With the acceleration of green-oriented transition of energy, metals have become inextricably linked to traditional, renewable energy and carbon markets due to their "carbon-intensive" and "carbon-reducing" properties. Consequently, we established a comprehensive "metal-energy-carbon" system. By integrating the GJR-GARCHSK model with the TVP-VAR (time-varying vector autoregressive) extended joint connectedness approach, this study systematically analyzed the multidimensional risk spillover effects among the markets since 2019 and established multivariate portfolio strategies. Additionally, the TVP-VAR-SV model is employed to dynamically explore the time-varying impact of geopolitical risks on the spillover effects of this system. Our findings indicated that China's "metal-energy-carbon" system exhibited significant risk spillovers across different dimensions, with the volatility spillover being the most pronounced. The system's dynamic connectedness showed significant timevarying features. The total connectedness level was closely tied to major events, notably surging during the COVID-19 and the Russia-Ukraine conflict. Furthermore, geopolitical risk had a significantly different dynamic impact on lower and higher-order moment spillovers, with the impact on the latter being more rapid and conspicuous. We emphasize that these findings are of great importance and provide valuable insights for investors and policymakers, offering a deeper understanding of risk dynamics within "metal-energy-carbon" system.

To establish an effective system for managing systemic financial risk, it is critical to understand and prevent contagion between cross-country financial assets. We provide an in-depth analysis of international linkages across markets (stock, bond and foreign exchange), analyzing the dynamics of the linkages in terms of three key levels, namely, returns, volatility and tail risk correlations, as well as an event-based research methodology. For the first time, we propose a multidimensional framework for analyzing the stability of financial networks, based on the consideration of an individual market, asset diversity, and national financial systems. The results show that European market in particular highlights the “too connected to fail” phenomenon, which is consistent at the level of returns, volatility and tail correlations. Crisis events or extreme adverse conditions increase the correlation between global financial markets, increasing the intensity and efficiency of contagion between and within developed and emerging markets. The comprehensive impact of the Italian financial market increased particularly during these periods, especially in terms of its linkages with the core European economies. Meanwhile, the results of the simulated attacks show that the financial systems of countries in Europe and the Americas, such as the UK, France, and Germany, are critical to the stability of the network, especially their stock and bond markets. In particular, the stock market is the most critical to financial stability. Stock markets, especially in Mexico and South Africa, is the key link between emerging and developed markets.

Existing research pays less attention to the risk characteristics and connectedness of higher moments of cryptocurrencies. We dynamically analyze the risk characteristics of cryptocurrencies and their connectedness at four levels: return, volatility, skewness, and kurtosis. First, there are price bubbles in five popular cryptocurrencies, with long bubble periods during the COVID-19 epidemic. The volatility, skewness and kurtosis of cryptocurrencies are characterized by high persistence, with the exception of BNB. Second, the connectedness between the five cryptocurrencies is significant at higher moment conditions. The results for time-varying connectedness show that the total spillover of volatility, skewness and kurtosis varies over a wider range than returns, and that the total spillover of returns, volatility and skewness peaks during COVID-19. According to the pairwise results, there is strong connectedness between Ether and Bitcoin at the level of returns, volatility, skewness, and kurtosis, which is more significant at higher moments. Third, the role of cryptocurrencies changes not only over time, but also over order moments. The cryptocurrency market experienced significant volatility during 2018 and the first half of 2019, with Bitcoin being the most significant net exporter of returns and volatility spillovers. Cardano is a net exporter of both skewness and kurtosis spillover, and it is highly persistent with respect to volatility, skewness, and kurtosis. Overall, the leader in risk spillover at all order moments is Ether, and the net receiver is BNB.

This paper analyzes the connectedness between the financial markets in the U.S. and China based on non-linear Granger causality and quantile Granger causality networks. We extend the linear Granger causality network proposed by Billio et al. (2012) and support the importance of multi-class market interactions highlighted by Bollerslev et al. (2018). We find that non-linear Granger causality between stock, bond, and foreign exchange markets in the U.S. and China provides more interconnectedness than linear Granger causality. Both the strength and the direction of the connectedness are related to the quantile of market returns, as evidenced by the fact that the left (right) tail quantiles provide more causal effects than the middle quantiles and the mean. We characterize this phenomenon as the "smiling curve" of causality connectedness, and we show the "smiling curve" for each country and financial submarket. Compared to the financial markets in China, those in the U.S. appear to be more central to the connectedness network, with the main route being the stock market. When market returns are in the right tail quantile interval, the U.S. financial market has more capacity to receive information from various markets than China does. When market returns are in the left tail quantile interval and at the middle quantiles, the Chinese financial markets are more susceptible to shocks than those in the United States, with the Chinese stock and bond markets being more sensitive than the foreign exchange market.

