The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt ...The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.展开更多
The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack...The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack a unified data structure,and depend heavily on manual intervention to process high-frequency and retroactive transactions.To address these limitations,a graph-based unified settlement framework is proposed to enhance automation,flexibility,and adaptability in electricity market settlements.A flexible attribute-graph model is employed to represent heterogeneousmulti-market data,enabling standardized integration,rapid querying,and seamless adaptation to evolving business requirements.An extensible operator library is designed to support configurable settlement rules,and a suite of modular tools—including dataset generation,formula configuration,billing templates,and task scheduling—facilitates end-to-end automated settlement processing.A robust refund-clearing mechanism is further incorporated,utilizing sandbox execution,data-version snapshots,dynamic lineage tracing,and real-time changecapture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions.Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach,demonstrating marked improvements in computational efficiency,system robustness,and automation.Moreover,enhanced settlement accuracy and high temporal granularity improve price-signal fidelity,promote cost-reflective tariffs,and incentivize energy-efficient and demand-responsive behavior among market participants.The method not only supports equitable and transparent market operations but also provides a generalizable,scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.展开更多
At a critical juncture for the textile industry's transition towards high quality,the cocoon and silk sector—carrying millennia of cultural heritage and harboring modern industrial potential—is embracing dual op...At a critical juncture for the textile industry's transition towards high quality,the cocoon and silk sector—carrying millennia of cultural heritage and harboring modern industrial potential—is embracing dual opportunities driven by both policy empowerment and market forces.Recently,the Ministry of Commerce,jointly with multiple departments,issued the"Notice on Carrying Out the'Eastern Silk,Western Consolidation'Work in the Cocoon and Silk Industry,"anchoring the core directions of"optimizing the industrial chain,strengthening innovation-driven development,and promoting coordinated development"to push the industry's gradient relocation to central and western regions.This profound adjustment in regional layout not only injects strong momentum into the cocoon and silk sector but also serves as a vivid practice of implementing"regional coordination and dual improvement of quality and efficiency"in the textile field.展开更多
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
This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dyn...This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk(ADCC-CoVaR)model and a bootstrapped Kolmogorov–Smirnov(KS)test,we analyze the period from December 30,2005,to February 28,2024,examining correlations,downside and upside risk spillovers,and highlighting the effects of major events such as the global financial crisis of 2008,the COVID-19 pandemic,and the Russia-Ukraine war.The results show heightened correlations during crises and significant risk spillovers across market pairs,with downside risks often outweighing upside risks.Gold displays minimal risk spillover,highlighting its unique role as a haven asset.We find that spillovers between gold,global commodities,and stocks increased during the pandemic and the Russia-Ukraine conflict,while those involving crude oil remained stable.These findings provide valuable guidance for portfolio managers in navigating volatile markets.展开更多
The marine economy has emerged as a vital driver of economic growth in both China and ASEAN countries.It encompasses a wide range of activities including fisheries,maritime transport,offshore oil and gas exploration,a...The marine economy has emerged as a vital driver of economic growth in both China and ASEAN countries.It encompasses a wide range of activities including fisheries,maritime transport,offshore oil and gas exploration,and marine tourism.At the same time,demographic structures are undergoing notable changes,with an increasing share of the older population.This trend of population aging has profound implications for various sectors,including the marine economy.Understanding the interplay between these dynamics is essential for sustainable development and policy-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 integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate fo...The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate forecasting is crucial for ensuring grid stability,optimizing market operations,and minimizing economic risks.This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models,fractal-based feature enginering,and deep learning architectures to improve renewable energy forecasting accuracy.Fractional autoregressive integrated moving average(FARIMA)and fractional exponential smoothing(FETS)models are explored for capturing long-memory dependencies in energy time-series data.Additionally,multifractal detrended fluctuation analysis(MFDFA)is used to analyze the intermittency of renewable energy generation.The hybrid approach further integrates wavelet transforms and convolutional long short-term memory(CNN-LSTM)networks to model shortand long-term dependencies effectively.Experimental results demonstrate that fractional and fractal-based hybrid forecasting techniques significantly outperform traditional models in terms of accuracy,reliability,and adaptability to energy market dynamics.This research provides insights for market participants,policymakers,and grid operators to develop more robust forecasting frameworks,ensuring a more sustainable and resilient electricity market.展开更多
