This paper investigates the spillover dynamics and diversification benefits between China's national carbon emission allowance (CEA) market, the largest of its kind globally, and industrial stock portfolios categorized by their carbon emission levels. While a growing number of industrial firms now participate in carbon trading, the specific interactions between CEA prices and their stock performance remain unexplored. To address this gap, we employ a time-varying parameter vector autoregression (TVP-VAR) spillover analysis alongside two novel portfolio allocation strategies derived from spillover indices. Our analysis reveals substantial total spillover effects, with the CEA market consistently acting as a net spillover receiver, signaling its potential as a diversification asset. We find that optimal portfolio weights for CEAs and the hedging effectiveness for individual stocks vary markedly across high-, medium-, and low-emission portfolios and differ according to the allocation method applied. Although no single allocation strategy dominates in terms of absolute profitability, a method based on the net pairwise directional spillover index yields the most predictable cumulative returns. Critically, our Sharpe ratio analysis demonstrates that integrating CEAs consistently enhances risk-adjusted returns for all carbon-stock portfolios. Notably, portfolios combining CEAs with high- and low-carbon stocks significantly outperform their medium-carbon counterparts. These findings offer crucial insights for investors developing differentiated risk management strategies and for regulators refining China's carbon pricing mechanism.

Crude oil markets are characterized by noise and multifractal features, which undermine the reliability of traditional hedge ratio estimation models. Therefore, this study develops a denoising-multifractal dual intelligent integration framework for estimating optimal hedge ratios in the Shanghai crude oil futures on the Shanghai International Energy Exchange (INE) and crude oil spot markets, with a primary focus on minimizing spot risk while enhancing hedging returns. The framework begins with an innovative use of the Complementary Ensemble Empirical Mode Decomposition (CEEMD) approach to remove high-frequency noise, improving data quality. It then employs the Multifractal Detrended Cross-Correlation Analysis (MF-DCCA) method to capture the multifractal structure of crude oil futures and spot markets. Building on these results, the Flower Pollination Algorithm (FPA) is employed to integrate hedge ratios in a two-stage manner. First, local hedge ratios are aggregated across time scales within each fluctuation level. Second, hedge ratios are further integrated across different fluctuation amplitudes. This unique design allows the model to fully exploit the multi-scale and multi-fluctuation information for deriving the optimal hedge ratios. Empirical analysis confirms the existence of significant noise and multifractal properties in crude oil markets. Moreover, the hedging results show that the proposed model outperforms all competing methods, achieving higher accumulated returns, Hedging Effectiveness (HE), and Sharpe ratios in most cases. The study offers an effective hedge ratio estimation method for investors in the INE crude oil futures and spot markets.

As China plays a pivotal role in shaping global energy trends, the healthy development of energy markets faces formidable challenges, making the achievement of carbon neutrality, particularly, crucial. Unfortunately, geopolitical risk (GPR) introduces significant uncertainties for the interactions between China's carbon neutrality process and energy markets. To this end, we explore the connectedness between China's carbon neutrality index and energy futures prices from both return and risk perspectives using the TVP-VAR method. Additionally, we analyse the impact mechanisms and predictive effects of GPR on the volatility of this connectedness applying the GARCH-MIDAS method. Our empirical findings reveal several key insights. First, the GPR drives the volatility in connectedness between China's carbon neutrality index and energy futures prices, particularly, within the petrochemical industry, while the opposite impact is observed in certain coal-related sectors. Moreover, the models indicate that poor GPR predictive effects are primarily evident in industries with a close link to coal energy in their production processes. Secondly, the risk connectedness of China's carbon neutrality index and energy futures prices contains more implicit information and is more responsive to GPR shocks compared to return connectedness. This explains why the GPR is less effective in forecasting the volatility of risk connectedness. Finally, the global GPR is more helpful than the China-specific GPR in forecasting the volatility of connectedness in China's carbon neutrality index and energy futures prices. These insights reveal the potential vulnerability of China's carbon neutrality and energy markets to GPR, offering actionable guidance for strengthening risk management strategies.

