Against the backdrop of deepening globalization and digital integration,emerging international markets,characterized by large populations,rapidly growing educational demands,and progressively upgraded digital infrastr...Against the backdrop of deepening globalization and digital integration,emerging international markets,characterized by large populations,rapidly growing educational demands,and progressively upgraded digital infrastructure,have become pivotal hubs for educational technology(EdTech)enterprises to expand their global presence.However,the unique characteristics of these markets,including cultural diversity,divergent consumer behaviors,and uneven digital maturity,pose challenges to traditional digital marketing strategies.This results in EdTech products facing issues such as inefficient user acquisition,insufficient brand awareness,and suboptimal conversion rates.To address these challenges,this paper focuses on optimizing digital marketing strategies for EdTech product sales in emerging international markets.This paper focuses on the optimization of digital marketing strategies for Ed Tech product sales in emerging international markets.Through analyzing the pain points in the application of current strategies,this paper proposes a systematic optimization path from four dimensions:localized content construction,multi-channel coordination and integration,user life cycle operation,and data-driven decision making.展开更多
Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,...Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models,comprehensively using the Markov state transition GARCH model with skewed distribution(Skew-MSGARCH)and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor,and selecting the earliest developed and the most mature currency price volatility daily return series,to deeply explore the volatility risk of digital cryptocurrencies.risk.Finally,it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies.It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy,blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies,thereby maintaining the security and stability of emerging financial markets.展开更多
Chinese companies are transitioning from“manufacturing export”to brand export and from“value-for-money competition”to“value competition”,continuously expanding their presence in Russian-speaking markets.
The Emerging Markets and Developing Countries Forum on Bridging the AI Divide and Opening Ceremony of AIM Global CoE were held on July 27 in Minhang district,Shanghai.The event was hosted by AIM Global CoE,and co-orga...The Emerging Markets and Developing Countries Forum on Bridging the AI Divide and Opening Ceremony of AIM Global CoE were held on July 27 in Minhang district,Shanghai.The event was hosted by AIM Global CoE,and co-organized by Shanghai Artificial Intelligence Research Institute(SAIRI),Shanghai Technology Innovation Center,Shanghai Grand Neobay Investment Development Group,and Organizing Committee of AI Journey Conference.Asian Association of Business Incubation(AABI)and SAIRI served as the international partners.展开更多
With the intensifying global climate crisis,carbon emissions trading has emerged as a crucial market-based instrument for emissions reduction,attracting significant attention from government agencies and academia worl...With the intensifying global climate crisis,carbon emissions trading has emerged as a crucial market-based instrument for emissions reduction,attracting significant attention from government agencies and academia worldwide.As of January 2024,28 carbon trading markets have been established globally,encompassing approximately 17%of global greenhouse gas emissions and serving approximately 1/3 of the global population.With various nations setting carbon neutrality targets and delineating carbon reduction pathways,the con-struction,operation,and regulatory frameworks of carbon markets are becoming increasingly refined and comprehensive.This study elucidates the importance and necessity of establishing carbon markets from the perspective of energy system transformation and sus-tainable economic development.Second,it provides a comparative analysis of the operational mechanisms,trading scales,and emission reduction outcomes of major carbon markets in the European Union,United States,and New Zealand,systematically summarizing their development processes and recent advancements.Finally,this study addresses issues and challenges in the construction of China’s carbon market.Drawing on the successful experiences of leading global carbon markets in institutional design and market operations,we pro-pose development strategies and recommendations for a carbon market with Chinese characteristics.These strategies are intended to align with international standards while meeting China’s national conditions,thereby contributing insights into the global carbon market trading system.展开更多
The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for th...The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.展开更多
The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,inve...The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,investors should consider investing in more secure assets,such as real estate property,cash,gold,and crypto assets.In recent years,among secure assets,cryptoassets are gaining more attention than traditional investments.This study compares the Bitcoin market,the gold market,and American stock indexes(S&P500,Nasdaq,and Dow Jones)before and during the COVID-19 pandemic.For this purpose,the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets.Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin,gold,and stock markets.In particular,we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period.This paper provides practical impacts on risk management and portfolio diversification.展开更多
