基于Copula的金融资产相关结构建模

分析了几种相关结构函数(Copula)表示的相关结构模型,给出了用相关结构函数对金融资产间的相关结构进行建模的方法.结果表明混合Gumbel(M-Gumbel)相关结构函数能较全面地描述上海深圳两证券指数的相关结构,模拟计算VaR的结果支持了实证分析的结论.     

开放式基金投资组合风险实证—基于copula的分析

利用多元阿基米德Copula捕捉多个金融资产间的相关结构,并利用非参数核密度估计描述单个金融资产的边缘分布,建立Copula-Kernel模型。利用该模型和VaR风险测度,结合Mente Carlo模拟技术,对我国股票型开放式基金-华夏成长基金的投资组合进行风险分析。     

多元Copula-GARCH模型及其在金融风险分析上的应用

针对传统风险分析模型的不足,结合Copula技术和GARCH模型,提出了多元Copula-GARCH模型。指出该模型不仅可以捕捉金融市场间的非线性相关性,还可以得到更灵活的多元分布进而用于资产投资组合VaR分析。在详细探讨了基于Copula技术的资产投资组合的MonteCarlo仿真技术的基础上,运用具有不同边缘分布的多元Copula-GARCH模型,对上海股市进行了研究,结果证实了所提模型和方法的可行性和有效性。     

半参数阿基米德Copula的实证研究

半参数阿基米德Copula族的生成元可由现有阿基米德Copula生成元得到,由于有独特的构造方式,该Copula族具有灵活的相关结构,能"自适应"地描述数据中包含的相关结构.外汇市场的实证分析证实了该Copula族在描述相关结构时的灵活性,对选择何种Copula描述金融资产间的相关结构有一定的参考意义.     

基于Copula理论的上证指数与上证基金的相依性实证分析

选取上证指数、上证基金的日收益率数据,根据Sklar提出的Copula理论,刻画随机变量间相关性的信息,用于描述金融市场间的相关模式.首先针对二维变量,通过比较参数法与非参数法拟合的优度来确定边缘分布,从而选择合适的Copula函数来刻画二者之间的相关性,最后对模型进行评价.     

Copula的局部变结构点诊断的实证研究

基于Copula函数对相关性研究的特有优势,构建了二元正态Copula模型,提出了在时变相关系数的基础上对局部变结构点的诊断方法.以上证煤炭指数及有色金属指数作为实证样本,研究了煤炭指数和有色金属的相关性发生显著变化的时刻,并分析其变化原因.本文的研究结果能更敏锐地捕捉金融市场的动向和指导风险投资.     

基于扭曲混合Copula和ARMA-GARCH-t模型的投资组合风险分析——以上证综指、中证综合债和上证基金为例

利用扭曲混合Copula和ARMA-GARCH-t模型,对包含2015年股灾和2016年熔断期间的上证综指、中证综合债和上证基金的投资组合风险相关性进行建模分析。研究表明:扭曲混合Copula模型较混合Copula模型能更好地拟合各资产日收益率间的相关结构,尤其是"厚尾"特性。并运用蒙特卡罗模拟法计算各资产的风险价值、预期损失和中位数损失并讨论其差异性,以期为关注风险管理的人们提供更多借鉴。     

国际多元化下多维金融市场相关结构测度——以金砖国家新兴市场为对象

在国际多元化背景下研究金砖国家新兴金融市场的相关结构对厘清多维资产收益分布现实意义重大。考虑到高维建模的困难性,构建了基于藤结构Copula的多维金融市场相关结构测度模型,利用其层次结构、节点顺序、函数类型及估计参数等信息联合反映多维金融市场的复杂相关特征。接着,对金砖国家股票市场内部及外部相关结构进行测度。从结果来看,金砖国家股市受到全球主要资本市场显著影响,故国际多元化是顺应这种趋势的必然选择;金砖国家股市内部相关性较低,可作为国际多元化投资的优选对象,但也说明金融发展方面还缺乏实质性的合作交流。     

股票收益率尾部相关性的Copula度量及模拟

股票收益率尾部相关性是研究金融市场关联性的重要内容.由于传统的τ、ρ等相关系数是对随机变量的全局度量,不适合用于收益率分布尾部这种局部特征的相关性度量.因此,在引入左尾(右尾)相关系数的基础上,讨论了它们的Copula度量及其相关性质.最后,通过计算机模拟分析了沪、深股指收益率尾部相关性的变化趋势,有效避免了Copula模型的设定困难,并得到了尾部相关性增强、相关不对称等结论.     