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
Social media platforms collect information from complex human social behaviors.It has been shown that they significantly impact capital markets.This article conducts a bibliometric analysis of 363 high-quality article...Social media platforms collect information from complex human social behaviors.It has been shown that they significantly impact capital markets.This article conducts a bibliometric analysis of 363 high-quality articles from the Web of Science on social media and capital markets.First,a descriptive analysis was performed,including discipline distribution,growth trends,institutions,publication sources,influential authors,articles,and countries.Second,collaborative networks identify cooperation between countries and institutions in the network analysis section.Bibliometric coupling analysis determines the relationship between journals of different disciplines.Cluster analysis and keyword co-occurrence networks identify hot research topics.Burst detection and keyword time zone analysis track field development.Finally,keyword analysis and clustering analysis are conducted on the research of different disciplines.These sample papers mainly include seven hot topics,including sentiment analysis and financial market prediction.Articles in business and finance focus more on social media’s impact on capital markets.In contrast,information technology articles focus more on extracting information from social media.Each field also has its unique research directions.This paper contributes to a comprehensive understanding of the research on social media and capital markets.It offers perspectives on disciplinary differences,serving as a valuable scholarly reference for conducting in-depth research.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
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
What is it like to open a bookstore in a busy food market?At first glance,the quiet world of a bookstore and the noisy market seem worlds apart,yet they are gradually blending together.During the recent summer holiday...What is it like to open a bookstore in a busy food market?At first glance,the quiet world of a bookstore and the noisy market seem worlds apart,yet they are gradually blending together.During the recent summer holiday,in the busy Shuangjingtou Market of Wenzhou,a new bookstore became an unexpected community center.展开更多
The 53rd issue of Financial Innovation(FIN),Volume 11,No.5(2025),features 21 papers that can be classified into four main themes:the Special Issue on The Anomie of Artificial Intelligence(AI)in Finance:Bridging the Ga...The 53rd issue of Financial Innovation(FIN),Volume 11,No.5(2025),features 21 papers that can be classified into four main themes:the Special Issue on The Anomie of Artificial Intelligence(AI)in Finance:Bridging the Gap Between Technical Power and Human Wisdom,and Financial Markets and Investments,Economic and Policy Analysis,Corporate Governance and Related Market Dynamics.展开更多
This paper examines the dependence,systemic risk spillover,return and volatility spillover,and portfolio implications across various timescales between the Green Bond(GB)and U.S.S&P 500 Stock(SP),Vanguard Total Wo...This paper examines the dependence,systemic risk spillover,return and volatility spillover,and portfolio implications across various timescales between the Green Bond(GB)and U.S.S&P 500 Stock(SP),Vanguard Total World Stock Index Fund(VT),Bitcoin(BTC),Ethereum(ETH),Ripple,OIL,and GOLD markets.The sample period is August 07,2015–October 6,2023,covering periods of instability during the COVID-19 pandemic and the Russia–Ukraine conflict.Using the wavelet–copula–conditional value-atrisk and wavelet-multivariate asymmetric-GARCH framework,our main results show that the systemic risk and return,volatility spillovers,and diversification opportunities are portfolio-specific and timescale-dependent.Specifically,there is a negative long-term correlation for the pairs GB-SP and GB-OIL,whereas the pair GB–GOLD pair is positively correlated in the short term.GB can mitigate the risk of other markets.In terms of the portfolio implications,GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term.Regarding hedging effectiveness,the role of GB for GOLD and VT is noted.展开更多
Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional ...Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional data degrade performance,contrary to common expectations.While more data can still be beneficial,it may introduce systemic concept drift due to the complex nonstationarities of stock price index time series,thereby exacerbating overfitting.One such drift is memory inconsistency:locally measured long memories fluctuate over time,alternately approaching and deviating from the random walk condition.We address this problem by typifying memory inconsistencies into two simplified forms:long-term dependentto-independent(D2I)and long-term independent-to-dependent(I2D)inconsistencies.The first experiment,which uses U.S.stock price indices,suggests that additional training examples may lead to performance deterioration of long short-term memory(LSTM)networks,especially when memory inconsistencies are prominent.Since stock markets are influenced by numerous unknown dynamics,the second experiment,which uses simulated mean-reverting time series derived from the fractional Ornstein–Uhlenbeck(fOU)process,is conducted to focus solely on challenges arising from memory inconsistencies.The experimental results demonstrate that memory inconsistencies disrupt the performance of LSTM networks.Theoretically,additional errors from D2I and I2D inconsistencies increase as the time lag increases.Since LSTM networks are inherently recurrent,causing information from distant steps to attenuate,they fail to effectively capture memory inconsistencies in practical offline learning schemes.Nonetheless,transplanting pretrained memory-consistent gate parameters into the LSTM model partially mitigates the performance deterioration caused by memory inconsistencies,suggesting that memory augmentation strategies have the potential to overcome this problem.As such a memory augmentation method,we propose the Gate-of-Gates(GoG)model,which extends the capacity of LSTM gates and demonstrates that it can mitigate additional errors arising from memory inconsistencies.展开更多