The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This s...The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets,focusing on gold and six major stock markets(Japan,USA,UK,China,Germany,and France)using daily data from January 2,2011,to June 2,2022,with 2958 daily observations.We employ DCC-GARCH,wavelet coherence,and cascade-correlation network models to analyze the relationship between Bitcoin and those markets.Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty.We also find evidence of long-term contagion between Bitcoin and the six stock markets,with short-term contagion observed in Chinese and Japanese markets during COVID-19.These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets.Hedge funds,mutual funds,and individual and institutional investors can benefit from using our findings in their risk management strategies.Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions.It holds significant implications for stock market investors and policymakers,providing evidence for potentially using cryptocurrencies for hedging,for diversification,or as a safe haven.展开更多
With the deepening of globalization,the development speed of capital markets is constantly accelerating,presenting a trend of globalization.At the same time,the emergence of multiple forms of trading platforms and div...With the deepening of globalization,the development speed of capital markets is constantly accelerating,presenting a trend of globalization.At the same time,the emergence of multiple forms of trading platforms and diversified financial products further highlights the competitive relationship between security exchanges and other trading platforms.While promoting the transformation of security exchange forms in various countries,it also prompts governments to re-examine the financial regulatory system of securities markets.In this situation,it is very important to research the international financial market and financial regulatory system.This article explores the regulatory issues and countermeasures in the international financial market,intending to promote the stability and healthy development of the international financial market.展开更多
Introduction: Noise is the second leading cause of hearing loss in adults after presbycusis. The objective of this work was to study hearing loss induced by the noise of mills in the markets of Parakou. Methods: This ...Introduction: Noise is the second leading cause of hearing loss in adults after presbycusis. The objective of this work was to study hearing loss induced by the noise of mills in the markets of Parakou. Methods: This was a descriptive and analytical cross-sectional study, conducted from February 3 to June 3, 2021 in the markets of Parakou. It concerned millers and sellers located within a 5 meter radius around the mills and among whom pure-tone audiometry was performed to detect a hearing loss. Subjects with no particular medical health history, under 55 years of age and having been working in these markets since more than 12 months, were included. Results: In this study, 103 subjects were selected, including 43 millers and 61 sellers. Their average age was 29 ± 13 years. The sex ratio was 0.49. The average length of service in the profession was 8 years with the extremes of 3 months and 47 years. They were exposed to noise on average 10 hours per day and 6 days a week. The average duration of weekly noise exposure was 23 h 28 min ± 13 h 32 min with the extremes of 5 h 00 min and 52 h 30 min. The average level of noise exposure was 90 dB with the extremes of 72 and 110 dB. 24 subjects reported symptoms related to noise such as headache, tinnitus, and hearing loss, with respective proportions of 22.33%, 20.39% and 06.80%. The prevalence of noise-related hearing loss was 26.21% (n = 27/103). Subjects with a notch at 4000 Hz and normal Average Hearing Loss (AHL) (20.39%) had a seven-time greater risk of developing noise-induced hearing loss (OR = 6.58;95% CI [2.54 - 18.8], p Conclusion: Hearing loss related to the noise of mills affected both millers and sellers near the mills in markets, hence the importance of regulating mills.展开更多
We propose a new predictor-the innovation in the daily return minimum in the U.S.stock market(△MIN^(US))-for predicting international stock market returns.Using monthly data for a wide range of 17 MSCI international ...We propose a new predictor-the innovation in the daily return minimum in the U.S.stock market(△MIN^(US))-for predicting international stock market returns.Using monthly data for a wide range of 17 MSCI international stock markets dur-ing the period spanning over half a century from January 1972 to July 2022,we find that △MIN^(US) have strong predictive power for returns in most international stock markets:△MIN^(US) negatively predicts the next-month stock market returns.The results remain robust after controlling for a number of macroeconomic predictors and con-ducting subsample and panel data analyses,indicating that △MIN^(US) has significant predictive power and it outperforms other variables in international markets.Notably,△MIN^(US) demonstrates excellent predictive power even during the periods driven by financial upheavals(e.g.,Global Financial Crisis and European Sovereign Debt Crisis).Both panel regressions and out-of-sample tests also support the robust predictive performance of △MIN^(US).The predictive power,however,disappears during the non-financial crisis caused by COVID-19 pandemic,which is originated from the health sector rather than the financial sector.The results provide a new perspective on U.S.extreme indicator in stock market return predictability.展开更多
