运用Student T-Copula的极值理论度量我国商业银行的操作风险

银行操作风险损失数据具有厚尾性,同时不同损失事件之间具有相关性.依据巴塞尔委员会对操作风险损失类型的界定,利用从公开渠道收集的我国商业银行内部欺诈和外部欺诈损失数据.运用基于Studentt-Copula的极值理论研究我国商业银行面临的操作风险,得出极值理论的POT模型能够有效地捕捉损失厚尾性,计算出的VaR比较准确,只是不同的损失数据对阈值的选取存在一定差异.进一步研究表明采用Studentt-Copula刻画两种损失事件之间的相关性,能够有效地降低VaR,降幅甚至高达40%以上.既可以为银行节省大量经济资本,有利于日常经营,又可以准确计提经济资本,有利于监管当局的监管.     

基于POT方法的商业银行操作风险极端值估计

对于商业银行而言,操作风险已经成为与市场风险和信用风险同样重要的风险。本文利用极值理论超越样本的估计能力,采用极值理论中对数据要求量较少,可以进行单步预测的超阈值(POT)方法对我国商业银行操作损失极端值分布进行估计,以均值超额函数图和拟合直线的交点确定阈值,估计出给定置信水平之下操作风险损失的分位数,从而使得国内商业银行操作风险监管资本的计算成为可能。     

基于贝叶斯极值估计的商业银行内部欺诈风险度量研究

内部欺诈风险是我国商业银行面临的一个重大风险来源。本文针对内部欺诈具有的低频率高损失的特点,采用不同分布分段刻画其损失统计分布规律,对于低于和高于门限值的样本点,采用Box-Cox变换和全Paretian分布模型进行分析,然后采用贝叶斯估计对全Paretian分布模型的参数进行估计,接着在此基础上对建立了一个内部欺诈风险度量模型,然后使用所构建的风险度量模型对操作风险在险风险值、经济资本和最大可能损失进行了测算,最后提出了防范操作风险的政策建议。     

商业银行操作风险的度量——基于非参数方法

在损失分布方法的基础上,本文基于非参数方法对商业银行操作风险的度量进行了研究。非参数方法对损失额的分布不作过多的设定,避免了由于分布误设可能出现的偏差。古典的核密度估计对损失额拟合的效果不太好,特别是尾部的拟合效果更差。变换后的核密度估计的拟合效果比古典的核密度估计改善很多.基于变换后的核密度估计对商业银行操作风险损失度量可以得到不同置信水平的VaR与ES,并且不同置信水平的差距比较大。基于非参数与基于参数方法得到的各个置信水平的VaR与ES有一定差距。     

基于分段损失分布法和Copula的银行操作风险集成度量

多个风险单元的集成度量是银行操作风险管理的关键步骤之一。立足于操作风险的“厚尾”、“截断”性,从分段损失分布法的视角出发,探讨操作风险集成度量的模式和数值方法。首先,引入两阶段损失分布法来拟合单个风险单元边际损失分布,用双截尾分布代替传统的完整分布来刻画“高频低损”损失数据的双截断特性,利用POT模型捕获“低频高损”事件的厚尾特性。再次,基于分段建模思路,对传统度量过程中边际分布为单一、完整分布的Copula模型进行了扩展,研究边际分布为分段分布、截尾分布条件下使用Copula函数集成度量操作风险的框架和步骤,并设计了Monte Carlo模拟算法。最后,以实证分析的形式验证所构建模型。通过对中国商业银行416个操作风险损失数据的实证分析,结果表明分段分布、截尾分布能对单个风险单元边际分布有更好的拟合效果,能减小由于分布选择不当而引发的模型风险。分段度量视角下Copula函数的引入能灵活处理多个操作风险单元间的相依结构,使风险度量结果更为合理。     

多变量极值理论在银行操作风险度量中的运用

针对银行操作风险损失分布的厚尾性和损失事件之间的尾部相依性,首先用单变量极值理论建立了单个损失事件计量模型,然后用多变量极值的连接函数反映了损失事件之间的尾部相依性,避免了计量中对银行操作风险的低估和对监管资本要求高估.     