Our study investigates the relationship between ESG (Environmental, Social, and Governance) performance and the stock liquidity of Chinese listed companies. Using 14,334 observations of A-share listed companies in China from 2018 to 2023, we demonstrate that (1) Improvements in ESG performance contribute to increased stock liquidity, but this relationship is non-linear. The primary reason for this is that the marginal utility of improvement diminishes as ESG performance rises; (2) The impact of performance across the environmental (E), social (S) and governance (G) dimensions differs in terms of liquidity, reflecting investors' varying sensitivity to information across these dimensions; (3) Tests conducted to investigate heterogeneity across the three dimensions (property rights, geography and information disclosure) validate the significance of signaling theory in China's stock market. Our findings provide a theoretical basis for further strengthening ESG development and offer new insights into the market's understanding of ESG information.

During Donald Trump's presidency, his embrace of unilateral policies, coupled with a focus on trade protectionism—evidenced by the imposition of high tariffs and withdrawal from multilateral agreements—significantly influenced not only the dynamics but also the interactions among cryptocurrencies, US commodity markets, and US capital markets. This paper provides a quantitative analysis of how Trump's tenure shaped the spillover effects across these markets. The results reveal that, during Trump's presidency, the total spillover across assets was notably dampened, primarily due to the diminishing spillover influence of the US dollar. Additionally, the spillover intensity of Bitcoin to other assets strengthened significantly during this period, with Treasury bills and crude oil following closely behind. In contrast, the US stock market and the US dollar clearly experienced a reduction in their spillover dominance. These results carry significant economic implications for investors in their portfolio allocation.

Purpose This study aims to examine whether the adoption of artificial intelligence (AI) can alleviate financial mismatches and enhance capital allocation efficiency through firms' investment decisions, particularly in the context of rapid digitalization and structural financial reforms.Design/methodology/approach The study develops a "technology-efficiency-allocation" analytical framework to explain how AI promotes investment efficiency and mitigates financial mismatches. Using the Skip-gram model in Word2vec, firm-level indicators of AI application are constructed to overcome the biases of patent-based or industry-level measures. Inefficient investment is introduced as an instrumental variable, and a double machine learning (DML) framework is employed to control for high-dimensional covariates and specification errors, ensuring credible causal identification.Findings The study develops a "technology-efficiency-allocation" analytical framework to explain how AI promotes investment efficiency and mitigates financial mismatches. Using the Skip-gram model in Word2vec, firm-level indicators of AI application are constructed to overcome the biases of patent-based or industry-level measures. Inefficient investment is introduced as an instrumental variable, and a DML framework is employed to control for high-dimensional covariates and specification errors, ensuring credible causal identification.Originality/value This study provides firm-level empirical evidence clarifying AI's role in reducing financial mismatches and improving capital allocation efficiency. It extends the literature by integrating advanced textual measurement of AI application with causal machine learning methods and offers practical implications for promoting technological integration and enhancing financial efficiency in emerging economies.

This paper investigates the diversification effects of China's new photovoltaic weather index (PVWI) on commodity portfolios. Using a TVP-VAR spillover analysis, we find the PVWI is highly insulated from the commodity system, acting as a net shock receiver and confirming its diversification potential. We then construct novel "minimum-connectedness portfolios" (MCoP). Results show that portfolios incorporating the PVWI, particularly the MCoP, significantly enhance risk-adjusted returns and demonstrably outperform traditional minimum-variance benchmarks. This study validates the practical benefits of well-designed weather derivatives and the superiority of spillover-aware asset allocation frameworks.