Because of the contradiction between the scale of new energy installations and the continuous load growth in the central and eastern regions of China,the balance problems of the electricity market are becoming increas...Because of the contradiction between the scale of new energy installations and the continuous load growth in the central and eastern regions of China,the balance problems of the electricity market are becoming increasingly prominent,and it is urgent to solve such problems through inter-provincial electricity spot markets.First,the development history and construction status of the inter-provincial electricity spot market are summarized;second,the mechanism design of the inter-provincial electricity spot market is sorted out in terms of the market operation framework,transaction declaration,and clearing methods;subsequently,the evaluation index system of the inter-provincial electricity spot market is constructed,including four themes of electricity mutual aid and support,new energy consumption,economic benefits of market-based allocation,and social benefits of market-based allocation;finally,the operation of the inter-provincial electricity spot market is comprehensively analyzed by the algorithm based on the market operation data of 2022,which proves the feasibility and practicality of the proposed index system.展开更多
An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between th...An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between the carbon mar-ket and energy market returns.To achieve that,we improve the Diebold-Yilmaz index model by a time-varying vector autoregressive(TVP-VAR)model.In a unified network,our daily dataset includes the closing prices of the Hubei carbon market,Shenzhen carbon market,coal futures,and energy stock index.The findings reveal that both the Hubei and Shen-zhen pilots typically generate net information spillovers on energy futures.In connection with energy stocks,the Hubei carbon market acts as a net receiver,while the Shenzhen carbon market is a net transmitter.Compared with the Hubei pi-lot,the Shenzhen pilot is more tightly connected to the energy markets.Furthermore,the spillovers of the carbon markets exhibit significant asymmetry.In most cases,they have more substantial impacts on the energy markets when the prices of emission allowances rise.The direction and magnitude of asymmetric spillovers across markets vary over time and can be influenced by certain economic or political events.展开更多
The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and con...The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.展开更多
How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop tradi...How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data,is struggling to deal with fastchanging markets due to sample inefficiency.This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning(RL)approaches in non-stationary markets for the first time.In our work,the history trading data is divided into multiple task data and for each of these data themarket condition is relatively stationary.Then amodel agnosticmeta-learning(MAML)-based tradingmethod involving a meta-learner and a normal learner is proposed.A trading policy is learned by the meta-learner across multiple task data,which is then fine-tuned by the normal learner through a small amount of data from a new market task before trading in it.To improve the adaptability of the MAML-based method,an ordered multiplestep updating mechanism is also proposed to explore the changing dynamic within a task market.The simulation results demonstrate that the proposed MAML-based trading methods can increase the annualized return rate by approximately 180%,200%,and 160%,increase the Sharpe ratio by 180%,90%,and 170%,and decrease the maximum drawdown by 30%,20%,and 40%,compared to the traditional RL approach in three stock index future markets,respectively.展开更多
We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed ...We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets.We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets,focusing on the role of stablecoin issuances.We study the stock,bond,cryptocurrency,and stablecoin markets and find very high connectedness,which varies over time in response to up/down trends in financial markets.The results show that during financial turmoil,cryptocurrencies amplify downside risks rather than serve as diversifiers.In addition to risky assets from conventional financial markets,cryptocurrencies champion the transmission of spillovers to digital and conventional markets.In contrast,changes in stablecoin issuances produce few shocks because of their pegged prices,but they facilitate investors’switch from volatile cryptos to more stable digital instruments;that is,we observe a phenomenon designated by us as the“flight-to-cryptosafety.”We draw insightful conclusions,provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance.展开更多
This paper analyzes the degree of dynamic connectedness between energy and metal commodity prices in the pre and post-COVID-19 era,using the time-varying parameter vector autoregressive connectedness approach of Anton...This paper analyzes the degree of dynamic connectedness between energy and metal commodity prices in the pre and post-COVID-19 era,using the time-varying parameter vector autoregressive connectedness approach of Antonakakis et al.(J Risk Financ Manag 13(4):84,2020).The results suggest that market interconnectedness increased slightly following the outbreak of COVID-19,although this increase was lower and less persistent than that observed after the Global Financial Crisis of 2008.Furthermore,we find that crude oil was the main net transmitter of shocks before COVID-19 while heating oil,gold,and silver were the main net transmitters of shocks during the COVID-19 pandemic.In contrast,natural gas and palladium were the main net receivers of shocks during the entire sample period,making these two commodities attractive hedging and safe haven options for investors during the pandemic.Overall,our results suggest that hedging and diversification opportunities decrease during crises.Furthermore,they indicate that accurate forecasts of the volatility of several commodities,such as natural gas and different metals,can be obtained by exploiting the information content of crude oil.However,they also reveal that crude oil lost its leading position as a net shock transmitter during the COVID-19 pandemic.展开更多
