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.

由于下偏矩测度方法具有明显优于最小方差风险度量方法的特征，因此是更为合理的套期保值效率测度准则。本文针对已有的计算最小下偏矩套期保值比率的非参数方法与参数方法存在的局限性问题，提出使用时变Copula函数来估计现货与期货收益率的联合密度函数，然后通过数值方法计算最小下偏矩套期保值比率的新方法。并且运用上海期货交易所交易的铜期货合约价格与上海金属网公布的铜现货价格数据进行实证检验，发现使用具有随时间变化的相关系数的Copula函数，与非参数方法相比，可以得到更小下偏矩的套期保值率。

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。

结合相依结构函数Copula和极值理论EVT，构建了我国股票市场经流动性调整的La-Copula-EVT风险价值模型，并用沪深收益序列的分笔高频数据进行了实证分析，发现我国沪深股市收益序列的上尾和下尾都存在较高相关性，后验测试结果表明构建的模型能够对实际损失进行很好的拟合；然后在该模型的基础上进一步分析了我国沪深股市的风险价值和预期不足在不同置信区间的敏感度差异，确定了适合La-Copula-EVT模型的最优置信度区间。