我国上市保险公司及其投资组合的相关性实证研究——基于Copula方法

以Sklar定理为依据,先根据Copula函数的特性构造出混合Copula函数,它能更好的刻画研究对象之间的相关性,又根据时间序列的波动聚集特性,选取GRACH-(t1,1)模型来刻画边缘分布。最后,选取中国人寿股及其投资组合股,中国平安股及其投资组合股,作为实证研究对象,并根据Copula-GARCH-(t1,1)模型来计算相关性系数,从而得出我国上市保险公司股及其投资组合股的相关性。     

我国上市商业银行整合风险度量研究

金融机构面临信用风险、市场风险和操作风险等诸多风险组成的整合风险。本文利用Copula方法对整合风险度量进行研究。以12家中国上市商业银行为研究对象,首先确定其信用风险、市场风险这两种风险收益率的分布,然后利用Copula构建相依结构模型,最后用蒙特卡罗模拟算法计算不同风险组合的VaR和CVaR,并利用返回测试检验模型的有效性。     

Multiscale financial risk contagion between international stock markets: Evidence from EMD-Copula-CoVaR analysis

Considering the frequency domain and nonlinear characteristics of financial risks, we measure the multiscale financial risk contagion by constructing EMD-Copula-CoVaR models. Using a sample composed of nine international stock markets from January 4, 1999, to May 13, 2021, the empirical study reveals that: (1) EMD-Copula-CoVaR models can effectively measure the multiscale financial risk contagion, and the financial risk contagion is significant at all time scales; (2) The high-frequency component is the major contributor of financial risk contagion; meanwhile, the low-frequency component is the smallest among all time scale components; (3) The risk export of the US financial market to other markets, except the UK under the original and medium-frequency component, is higher than that it receives; and (4) Even though the magnitude of overall financial risk contagion is similar for the COVID-19 pandemic, Subprime Crises, 9/11 terrorist attack and other crises, the relative importance of different frequency components is heterogeneous. Therefore, the countermeasures of risk contagion should be designed according to its multiscale characteristics.     

Studying the properties of the Bitcoin as a diversifying and hedging asset through a copula analysis: Constant and time-varying

In this study, we analyze the properties of Bitcoin as a diversifier asset and hedge asset against the movement of international market stock indices: S&P500 (US), STOXX50 (EU), NIKKEI (Japan), CSI300 (Shanghai), and HSI (Hong Kong). For this, we use several copula models: Gaussian, Student-t, Clayton, Gumbel, and Frank. The analysis period runs from August 18, 2011 to June 31, 2019. We found that the Gaussian and Student-t copulas are best at fitting the structure dependence between markets. Also, these copulas suggest that under normal market conditions, Bitcoin might act as a hedge asset against the stock price movements of all international markets analyzed. However, the dependence on the Shanghai and Hong Kong markets was somewhat higher. Also, under extreme market conditions, the role of Bitcoin might change from hedge to diversifier. In a time-varying copula analysis, given by the Student-t copula, we found that even under normal market conditions, for some markets, the role of Bitcoin as a hedge asset might fail on a high number of days.     

Copula dynamics in CDOs

Values of tranche spreads of collateralized debt obligations (CDOs) are driven by the joint default performance of the assets in the collateral pool. The dependence between the entities in the portfolio mainly depends on current economic conditions. Therefore, a correlation implied from tranches can be seen as a measure of the general situation of the credit market. We analyse the European market of standardized CDOs using tranches of the iTraxx index in the periods before and during the global financial crisis. We investigate the evolution of the correlations using different copula models: the standard Gaussian, the NIG, the double-t, and the Gumbel copula model. After calibration of these models, one obtains a time varying vector of parameters. We analyse the dynamic pattern of these coefficients. That enables us to forecast future parameters and consequently calculate Value-at-Risk measures for iTraxx Europe tranches.     

Bivariate Survival Models for Coupled Lives

This article presents alternative models for modeling the dependence of the time of deaths of coupled lives and applies these to a data set from a life annuity portfolio. The data indicates a very high positive correlation in the time of deaths between coupled lives. The analysis concludes that a mixed frailty copula model with Gompertz marginals fits the data very well. Another model that fits the data well is based on a generalized Frank copula.     

Detecting contagion in Asian exchange rate markets using asymmetric DCC-GARCH and R-vine copulas

This study uses asymmetric DCC-GARCH models and copula functions to study exchange rate contagion in a group of twelve Asia-Pacific countries. Using daily data between November 1991 and March 2017, we show that extreme market movements are mainly associated with the high degree of interdependence registered by countries in this region. Evidence of contagion is scarce. Asymmetries do not appear to be important. Specifically, currency co-movements are statistically identical during times of extreme market appreciation and depreciation, indicating that phenomena such as the fear of “appreciation” do not appear to be relevant in the region’s foreign exchange markets.     

Time-varying dependence structures of equity markets of China,ASEAN and the USA

Popular time-varying Copulas are used to analyse the dependence structure between the CSI 300 index return, the S&P 300 index return and the Association of South East Asian Nations (ASEAN) 80 index return. Results show that these three types of stock index returns have obvious time-varying characteristics. The US sub-prime mortgage crisis has strengthened the correlation among the three-stock index returns, whereas the dependence between China and the ASEAN stock markets is more sensitive to the financial crisis. The time-varying features of the extreme dependence risk between China-ASEAN and China-US are very different.     

Negative default dependence in supplier networks

The financial defaults of suppliers in a supplier network are significant risks and causes of uncertainty for buying firms. Hitherto, it has been largely neglected that default probabilities of suppliers in supplier networks are not independent of each other. We aim to overcome this shortcoming by studying negative supplier default dependencies: situations where a surviving supplier may benefit from the default of another supplier, resulting in a lower default probability. We use empirical data from the automotive supplier industry and copula functions, a method of representing joint distribution functions with particular marginals, to capture the default dependency between automotive suppliers and simulate various scenarios with negative default dependency. We also conduct a comparative static analysis illustrating the significant impact of negative default dependence. Our findings should spur managers to analyze their supplier networks with respect to default dependencies, and to take this phenomenon into consideration when making sourcing decisions.