Gold and stocks, which are conventionally regarded as a safe haven and risk assets, respectively, exhibit complex interrelationships, with significant implications for financial risk management. This paper builds on the sentiment categorization proposed by Liang et al. (2020) to distinguish between private and public sector sentiment. The construction of sentiment indices for both sectors aims to allow the exploration of the heterogeneous effects of these sector-specific sentiments on the gold-stock market linkages in China under different market conditions. The empirical results demonstrate a notable asymmetry in the impact of market sentiment between the public and private sectors, with distinct manifestations in stable versus highly volatile market environments. Specifically, positive sentiment in the public sector tends to diminish the safe-haven function of gold, whereas positive sentiment in the private sector tends to reinforce it. This disparity becomes particularly evident during periods of extreme market volatility. Our findings not only underscore the diverse impacts of market sentiment but also provide novel insights into the importance of incorporating sector-specific sentiment when devising hedging strategies for specific industries.

This study examines stock behavior around sessions of the Chinese Provincial People’s Congress (PPC). We find that listed companies undergo a decrease in stock returns and an increase in return volatility around PPC sessions that introduce changes in government policies, regardless of whether they involve gubernatorial elections. We find some evidences the beneficial government policies have positive effects on stock performance during the PPC periods using the real estate industry sample. Firms have higher stock returns and lower stock volatility during PPC sessions that have less political uncertainty. The effects of the PPC sessions are more prominent during the first year of the five-year plans and less prominent for state-owned listed firms, firms with PPC deputy Chairman or CEOs, or firms in regulated industries.

PurposeThis study aims to explore the impacts of U.S. debt ceiling uncertainty on crude oil markets and further reveal the specific influence mechanisms.Design/methodology/approachThis paper introduces a debt ceiling uncertainty index based on news reports and selects six representative crude oil futures and spot markets to investigate the heterogeneous impacts of U.S. debt ceiling uncertainty on crude oil markets. More specifically, on the one hand, the nonparametric causality-in-quantiles test method is used to discuss the asymmetric impacts of debt ceiling uncertainty on the different conditional distributions of crude oil series. On the other hand, the dynamic effects of debt ceiling uncertainty on crude oil markets are analyzed, combining with the time-varying parameter vector autoregressive model.FindingsThe conclusions of this paper are as follows: First, the U.S. debt ceiling uncertainty has obvious nonlinear impacts on each crude oil market, and the effects are greater under the normal condition of crude oil markets rather than under their extreme conditions. Second, the shocks from debt ceiling uncertainty to crude oil markets are mainly illustrated as negative and can be significantly enhanced by important debt-related events. Over time, the reactions of crude oil markets turn to weak positive and gradually dissipate after 6 months. Finally, enterprise production, investor sentiment and government shutdown play important roles as transmission intermediaries for the influence of debt ceiling uncertainty on the crude oil market.Originality/valueThe findings are beneficial for investors to accurately judge oil price trends and prevent investment losses caused by debt risks and also help producers prevent the impacts of crude oil price changes on production and operation activities. Moreover, it is conducive to the management department to maintain the stability of the crude oil market, thus enabling the crude oil financial market to better serve the real economy.

The relationship between climate risks and commodity markets remains insufficiently explored, especially when analyzed through the lens of high-frequency data. This study seeks to address this gap by investigating the spillover effects of global climate risks, both physical and transitional, on key commodity markets and employs a novel analytical framework. By utilizing newly developed climate risk indices alongside the innovative mixed-frequency spillover measure, this research combines high-frequency climate risk data with the responses of low-frequency commodity prices. Our results highlight notable spillover effects, demonstrating that climate risks serve as the primary drivers of spillovers to commodity markets in a mixed-frequency data context, whereas such effects are not observed within a common-frequency data environment. These findings have important implications for policy-makers and investors, indicating that current market analyses may not capture the influence of climate risk adequately.