High-frequency trading(HFT)practices in the global financial markets involve the use of information and communication technologies(ICT),especially the capabilities of high-speed networks,rapid computation,and algorith...High-frequency trading(HFT)practices in the global financial markets involve the use of information and communication technologies(ICT),especially the capabilities of high-speed networks,rapid computation,and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds.HFT practices exist because a variety of new technologies have made them possible,and because financial market infrastructure capabilities have also been changing so rapidly.The U.S.markets,such as the National Association for Securities Dealers Automated Quote(NASDAQ)market and the New York Stock Exchange(NYSE),have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable.In this article,we explore the technological,institutional and market developments in leading financial markets around the world that have embraced HFT trading.From these examples,we will distill a number of common characteristics that seem to be in operation,and then assess the extent to which HFT practices have begun to be observed in Asian regional financial markets,and what will be their likely impacts.We also discuss a number of theoretical and empirical research directions of interest.展开更多
基金supported by NARI Relays Electric Co.,Ltd.under the Project“Research on Evaluation of Clearing Results and Switching Criteria for Primary-Backup Systems in Electricity SpotMarkets”(Project No.CGSQ240800443).
文摘The construction of spot electricity markets plays a pivotal role in power system reforms,where market clearing systems profoundly influence market efficiency and security.Current clearing systems predominantly adopt a single-system architecture,with research focusing primarily on accelerating solution algorithms through techniques such as high-efficiency parallel solvers and staggered decomposition of mixed-integer programming models.Notably absent are systematic studies evaluating the adaptability of primary-backup clearing systems incontingency scenarios—a critical gap given redundant systems’expanding applications in operational environments.This paper proposes a comprehensive evaluation framework for analyzing dual-system adaptability,demonstrated through an in-depth case study of the Inner Mongolia power market.First,we establish the innovative“Dual-Active Heterogeneous”architecture that enables independent parallelized operation and fault-isolated redundancy.Subsequently,key performance indices are quantitatively evaluated across four critical dimensions:unit commitment decisions,generator output constraints,transmission section congestion patterns,and clearing price formation mechanisms.An integrated fuzzy evaluation methodology incorporating grey relational analysis is employed for objective indicator weighting,enabling systematic quantification of system superiority under specific grid operating states.Empirical results based on actual operational data from 200 generation units demonstrate the framework’s efficacy in guiding optimal system selection,with particularly strong performance observed during peak load periods.The proposed approach shows high generalization potential for other regional markets employing redundant clearing mechanisms—particularly those with increasing renewable penetration and associated uncertainty.
基金funded by the Science and Technology Project of State Grid Corporation of China(5108-202355437A-3-2-ZN).
文摘The increasing complexity of China’s electricity market creates substantial challenges for settlement automation,data consistency,and operational scalability.Existing provincial settlement systems are fragmented,lack a unified data structure,and depend heavily on manual intervention to process high-frequency and retroactive transactions.To address these limitations,a graph-based unified settlement framework is proposed to enhance automation,flexibility,and adaptability in electricity market settlements.A flexible attribute-graph model is employed to represent heterogeneousmulti-market data,enabling standardized integration,rapid querying,and seamless adaptation to evolving business requirements.An extensible operator library is designed to support configurable settlement rules,and a suite of modular tools—including dataset generation,formula configuration,billing templates,and task scheduling—facilitates end-to-end automated settlement processing.A robust refund-clearing mechanism is further incorporated,utilizing sandbox execution,data-version snapshots,dynamic lineage tracing,and real-time changecapture technologies to enable rapid and accurate recalculations under dynamic policy and data revisions.Case studies based on real-world data from regional Chinese markets validate the effectiveness of the proposed approach,demonstrating marked improvements in computational efficiency,system robustness,and automation.Moreover,enhanced settlement accuracy and high temporal granularity improve price-signal fidelity,promote cost-reflective tariffs,and incentivize energy-efficient and demand-responsive behavior among market participants.The method not only supports equitable and transparent market operations but also provides a generalizable,scalable foundation for modern electricity settlement platforms in increasingly complex and dynamic market environments.
