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.     

基于Copula函数的保险公司经济资本配置研究

经济资本的度量及配置是风险管理的核心内容。本文利用Copula函数构建保险公司总体风险的联合分布函数,结合TCE方法来度量保险公司经济资本,并利用动态规划方法对经济资本最优配置模型求解。最后结合中国人民财产保险股份有限公司的数据进行实证。通过研究发现,我国财险公司内部偿付能力状况较好,但险种结构有待优化。     

Dependence patterns among Asian banking sector stocks: A copula approach

Asian banks have recorded 22 banking crisis between 1945 and 2008 and its total share of years in a banking crisis since 1945 is 12.4%, the highest compared to all regions. Interestingly, most of the financial institutions in the region remained largely unscathed during the recent global financial crisis, mainly due to their strong liquidity and capital buffers. Yet, given the episodes of past crisis, the rapid increase in regional corporations and cross-border flows in the region, as well as the paramount importance of the banking sector in the Asian region, it is interesting to study how the banking sectors in the various economies co-move with each other. Against this backdrop, we examine the dependence structure between banking sectors in the region using copula functions. Several findings are documented. First, average dependence generally remain at moderate levels, though dependence between the banking sectors of the developed Asian markets are relatively higher than the emerging markets. Second, we find evidence of asymmetric dependence, suggesting that banking sector returns co-movement varies in bearish and bullish markets. Third, our results show a mild increase in the bivariate dynamic correlations during crisis periods, indicating very limited risk of contagion. Our results provide significant implications for portfolio managers and policymakers.     

Assessing stock market dependence and contagion

This paper assesses evidence of the linkages and contagion among important stock markets in Latin America (Brazil, Mexico and Argentina), Europe (UK and Germany), Asia (Japan and Singapore) and the USA from 6 September 1995 to 19 April 2013. To accomplish this task, this paper combines copula modelling with time-varying parameters and pair-copula composition of multiple dependence. The bivariate analyses show an asymmetric dependence between the stock markets as well as contagion. In addition, this work proposes a method to assess the linkages and contagion between two stock markets which takes into account the effects of a third stock market. In applying this method, conditioned on the USA market, most of the evidence of contagion between the Latin American or European markets disappears, but important dependence levels still remain.     

基于动态因子Copula模型的我国银行系统性风险度量

本文基于2007年至2019年我国14家上市银行的股票收益率,构建偏态t-分布动态因子Copula模型,利用时变荷载因子刻画单家银行与整个系统的相关性,计算联合风险概率作为系统性风险整体水平的度量,基于关联性视角提出了新的单家机构系统脆弱性和系统重要性度量指标——系统脆弱性程度和系统重要性程度。该方法充分考虑了银行个体差异性和系统的内在关联性以及收益率的厚尾性和非对称性,从而能够捕捉到更多的信息且兼具时效性。研究表明:银行机构在风险聚集时期相关程度更大,联合风险概率能够准确识别出系统性风险事件且在我国推行宏观审慎评估体系以后有明显降低;整体而言,大型商业银行系统重要性水平最高,同时风险抗压能力也最强;本文使用的度量方法降低了数据获取成本且更具时效性,有助于为宏观审慎差异化监管工作提供借鉴和参考。     

Co-movement of ASEAN stock markets: New evidence from wavelet and VMD-based copula tests

This paper aims to study the co-movement and the volatility fluctuation between stock markets in the Association of Southeast Asian Nations (ASEAN) countries from a new perspective. The analyses also delve more deeply into the effect of ASEAN trading link establishment on the short-term interdependency. By applying three-dimensional continuous wavelet transform (CWT) on daily returns of stock markets for the period 2009 to 2016, the interdependence level and lag-lead relationship among ASEAN trading link participants are estimated. The degree of interdependence in ASEAN stock markets is found to be stronger in the short term, especially following particular external shocks. A Variational Modes Decomposition (VMD)-based copula estimation shows that the effect of economic shock – in our case, ASEAN trading link establishment – on the stock markets’ level of comovement is only temporary and will progressively diminish within approximately two years. Only Indonesia and Malaysia display strong fundamental linkages between each other. Both the CWT and Copula methods consistently show that Vietnam (Indonesia) has the lowest (highest) interdependence with the rest of ASEAN trading link participants, as opposed to previous empirical evidence obtained from conventional methods. Investors who want to construct optimal portfolios and policymakers who aim to make effective macroeconomic policies should take these findings into account.     

基于Copula-GARCH的金融市场时变相关性分析

进入21世纪全球各金融市场之间的联系日益紧密,一个国家的金融状况会不同程度的影响到其他的国家,尤其在金融危机爆发的境况下,各主要金融市场之间的相关性分析更是具有实践意义。由于Copula函数其自身的性质,在研究相关性分析上的优势,逐渐被应用到金融分析的模型当中。本文正是以二元正态Copula—GARCH（1,1）一t模型借助MATLAB分析工具箱,在金融危机的大背景下,对主要金融国家的金融市场波动情况的相关性分析。     

Copula-based multivariate GARCH model with uncorrelated dependent errors

Multivariate GARCH (MGARCH) models are usually estimated under multivariate normality. In this paper, for non-elliptically distributed financial returns, we propose copula-based multivariate GARCH (C-MGARCH) model with uncorrelated dependent errors, which are generated through a linear combination of dependent random variables. The dependence structure is controlled by a copula function. Our new C-MGARCH model nests a conventional MGARCH model as a special case. The aim of this paper is to model MGARCH for non-normal multivariate distributions using copulas. We model the conditional correlation (by MGARCH) and the remaining dependence (by a copula) separately and simultaneously. We apply this idea to three MGARCH models, namely, the dynamic conditional correlation (DCC) model of Engle [Engle, R.F., 2002. Dynamic conditional correlation: A simple class of multivariate generalized autoregressive conditional heteroskedasticity models. Journal of Business and Economic Statistics 20, 339–350], the varying correlation (VC) model of Tse and Tsui [Tse, Y.K., Tsui, A.K., 2002. A multivariate generalized autoregressive conditional heteroscedasticity model with time-varying correlations. Journal of Business and Economic Statistics 20, 351–362], and the BEKK model of Engle and Kroner [Engle, R.F., Kroner, K.F., 1995. Multivariate simultaneous generalized ARCH. Econometric Theory 11, 122–150]. Empirical analysis with three foreign exchange rates indicates that the C-MGARCH models outperform DCC, VC, and BEKK in terms of in-sample model selection and out-of-sample multivariate density forecast, and in terms of these criteria the choice of copula functions is more important than the choice of the volatility models.     

期货投资组合交易保证金设置研究——基于Copula模型和极值理论的分析

本文以棉花、铜、天然橡胶三个期货合约为研究对象,基于t-Copula模型,利用Monte Carlo模拟法计算在一定权重下由三个品种构成的期货投资组合的VaR和ES值作为投资组合的保证金数值。Kupiec回溯测试结果表明,t-Copula模型结合极值理论计算出的期货投资组合保证金相比其他方法能够在较好覆盖极端风险的同时降低投资成本。     

投资组合风险承受能力既定的Achimedien copula实际风险模拟

Copula是用来描述多个随机变量闻相依的统计方法,利用随机向量的边缘分布,确定随机向量的联合分布.Copula函数广泛地应用与金融领域,特别在投资组合选择,金融市场风险管理方面成为一个有力的工具.本文选取模拟金融数据效果更好的Achi-medien copula函数对给定风险下的投资组合面临的实际风险进行了分析.