Various economic policy uncertainties (EPUs) are closely related to economic growth. This study investigates the relationship between economic policy uncertainties (EPUs) and GDP growth in China over different time periods by using Granger causality test based on Mixed Frequency VAR (MF-VAR) model and Mixed Frequency Data Sampling (MIDAS) quantile regression, which have the advantages over traditional Granger causality test and quantile regression approaches in dealing with the problem of sampling difference in EPUs and GDP. The results show that the impact of EPUs on GDP varies depending on GDP growth rates, both in terms of magnitude and direction. Furthermore, the effects are asymmetric when GDP growth rates fall within extreme quantiles. Specifically, EPUs have a greater impact on GDP during periods of slower GDP growth. Finally, the impacts of COVID-19 and the Global Financial Crisis (GFC) have caused a shift in the magnitude and direction of the responses of GDP to EPUs, resulting in more apparent asymmetric effects. These findings can assist policymakers and investors in evaluating policy measures and investment decisions.

Green stocks attract investors and policymakers as they can generate economic returns and promote environmental sustainability and social responsibility goals. However, green stocks are also perceived as riskier than traditional brown stocks. This study examines how brown stocks diversify green stocks in China using portfolio allocation based on centrality measures in a green-brown stock network. The empirical results show that most brown stocks are located at the edge of the stock network, with lower centrality. Portfolios with low-centrality stocks always include some brown stocks with large weights. The mean-variance allocation method outperforms the Equal Weighted and Minimum Variance models. Finally, adding brown stocks to the green stock portfolio significantly increases the expected return and reduces portfolio risk. In particular, brown stocks with moderate centrality offer better diversification effects. Our findings have significant investment and policy implications for investors and regulators.

This study measures global stock market dynamic returns and extreme risk spillover intensities using the spillover index method based on the time-varying parameter vector autoregression model. We discuss the asymmetric effects of climate policy uncertainty (CPU) on three types of information spillovers at different quantiles. Finally, the shock and diffusion channels from climate policy uncertainty to global stock markets are also revealed. The empirical results indicate that CPU positively affects stock market information spillover intensities, with a clear asymmetry across quantiles and spillover relationships. Furthermore, the influences of CPU become more pronounced after three months and gradually dissipate after six months. CPU affects the Russian, Chinese, and Indian stock markets, and the spillover pathways to extreme risks in the global stock market are more complex.

The aim of this paper is to investigate the extreme spillover effects between the newly launched China's national carbon emission allowance and agricultural futures markets with the influence of global climate risk. The results show that, first, under both normal and extreme market (climate risk) conditions, the three edible oil futures, i.e., soybean oil, palm oil and rapeseed oil, are strong spillover senders to other agricultural futures. Second, under low and normal climate risk states, climate transition risk is a spillover transmitter, while under high climate risk states, climate physical risk becomes a spillover sender. In addition, the network analysis shows that climate risk is an important information receiving and disseminating node, and large changes in it can lead to increased systemic risk across the network. Finally, China's carbon emission allowance market is always a spillover receiver across different market (climate risk) conditions, and can be used as an appropriate hedging instrument for climate risk and agricultural futures. These findings have valuable implications for both policymakers and agricultural investors.

PurposeWe phrase our analysis around the connectedness effects and portfolio allocation in the “Carbon-Energy-Green economy” system.Design/methodology/approachThis paper utilizes the TVP-VAR method provided by Antonakakis et al. (2020) and Chatziantoniou et al. (2021), and portfolio back-testing models, including bivariate portfolios and multivariate portfolios.FindingsFirstly, the connectedness within the “Carbon-Energy-Green economy” system is strong, and is mainly driven by short-term (weekly) connectedness. Notably, the COVID-19 pandemic leads to a vertical increase in the connectedness of this system. Secondly, in the “Carbon-Energy-Green economy” system, most of the sectors in the green economy stocks tend to be the transmitters of shocks to other markets (particularly the energy efficiency sector), while the carbon and energy markets are always the recipients of shocks from other markets (particularly the crude oil market). Thirdly, Green economy sector stocks have satisfactory hedging effects on the market risk of carbon and energy assets. Interestingly, hedging risks in relatively “dirty” assets requires more green economy stocks than in relatively “clean” assets. Finally, the results indicate that portfolios that include green economy stocks significantly outperform portfolios that do not contain green economy stocks, further demonstrating the crucial role of green economy stocks in this system.Originality/valueUnderstanding the interactions and portfolio allocation in the “Carbon-Energy-Green economy” system, especially identifying the role of the green economy performance in this system, is important for investors and policymakers.

