Research on the Dynamic Volatility Relationship between Chinese and U.S. Stock Markets Based on the DCC-GARCH Model under the Background of the COVID-19 Pandemic

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.

Identification of Antioxidant Additives in Food Products Sold in Dakar Markets

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.

Prevalence of Noise-Induced Hearing Loss Related to the Mills in the Markets of the Municipality of Parakou in 2021

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.

Trading in Fast-ChangingMarkets withMeta-Reinforcement Learning

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.

Regulatory Issues and Countermeasures in International Financial Markets

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.

China’s Monetary Policy Impacts on Money and Stock Markets

This study investigated the impact of China’s monetary policy on both the money market and stock markets,assuming that non-policy variables would not respond contemporaneously to changes in policy variables.Monetary policy adjustments are swiftly observed in money markets and gradually extend to the stock market.The study examined the effects of monetary policy shocks using three primary instruments:interest rate policy,reserve requirement ratio,and open market operations.Monthly data from 2007 to 2013 were analyzed using vector error correction(VEC)models.The findings suggest a likely presence of long-lasting and stable relationships among monetary policy,the money market,and stock markets.This research holds practical implications for Chinese policymakers,particularly in managing the challenges associated with fluctuation risks linked to high foreign exchange reserves,aiming to achieve autonomy in monetary policy and formulate effective monetary strategies to stimulate economic growth.

Multi-Blockchain Based Data Trading Markets With Novel Pricing Mechanisms

In the era of big data,there is an urgent need to establish data trading markets for effectively releasing the tremendous value of the drastically explosive data.Data security and data pricing,however,are still widely regarded as major challenges in this respect,which motivate this research on the novel multi-blockchain based framework for data trading markets and their associated pricing mechanisms.In this context,data recording and trading are conducted separately within two separate blockchains:the data blockchain(DChain) and the value blockchain(VChain).This enables the establishment of two-layer data trading markets to manage initial data trading in the primary market and subsequent data resales in the secondary market.Moreover,pricing mechanisms are then proposed to protect these markets against strategic trading behaviors and balance the payoffs of both suppliers and users.Specifically,in regular data trading on VChain-S2D,two auction models are employed according to the demand scale,for dealing with users’ strategic bidding.The incentive-compatible Vickrey-Clarke-Groves(VCG)model is deployed to the low-demand trading scenario,while the nearly incentive-compatible monopolistic price(MP) model is utilized for the high-demand trading scenario.With temporary data trading on VChain-D2S,a reverse auction mechanism namely two-stage obscure selection(TSOS) is designed to regulate both suppliers’ quoting and users’ valuation strategies.Furthermore,experiments are carried out to demonstrate the strength of this research in enhancing data security and trading efficiency.

The linkage between Bitcoin and foreign exchanges in developed and emerging markets

This study investigates the connectedness between Bitcoin and fiat currencies in two groups of countries:the developed G7 and the emerging BRICS.The methodology adopts the regular(R)-vine copula and compares it with two benchmark models:the multivariate t copula and the dynamic conditional correlation(DCC)GARCH model.Moreover,this study examines whether the Bitcoin meltdown of 2013,selloff of 2018,COVID-19 pandemic,2021 crash,and the Russia-Ukraine conflict impact the linkage with conventional currencies.The results indicate that for both currency baskets,R-vine beats the benchmark models.Hence,the dependence is better modeled by providing sufficient information on the shock transmission path.Furthermore,the cross-market linkage slightly increases during the Bitcoin crashes,and reaches significant levels during the 2021 and 2022 crises,which may indicate the end of market isolation of the virtual currency.

Editorial to special issue“Hidden market linkages between Bitcoin,cryptocurrencies and financial markets:Evidence from high‑frequency data and higher‑order moments”in financial innovation

The cryptomarket has evolved into a complex system of different types of cryptoassets,each playing an important role within the system.With specific features,opportunities,and risks.Studying their apparent and hidden linkages and general connectedness not only inside the system but also the linkages to the outer markets,being it either the traditional financial markets or the macroeconomic and monetary indicators and variables,plays a crucial role in understanding the market,managing risks,and aiming for profitable opportunities.The cryptomarkets are far from being simply Bitcoin or even just the most popular and capitalised cryptocurrencies and tokens which might have been the case just a few years back.

