银行操作风险贝叶斯网络量化控制研究

在银行的风险管理中,操作风险一直是管理难点,具有涉及面广、管理半径长、不易识别、不易控制、不易量化等特点,且在管理工具和量化方法上都没有信用风险、市场风险那么成熟。近年来,银行业因操作风险导致的巨大损失却日益增多。本文利用贝叶斯网络进行建模,量化研究关键风险指标与关键风险诱因的因果关系,正向分析各种操作风险诱因的影响程度,反向分析操作风险指标出现预警后,风险诱因的后验概率如何变化,以期不断优化模型,以更好地管理操作风险。此外,本文还初步探讨了关键风险指标阈值设置方法和控制成本与损失成本的关系。同时,结合银行操作风险管理工作的实践,提出了防范操作风险的建议。     

基于Bow-tie模型的模糊贝叶斯网络在风险评估中的应用

本文以Bow-tie模型为基础,借鉴工业部门风险分析方法,建立基于Bow-tie模型的模糊贝叶斯网络风险评估框架,实现从静态层次分析向动态网络推理转变、从风险评估向风险预测深化、从风险发生前评估向风险发生后分析发展,为内部审计职能拓展与价值深化提供借鉴。     

基于上市企业财务数据的贝叶斯统计与知识推理

本文主要使用matlab和R自带软件包对上市企业财务数据建立贝叶斯网。用matlab对建立好的贝叶斯网络进行参数学习,利用多种推理引擎进行精确推理,并对结果进行比较分析。     

中小金融机构软件项目风险量化管理实践研究

正近年来,随着中小金融机构的快速发展,软件项目风险管理中的许多问题逐渐显现出来。云南省农村信用社针对中小金融机构软件研发风险的特点,以CMMI风险管理实践为指导,科学确定风险参数,运用贝叶斯网络构建出适用的风险评估和缓降模型,创造性地提出了适合于中小金融机构的风险量化评估和缓降方法。本文基于以上研究成果,设计实现了云南省农村信用社软件项目风险管理系统,完成了CMMI风险管理的各个特定实践,并     

保险公司操作风险损失分布量化及其应用

为探讨适合我国保险公司操作风险量化模型应用,本文尝试使用损失分布法对操作风险损失分布进行量化研究,并运用案例对某保险公司操作风险损失数据取得经验分布,寻找损失数据的规律,评估其风险影响程度,并使用该模型进行风险监控,有助于保险公司对未来风险及时防范。国内保险公司可以积极尝试建立本公司操作风险损失数据库,并探索操作风险数据量化分析模型,有助于提高公司操作风险管理水平,提升决策的科学性和稳健性。     

基于贝叶斯网络的商业银行操作风险管理

在巴塞尔新资本协议框架下,操作风险成为商业银行面,临的三大风险之一.文章采用用于工程学领域的贝叶斯网络模型对商业银行的操作风险管理进行研究,通过实例分析了贝叶斯网络在银行操作风险方面的建模与应用,并对其进行了评价.     

银行风险管理前沿理论及对我国银行业监管的影响

信用风险、操作风险和市场风险的控制与实施对于银行业的安全运行至关重要.随着"金融脱媒"现象日益显现、世界范围内金融业竞争加剧,同时,伴随着网络等高科技的发展,银行业的风险量化管理、全面管理等前沿理论逐渐被提出并付之实践,这将对我国金融监管的思路与理念产生较大影响.传统的合规性监管向审慎性监管、试行风险量化式监管、风险预约选择/承诺制监管等前沿性理论的转变必将对我国银行业监管制度产生深远影响.根据新巴塞尔协议精神,建立科学、严密、高效的治理制度和银行内部控制制度势在必行,以风险意识为核心的企业文化建设待以推行.     