基于g-h分布的操作风险损失强度分布拟合及风险度量

操作风险损失强度分布呈现的"右偏性、厚尾性"特点,使操作风险损失强度的拟合面临着许多困难。为了解决传统损失分布难以拟合损失分布尾部的问题,同时又为了克服极值理论的弊端,将四参数的g-h分布用于操作风险损失强度拟合中,设计当损失强度分布为g-h分布时使用Monte Caro模拟的基本步骤。并以我国的517个风险损失数据为基础,以实证分析的形式验证所提出的方法,将拟合结果与尾部风险及其他4种常用分布进行比较。实证结果显示,在损失强度的拟合中,g-h分布能较好的捕获操作风险损失分布的"厚尾"特性,对操作风险损失分布的拟合效果最好,尾部风险计算较为合理。     

操作风险计量研究——基于贝叶斯Copula方法

商业银行操作风险的计量存在两个重要的问题,一个问题是损失数据缺乏,另一个问题是各部分之间的风险相关问题.结合Bayes估计和Copula函数解决了上述两个问题并基于中国商业银行操作风险的损失数据对对操作风险的计量进行了实证分析.实证分析的结果表明无论考虑风险相关与否,基于极大似然估计的VaR与基于Bayes估计的VaR具有一定的差距.     

基于Copula函数对巴塞尔协议中操作风险的度量

利用贝叶斯网络,将搜集到的操作风险事件分类建立数据网络;在假设一定的分布条件下,分别估计各类损失事件发生频率和损失量的分布参数,用copula函数处理相关节点,再估计总体分布的VaR和ES,从而为巴塞尔协议中操作风险损失的估计提供一种具体的可选方法。     

运用聚类分析法对我国企业信贷风险的评估与预测

本文通过对企业财务指标的分析与选取,运用聚类分析法对我国企业的信用风险贷款质量进行了评估与预测。根据企业信用风险程度,将商业银行对企业的贷款质量分为正常、关注、次级、可疑、损失五类形态。以此对上市公司的运行状况作出评价,为商业银行是否对其贷款提供依据。     

Operational risk modelling and organizational learning in structured finance operations: a Bayesian network approach

This paper describes the development of a tool, based on a Bayesian network model, that provides posteriori predictions of operational risk events, aggregate operational loss distributions, and Operational Value-at-Risk, for a structured finance operations unit located within one of Australia's major banks. The Bayesian network, based on a previously developed causal framework, has been designed to model the smaller and more frequent, attritional operational loss events. Given the limited availability of risk factor event information and operational loss data, we rely on the elicitation of subjective probabilities, sourced from domain experts. Parameter sensitivity analysis is performed to validate and check the model's robustness against the beliefs of risk management and operational staff. To ensure that the domain's evolving risk profile is captured through time, a formal approach to organizational learning is investigated that employs the automatic parameter adaption features of the Bayesian network model. A hypothetical case study is then described to demonstrate model adaption and the application of the tool to operational loss forecasting by a business unit risk manager.     

随机波动率环境下企业内生破产的期权博弈分析

将企业破产作为委托人-代理人的博弈问题,应用期权博弈分析的方法讨论此问题．在企业的资产价值的波动率服从快速均值回复OU过程的假设下,导出各博弈方的权益满足的偏微分方程,利用Taylor级数展开及求解一组Poisson方程的技巧,得到各博弈方的价值表达式．进而,讨论各博弈方的破产决策;企业的投资决策;融资决策以及贷方避免在破产时遭受损失的激励合同．最后,总结波动率的随机性对文献结论的影响．     

Maxentropic approach to decompound aggregate risk losses

A risk manager may be faced with the following problem: she/he has obtained loss data collected during a year, but the data only contains the total number of events and the total loss for that year. She/he suspects that there are different sources of risk, each occurring with a different frequency, and wants to identify the frequency with which each type of event occurs and if possible, the individual losses at each risk event.The purpose of this methodological note is to examine a combination of disentangling and decompounding procedures, to get as close as possible to that goal. The disentangling procedure is actually a two step process: First, a preliminary analysis is carried out to determine the number of risks groups present. Once that is decided, the underlying model for the frequency of each type of risk is worked out. After that we use the maxentropic techniques in the decompounding stage to determine the distribution of individual losses that aggregated yield the observed total loss.     

