银行操作风险度量的实证分析

一、引言自1995年巴林银行倒闭以来,银行操作风险引起了人们的广泛关注。普华永道出版的报告称,由于操作风险给金融机构造成的损失在1998年超过70亿美元。操作风险有限公司的资料也显示,自从1980年,操作风险给金融机构造成的损失超过2000亿美元[1]。以广东开平的余振东案和黑龙江的高山案件为标志,操作风险也引起了国内银行界的关注。根据巴塞尔银行委员会资料显示,银行面临的风险以信用风险为最高,约为60%,其次就是操作风险,约为30%,市场风险和其他风险如信誉风险等则较低,各占5%[2]。1999年巴塞尔委员会在新资本协议咨询意见稿中确立了操…     

违约相关性分析

 企业违约相关性是指具有相关关系的企业之间违约的相互关系,即一个企业违约引起另一个企业违约的可能性。如果两个企业完全不相关,则两个企业的违约就是相互独立的,违约相关性是“零”;当两个企业有关时,意味着双方同时违约的可能性增大;当两个企业完全相关时,其违约相关性趋于“1”。因此,企业违约相关性是[0,1]上的一个区间值,且违约与违约相关性均具有“模糊性”。     

我国国债风险的实证分析

 近年来,我国国民经济出现明显的通货紧缩迹象,有效需求不足,物价指数下跌,经济增长率也从1992年的14.2%下降到1999年的7.1%。在货币政策实施效果不明显的情况下,1998年下半年,政策决定采取积极的财政政策,刺激消费和投资,拉动经济增长。在财政收入的“两个比重”下降的情况下,政府不得不采取积极的国债政策,扩大国债发行规模,筹集更多资金,以加大公共投资支出。因此,国债政策已成为当前积极财政政策的重要组成部分。但从动态趋势来看,中国国债发行规模自1994年以来以30%的速度急剧扩张,正面临着巨大的压力及与日俱增的财政信用风险。因此,在实施积极财政政策时,既要充分发挥国债对国民经济增长的拉动作用及对经济运行的反周期调节作用,又要防止国债规模过大对财政收支造成难以承受的压力,避免像某些发展中国家那样因债务问题而陷入财政信用危机。     

基于极值Copula的投资组合集成风险度量方法

 应用极值的阈值与峰值模型来度量单个资产的风险价值,用两种不同的方法度量了基于Copula函数的沪深指数收益率的相关结构,比较了不同Copula函数下基于沪深指数的二元投资组合集成风险值,结果说明：Gauss Copula函数对沪深指数收益率的相关结构拟合较好,阈值模型的极值Copula能较好的度量投资组合的集成风险值,在高置信度下（0.99以上）,基于Gumble Copula函数的上尾（正收益）集成风险值、基于Clayton Copula函数的下尾（负收益）集成风险值与真实值最为接近。直接加权的方法会高估投资组合的风险,假设沪深指数的收益率服从二元正态分布会低估风险。峰值法的集成风险值误差较大。     

股票市场风险的多重分形分析

一、引言 众所周知,证券投资历来存在风险,尤其中国的证券市场,运行时间短,指数、股价起伏跌宕,令人有"高处不胜寒"感觉.通常人们认为我国的证券市场与国外一些证券市场相比,风险较大,并把此归因于我国在这方面起步较晚,成熟程度较低.     

动态资产定价理论中风险估计及其统计性质分析

一、引言古典资产定价理论,如Ｓｈａｒｐｅ的资产定价模型（ＣＡＰＭ）和Ｒｏｓ的套利定价理论（ＡＰＴ）,有助于大家对风险定价及其概念的理解。这些理论解释了资产回报的横截面行为,允许根据不同的风险水平对资产进行评价和计算。这些理论的经验证明必须对风险进行估...     

沪深股票市场相关性的实证研究

沪深股票市场相关性的实证研究韩德宗,徐剑刚中国大陆的股票市场是个年青的、发展和完善中的证券市场。过去三年多的时间,上海股市和深圳股市的股价变化是中国大陆股市发展的一个重要的侧面,它所留下的轨迹,值得我们去探索和总结。本文希望通过对上海股市以下简称沪市...     

沪深股市的风险测度研究

本文比较风险测度方法在不同置信水平下是否能力有效测度沪深市场风险.针对上证综指收益率具有自相关、波动集聚性和杠杆效应特征,运用ARMA-GJR模型对上证综指的负收益率序列进行MLE以求出条件均值和方差以及标准残差序列,运用10%的数据作为极值数据运用MLE方法来估计广义帕累托分布,还对风险测度方法的估计效果进行分析,认为极值VaR能有效测度沪深股市风险.     