几种Copula函数在沪深股市相关性建模中的应用

研究了Copula函数对沪深股市的相关性建模问题.许多学者用Gaussian Copula建模,但是它无法捕捉到尾部变化,尾部相关系数不存在.用t-Copula度量中国股市的相关性,捕捉到了尾部变化,并计算出了尾部相关系数,克服了Gaussian Copula对相关性建模的不足,并通过AIC准则比较得到t-Copula优于Gaussian Copula.最后对3种Archimedean Copula进行比较,通过比较它们与经验分布函数的距离,说明Gumble Copula更加适用于中国的金融市场.     

Measuring the subprime crisis contagion: Evidence of change point analysis of copula functions

In this paper, we first determine the existence of structural changes in the dependence between time series of equity index returns of two markets using the change point testing method. The method is based on Archimedean copula functions, which are able to comprehensively describe dependence characteristics of random variables. The degree of financial contagion between markets is subsequently estimated using the tail dependence coefficient of copula functions before and after the change point. We empirically test our method by investigating financial contagion during the subprime crisis between the US S&P 500 index and five Asian markets, namely China, Japan, Korea, Hong Kong and Taiwan. Our results show that a statistically significant change point exists in the dependence between the US market and all Asian stock markets except Taiwan. The upper tail dependence is larger after the time of change, implying the existence of contagion during the banking crisis between the US and the Asian economies. The degree of financial contagion is also estimated and found to be consistent with market events and media reports during that period.     

On a bivariate copula with both upper and lower full-range tail dependence

Copula functions can be useful in accounting for various dependence patterns appearing in joint tails of data. We propose a new two-parameter bivariate copula family that possesses the following features. First, both upper and lower tails are able to explain full-range tail dependence. That is, the dependence in each tail can range among quadrant tail independence, intermediate tail dependence, and usual tail dependence. Second, it can capture upper and lower tail dependence patterns that are either the same or different. We first prove the full-range tail dependence property, and then we obtain the corresponding extreme value copula. There are two applications based on the proposed copula. The first one is modeling pairwise dependence between financial markets. The second one is modeling dynamic tail dependence patterns that appear in upper and lower tails of a loss-and-expense data.     

金融市场相关性的Chi-plot测度

金融市场相关性研究是分析跨市场金融风险传导、投资组合理论等方面的重要内容.在传统的相关测度中,线性相关系数、秩相关系数和Granger因果检验等都存在一定的局限.因此引人了一种新的图方法—Chi-plot,它可以考察金融市场的复杂相关关系及局部相关特征,并且简单易行.在分析了该方法的原理及使用步骤后,通过对中国股票市场的关联性分析,进一步验证了该方法的有效性.     

基于GAS t-Copula模型的金融市场非对称风险溢出效应测度研究

准确测度金融风险溢出效应对于金融风险管理和构建投资组合具有重要意义,而金融市场之间的非线性及动态相关结构一直是风险溢出效应研究中的难点问题之一。本文通过引入GAS t-copula模型与CoVaR方法,结合能够刻画重要典型事实特征的边缘分布模型,构建了金融市场间的风险溢出效应测度模型,以中国内地等五个股市为研究对象,测度美国股市对中国内地等四个重要股市的风险溢出效应,以检验模型的可靠性与准确性。实证结果表明:中国内地等四个股市与美国股市之间呈现出显著为正且时变相关结构,随着金融危机的爆发,相关系数逐渐增加达到最大值;中国内地等四个股市受到美国股市的风险溢出效应呈现出非对称特征,即下跌风险溢出效应强度显著大于上涨风险溢出效应;中国内地股市受到的金融风险溢出效应显著小于香港、日本以及英国股市。     