Expected Shortfall风险度量下的最优再保险（英文）

为了避免由高理赔额造成的违约,保险公司通常通过签订再保合约将一部分风险转移给再保险公司.近年来对最优再保策略的研究着眼于最小化自留损失的方差,保险公司总风险的value-at-risk或conditional tail expectation.本文研究了在expected shortfall准则下的再保策略.我们给出了最优的增凸转移损失函数,并分别讨论了有无保费限制的情形.     

基于VaR和集中度约束的贷款组合优化模型

资产负债管理是把资产与负债组合视为有机整体,协调流动性、安全性和赢利性,本文通过资产的集中度约束把银行资产合理分配在不同行业中,有效降低银行资产集中度风险,通过能反映银行风险承受能力的VaR约束控制了贷款组合风险,应用实例的结果表明,本模型能够谋求"三性"的最佳配置,有效降低银行经营过程中的集中度风险和流动性风险,并实...     

Multivariate risk measures based on conditional expectation and systemic risk for Exponential Dispersion Models

Exponential dispersion models are well used and studied in quantitative risk management and actuarial science. One of the main interests is the risk measurement analysis of such models when facing extreme loss events. In this paper, we propose two multivariate risk measures based on conditional expectation and derive the explicit formulae for exponential dispersion models. In particular, our multivariate risk measures could facilitate a systemic risk measure with explicit expressions for exponential dispersion models subject to any pre-specified “systemic event.” We provide two numerical examples based on practical data to show the advantages of our approach in the context of exponential dispersion models.     

A Bayesian network structure for operational risk modelling in structured finance operations

This paper is concerned with the design of a Bayesian network structure that is suitable for operational risk modelling. The model's structure is designed specifically from the perspective of a business unit operational risk manager whose role is to measure, record, predict, communicate, analyse and control operational risk within their unit. The problem domain modelled is a functioning structured finance operations unit within a major Australian bank. The network model design incorporates a number of existing human factor frameworks to account for human error and operational risk events within the domain. The design also supports a modular structure, allowing for the inclusion of many operational loss event types, making it adaptable to different operational risk environments.     

机场安全运行监管机制的动态博弈分析

监管机制是否行之有效是决定机场能否长期安全运行的关键所在.针对机场安全运行监管现状,进一步挖掘机场与政府之间内在博弈机理,在引入机场按章运行与违章运行下发生不安全事件概率因子的基础上,构建了机场与政府之间的安全运行动态监管博弈模型,研究了二者在机场安全运行监管过程中博弈双方的策略选择问题.通过对模型混合策略纳什均衡进行...     

Dynamic asset allocation with event risk,transaction costs and predictable returns

We examine the interplay between event risk, transaction costs and predictability on the dynamic asset allocation of an investor with discrete trading opportunities. The model is calibrated to the U.S. stock market and a Gauss–Hermite quadrature approach is used to solve the investor’s dynamic optimization problem. Numerical scenarios are examined to show the impact of event risk on asset allocations, hedging demands, no-trading regions, and certainty equivalent returns. It is found that event risk shrinks hedging demand. Neglecting event risk can also lead to sizeable certainty equivalent return losses.     

双险种最优再保险策略

本文对双险种风险模型,在一险种采取比例再保险,另一险种采取超出损失再保险策略下,得到调节系数与再保险自留水平之间的函数关系式,在理赔额为指数分布和Erlang(2)分布的条件下,得到最优比例再保险和超出损失再保险的自留水平,以及调节系数最大值。