综合评价中指标相关性的处理方法

一、引言在许多领域中都涉及到多指标综合评价问题 ,如综合国力的比较 ,国民经济运行态势的综合判断 ,上市公司经营业绩的综合评价 ,投资方案的优选 ,人才的选拔等等。综合评价的方法也很多 ,但其基本思路是大体相同的。但各评价指标间总会有一定的相关性 ,综合时必然会导致信息的重迭 ,影响到评价结果的合理性 ,甚至会歪曲评价对象间的相对位置。文献 [1]认为 ,指标间信息的重迭一般会夸大评价结果 ,夸大的程度取决于综合的方式。这种信息的重迭有时还会导致人们对多指标综合评价的必要性产生怀疑 ,文献 [4 ]中提到 :“近年来国家统计局从…     

应积极推动社会风险的调查与统计

这几年来,随着改革开放的进展,社会统计已有了较大的发展,然而有关风险、灾害、安全这一类的统计工作,还没有受到广泛的注意。本文试图结合国内的情况,介绍一些国外在这方面的工作,来说明开展这一项工作的意义和它的紧迫性。我国幅员辽阔,地理、气候、人文、自然条件差别很大,对于自然灾害的频繁程度和人们心理以及财力上的承受能力,过去有过一些统计,但多是记录一些较大的灾害。而对于如森林火灾、洪水灾害,在各个地区之间有多大的差异,还缺乏认真、系统的调查和分析。对于一些较常见的自然灾害,如雷击引起的死亡和财产损失,溺水引起的死亡,坠落引起的死亡等等,还没有专门的统计,而且还没有认识到这一类统计数字对国家的建设有什么意义。     

A dynamic double asymmetric copula generalized autoregressive conditional heteroskedasticity model: application to China's and US stock market

Modeling the relationship between multiple financial markets has had a great deal of attention in both literature and real-life applications. One state-of-the-art technique is that the individual financial market is modeled by generalized autoregressive conditional heteroskedasticity (GARCH) process, while market dependence is modeled by copula, e.g. dynamic asymmetric copula-GARCH. As an extension, we propose a dynamic double asymmetric copula (DDAC)-GARCH model to allow for the joint asymmetry caused by the negative shocks as well as by the copula model. Furthermore, our model adopts a more intuitive way of constructing the sample correlation matrix. Our new model yet satisfies the positive-definite condition as found in dynamic conditional correlation-GARCH and constant conditional correlation-GARCH models. The simulation study shows the performance of the maximum likelihood estimate for DDAC-GARCH model. As a case study, we apply this model to examine the dependence between China and US stock markets since 1990s. We conduct a series of likelihood ratio test tests that demonstrate our extension (dynamic double joint asymmetry) is adequate in dynamic dependence modeling. Also, we propose a simulation method involving the DDAC-GARCH model to estimate value at risk (VaR) of a portfolio. Our study shows that the proposed method depicts VaR much better than well-established variance–covariance method.     

Analysis of directional dependence using asymmetric copula-based regression models

The directional dependence between variables using asymmetric copula regression has drawn much attention in recent years. There are, however, some critical issues which have not been properly addressed in regards to the statistical inference of the directional dependence. For example, the previous use of asymmetric copulas failed to fully capture the dependence patterns between variables, and the method used for the parameter estimation was not optimal. In addition, no method was considered for selecting a suitable asymmetric copula or for computing the general measurements of the directional dependence when there are no closed-form expressions. In this paper, we propose a generalized multiple-step procedure for the full inference of the directional dependence in joint behaviour based on the asymmetric copula regression. The proposed procedure utilizes several novel methodologies that have not been considered in the literature of the analysis of directional dependence. The performance and advantages of the proposed procedure are illustrated using two real data examples, one from biological research on histone genes, and the other from developmental research on attention deficit hyperactivity disorder.     

An investigation of duration dependence in the American stock market cycle

This paper investigates the duration dependence of the US stock market cycles. A new classification method for bull and bear market regimes based on the crossing of the market index and its moving average is proposed. We show evidence of duration dependence in whole cycles. The half cycles, however, are found to be duration independent. More importantly, we find that the degree of duration dependence of the US stock market cycles has dropped after the launch of the NASDAQ index.     

Analysis of stock market indices through multidimensional scaling

Two similarity measures are employed to compare historic stock market indices over time. The more traditional Euclidean similarity is employed to provide a reference. As a comparison, dynamic time warping is introduced as a similarity measure. Multidimensional scaling is employed to compare these dissimilarities on 15 financial indices sampled daily over a 10-year period. In addition to investigating the whole period, 1-year tranches are also considered. This analysis is compared to a recent study of Machado et al. [Analysis of stock market indices through multidimensional scaling, Commun. Nonlinear Sci. Numer. Simul. 16(12) (2011), pp. 4610–4618], who examined these same indices using correlation as a similarity measure. It is suggested that this approach may be problematic. Doubt is also cast on the efficacy of the ‘histogram’ similarity they also propose.     

