and China’s financial market, and the risk spillover effect is very weak. Specifically, the spillover of cryptocurrencies to China’s financial market is significantly stronger than the spillover of China’s financial market to cryptocurrencies. Cryptocurrencies have a stronger spillover effect to China’s exchange rate and gold. The net spillover effect of cryptocurrencies is weakening over time. Overall, the return spillover impact of cryptocurrencies on China’s financial market is greater than the volatility spillover impact, and the degree of impact of different cryptocurrencies is heterogeneous. The findings of this study have several implications for policymakers and investors.

Extreme events have further complicated the already closely related carbon-energy system, but little research has focused on the extreme spillovers between energy and carbon markets. This paper combines quantile vector autoregression with the extended joint connectedness approach to introduce a new quantile extended joint connectedness approach to study the extreme spillover between the carbon market, fossil energy and clean energy markets, using daily data spanning from October 15, 2010 to February 25, 2022. The results show that markets are more closely linked at extreme risk, and the spillover is time-varying and cyclical. The impact of extreme events will strengthen the links between markets. Further research shows different clean energy have heterogeneous spillovers on the carbon market, especially when impacted by extreme events. Finally, the hedging and portfolio effectiveness of clean energy to carbon market also show the existence of heterogeneity, and clean energy can diversify the portfolio of carbon market.

Taking six representative futures in the international energy and agricultural markets as the research objects, we use multifractal analysis methods to study the fluctuation characteristics, market risks and cross-correlations within and between these markets before and after the outbreak of the Russia–Ukraine conflict in this paper. The empirical results show that both the auto-correlations and cross-correlations have obvious multifractal features. It is confirmed that the multifractal strength and market risks of the international energy markets have weakened, while those of the international agricultural markets have enhanced after the Russia–Ukraine conflict broke out. In addition, the Russia–Ukraine conflict has intensified the strength of the multifractality and the degree of fluctuation complexity between these two classes of international markets. Further, the intrinsic multifractal natures of cross-correlations are tested, and the apparent and intrinsic multifractality before and after the conflict are revealed. Finally, some policy suggestions are put forward based on the empirical results.

This paper mainly studies the interdependence structure and risk transmission between China’s north–south capital flow and the stock market and foreign exchange market. The results show that the correlation between southward capital and the mainland stock market is stronger, and the correlation between northward capital and the Hong Kong stock market is stronger. The correlation between northward capital and RMB exchange rate is greater than that between southward capital and RMB exchange rate. After the outbreak of COVID-19, the correlation between capital flows and the stock market has strengthened, while the correlation with the foreign exchange market has weakened. Furthermore, there is a bidirectional risk transmission between the north–south capital flow and China’s stock market and foreign exchange market. In general, the north–south capital flow has a greater impact on the stock market, and the exchange rate of RMB/US dollar has a greater impact on the north–south capital flow. This study has implications for investors and policymakers.

In this paper, we first proposed a statistical test for the detrended multiple moving average cross-correlation coefficient [Formula: see text]. The [Formula: see text] mainly was used to analyze the correlation between the dependent variable y and other n independent variables [Formula: see text]. We proved that [Formula: see text] approximately obeys the chi-square distribution. We studied the statistical properties of the [Formula: see text] between normally distributed random sequences and power-law [Formula: see text] long memory random sequences. Furthermore, we discussed the influence of the cross-correlation among the target variable and independent variables on [Formula: see text]. Finally, we further study the application of [Formula: see text] to China’s stock markets and China carbon emission trading markets to investigate multiple cross-correlation. The empirical results show that there is a strong multiple correlation between China’s Shanghai, Shenzhen and Hong Kong stock markets, while the correlation between China’s carbon markets is not significant. This paper provides new ideas and theoretical support for exploring the correlation between multiple variables, which has implications for investors and policymakers.

Providing a scientific basis and method to ensure the smooth functioning of the Chinese crude oil market would be hugely significant to China’s future economic development and security. To this end, attempts have been made to both internationalize the Shanghai crude oil market and minimize financial risks. This paper selects three crude oil markets (INE, WTI and Brent) and five Chinese financial markets (the futures, bond, fund, stock and foreign exchange markets) as the research objects. The Diebold and Yilmaz spillover index model and the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method are used to study the volatility spillover effect and the asymmetric cross-correlation between crude oil markets and financial markets. When the volatility spillover effect and the asymmetric relationship that exists between the financial markets are examined, the volatility spillover of the oil market to the financial market is found to be significantly higher than that of the financial market to the oil market. In particular, the spillover effect was even more significant from late-2019 to early-2020. Analysis demonstrates an asymmetric cross-correlation between crude oil markets and the abovementioned five Chinese financial markets. In particular, the impact of the Chinese crude oil market on the stock market is greatest, especially with respect to the Brent and WTI crude oil markets. Except for the bond market, when the INE and Brent markets are increasing, the risk exposure to financial markets is more significant. Among financial markets, INE-Bond market asymmetry is stronger than WTI-bond market asymmetry, but weaker than that of the Brent-Bond market when there are large fluctuations.