文摘At a critical juncture for the textile industry's transition towards high quality,the cocoon and silk sector—carrying millennia of cultural heritage and harboring modern industrial potential—is embracing dual opportunities driven by both policy empowerment and market forces.Recently,the Ministry of Commerce,jointly with multiple departments,issued the"Notice on Carrying Out the'Eastern Silk,Western Consolidation'Work in the Cocoon and Silk Industry,"anchoring the core directions of"optimizing the industrial chain,strengthening innovation-driven development,and promoting coordinated development"to push the industry's gradient relocation to central and western regions.This profound adjustment in regional layout not only injects strong momentum into the cocoon and silk sector but also serves as a vivid practice of implementing"regional coordination and dual improvement of quality and efficiency"in the textile field.
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This study explores correlations and risk spillovers,essential concepts for financial risk management,among commodities(crude oil,gold,and a global commodities index)and emerging stock markets.Using the Asymmetric Dynamic Conditional Correlation–Conditional Value-at-Risk(ADCC-CoVaR)model and a bootstrapped Kolmogorov–Smirnov(KS)test,we analyze the period from December 30,2005,to February 28,2024,examining correlations,downside and upside risk spillovers,and highlighting the effects of major events such as the global financial crisis of 2008,the COVID-19 pandemic,and the Russia-Ukraine war.The results show heightened correlations during crises and significant risk spillovers across market pairs,with downside risks often outweighing upside risks.Gold displays minimal risk spillover,highlighting its unique role as a haven asset.We find that spillovers between gold,global commodities,and stocks increased during the pandemic and the Russia-Ukraine conflict,while those involving crude oil remained stable.These findings provide valuable guidance for portfolio managers in navigating volatile markets.
文摘The marine economy has emerged as a vital driver of economic growth in both China and ASEAN countries.It encompasses a wide range of activities including fisheries,maritime transport,offshore oil and gas exploration,and marine tourism.At the same time,demographic structures are undergoing notable changes,with an increasing share of the older population.This trend of population aging has profound implications for various sectors,including the marine economy.Understanding the interplay between these dynamics is essential for sustainable development and policy-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.
基金funded under research grant from the Research,Development,andInnovation Authority(RDIA),Saudi Arabia,grant No.13010-Tabuk-2023-UT-R-3-1-SE.
文摘The integration of renewable energy sources into electricity markets presents significant challenges due to the inherent variability and uncertainty of power generation from wind,solar,and other renewables.Accurate forecasting is crucial for ensuring grid stability,optimizing market operations,and minimizing economic risks.This paper introduces a hybrid forecasting framework incorporating fractional-order statistical models,fractal-based feature enginering,and deep learning architectures to improve renewable energy forecasting accuracy.Fractional autoregressive integrated moving average(FARIMA)and fractional exponential smoothing(FETS)models are explored for capturing long-memory dependencies in energy time-series data.Additionally,multifractal detrended fluctuation analysis(MFDFA)is used to analyze the intermittency of renewable energy generation.The hybrid approach further integrates wavelet transforms and convolutional long short-term memory(CNN-LSTM)networks to model shortand long-term dependencies effectively.Experimental results demonstrate that fractional and fractal-based hybrid forecasting techniques significantly outperform traditional models in terms of accuracy,reliability,and adaptability to energy market dynamics.This research provides insights for market participants,policymakers,and grid operators to develop more robust forecasting frameworks,ensuring a more sustainable and resilient electricity market.
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financially supported by the National Natural Science Foundation of China(No.72471223,No.72231010)。
文摘Social media platforms collect information from complex human social behaviors.It has been shown that they significantly impact capital markets.This article conducts a bibliometric analysis of 363 high-quality articles from the Web of Science on social media and capital markets.First,a descriptive analysis was performed,including discipline distribution,growth trends,institutions,publication sources,influential authors,articles,and countries.Second,collaborative networks identify cooperation between countries and institutions in the network analysis section.Bibliometric coupling analysis determines the relationship between journals of different disciplines.Cluster analysis and keyword co-occurrence networks identify hot research topics.Burst detection and keyword time zone analysis track field development.Finally,keyword analysis and clustering analysis are conducted on the research of different disciplines.These sample papers mainly include seven hot topics,including sentiment analysis and financial market prediction.Articles in business and finance focus more on social media’s impact on capital markets.In contrast,information technology articles focus more on extracting information from social media.Each field also has its unique research directions.This paper contributes to a comprehensive understanding of the research on social media and capital markets.It offers perspectives on disciplinary differences,serving as a valuable scholarly reference for conducting in-depth research.
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.