This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid t...This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.展开更多
The use of food additives in industrial production has the advantage of improving sensory properties, technological quality and extending the shelf life of foods. Among the most widely used additives are antioxidants,...The use of food additives in industrial production has the advantage of improving sensory properties, technological quality and extending the shelf life of foods. Among the most widely used additives are antioxidants, which prevent oxidation, browning and rancidity reactions in foods. However, the strong presence of these additives on the market is not without risks for human health, and should be controlled to guarantee food safety. Analysis of the risks associated with consumption of foods containing these additives requires, among other things, information on the frequency of use of these additives in various consumer products. The aim of this study is therefore to identify the antioxidants present in industrial food products distributed in Dakar. The methodology adopted consists of a qualitative analysis based on the identification of additives from food labels. Investigations were carried out in 9 stores, 4 superettes and 2 supermarkets located in different districts of Dakar. The results revealed the presence of 12 antioxidant additives, dominated by citric acid (53%) and ascorbic acid (29%). These studies have also highlighted the simultaneous use of several antioxidants in the same food product. Moreover, for some artificial antioxidants identified antioxydant such as BHA and BHT, health risks are associated with their consumption. The results of this study open up prospects for the development of information databases on food additives.展开更多
文摘Against the backdrop of deepening globalization and digital integration,emerging international markets,characterized by large populations,rapidly growing educational demands,and progressively upgraded digital infrastructure,have become pivotal hubs for educational technology(EdTech)enterprises to expand their global presence.However,the unique characteristics of these markets,including cultural diversity,divergent consumer behaviors,and uneven digital maturity,pose challenges to traditional digital marketing strategies.This results in EdTech products facing issues such as inefficient user acquisition,insufficient brand awareness,and suboptimal conversion rates.To address these challenges,this paper focuses on optimizing digital marketing strategies for EdTech product sales in emerging international markets.This paper focuses on the optimization of digital marketing strategies for Ed Tech product sales in emerging international markets.Through analyzing the pain points in the application of current strategies,this paper proposes a systematic optimization path from four dimensions:localized content construction,multi-channel coordination and integration,user life cycle operation,and data-driven decision making.
文摘Cryptocurrency,a booming decentralised asset designed based on the blockchain architecture,is particularly important to the market at the present time by studying the volatility risk of cryptocurrencies.In this paper,we empirically analyse the volatility risk of cryptocurrencies through quantitative analysis models,comprehensively using the Markov state transition GARCH model with skewed distribution(Skew-MSGARCH)and the autoregressive conditional volatility density ARJI model introducing the Poisson jump factor,and selecting the earliest developed and the most mature currency price volatility daily return series,to deeply explore the volatility risk of digital cryptocurrencies.risk.Finally,it can be seen through in-depth analyses that the expectation factor and information inducement are the main reasons leading to the exacerbation of the volatility risk of digital cryptocurrencies.It is recommended that this situation be optimised and improved in terms of the value function of digital cryptocurrencies themselves and the implementation of systematic risk management and regulatory innovation.As an important component of the digital economy,blockchain technology can effectively regulate and improve the volatility of digital cryptocurrencies under macroeconomic policies,thereby maintaining the security and stability of emerging financial markets.
文摘Chinese companies are transitioning from“manufacturing export”to brand export and from“value-for-money competition”to“value competition”,continuously expanding their presence in Russian-speaking markets.
文摘The Emerging Markets and Developing Countries Forum on Bridging the AI Divide and Opening Ceremony of AIM Global CoE were held on July 27 in Minhang district,Shanghai.The event was hosted by AIM Global CoE,and co-organized by Shanghai Artificial Intelligence Research Institute(SAIRI),Shanghai Technology Innovation Center,Shanghai Grand Neobay Investment Development Group,and Organizing Committee of AI Journey Conference.Asian Association of Business Incubation(AABI)and SAIRI served as the international partners.