This study uses generalized impulse response, generalized variance decomposition, and quantile VAR model to analyze the spillover effects of EPUs among the United States, Japan, mainland China, and Hong Kong, China. This study analyzes for the first time the correlation between developed and emerging economies under different EPU conditions, and the results show that the correlation is stronger in the extreme case than in the normal case. This study finds that the international spillover effect between EPUs is significant and asymmetric. This asymmetric relationship is not only between pairwise countries (regions), but also between the left and right tails of EPUs. When the EPUs of other countries (regions) are at the right tail quantiles, the spillover effect is significantly stronger in both the U.S. and Japan than at the left tail quantiles and the median, and they are net exporters of EPU risk, while HK and mainland China are net receivers. Both financial shocks and major public health emergencies lead to increased EPU connectedness among countries (regions).

In this paper, some laws of large numbers are established for random variables that satisfy the Pareto distribution, so that the relevant conclusions in the traditional probability space are extended to the sub-linear expectation space. Based on the Pareto distribution, we obtain the weak law of large numbers and strong law of large numbers of the weighted sum of some independent random variable sequences.

在全球不确定性日益加剧背景下,本文阐释了经济政策不确定性、汇率与国际资本流动之间的互动机制,并进行实证研究.根据初步检验结果,构建多类包括非线性结构和异方差性质的VAR模型,并通过贝叶斯模型比较准则选取TVP-SV-VAR模型进行分析.实证结果表明:汇率变动冲击对国际资本流动存在显著的即时传导影响,但国际资本流动对汇率的传导则相对较弱.人民币贬值会显著增加我国经济政策不确定性,而经济政策不确定性增加会反过来在短期内引起人民币有升值之势.此外,经济政策不确定性增加对国际资本流入的影响较突出.2012年后,经济政策不确定性对汇率和国际资本流动的冲击效果均明显强化.

We use quantile Granger causality and quantile spillover indices to analyze the connectedness of returns and volatility between crude oil and China's energy-intensive sectors (steel, electricity, coal, and petrochemical). The results show that both the strength and the direction of the connectedness are related to financial conditions (measured by quantiles), with the tail quantiles having much stronger connectedness than the middle level. Bidirectional Granger causality between crude oil and energy-intensive sectors occurs in the left (right) tail of returns as well as in the right tail of volatility. According to the quantile spillover indices, the spillover indices of crude oil's return and volatility are 2.79 % and 0.09 %, respectively, in the median condition, while they are as high as 71.09 % and 287.42 %, respectively, in the extreme condition. We emphasize that the volatility contagion from crude oil to energy-intensive sectors is significant only in the right tail quantiles. Among different sectors, the strongest connectedness between crude oil and the coal sector is observed at the extremes, followed by the petrochemical sector. The impact of China's energy-intensive sectors on the crude oil market deserves attention, as crude oil is a net receiver of information from China's energy-intensive sectors during certain periods.

Based on the new perspective of high-dimensional and time-varying methods, this paper analyzes the contagion effects of US financial market volatility on China's nine financial sub-markets. The results show evidence of non-linear Granger causality from the US financial volatility (VIX) to the China's financial markets. Increased US financial volatility has a negative next-day impact on the stock, bond, fund, interest rate, foreign exchange, industrial product and agricultural product markets, and a positive next-day impact on the gold and real estate markets. US financial vola-tility has the greatest impact on industrial product market, following by stock, agricultural product, fund, real estate, bond, gold, foreign exchange, and interest rates. Major risk events such as the global financial crisis can cause an enhanced contagion effect of US financial volatility to China's financial markets. This paper supports the achievements of China's actions to prevent and resolve major financial risks in the period of the COVID-19 epidemic.

Under the condition that the Choquet integral exists, we study the complete convergence theorem for negatively dependent random variables under sub-linear expectation space. Two general complete convergence theorems under sub-linear expectation space are obtained, where the coefficient of weighted sum is the general function. This paper not only extends the complete convergence theorem in the traditional probability space to the sub-linear expectation space, but also extends the coefficient of weighted sum as a general function.

From the perspective of realized volatility spillover,the contagion mechanism of financial market risk can be traced back to the contagion route and spillover direction of high frequency and high order financial risk in international financial market.This article first analyzes the spillover effect of stock market volatility risk from two levels: "traditional economic basic theory " and "financial risk contagion conjecture".Then introduce the time-varying parameter（TVP）factor and the stochastic volatility（SV） factor to model the intra-day realized variance（RV） time series of the United States,Japan,Hong Kong,and the Shanghai stock market from the perspective of time difference and information shock effects.Through multiple robustness tests,it is found that the volatility between securities markets has significant risk spillover effects and financial contagion effects,as well as time-varying and structural mutation characteristics.In the global risk transmission chain,the US market dominates the volatility transmission,and the impact will spill over quickly to other markets,where volatility risk will affect Japan to the greatest extent.The Chinese mainland stock market not only responds rapidly and strongly to the fluctuation information of the peripheral market,but also can significantly affect the peripheral market.Hong Kong,China,is one of the central regions of Asian stock market volatility spillover,and the short-term two-way spillover effect with mainland stock market is the most significant.In this paper,the new time-varying impulse function system has a strong overall characterization ability to market risk contagion,especially in Hong Kong stock jump to the top three global stock market forecast has reached a very satisfactory conclusion.The time-varying impulse system can be applied to many fields such as financial market and macro-econometric.