Extreme dependencies and spillovers between gold and stock markets:evidence from MENA countries

This study addresses whether gold exhibits the function of a hedge or safe haven as often referred to in academia.It contributes to the existing literature by(i)revisiting this question for the principal stock markets in the Middle East and North Africa(MENA)region and(ii)using the copula-quantile-on-quantile and conditional value at risk methods to detail the risks facing market participants provided with accurate information about various gold and stock market scenarios(i.e.,bear,normal,bull).The results provide strong evidence of quantile dependence between gold and stock returns.Positive correlations are found between MENA gold and stock markets when both are bullish.Conversely,when stock returns are bearish,gold markets show negative correlations with MENA stock markets.The risk spillover from gold to stock markets intensified during the global financial and European crises.Given the risk spillover between gold and stock markets,investors in MENA markets should be careful when considering gold as a safe haven because its effectiveness as a hedge is not the same in all MENA stock markets.Investors and portfolio managers should rebalance their portfolio compositions under various gold and stock market conditions.Overall,such precise insights about the heterogeneous linkages and spillovers between gold and MENA stock returns provide potential input for developing effective hedging strategies and optimal portfolio allocations.

Evaluation method for assessing the auxiliary service transaction mechanism in regional power markets

Accurate and seamless auxiliary services in the power market can guarantee smooth and continuous power system operation. China’s new round of power system reform has entered a critical period, and reform implementation requires comprehensive improvements in the maturity of the supporting auxiliary service market. This study reviews the development status and evolution path of the European unified power market and the US regional power market, provides experience for the development of China’s regional power market, then identifies the key influencing factors of auxiliary service trading mechanism design in regional power markets. To analyze the rationality of the auxiliary service trading evaluation index, this paper established an evaluation model for assessing regional power markets. Using combined weight optimization, the gray correlation TOPSIS method was applied to comprehensively evaluate auxiliary service trading in the regional power market. Finally, the application of the proposed evaluation method was briefly analyzed to examine four regional power markets in China and evaluate the effectiveness of current market construction in different regions and provide suggestions for future market construction.

对外开放对全国统一大市场建设的影响研究:基于我国市场分割的视角

双循环新发展格局下,进一步扩大对外开放将成为我国构建全国统一大市场的关键。在分析对外开放影响全国统一大市场建设的理论机制基础上,选取2005—2020年中国省级面板数据,构建空间计量模型,检验对外开放对我国市场分割产生的空间溢出效应,并通过中介效应模型对理论机制进行检验。研究表明:对外开放能显著降低本地区的市场分割程度,具有正向的空间外溢效应;理论机制检验进一步发现,对外开放通过国企股份制改革、基础设施建设以及提高地区经济发展水平三条路径打破市场分割局面,对全国统一大市场建设产生积极效应。

Social interactions in periodic urban markets and their contributions to sustainable livelihoods:Evidence from Ghana

Periodic markets are an important aspect of local economies,providing a platform for farmers(producers),wholesalers,retailers,and consumers to interact face-to-face and exchange goods and services.These markets have been increasing in urban areas in Africa,Asia,and South America because of urbanization.The increase of periodic urban markets(PUMs)in urban areas is observed as an index of modernization,reflecting a response to transition process.However,there are limited studies on how social interactions in PUMs contribute to sustainable livelihoods.This study investigated the types of social interactions occurring in PUMs in Ghana,the benefits of social interactions for participants of PUMs,and how social interactions contribute to sustainable livelihoods.This research interviewed 162 participants,comprising 27 farmers(farmers were regarded as producers in this study),61 retailers,47 wholesalers from 9 selected PUMs across Ghana,and 27 officers from government institutions and non-governmental market associations to obtain their opinions.We analyzed the interview data using the NVivo software.The results showed that there are seven kinds of social interactions in PUMs,including(i)producer-wholesaler relationship,(ii)producer-consumer relationship,(iii)wholesaler-retailer relationship,(iv)retailer-consumer relationship,(v)trader-driver relationship,(vi)trader-institution relationship,and(vii)trader-international buyer relationship.We found that these social interactions in PUMs enhance sustainable livelihoods by supporting human,social,financial,natural,and physical assets of traders(traders refer to producers,wholesalers,and retailers in this study).Therefore,we concluded that the development of policies to improve PUMs could strengthen social interactions,enabling the achievement of sustainable livelihoods in developing countries.

2023年蔬菜市场形势分析和2024年展望

蔬菜是重要“菜篮子”农产品之一。2023年,蔬菜市场价格总体基本符合常年季节性波动规律,偏高位运行,夏季菜价跌幅不明显。受多次寒潮和补偿性上涨因素影响,预计冬春菜价上行较往年明显,春节后可能保持高位运行一段时间。考虑到2023年菜价整体偏高,在田面积同比增加,大白菜、萝卜等耐贮蔬菜供大于求,北方设施蔬菜和“南菜北运”蔬菜上市稳健,部分产区寒潮受灾但恢复供应的可能性大,预计2024年春季蔬菜供应总体有保障。总体看,在蔬菜供给总量宽松、结构调优的背景下,建议加快发展特色蔬菜,优化布局设施蔬菜,推进绿色转型,稳定供应链,升级产业链,充分发挥蔬菜产业促进乡村振兴的重要作用。