基金support of the SGCC Science and Technology Project“Cost Analysis,Market Bidding Mechanism Research and Validation of New Power Sys-tem Transformation under a Diversified Value System”(1400-202357380A-2-3-XG)for this article.
文摘With the intensifying global climate crisis,carbon emissions trading has emerged as a crucial market-based instrument for emissions reduction,attracting significant attention from government agencies and academia worldwide.As of January 2024,28 carbon trading markets have been established globally,encompassing approximately 17%of global greenhouse gas emissions and serving approximately 1/3 of the global population.With various nations setting carbon neutrality targets and delineating carbon reduction pathways,the con-struction,operation,and regulatory frameworks of carbon markets are becoming increasingly refined and comprehensive.This study elucidates the importance and necessity of establishing carbon markets from the perspective of energy system transformation and sus-tainable economic development.Second,it provides a comparative analysis of the operational mechanisms,trading scales,and emission reduction outcomes of major carbon markets in the European Union,United States,and New Zealand,systematically summarizing their development processes and recent advancements.Finally,this study addresses issues and challenges in the construction of China’s carbon market.Drawing on the successful experiences of leading global carbon markets in institutional design and market operations,we pro-pose development strategies and recommendations for a carbon market with Chinese characteristics.These strategies are intended to align with international standards while meeting China’s national conditions,thereby contributing insights into the global carbon market trading system.
基金support by the Science and Technology Project of Guangdong Power Exchange Center Co.,Ltd.(No.GDKJXM20222599)National Natural Science Foundation of China(No.52207104)Natural Science Foundation of Guangdong Province(No.2024A1515010426).
文摘The rapid development of electricity retail market has prompted an increasing number of electricity consumers to sign green electricity contracts with retail electricity companies,which poses greater challenges for the market service for green energy consumers.This study proposed a two-stage feature extraction approach for green energy consumers leveraging clustering and termfrequency-inverse document frequency(TF-IDF)algorithms within a knowledge graph framework to provide an information basis that supports the green development of the retail electricity market.First,the multi-source heterogeneous data of green energy consumers under an actual market environment is systematically introduced and the information is categorized into discrete,interval,and relational features.A clustering algorithm was employed to extract features of the trading behavior of green energy consumers in the first stage using the parameter data of green retail electricity contracts.Then,TF-IDF algorithm was applied in the second stage to extract features for green energy consumers in different clusters.Finally,the effectiveness of the proposed approach was validated based on the actual operational data in a southern province of China.It is shown that the most significant discrepancy between the retail trading behaviors of green energy consumers is the power share of green retail packages,whose averaged values are 25.64%,50%,39.66%,and 24.89%in four different clusters,respectively.Additionally,power supply bureaus and electricity retail companies affects the behavior of the green energy consumers most significantly.
基金supported by the Department of Economics and Management,University of Luxembourgfinancial support from the Department of Economics and Management,University of Luxembourg.
文摘The spread of the coronavirus has reduced the value of stock indexes,depressed energy and metals commodities prices including oil,and caused instability in financial markets around the world.Due to this situation,investors should consider investing in more secure assets,such as real estate property,cash,gold,and crypto assets.In recent years,among secure assets,cryptoassets are gaining more attention than traditional investments.This study compares the Bitcoin market,the gold market,and American stock indexes(S&P500,Nasdaq,and Dow Jones)before and during the COVID-19 pandemic.For this purpose,the dynamic conditional correlation exponential generalized autoregressive conditional heteroskedasticity model was used to estimate the DCC coefficient and compare this model with the artificial neural network approach to predict volatility of these markets.Our empirical findings showed a substantial dynamic conditional correlation between Bitcoin,gold,and stock markets.In particular,we observed that Bitcoin offered better diversification opportunities to reduce risks in key stock markets during the COVID-19 period.This paper provides practical impacts on risk management and portfolio diversification.