次贷危机下中国股市与国外股市相依性分析——基于Markov机制转换模型

文章通过选取2004年1月1日到2009年6月30日中国、香港、日本、英国和澳大利亚五个股票市场日收盘价的道琼斯数据,采用三状态Markov机制转换模型研究这些股市间相依性结构的变化。通过对目标股市结构变化的研究,可以描述并预测股市的波动性,从而指导风险管理。实证分析表明,在有机制转换条件下,澳大利亚与英国、日本股市间的相依性比无机制转换条件下均有所下降,而中国与香港股市间相依性却大幅上升。同时,本文采用总体拟合效果法来选取合适的copula函数并运用基于copula理论的相关系数法进行对比研究,发现次贷危机后各股市间的尾部相依性出现不同的变化,市场收益率呈现下降趋势,波动性均有所增加。其中,澳大利亚与英国股市间的尾部相依性最强,而中国股市与其他股市之间的相依性较弱,说明受到影响的程度较小。     

基于时变t-Copula的次贷危机前后亚洲股市相关结构的比较分析

将时变t-Copula函数与GARCH模型结合起来刻画金融市场间的相关结构并用于亚洲股市作实证研究.结果表明,次贷危机加剧了亚洲股市的波动溢出效应,提示次贷危机是亚洲股市相关结构的一个结构性变点.     

Contagion modeling between the financial and insurance markets with time changed processes

This study analyzes the impact of contagion between financial and non-life insurance markets on the asset–liability management policy of an insurance company. The indirect dependence between these markets is modeled by assuming that the assets return and non-life insurance claims are led respectively by time-changed Brownian and jump processes, for which stochastic clocks are integrals of mutually self-exciting processes. This model exhibits delayed co-movements between financial and non-life insurance markets, caused by events like natural disasters, epidemics, or economic recessions.     

基于时变SJC-Copula对商品期货市场间风险传染的探究

分别选取WIND商品指数和CRB指数作为衡量我国商品期货市场及国际商品期货市场综合价格的指标,利用时变SJC-Copula模型构建两者之间的动态相依结构,通过动态的尾部相关系数来探究我国商品期货市场与国际市场间的尾部相关性.实证结果表明,我国商品期货市场与国际市场间的上尾相关性要强于下尾相关性,即当商品期货价格上涨时,两个市场间更易发生风险传染.     

Real and financial interacting markets: A behavioral macro-model

In the present paper we propose a model in which the real side of the economy, described via a Keynesian good market approach, interacts with the stock market with heterogeneous speculators, i.e., optimistic and pessimistic fundamentalists, that respectively overestimate and underestimate the reference value due to a belief bias. Agents may switch between optimism and pessimism according to which behavior is more profitable. To the best of our knowledge, this is the first contribution considering both real and financial interacting markets and an evolutionary selection process for which an analytical study is performed. Indeed, employing analytical and numerical tools, we detect the mechanisms and the channels through which the stability of the isolated real and financial sectors leads to instability for the two interacting markets. In order to perform such analysis, we introduce the “interaction degree approach”, which allows us to study the complete three-dimensional system by decomposing it into two subsystems, i.e., the isolated financial and real markets, easier to analyze, that are then linked through a parameter describing the interaction degree between the two markets. We derive the stability conditions both for the isolated markets and for the whole system with interacting markets. Next, we show how to apply the interaction degree approach to our model. Among the various scenarios we are led to analyze, the most interesting one is that in which the isolated markets are stable, but their interaction is destabilizing. We choose such setting to give an economic interpretation of the model and to explain the rationale for the emergence of boom and bust cycles. Finally, we add stochastic noises to the optimists and pessimists demands and show how the model is able to reproduce the stylized facts for the real output data in the US.     

A case study on the shareholder network effect of stock market data: An SARMA approach

One of the key research problems in financial markets is the investigation of inter-stock dependence. A good understanding in this regard is crucial for portfolio optimization. To this end, various econometric models have been proposed. Most of them assume that the random noise associated with each subject is independent. However, dependence might still exist within this random noise. Ignoring this valuable information might lead to biased estimations and inaccurate predictions. In this article, we study a spatial autoregressive moving average model with exogenous covariates. Spatial dependence from both response and random noise is considered simultaneously. A quasi-maximum likelihood estimator is developed, and the estimated parameters are shown to be consistent and asymptotically normal. We then conduct an extensive analysis of the proposed method by applying it to the Chinese stock market data.