This work models the spillover of liquidity and volatility and their joint dynamics in the Chinese stock market. Methodologically, we implement a copula-based vector multiplicative error model for sectors. Utilizing intraday data from 2014 to 2022, our empirical analysis reveals strong interdependence between liquidity and volatility at the sectoral level. Moreover, different sectors dominate the transmission of liquidity and volatility shocks at different times. In normal times, sector volatilities transmit shocks notably (though not always dominantly), while in turbulent times, illiquidity is the key channel through which shocks spread. We also pay special attention to how two catastrophic events impacted the Chinese stock market: the 2015/16 stock market crash and the COVID-19 pandemic. Our ffndings are useful for policymakers monitoring and making policy at the sectoral level, as well as for institutional and private investors making investment decisions.

近年来中国金融市场波动加剧，系统性风险问题凸显，亟需立足中国金融实情，准确衡量金融系统性风险。本文基于广义自回归得分模型（GAS）和 BE—MES 模型，构建了包括旋转 Copula 和其本身的时变混 Copula 模型—DMC-MES 模型，并将其用于度量 2012 年至 2018 年沪深 300 成分股中不同行业的边际期望损失（MES），力求对系统性风险有更好把握。研究结果发现：（1）该模型可以很好捕获非线性相依关系，将非线性相依结构清晰分为上下尾相依结构；（2）除部分金融机构外，其他机构的左尾相依性要大于右尾；（3）各个机构的风险贡献很大程度上取决于活动领域，相同部门机构的 MES 相近；（4）与国外研究相反，国内银行机构的风险贡献度最小，风险贡献度最大的行业为券商和房地产行业；（5）金融类机构在市场下跌期的波动性小于其他行业，并发现在2015年股灾发生前这类机构的 MES 值就已开始下降。

This paper investigates the impact of the COVID-19 pandemic on the volatility spillover and dependence structure among the major developed and emerging stock markets. The TVP-VAR connectedness decomposition approach and R-vine copula are implemented in this research. The results of the TVP-VAR connectedness decomposition approach reveal that the volatility spillover among the major developed and emerging stock markets has been significantly strengthened by the outbreak of the COVID-19 pandemic, although it has gradually faded over time. In addition, during the pandemic, the UK, German, French and Canadian stock markets are the spillover transmitters, while the Japanese, Chinese Hong Kong, Chinese and Indian stock markets are the receivers. It is also found that the US and Brazilian stock markets have undergone role shifts after the outbreak of the COVID-19 pandemic. The results of the R-vine copula model indicate that during the pandemic, the Canadian, French, and Chinese Hong Kong stock markets are the most important financial centre in the American, European, and Asian stock markets, respectively. Furthermore, the effect of the extreme risk contagion has been strengthened by the pandemic, particularly the downside risk contagion.

The crypto and noncrypto markets used to be separated from each other. We argue that with the rapid development of stablecoins since 2018, risks are now transmitted between the crypto and noncrypto markets through stablecoins, which are both pegged to noncrypto assets and play a central role in crypto trading. Applying copula-based CoVaR approaches, we find significant risk spillovers between stablecoins and cryptocurrencies as well as between stablecoins and noncrypto markets, which could help explain the tail dependency between the crypto and noncrypto markets from 2019 to 2021. We also document that the risk spillovers through stablecoins are asymmetric—stronger in the direction from the US dollar to the crypto market than vice versa—which suggests the crypto market is re-dollarizing. Further analyses consider alternative explanations, such as the COVID-19 pandemic and institutional crypto holdings, and determine that the primary channels of risk transmission are stablecoins’ US dollar peg to the noncrypto market and their transaction-medium function in the crypto ecosystem. Our results have important implications for financial stability and shed light on the future of stablecoin regulation.