对RiskMetrics模型两个假设做出改进,并运用改进的RiskMetrics模型对2007年1月至2018年9月的国内外股票指数日收盘价序列进行建模,实证结果表明:改进的RiskMetrics模型可以更加精准刻画三类股指序列的在险价值.美国股市对利空消息的反应非常剧烈,沪深股市与香港股市之间具有趋同性,但两者对新息冲击的反应有所不同,沪深股市对利空消息与利好消息的反应区别不明显,而香港股市对利空消息的反应明显强于利好消息.另外,三类指数的收益率序列均呈"尖峰厚尾"特性;股票价格波动对冲击的反应速度由高到低依次是美国股市、香港股市、内地股市,而对冲击的持久性由强至弱的排序则恰恰相反.

This paper analyzes the time-varying impacts of Chinaʼs economic growth, energy efficiency, and industrial development on carbon dioxide (CO2) emissions from 1970 to 2019. First, we examined and found that there are two significant structural changes in the CO2 sequence over the years, and there was a significant nonlinear relationship among the four. The first nonlinear structural model constructed is the TVP regression model. According to the Bayesian model comparison criterion, TVP-SV-VAR was selected as the second constructed model from four types of VAR models containing nonlinear structures. The results show that the conduction intensity value of energy use efficiency to CO2 emissions has increased year by year, from 0.45 in 1971 to 0.97 in 2019. The short-term transmission mechanism of energy use efficiency to carbon emissions is the most significant. The conduction intensity of Chinaʼs economic growth on CO2 emissions increases year by year. Chinaʼs economic growth plays a major role in long-term CO2 emission reduction. The impact of industrial development on CO2 emissions reached a peak of 0.34 in 1977, and the intensity of the impact has basically stabilized at 0.26.

中国利率汇率市场化、中国股市与国际金融市场联系日益密切.在贝叶斯框架下建立时变参数向量自回归(TVP-SV-VAR)模型,对中国股市、利率、汇率和原油价格之间的动态传导机制进行研究.研究发现:四者相互传导过程中,利率渠道相对最不通畅,这可能是由于目前中国利率市场化进程仍未完成,利率的价格机制作用较为有限.此外,利率对股市的长期调控是行之有效的;股市对汇市冲击的反应伴有“隔夜回调”现象;汇率对利率的传导效应长期有效且稳定,但与非平抛利率曲线相驳,这或许与中国资本账户管制与国际资本外流有关.2018年之前原油市场与中国股市有脆弱的“共热”现象,此后原油价格的溢出效应呈现不断增强之势,因此原油价格风险应成为股票定价的重要因素.为避免股市的风险溢出至其他市场,建议继续深化“两率”市场化改革.要引导股价在合理范围内波动,或者为风险在不同市场间的溢出构建相对应的缓冲机制,还需注意不同货币政策之间的协同联动效应从而增强货币市场的内外均衡性.

利用我国的GDP、货运量和客运量时间序列数据,采用贝叶斯模型比较方法选取最优VAR模型来分析三者间联动关系,进而捕捉变量结构的时变性和周期性特征.首先比较六种时变参数向量自回归(TVP-VAR)模型,再比较各种机制转换向量自回归(RS-VAR)模型,发现一类特殊的TVP-SV模型的对数边际似然最大.研究表明,近年来运输业与经济增长具有同期同向发展的关系,经济增长对货运量和客运量的影响非常稳定,并且经济增长有利于带动货运量和客运量需求上涨;货运的繁荣发展有利于推动客运量的上涨;改革开放以后,客运量增长是推动经济增长的重要来源,并且经济增长对客运量冲击的脉冲响应走势具有明显的时变特征;我国运输业和经济增长之间的联动关系在很大可能性上不存在机制转换,即不存在阶段性特征.

在Choquet积分存在条件下,研究并建立次线性期望空间中的独立同分布随机变量序列的一般强收敛性定理,从而将传统概率空间的一般强收敛定理推广到次线性期望空间中.我们的结果推广了MENG(2019)的相应结果,得到两个一般的强大数定律(SLLN),其中加权和的系数是一般函数,作为推论,我们得到独立同分布随机变量序列的Marcinkiewicz型SLLN、对数SLLN和Marcinkiewicz SLLN.

通过问卷调查和访问调查采集数据,分析了桂林市各高校大学生对传统文化了解的基本现状;不同年级、专业、性别的学生对传统文化的认知差异;桂林高校关于弘扬和传承中国优秀传统文化的宣传教育现况.提出了引导大学生弘扬和传承中华优秀传统文化、培养"文化自信"的建议.