考虑电动汽车充放电的输配协同能量-灵活性市场出清机制

为应对输电网对灵活性资源的迫切需求,挖掘配电网分布式资源的灵活性支撑潜力,首先,构建市场环境下电动汽车的车-网互动响应模型,分析不同充放电方式下电动汽车的需求响应能力,为电动汽车参与市场环境下电网调度控制提供模型基础;然后,计及配电网运营商对需求侧灵活性资源的聚合作用,以购买电力和灵活性资源总成本最低为目标,构建考虑电动汽车充放电方式的输配协同能量-灵活性市场出清模型,通过协调控制输配电网中各类灵活性资源来满足输电网的灵活性需求;最后,通过修改的IEEE 33节点输电网与2个IEEE 33节点配电网耦合的测试系统对所提输配协同能量-灵活性市场出清方法的有效性进行分析与验证。结果表明,所提方法可提升电力系统的运行经济性和灵活性。

中国特色的有效市场与有为政府

当今世界的经济发展主要存在两种制度类型:一是受新古典自由主义经济学影响并以“大市场+小政府”为特征的西方制度,二是以当代中国马克思主义经济学为指导并将有效市场和有为政府有机结合的中国制度。西方社会的市场失灵和政府失灵根源于资本主义市场经济与政党政治相结合的资本主义制度弊端。相比之下,社会主义市场经济和民主集中制相结合的中国特色社会主义制度优势是实现有效市场和有为政府有机结合的体制根源。

统一大市场促进形成强大国内市场的机理与路径——基于交易成本的视角

促进形成强大国内市场是新发展格局下我国经济高质量发展的重大战略部署。建设统一大市场是形成强大国内市场的重要举措,降低交易成本是建设统一大市场促进形成强大国内市场的关键。为此,基于交易成本的视角,分别从促进市场流通、交易环节精简、交易基础制度规则保障与多地域文化认同四个方面剖析建设统一大市场促进形成强大国内市场的机理,进而提出其实现路径:加快建设现代流通体系,促进流通业数字化转型;推进交易平台共享,打造全国统一交易平台;降低制度性交易成本,实现全国产权交易市场互联互通;强化文化认同,形成建设全国统一大市场的共有理念。

2023年马铃薯市场形势回顾和2024年展望

2023年中国马铃薯市场行情具有薯价前期高企、后期同比偏低、年度价格处于近10年最高位等3个显著特点,预计2024年马铃薯市场价格将总体低于2023年水平。目前,中国马铃薯产业仍存在产业发展不均衡、加工水平亟待提高和区域公用品牌保护不足等值得关注的问题。建议各地实施有针对性的扶持政策、加快全产业链深度开发以及强化区域公用品牌保护,着力推动中国马铃薯产业进一步提档升级。

基于改进ORB算法的遥感图像匹配

针对遥感图像匹配中存在的误匹配率高、匹配速度慢等问题,结合相似图像局部结构一致的思想,提出一种将ORB特征提取算子与BRISK特征描述算子相结合的遥感图像匹配算法。首先使用ORB算法的特征点提取算子对特征点进行检测,再使用BRISK算法的特征点描述算子对检测到的特征点建立特征描述符,然后基于汉明距离使用暴力匹配完成特征点匹配,最后使用LPM算法剔除错误匹配点。实验结果表明:本文算法提取的特征点数量、匹配精确度和运行速度都满足遥感图像匹配的要求;相较于ORB、BRISK算法,做到了速度和精度的较好平衡,展现出了良好的匹配性能。

数字经济对劳动力市场一体化影响研究——基于中国省际面板数据的实证检验

数字经济已逐渐成为推动我国经济高质量发展的重要引擎,也为劳动力市场提供了新动力与新机遇。本文利用2011—2021年我国省际面板数据,采用空间面板杜宾模型实证研究了我国数字经济建设对劳动力市场一体化的影响。研究发现:数字经济发展水平对劳动力市场一体化具有显著的促进作用;数字经济具有空间溢出效应,可以有效推动本地与邻近地区的劳动力市场一体化进程;数字经济主要通过增强劳动力流动与谈判力量来间接影响劳动力市场一体化;数字经济对劳动力市场一体化的作用存在明显的区域异质性,中西部地区和人力资本水平较低的地区受到的影响效应较大。因此,应不断夯实劳动力流动基础、激发劳动力流动活力、拓展劳动力流动空间,弥补数字鸿沟,激发数字经济红利,助力全国统一大市场建设。