Measures of dependence for the multivariate t distribution with applications to the stock market

Two measures of dependence for multivariate t and Cauchy random variables are developed based on Kullback–Leibler number. The mutual information number T(X) is obtained in a closed expression form, as well as its asymptotic distribution. A dependence coefficient ρ₁, is defined (based on the Kullback–Leibler number) with the properties of ρ₁=0 indicating independence and ρ₁=1indicating degeneracy. Two real life examples from the stock market are used to analyze the level of dependence and correlation among stocks.     

中国股市波动的周期性研究

一、股市周期定义及特性数学中的周期是这样定义的 :对于函数y =f(x) ,如果存在一个非零常数C ,使得当x取定义域内的任何一个数值时 ,f(x+C) =f(x)都成立 ,那么函数y =f(x)就叫做周期函数 ,常数C叫做这个周期函数的一个周期。股市周期是一种重复出现的价格周期 ,它属于时间周期。价格波动的底部称为波谷 ,顶部称为波峰 ,周期长度是从波谷到波谷测量的 ,也可以从波峰到波峰测量 ,但时间序列中波峰通常不如波谷那样稳定、可靠。股市周期具有三方面特性 :波长、波幅和相位。波长是相邻的两波谷之间的时间差 ;波幅是波的高度 ,即波谷到波峰的…     

Nonparametric estimation of copula functions for dependence modelling

Copulas characterize the dependence among components of random vectors. Unlike marginal and joint distributions, which are directly observable, the copula of a random vector is a hidden dependence structure that links the joint distribution with its margins. Choosing a parametric copula model is thus a nontrivial task but it can be facilitated by relying on a nonparametric estimator. Here the authors propose a kernel estimator of the copula that is mean square consistent everywhere on the support. They determine the bias and variance of this estimator. They also study the effects of kernel smoothing on copula estimation. They then propose a smoothing bandwidth selection rule based on the derived bias and variance. After confirming their theoretical findings through simulations, they use their kernel estimator to formulate a goodness-of-fit test for parametric copula models.     

Positive quadrant dependence testing and constrained copula estimation

Positive quadrant dependence is a specific dependence structure that is of practical importance in for example modelling dependencies in insurance and actuarial sciences. This dependence structure imposes a constraint on the copula function. The interest in this paper is to test for positive quadrant dependence. One way to assess the distribution of the test statistics under the null hypothesis of positive quadrant dependence is to resample from a constrained copula. This requires constrained estimation of a copula function. We show that this use of resampling under a constrained copula improves considerably the power performance of existing testing procedures. We propose two resampling procedures, one based on a parametric constrained copula estimation and one relying on nonparametric estimation of a positive quadrant dependence copula, and discuss their properties. The finite‐sample performances of the resulting testing procedures are evaluated via a simulation study that also includes comparisons with existing tests. Finally, a data set of Danish fire insurance claims is tested for positive quadrant dependence. The Canadian Journal of Statistics 41: 36–64; 2013 © 2012 Statistical Society of Canada     

中国股市的星期效应研究

一、引言星期效应是股票市场上普遍存在的一种现象 ,即一周中各交易日股票收益率存在一定规律。西方股市的星期效应一般表现为周一收益率为负数 ,即所谓周末效应。星期效应的存在意味着在不考虑交易成本的前提下 ,基于股票价格的历史数据就可获得一定收益。Ｋｅｌｌｙ(1930 )在《输赢的原因》一文中第一次论及股票市场的周末效应 ,并认为造成这种现象的原因是投资者群体中存在一种在周一出售股票的倾向。Ｃｒｏｓｓ(1973)和Ｆｒｅｎｃｈ(1980 )研究了纽约证券市场的周末效应。Ａｔｈａｎａｓｓａｋｏｓ(1994 )考察了加拿大股市1975— 1989年…     

Tail-weighted measures of dependence

Multivariate copula models are commonly used in place of Gaussian dependence models when plots of the data suggest tail dependence and tail asymmetry. In these cases, it is useful to have simple statistics to summarize the strength of dependence in different joint tails. Measures of monotone association such as Kendall's tau and Spearman's rho are insufficient to distinguish commonly used parametric bivariate families with different tail properties. We propose lower and upper tail-weighted bivariate measures of dependence as additional scalar measures to distinguish bivariate copulas with roughly the same overall monotone dependence. These measures allow the efficient estimation of strength of dependence in the joint tails and can be used as a guide for selection of bivariate linking copulas in vine and factor models as well as for assessing the adequacy of fit of multivariate copula models. We apply the tail-weighted measures of dependence to a financial data set and show that the measures better discriminate models with different tail properties compared to commonly used risk measures – the portfolio value-at-risk and conditional tail expectation.