基于贝叶斯网络的上市证券公司风险预警模型研究

上市证券公司的风险预警模型能够为政府监管、证券公司稳健发展以及投资者研判提供依据。以上市证券公司风险管理指标体系为基础,利用贝叶斯网络方法以及支持向量机、随机森林和多项Logit模型分别建立风险预警模型进行比较,并在实证中针对上市证券公司的不平衡数据特征,用 SMOTE抽样对数据进行预处理。最终实证表明：从平均准确率和标准差两个角度比较,SOMTE抽样增加了贝叶斯网络的预测效果,机器学习方法要优于多项Logit模型,贝叶斯网络方法效果最佳。     

操作风险损失分类的原理与方法探讨

银行操作风险管理,特别是量化管理,一段时间以来面临的主要困难之一是数据的采集和数据库的建立。由于操作风险几乎涉及银行全部的业务和流程,因此,要获得可以用于风险管理并进行数据处理的信息,最首要的是建立一套合理的操作风险损失分类体系,否则操作风险量化将无从谈起。认真分析我国商业银行操作风险领域的案件可以发现,这些案件中有许多是同类事件的简单重复,暴露出我国商业银行操作风险管理和控制的薄弱,     

金融风险中的新领域--操作风险的度量与管理

近年来金融学界对于金融风险的微观研究日益深入，新的理论模型和新思想不断出现。但这些研究主要是针对市场风险和信用风险，对于金融风险中非常重要的一方面-操作风险的研究进展缓慢，实践操作部门也未给予足够的重视，本文拟通过贝叶斯模型的引入，对操作风险作一定程度上的量化分析，并通过这种分析方法了解操作风险与市场风险、信用风险是如何结合的。     

Modeling Operational Risk With Bayesian Networks

Bayesian networks is an emerging tool for a wide range of risk management applications, one of which is the modeling of operational risk. This comes at a time when changes in the supervision of financial institutions have resulted in increased scrutiny on the risk management of banks and insurance companies, thus giving the industry an impetus to measure and manage operational risk. The more established methods for risk quantification are linear models such as time series models, econometric models, empirical actuarial models, and extreme value theory. Due to data limitations and complex interaction between operational risk variables, various nonlinear methods have been proposed, one of which is the focus of this article: Bayesian networks. Using an idealized example of a fictitious on line business, we construct a Bayesian network that models various risk factors and their combination into an overall loss distribution. Using this model, we show how established Bayesian network methodology can be applied to: (1) form posterior marginal distributions of variables based on evidence, (2) simulate scenarios, (3) update the parameters of the model using data, and (4) quantify in real‐time how well the model predictions compare to actual data. A specific example of Bayesian networks application to operational risk in an insurance setting is then suggested.     

我国商业银行操作风险度量模型的选择

操作风险涉及银行经营活动的所有领域、各个环节和所有人员,不同银行、不同业务、不同环节的操作风险特征都不相同。操作风险度量是对操作风险进行经济资本配置的基础,目前还没有普遍适用的操作风险度量方法,现有的一些主流模型没有充分考虑内部控制对操作风险的影响和操作风险的因果性特征。因此,操作风险度量模型应考虑到其特征,既要综合主观和客观两方面的因素,也要可以灵活地进行动态调整。考虑到我国商业银行操作风险管理的实际,在操作风险度量模型的选择上,可用内部控制评价结果调整的基本指标法和标准法作为自上而下的度量模型,用贝叶斯网络技术作为自下而上的度量模型。     

Riesgo operacional en el proceso de pago del Procampo Un enfoque bayesiano

This paper identifies and quantifies through a Bayesian Network model (BN) the various factors of Operational Risk (OR) associated with the payment process PROCAMPO. The BN model is calibrated with data from events that occurred during the period 2008-2011. Unlike classical methods, the BN model calibration sources include both objective and subjective ones, allowing to more adequately capture the relationship (cause and effect) between the several elements of operational risk.     