Under the current carbon neutrality goal, energy structure transformation and international oil price fluctuations make research on the dependence of crude oil and clean energy has important theoretical and practical significance. This study proposes an asymmetric variable coefficient quantile regression model to measure the dependence and asymmetry of crude oil futures and clean energy stock markets under different market conditions. The results show that there is a positive dependence between crude oil futures and clean energy which is asymmetric in quantile. The positive and negative returns of crude oil futures have a time-varying asymmetric impact on the clean energy stock markets. In addition, the portfolio results show that options involving the Chinese clean energy market has a lower VaR.

Cryptocurrency has become an increasingly important tool in both portfolio investment and government regulation. As a relatively new asset class, cryptocurrencies are prone to extreme volatility, with the potential for significant downward movements over the short term. This paper uses MES and oCoVaR to forecast the systemic risk in the cryptocurrency market and subse-quently tests the validity based on unconditional coverage and independence. The results of this paper show that a DCC-GARCH model performs well in forecasting systemic risk. The paper also shows that Aoen, EOS and Sinacoin are the best forecasters of systemic risk across the 191 cryptocurrencies analysed over the full estimation period. Our findings have important implica-tions for investors and policy-makers with a vested interest in the cryptocurrency market.

本文主要以比特币、瑞波币和莱特币为研究对象,运用多重分形投资组合模型进行加密货币的组合投资,进一步研究了牛市和熊市投资组合效果的差异,采用收益、风险和夏普比率三个指标对投资组合进行样本内预测和样本外效果检验分析.实证结果表明:在不同的时期,各单一加密货币以及两两加密货币之间均表现出了标度效应、多重分形特征,且牛市期间长记忆特征更强.在全样本、牛市和熊市期间,不同波动幅度、不同时间尺度下多重分形投资组合模型与传统的投资组合模型相比能够显著地分散风险,并且牛熊市期间多重分形投资组合的有效边界显著向左移动.进一步研究发现,在适当的标度q下,多重分形投资组合模型在牛市和熊市均能取得最优的投资组合效果.

The carbon emission trading market is an important policy tool to promote the realization of China’s carbon peaking and carbon neutrality goals. Research on the relationship between the carbon market and other related ones supports policy formulation and risk aversion. Firstly, we construct the Carbon–Energy–Stock system to compare the information spillover between the three subsystems under a unified framework. Secondly, we adopt the connectedness network to identify the role and status of the carbon, energy, and stock markets. Thirdly, through the rolling window approach, we explore the dynamic evolution of the information spillover. The results show that (1) the information spillover effect between China’s pilot carbon markets, the energy market, and the stock market is relatively low; (2) in the Carbon–Energy–Stock system, China’s pilot carbon markets behave as the information transmitters, and the Guangdong pilot and Beijing pilot are core pilots. The coal market is the top information recipient, while the new energy industry is the top information transmitter; (3) the system connectivity shows the characteristics of increasing first and then decreasing. For investors and policymakers, looking at each market from a systems point of view will present a more accurate understanding of them and their interconnections.

随着我国高等教育的快速发展,高等院校科研经费投入日益增长,提升科研经费绩效水平对于促进科研事业发展以及优化高等院校科研经费管理具有积极意义.文章从科研投入、运行和产出等三个方面构建高等院校科研经费绩效评价指标体系,运用基于实数编码的加速遗传算法的投影寻踪聚类模型(RAGA-PPC),对江苏省36所高等院校2012-2017年的科研经费绩效水平进行综合评价,利用最佳投影方向确定不同指标的权重,分析了高等院校科研绩效的主要影响因素并提出了相应的对策建议.研究发现"双一流"重点高等院校的科研经费绩效水平优于普通高等院校,以东南大学、南京大学、苏州大学、南京航空航天大学、南京理工大学最为显著.科研绩效的关键影响因素主要包括科技课题拨入经费、科研经费支出总额、论文发表总量、课题投入人数、科研经费投入总额等三级指标.二级指标中论文产出、科研经费支出、科研经费投入是主要影响因素.文章丰富了科研经费绩效的研究案例与方法,为高等院校科研经费绩效评价提供借鉴.

In this paper, we constructed a volatility spillover index based on the time-varying parameter vector autoregressions (TVP-VAR) model to study the asymmetric volatility spillover effect between cryptocurrency and China's financial market. Our results show that the impact of cryptocurrency on China's financial market is relatively strong, but the impact of China's financial market on cryptocurrency is very weak. Furthermore, negative spillovers are stronger than positive spillovers. The average negative volatility spillover is dominant for Bitcoin and Ethereum, but the average positive volatility spillover is dominant for Ripple. This study has implications for investors and policymakers.