基于Cox过程的操作风险度量方法

文章讨论了操作风险的度量问题,将操作风险事件的发生假定为Cox过程--一种比Poisson过程更一般的计数过程,损失分布为广义帕累托分布(GPD),各风险事件的相关性由Copula来描述;分析了金融机构整体操作风险的度量.文章还讨论了模型中参数的设定与估计问题,为了使得模型可以在实际中应用,Monce Carlo模拟也给与了考虑.     

小样本下贝叶斯参数估计法对操作风险的度量

操作风险损失事件的数据一般较为匮乏,这会影响到模型参数估计的准确性,进而导致经济资本配置的偏差和风险控制能力的降低。在损失分布法的框架下,运用基于MCMC模拟的贝叶斯方法,借助WinBUGS软件包通过Gibbs抽样构造出负二项分布和帕累托分布的稳态马尔可夫链,以分别动态模拟操作风险损失频率和强度的后验分布,计算出操作风险所要求的经济资本。对比极大似然估计法,实证结果表明,在小样本条件下此方法可以取得较好的结果。     

商业银行操作风险的度量与资本需求模型

目前,商业银行操作风险的度量大都是在操作风险损失数据的分布假定下、根据VaR风险度量方法给出资本需求（风险准备金）,这一理论方法的基础是假定分布。然而商业银行操作风险的准备金往往又是一个基本确定的数值或需求区间,这就给风险准备金提出了比较严格的要求,否则将为商业银行操作带来一定的风险隐患。故根据分区多目标风险方法度量操作风险,并在此基础上根据信息熵的理论给出最优的资本需求（风险准备金）及其模型,其方法的优点是灵活简单,但要求初始密度函数的极值分布收敛于耿贝尔类型。为此给出实证分析,以说明两者之间的关系,这一理论方法可以为监管部门的管理提供一定程度的参考。     

贝叶斯网络模型在商业银行操作风险管理中的应用

操作风险是指由不完善或有问题的内部程序人员及系统或外部事件所造成损失的风险。本文采用广泛应用于工学领域的贝叶斯网络模型对商业银行的操作风险进行研究,尝试性地提出了运用这一模型管理商业银行操作风险的框架,并对其进行了评价。     

中国商业银行操作风险的度量

为了增强银行的抵御风险能力,新巴塞尔协议提出在银行监管资本估计中应包含操作风险资本金,然而因为数据和模型等方面的原因,导致准确估计操作风险资本金绝非易事.文章采用Monte Carlo模拟技术,同时考虑到操作风险损失数据的不完整性,利用损失分布方法来度量我国商业银行操作风险,该方法能够让银行操作风险资本金的估计变得简单、容易,且具实际可操作性.     

中国上市商业银行汇率风险实证研究

2005年中国汇率制度改革以后,汇率风险成为中国商业银行面临的主要市场风险之一.商业银行汇率风险估计主要有两种方法--资本市场法和现金流法,现有的现金流法模型不适用于中国商业银行,需要进行改进.通过资本市场法和现金流法对中国上市银行的汇率风险进行实证分析,结果发现:现金流法比资本市场法计算的汇率风险更敏感;中国银行面临最为显著的汇率风险,并且人民币升值会使得中国银行的股票收益率下跌,主营业务收入减少等.     

尾相关Copula在操作风险计量中的应用

新巴塞尔协议明确地提出,把商业银行操作风险纳入风险量化和监管领域.针对操作风险损失数据所具有的厚尾性及不同损失类型之间的相关性,文章应用尾相关Copula函数分析我国商业银行各类操作风险之间的相关性结构,运用极值理论的POT模型有效地捕捉损失的厚尾性,进而构建计算操作风险总体VaR值的尾相关Copula模型.研究表明,与传统计量模型对所有损失的VaR值进行简单加总相比,该模型能够有效地降低VaR,且降幅为1O％以上,这让银行有效地降低了计提的监管资本,增加了资金的流动性.     

BMM模型在商业银行操作风险度量中的应用

文章利用极值理论中的BMM模型对商业银行操作风险损失极端值分布进行估计,采用广义极值分布构建VaR模型,组建极值数据组,运用极大似然估计法估计两个参数,进而计算操作风险损失VaR。最后结合我国商业银行1994~2008年的220个操作风险损失数据进行实证研究,结果显示BMM模型具有超越样本的估计能力,在数据较少条件下能得到较准确结果,用其度量商业银行的操作风险损失VaR是合理的,这为我国商业银行操作风险度量和管理提供一定的量化依据。     

基于变点理论的POT模型阈值确定方法——对操作风险经济资本的度量

对操作风险所要求的经济资本的度量以及配置能极大提高金融机构的风险控制能力。采用PCIT模型对操作风险度量时，阈值的选取是关键所在，它决定了拟合操作风险损失分布的近似程度。通过变点理论来定位Hill估计曲线开始进入稳定状态的位置，以精确地估计出阈值的大小。同时，为确保误差更小，结果更稳定，用平方误差积分法来估计POT模型的参数。结果表明，所改进的方法能为经济资本的度量提供有效的方法支持。     

我国商业银行操作风险损失事件分布分析

一、我国商业银行操作风险现状分析 为了分析我国商业银行操作风险的损失事件类型分布情况和损失事件发生的业务部门分布情况,我们收集了2005-2012年国内媒体公开报道的189起操作风险损失事件。每一笔损失都记录了损失事件的类型、业务部门和损失金额。表1是对不同的事件类型和不同的业务部门的损失事件进行的分类统计。     

Bayesian Estimation for Deformed Modified Power Series Distribution

In this article, posterior distribution, posterior moments, and predictive distribution for the modified power series distributions deformed at any of a support point under linex and generalized entropy loss function are derived. It is assumed that the prior information can be summarized by a uniform, Beta, two-sided power, Gamma, or generalized Pareto distributions. The obtained results are demonstrated on the generalized Poisson and the generalized negative binomial distribution deformed at a given point.     