文摘The rapid rise of Bitcoin and its increasing global adoption has raised concerns about its impact on traditional markets,particularly in periods of economic turmoil and uncertainty such as the COVID-19 pandemic.This study examines the extent of the volatility contagion from the Bitcoin market to traditional markets,focusing on gold and six major stock markets(Japan,USA,UK,China,Germany,and France)using daily data from January 2,2011,to June 2,2022,with 2958 daily observations.We employ DCC-GARCH,wavelet coherence,and cascade-correlation network models to analyze the relationship between Bitcoin and those markets.Our results indicate long-term volatility contagion between Bitcoin and gold and short-term contagion during periods of market turmoil and uncertainty.We also find evidence of long-term contagion between Bitcoin and the six stock markets,with short-term contagion observed in Chinese and Japanese markets during COVID-19.These results suggest a risk of uncontrollable threats from Bitcoin volatility and highlight the need for measures to prevent infection transmission to local stock markets.Hedge funds,mutual funds,and individual and institutional investors can benefit from using our findings in their risk management strategies.Our research confirms the utility of the cascade-correlation network model as an innovative method to investigate intermarket contagion across diverse conditions.It holds significant implications for stock market investors and policymakers,providing evidence for potentially using cryptocurrencies for hedging,for diversification,or as a safe haven.
文摘With the deepening of globalization,the development speed of capital markets is constantly accelerating,presenting a trend of globalization.At the same time,the emergence of multiple forms of trading platforms and diversified financial products further highlights the competitive relationship between security exchanges and other trading platforms.While promoting the transformation of security exchange forms in various countries,it also prompts governments to re-examine the financial regulatory system of securities markets.In this situation,it is very important to research the international financial market and financial regulatory system.This article explores the regulatory issues and countermeasures in the international financial market,intending to promote the stability and healthy development of the international financial market.
文摘Introduction: Noise is the second leading cause of hearing loss in adults after presbycusis. The objective of this work was to study hearing loss induced by the noise of mills in the markets of Parakou. Methods: This was a descriptive and analytical cross-sectional study, conducted from February 3 to June 3, 2021 in the markets of Parakou. It concerned millers and sellers located within a 5 meter radius around the mills and among whom pure-tone audiometry was performed to detect a hearing loss. Subjects with no particular medical health history, under 55 years of age and having been working in these markets since more than 12 months, were included. Results: In this study, 103 subjects were selected, including 43 millers and 61 sellers. Their average age was 29 ± 13 years. The sex ratio was 0.49. The average length of service in the profession was 8 years with the extremes of 3 months and 47 years. They were exposed to noise on average 10 hours per day and 6 days a week. The average duration of weekly noise exposure was 23 h 28 min ± 13 h 32 min with the extremes of 5 h 00 min and 52 h 30 min. The average level of noise exposure was 90 dB with the extremes of 72 and 110 dB. 24 subjects reported symptoms related to noise such as headache, tinnitus, and hearing loss, with respective proportions of 22.33%, 20.39% and 06.80%. The prevalence of noise-related hearing loss was 26.21% (n = 27/103). Subjects with a notch at 4000 Hz and normal Average Hearing Loss (AHL) (20.39%) had a seven-time greater risk of developing noise-induced hearing loss (OR = 6.58;95% CI [2.54 - 18.8], p Conclusion: Hearing loss related to the noise of mills affected both millers and sellers near the mills in markets, hence the importance of regulating mills.
文摘We propose a new predictor-the innovation in the daily return minimum in the U.S.stock market(△MIN^(US))-for predicting international stock market returns.Using monthly data for a wide range of 17 MSCI international stock markets dur-ing the period spanning over half a century from January 1972 to July 2022,we find that △MIN^(US) have strong predictive power for returns in most international stock markets:△MIN^(US) negatively predicts the next-month stock market returns.The results remain robust after controlling for a number of macroeconomic predictors and con-ducting subsample and panel data analyses,indicating that △MIN^(US) has significant predictive power and it outperforms other variables in international markets.Notably,△MIN^(US) demonstrates excellent predictive power even during the periods driven by financial upheavals(e.g.,Global Financial Crisis and European Sovereign Debt Crisis).Both panel regressions and out-of-sample tests also support the robust predictive performance of △MIN^(US).The predictive power,however,disappears during the non-financial crisis caused by COVID-19 pandemic,which is originated from the health sector rather than the financial sector.The results provide a new perspective on U.S.extreme indicator in stock market return predictability.
基金State Grid Jibei Electric Power Company Limited(no.SGJBJY00GPJS2310051)Natural Science Foundation of Beijing Municipality(no.9242015).