The crypto and noncrypto markets used to be separated from each other. We argue that with the rapid development of stablecoins since 2018, risks are now transmitted between the crypto and noncrypto markets through stablecoins, which are both pegged to noncrypto assets and play a central role in crypto trading. Applying copula-based CoVaR approaches, we find significant risk spillovers between stablecoins and cryptocurrencies as well as between stablecoins and noncrypto markets, which could help explain the tail dependency between the crypto and noncrypto markets from 2019 to 2021. We also document that the risk spillovers through stablecoins are asymmetric—stronger in the direction from the US dollar to the crypto market than vice versa—which suggests the crypto market is re-dollarizing. Further analyses consider alternative explanations, such as the COVID-19 pandemic and institutional crypto holdings, and determine that the primary channels of risk transmission are stablecoins' US dollar peg to the noncrypto market and their transaction-medium function in the crypto ecosystem. Our results have important implications for financial stability and shed light on the future of stablecoin regulation.

The existing literature mainly documents the relationship between international and domestic fossil energy markets; however, empirical evidence of the dynamic relationships between fossil energy market and new energy market is lacking. This paper combines TGARCH model and copula model to explore the dynamic linkages and spillover effects between international fossil energy (crude oil, coal and natural gas) markets and China's new energy market using daily data from 4 January 2012 to 3 September 2018. The empirical results indicate that fossil energy returns and new energy returns are positive related over time. And the crude oil returns and new energy returns, as well as the coal returns and new energy returns have lower tail dependence, while there is upper tail dependence structure between natural gas returns and new energy returns. Furthermore, the extreme upside and downside risk spillover from international fossil energy markets to China's new energy market is asymmetric. Among the spillover effects, the downward risk spillover of crude oil market exerts the most significant impact on China's new energy market.

ABSTRACT：As an important derivative instrument, CDO is playing a crucial role in the financial crisis. With complicated structure, we have developed many pricing models, which all relay on complicated mathematical model. The paper, firstly, introduces the mainstream pricing model----structural model and reduced form model. Then we introduced the Hybrid Models based on two formal models, by discussing the parameter of pricing i.e. default probability, default free risk and default correlation. In this paper, we give the hybrid model by Monte Carlo simulation based on copula function. Finally, we consider the pricing sensitivity on various parameters. According to the result of simulation, the relationship between the tranches price and pricing parameters is various. For the equity tranche and mezzanine tranche, the price and recovery rate have a positive correlation, while the case is inverse for the senior tranche. We also can conclude that, higher default correlation can lower the price of equity tranche, and have an opposite effect on the senior tranche. The influence on the mezzanine tranche isn’t certain. Furthermore, by comparing two different copula function model, we can get that marginal distribution has different effect on the tranches price.

本文使用VaR(Value at Risk)和一致性风险度量指标ES(Expected Shortfall)作为风险度量指标，应 用 Copula 函数和极值理论度量不同市场间在极端情形下的相依性风险。应用研究中用 Copula 函数来替代 传统多元序列间的正态分布的假设，使得在组合投资中进行多元极值建模时更加灵活。实证中对上证综指 和香港恒生指数进行组合投资建模，度量不同市场间在极端情形下的相依性风险。通过不同权重下投资组 合风险指标的计算结果比较，为投资者选择合理投资权重规避风险提供了决策参考。

次贷危机引发的全球经济危机充分表明，缺乏对市场极端条件下风险溢出效应的考量，可能会导致各金融市场风险水平被严重低估。EVT-Copula模型能够有效地拟合极端市场条件下金融市场间的相关结构，CoVaR模型将风险溢出效应纳入VaR框架内，测度单个金融机构或金融市场发生风险事件时，对其它金融体系风险溢出效应的方向和大小。本文融合两个模型的分析特点，构建EVT-Copula-CoVaR模型，研究美国股票市场的风险溢出效应，结果表明美国股票市场对英国、法国、日本、中国香港及中国股票市场均存在很强的风险正溢出效应，以%CoVaR表示的平均风险溢出强度为56%，对我国上证指数的风险溢出强度最弱，但也高达33%。模型诊断和后验测试表明，该模型方法可以有效地对单个金融机构（或金融市场）的风险溢出进行衡量，有利于金融监管当局及时跟踪系统性风险的变化。

在文章的最初部分介绍了投资组合理论与Copula，然后给出基于概率p0的收益率等定义，建立基于概率p0的收益率的投资组合选择模型并给出具体解法，接着通过选取上证领先指数与深证领先指数2004年9月1日至2006年5月26日的日收盘数据进行实证分析，我们发现在收益率（基于概率p0的收益率）一定的情况下，通过投资组合可以降低风险。