On generalized semi-Pareto and semi-Burr distributions and random coefficient minification processes

A characterization of Pareto type III distribution is obtained. As a generalization of the Pareto distribution, a new class of distributions called the generalized Pareto distributions is introduced and a characterization of this class is obtained. We introduce a new class of autoregressive models with semi-Pareto marginals and study its properties. As a generalization of semi-Pareto distribution, we introduce semi-Burr distribution and develop a random coefficient autoregressive model with semi-Burr marginal distributions.     

Generalized Pickands estimators for the extreme value index

The Pickands estimator for the extreme value index is generalized in a way that includes all of its previously known variants. A detailed study of the asymptotic behavior of the estimators in the family serves to determine its optimally performing members. These are given by simple, explicit formulas, have the same asymptotic variance as the maximum likelihood estimator in the generalized Pareto model, and are robust to departures from the limiting generalized Pareto model in case the convergence of the excess distribution to its limit is slow. A simulation study involving a wide range of distributions shows the new estimators to compare favorably with the maximum likelihood estimator.     

A matching prior for extreme quantile estimation of the generalized Pareto distribution

Extreme quantile estimation plays an important role in risk management and environmental statistics among other applications. A popular method is the peaks-over-threshold (POT) model that approximate the distribution of excesses over a high threshold through generalized Pareto distribution (GPD). Motivated by a practical financial risk management problem, we look for an appropriate prior choice for Bayesian estimation of the GPD parameters that results in better quantile estimation. Specifically, we propose a noninformative matching prior for the parameters of a GPD so that a specific quantile of the Bayesian predictive distribution matches the true quantile in the sense of Datta et al. (2000).     

On the identification of extreme outliers and dragon-kings mechanisms in the upper tail of income distribution

The presence of extreme outliers in the upper tail data of income distribution affects the Pareto tail modeling. A simulation study is carried out to compare the performance of three types of boxplot in the detection of extreme outliers for Pareto data, including standard boxplot, adjusted boxplot and generalized boxplot. It is found that the generalized boxplot is the best method for determining extreme outliers for Pareto distributed data. For the application, the generalized boxplot is utilized for determining the exreme outliers in the upper tail of Malaysian income distribution. In addition, for this data set, the confidence interval method is applied for examining the presence of dragon-kings, extreme outliers which are beyond the Pareto or power-laws distribution.     

On generalized order statistics from generalized pareto distribution

The aim of this paper is to estimate parameters of generalized Pareto distribution based on generalized order statistics. Some non-Bayesian methods, such as MLE, bootstrap and unbiased estimators have been obtained to develop point and interval estimations. Bayesian estimations have also been derived under LSE and LINEX loss functions. To compare the performances of the employed methods, numerical results have been computed. To illustrate dependence and association properties of generalized order statistics, correlation coefficient and some informational measures in closed form have been obtained.     

Estimation of the Lognormal-Pareto Distribution Using Probability Weighted Moments and Maximum Likelihood

This article deals with the estimation of the lognormal-Pareto and the lognormal-generalized Pareto distributions, for which a general result concerning asymptotic optimality of maximum likelihood estimation cannot be proved. We develop a method based on probability weighted moments, showing that it can be applied straightforwardly to the first distribution only. In the lognormal-generalized Pareto case, we propose a mixed approach combining maximum likelihood and probability weighted moments. Extensive simulations analyze the relative efficiencies of the methods in various setups. Finally, the techniques are applied to two real datasets in the actuarial and operational risk management fields.     

地震死亡人数预测与巨灾保险基金测算

巨灾保险制度在很大程度上依赖于巨灾损失的建模分析。由于巨灾损失通常存在极端值,一般的统计分布很难对其进行有效拟合。本文以我国大陆地区1950-2015年期间的地震灾害为研究样本,基于二维泊松过程建立了地震灾害死亡人数的预测模型。根据地震死亡人数的分布特征,将地震灾害分为非巨灾事件和巨灾事件,分别用右截断的负二项分布和右截断的广义帕累托分布拟合死亡人数;用齐次泊松过程描述地震灾害在给定期间的发生次数;用Panjer迭代法和快速傅里叶变换计算地震死亡人数在特定时期的分布以及风险度量值;用蒙特卡罗模拟法测算我国地震死亡保险基金的规模和纯保费水平。与传统的巨灾模型相比,本文提出的方法同时考虑了地震灾害发生的时间和地震死亡人数两个维度,并用贝叶斯方法估计模型参数,对地震死亡人数的拟合更加合理,为完善我国地震死亡保险提供了一种新的思路。     

Entropy-based goodness-of-fit tests for the Pareto I distribution

In this article, having observed the generalized order statistics in a sample, we construct a test for the hypothesis that the underlying distribution is the Pareto I distribution. The Shannon entropy of generalized order statistics is used to test the null hypothesis.