文摘Because of the contradiction between the scale of new energy installations and the continuous load growth in the central and eastern regions of China,the balance problems of the electricity market are becoming increasingly prominent,and it is urgent to solve such problems through inter-provincial electricity spot markets.First,the development history and construction status of the inter-provincial electricity spot market are summarized;second,the mechanism design of the inter-provincial electricity spot market is sorted out in terms of the market operation framework,transaction declaration,and clearing methods;subsequently,the evaluation index system of the inter-provincial electricity spot market is constructed,including four themes of electricity mutual aid and support,new energy consumption,economic benefits of market-based allocation,and social benefits of market-based allocation;finally,the operation of the inter-provincial electricity spot market is comprehensively analyzed by the algorithm based on the market operation data of 2022,which proves the feasibility and practicality of the proposed index system.
基金supported by the National Natural Science Foundation of China(71973001).
文摘An intuitive portrayal of the correlation between the carbon and energy markets is essential for risk control and green financial investment management.In this paper,we investigate the asymmetric spillovers between the carbon mar-ket and energy market returns.To achieve that,we improve the Diebold-Yilmaz index model by a time-varying vector autoregressive(TVP-VAR)model.In a unified network,our daily dataset includes the closing prices of the Hubei carbon market,Shenzhen carbon market,coal futures,and energy stock index.The findings reveal that both the Hubei and Shen-zhen pilots typically generate net information spillovers on energy futures.In connection with energy stocks,the Hubei carbon market acts as a net receiver,while the Shenzhen carbon market is a net transmitter.Compared with the Hubei pi-lot,the Shenzhen pilot is more tightly connected to the energy markets.Furthermore,the spillovers of the carbon markets exhibit significant asymmetry.In most cases,they have more substantial impacts on the energy markets when the prices of emission allowances rise.The direction and magnitude of asymmetric spillovers across markets vary over time and can be influenced by certain economic or political events.
文摘The evaluation of the electricity market is crucial for fostering market construction and development.An accurate assessment of the electricity market reveals developmental trends,identifies operational issues,and contributes to stable and healthy market growth.This study investigated the characteristics of electricity markets in different provinces and synthesized a comprehensive set of evaluation indicators to assess market effectiveness.The evaluation framework,comprising nine indicators organized into two tiers,was constructed based on three aspects:market design,market efficiency,and developmental coordination.Furthermore,a novel fuzzy multi-criteria decision-making evaluation model for electricity market performance was developed based on the Fuzzy-BWM and fuzzy COPRAS methodologies.This model aimed to ensure both accuracy and comprehensiveness in market operation assessment.Subsequently,empirical analyses were conducted on four typical provincial-level electricity markets in China.The results indicate that Guangdong’s electricity market performed best because of its effective balance of stakeholder interests and adherence to contractual integrity principles.Zhejiang and Shandong ranked second and third,respectively,whereas Sichuan exhibited the poorest market performance.Sichuan’s electricity market must be improved in terms of market design,such that market players can obtain a fairly competitive environment.The sensitivity analysis of the constructed indicators verified the effectiveness of the evaluation model proposed in this study.Finally,policy recommendations were proposed to facilitate the sustainable development of China’s electricity markets with the objective of transforming them into efficient and secure markets adaptable to the evolution of novel power systems.
文摘How to find an effective trading policy is still an open question mainly due to the nonlinear and non-stationary dynamics in a financial market.Deep reinforcement learning,which has recently been used to develop trading strategies by automatically extracting complex features from a large amount of data,is struggling to deal with fastchanging markets due to sample inefficiency.This paper applies the meta-reinforcement learning method to tackle the trading challenges faced by conventional reinforcement learning(RL)approaches in non-stationary markets for the first time.In our work,the history trading data is divided into multiple task data and for each of these data themarket condition is relatively stationary.Then amodel agnosticmeta-learning(MAML)-based tradingmethod involving a meta-learner and a normal learner is proposed.A trading policy is learned by the meta-learner across multiple task data,which is then fine-tuned by the normal learner through a small amount of data from a new market task before trading in it.To improve the adaptability of the MAML-based method,an ordered multiplestep updating mechanism is also proposed to explore the changing dynamic within a task market.The simulation results demonstrate that the proposed MAML-based trading methods can increase the annualized return rate by approximately 180%,200%,and 160%,increase the Sharpe ratio by 180%,90%,and 170%,and decrease the maximum drawdown by 30%,20%,and 40%,compared to the traditional RL approach in three stock index future markets,respectively.
基金Fundacão para a Ciencia e a Tecnologia(Grant No.UIDB/04521/2020).