文摘What is it like to open a bookstore in a busy food market?At first glance,the quiet world of a bookstore and the noisy market seem worlds apart,yet they are gradually blending together.During the recent summer holiday,in the busy Shuangjingtou Market of Wenzhou,a new bookstore became an unexpected community center.
文摘The 53rd issue of Financial Innovation(FIN),Volume 11,No.5(2025),features 21 papers that can be classified into four main themes:the Special Issue on The Anomie of Artificial Intelligence(AI)in Finance:Bridging the Gap Between Technical Power and Human Wisdom,and Financial Markets and Investments,Economic and Policy Analysis,Corporate Governance and Related Market Dynamics.
文摘This paper examines the dependence,systemic risk spillover,return and volatility spillover,and portfolio implications across various timescales between the Green Bond(GB)and U.S.S&P 500 Stock(SP),Vanguard Total World Stock Index Fund(VT),Bitcoin(BTC),Ethereum(ETH),Ripple,OIL,and GOLD markets.The sample period is August 07,2015–October 6,2023,covering periods of instability during the COVID-19 pandemic and the Russia–Ukraine conflict.Using the wavelet–copula–conditional value-atrisk and wavelet-multivariate asymmetric-GARCH framework,our main results show that the systemic risk and return,volatility spillovers,and diversification opportunities are portfolio-specific and timescale-dependent.Specifically,there is a negative long-term correlation for the pairs GB-SP and GB-OIL,whereas the pair GB–GOLD pair is positively correlated in the short term.GB can mitigate the risk of other markets.In terms of the portfolio implications,GB weakly hedges BTC and ETH during normal and turbulent periods but has a strong ability to hedge VT in the short term and SP in the mid and long term.Regarding hedging effectiveness,the role of GB for GOLD and VT is noted.
基金supported by the Ministry of Education of the Republic of Korea and the National Research Foundation of Korea(NRF-2023S1A5A8077102).
文摘Deep learning enables neural networks to improve prediction performance through data supplementation.In financial time series forecasting,however,such data-driven approaches can encounter limitations where additional data degrade performance,contrary to common expectations.While more data can still be beneficial,it may introduce systemic concept drift due to the complex nonstationarities of stock price index time series,thereby exacerbating overfitting.One such drift is memory inconsistency:locally measured long memories fluctuate over time,alternately approaching and deviating from the random walk condition.We address this problem by typifying memory inconsistencies into two simplified forms:long-term dependentto-independent(D2I)and long-term independent-to-dependent(I2D)inconsistencies.The first experiment,which uses U.S.stock price indices,suggests that additional training examples may lead to performance deterioration of long short-term memory(LSTM)networks,especially when memory inconsistencies are prominent.Since stock markets are influenced by numerous unknown dynamics,the second experiment,which uses simulated mean-reverting time series derived from the fractional Ornstein–Uhlenbeck(fOU)process,is conducted to focus solely on challenges arising from memory inconsistencies.The experimental results demonstrate that memory inconsistencies disrupt the performance of LSTM networks.Theoretically,additional errors from D2I and I2D inconsistencies increase as the time lag increases.Since LSTM networks are inherently recurrent,causing information from distant steps to attenuate,they fail to effectively capture memory inconsistencies in practical offline learning schemes.Nonetheless,transplanting pretrained memory-consistent gate parameters into the LSTM model partially mitigates the performance deterioration caused by memory inconsistencies,suggesting that memory augmentation strategies have the potential to overcome this problem.As such a memory augmentation method,we propose the Gate-of-Gates(GoG)model,which extends the capacity of LSTM gates and demonstrates that it can mitigate additional errors arising from memory inconsistencies.
文摘High-frequency trading(HFT)practices in the global financial markets involve the use of information and communication technologies(ICT),especially the capabilities of high-speed networks,rapid computation,and algorithmic detection of changing information and prices that create opportunities for computers to effect low-latency trades that can be accomplished in milliseconds.HFT practices exist because a variety of new technologies have made them possible,and because financial market infrastructure capabilities have also been changing so rapidly.The U.S.markets,such as the National Association for Securities Dealers Automated Quote(NASDAQ)market and the New York Stock Exchange(NYSE),have maintained relevance and centrality in financial intermediation in financial markets settings that have changed so much in the past 20 years that they are hardly recognizable.In this article,we explore the technological,institutional and market developments in leading financial markets around the world that have embraced HFT trading.From these examples,we will distill a number of common characteristics that seem to be in operation,and then assess the extent to which HFT practices have begun to be observed in Asian regional financial markets,and what will be their likely impacts.We also discuss a number of theoretical and empirical research directions of interest.