The role of the carbon market in the market efficiency of cryptocurrencies has not received much attention from scholars, despite its particular relevance for integrating environmental externalities into the theory and practice of digital financial markets. We investigate whether and how the performance of the carbon market during busts and booms affects the market efficiency of both clean and dirty cryptocurrencies. Overall, our results underscore the complex quantile dependence of cryptocurrency market efficiency on carbon market performance, highlighting significant variability in how different types of cryptocurrencies respond to carbon market. These results have significant implications for regulatory policies, investment strategies, industry practices, public perceptions, and technological advances in the pursuit of aligning cryptocurrency markets with environmental sustainability goals.

PurposeThis study conducts a comparative analysis of the diversification effects of China's national carbon market (CEA) and the EU ETS Phase IV (EUA) within major commodity markets.Design/methodology/approachThe study employs the TVP-VAR extension of the spillover index framework to scrutinize the information spillovers among the energy, agriculture, metal, and carbon markets. Subsequently, the study explores practical applications of these findings, emphasizing how investors can harness insights from information spillovers to refine their investment strategies.FindingsFirst, the CEA provide ample opportunities for portfolio diversification between the energy, agriculture, and metal markets, a desirable feature that the EUA does not possess. Second, a portfolio comprising exclusively energy and carbon assets often exhibits the highest Sharpe ratio. Nevertheless, the inclusion of agricultural and metal commodities in a carbon-oriented portfolio may potentially compromise its performance. Finally, our results underscore the pronounced advantage of minimum spillover portfolios; particularly those that designed minimize net pairwise volatility spillover, in the context of China's national carbon market.Originality/valueThis study addresses the previously unexplored intersection of information spillovers and portfolio diversification in major commodity markets, with an emphasis on the role of CEA.

Extant research may still lack a nuanced comprehension of the impact and connectedness among fossil energy markets and multiple uncertainties, particularly climate uncertainty represented by the El Niño Southern Oscillation (ENSO), through normal to extreme conditions, despite the recognition that uncertainties play a pivotal role in shaping the price dynamics of fossil energy markets. In view of this, employing the quantile-on-quantile regression and quantile connectedness approaches, our study, to the best of our knowledge, pioneers a comprehensive evaluation of the impact and connectedness between three important fossil energy markets and four pivotal uncertainties under different market conditions. Our empirical findings underscore several key points. Firstly, the conventional mean-based methods risk underestimating the linkages between fossil energy markets and uncertainties, as the impact and connectedness between these variables are distinctly more pronounced under extreme market conditions. Secondly, we observe the ENSO predominantly operates as a net performer, while the crude oil market acts as a net participant in the takeover of information spillovers. Thirdly, our analysis reveals that strong La Niña events propagate more potent information spillovers when compared to strong El Niño events. Fourthly, the dynamic analysis lends further credence to a tighter time-varying connectedness between fossil energy markets and uncertainties under extreme downside and upside than under normal market conditions. Finally, the dynamic connectedness results propose that the ENSO consistently serves as a stable net contributor of information to all other variables under extreme market conditions. In contrast, geopolitical uncertainty and infectious disease uncertainty operate as both recipients and contributors over time and across quantiles with the dominance of a net contributor pattern in the extreme upside and downside, respectively. These findings provide fresh and valuable implications for enhancing resilience against shocks from uncertainties impacting fossil energy commodities.