文摘We provide empirical evidence supporting the economic reasoning behind the impossibility of diversification benefits and the hedge attributes of cryptocurrencies remaining in force during the downside trends observed in bearish financial markets.We employ a spillover connectedness model driven by time-varying parameter vector autoregressions on daily data covering January 2018 to November 2022 to analyze spillover transmissions between conventional and digital markets,focusing on the role of stablecoin issuances.We study the stock,bond,cryptocurrency,and stablecoin markets and find very high connectedness,which varies over time in response to up/down trends in financial markets.The results show that during financial turmoil,cryptocurrencies amplify downside risks rather than serve as diversifiers.In addition to risky assets from conventional financial markets,cryptocurrencies champion the transmission of spillovers to digital and conventional markets.In contrast,changes in stablecoin issuances produce few shocks because of their pegged prices,but they facilitate investors’switch from volatile cryptos to more stable digital instruments;that is,we observe a phenomenon designated by us as the“flight-to-cryptosafety.”We draw insightful conclusions,provoking new thinking regarding portfolio hedge strategies that could potentially benefit investors when searching for less volatile investment performance.
基金financial support from Ministerio de Ciencia e Innovación(MCIN/AEI/10.13039/501100011033)。
文摘This paper analyzes the degree of dynamic connectedness between energy and metal commodity prices in the pre and post-COVID-19 era,using the time-varying parameter vector autoregressive connectedness approach of Antonakakis et al.(J Risk Financ Manag 13(4):84,2020).The results suggest that market interconnectedness increased slightly following the outbreak of COVID-19,although this increase was lower and less persistent than that observed after the Global Financial Crisis of 2008.Furthermore,we find that crude oil was the main net transmitter of shocks before COVID-19 while heating oil,gold,and silver were the main net transmitters of shocks during the COVID-19 pandemic.In contrast,natural gas and palladium were the main net receivers of shocks during the entire sample period,making these two commodities attractive hedging and safe haven options for investors during the pandemic.Overall,our results suggest that hedging and diversification opportunities decrease during crises.Furthermore,they indicate that accurate forecasts of the volatility of several commodities,such as natural gas and different metals,can be obtained by exploiting the information content of crude oil.However,they also reveal that crude oil lost its leading position as a net shock transmitter during the COVID-19 pandemic.
文摘This study utilizes the Dynamic Conditional Correlation-Generalized Autoregressive Conditional Heteroskedasticity (DCC-GARCH) model to investigate the dynamic relationship between Chinese and U.S. stock markets amid the COVID-19 pandemic. Initially, a univariate GARCH model is developed to derive residual sequences, which are then used to estimate the DCC model parameters. The research reveals a significant rise in the interconnection between the Chinese and U.S. stock markets during the pandemic. The S&P 500 index displayed higher sensitivity and greater volatility in response to the pandemic, whereas the CSI 300 index showed superior resilience and stability. Analysis and model estimation suggest that the market’s dependence on historical data has intensified and its sensitivity to recent shocks has heightened. Predictions from the model indicate increased market volatility during the pandemic. While the model is proficient in capturing market trends, there remains potential for enhancing the accuracy of specific volatility predictions. The study proposes recommendations for policymakers and investors, highlighting the importance of improved cooperation in international financial market regulation and investor education.
文摘The use of food additives in industrial production has the advantage of improving sensory properties, technological quality and extending the shelf life of foods. Among the most widely used additives are antioxidants, which prevent oxidation, browning and rancidity reactions in foods. However, the strong presence of these additives on the market is not without risks for human health, and should be controlled to guarantee food safety. Analysis of the risks associated with consumption of foods containing these additives requires, among other things, information on the frequency of use of these additives in various consumer products. The aim of this study is therefore to identify the antioxidants present in industrial food products distributed in Dakar. The methodology adopted consists of a qualitative analysis based on the identification of additives from food labels. Investigations were carried out in 9 stores, 4 superettes and 2 supermarkets located in different districts of Dakar. The results revealed the presence of 12 antioxidant additives, dominated by citric acid (53%) and ascorbic acid (29%). These studies have also highlighted the simultaneous use of several antioxidants in the same food product. Moreover, for some artificial antioxidants identified antioxydant such as BHA and BHT, health risks are associated with their consumption. The results of this study open up prospects for the development of information databases on food additives.