Bayesian estimation of truncated data with applications to operational risk measurement

Data insufficiency and reporting threshold are two main issues in operational risk modelling. When these conditions are present, maximum likelihood estimation (MLE) may produce very poor parameter estimates. In this study, we first investigate four methods to estimate the parameters of truncated distributions for small samples—MLE, expectation-maximization algorithm, penalized likelihood estimators, and Bayesian methods. Without any proper prior information, Jeffreys’ prior for truncated distributions is used. Based on a simulation study for the log-normal distribution, we find that the Bayesian method gives much more credible and reliable estimates than the MLE method. Finally, an application to the operational loss severity estimation using real data is conducted using the truncated log-normal and log-gamma distributions. With the Bayesian method, the loss distribution parameters and value-at-risk measure for every cell with loss data can be estimated separately for internal and external data. Moreover, confidence intervals for the Bayesian estimates are obtained via a bootstrap method.     

关于农行开展小额现金代理支付业务的思考

2009年11月，吉林省农行推出了小额现金代理支付业务。此业务在延伸和拓展有效金融服务、解决金融机构乡镇金融服务方面空白的问题，加大支农服务力度方面进行了有效尝试，但试点过程中暴露的操作风险及制约业务发展的问题应予关注。本文对该项业务试点过程中的主要做法和存在问题进行了分析，并据此提出了相应的对策建议。     

利率市场化能降低企业营运风险吗?——基于融资约束和企业金融化的双重视角

如何提高金融服务实体经济的结构性调节功能并促进企业有序运营与健康发展是金融供给侧结构性改革的重要目标。利率市场化改革作为金融领域最重要的改革之一,其在微观层面如何影响实体经济运行引起了高度关注。本文首先从理论上诠释了利率市场化通过缓解融资约束、抑制金融化进而降低企业营运风险的逻辑关系。为识别利率市场化与企业营运风险之间的因果效应,本文以中国人民银行2004年10月取消金融机构贷款利率上限和2013年7月取消金融机构贷款利率下限为外生冲击,基于融资约束差异构造准自然实验为上述理论推断提供经验证据。特别地,利率市场化每增加1单位标准差,企业营运风险平均约下降样本标准差的2.39%。异质性分析表明,利率市场化对企业营运风险的抑制效应在融资约束程度较高、行业竞争程度较高、投资机会较多的企业中尤为明显。本文研究对于进一步优化资源配置效率、实现金融与实体经济高质量发展具有一定参考意义。     

A conceptual Object-Oriented Bayesian Network (OOBN) for modeling aircraft carrier-based UAS safety risk

This paper illustrates the conceptual development of a demonstration Object-Oriented Bayesian Network (OOBN) to integrate the hazards associated with an experimental Unmanned Aircraft System (UAS) planned for deployment from an aircraft carrier. The final Air/Ship Integration (A/SI) demonstration model is characterized by a top-level Bayesian network model with nine sub-nets comprising 70 causal factors with 15 mitigations. With the creation of a probabilistic model, inferences about changes to the states of the causal factors given the presence or absence of controls or mitigations can be ascertained. These inferences build on qualitative reasoning and enable an analyst to identify the most prominent causal factor groupings leading to a prioritization of the most influential causal factors. Mitigation effects can be systematically studied and assessed. The A/SI OOBN demonstration model illustrates the construction of an integrative safety risk model that may be used to compute a higher-order system mishap probability for an experimental UAS that interacts with ship operations in a highly severe, dynamic sea environment. In addition to computing mishap probabilities, the Bayesian approach may also be used to support control contingency management for possible mitigation implementation.     

当前金融市场环境下我国商业银行利率风险的防范

传统的存贷款政策、落后的金融市场、利息损益调整的被动局面以及在国际金融业务中风险意识淡薄等原因，严重影响我国商业银行的经营效益，同时也带来巨大的经营风险。建议结合我国商业银行的实际情况及利率市场化的进程，分不同阶段确定不同的重点，循序渐进地进行利率风险管理。     

Multivariate Cox Hidden Markov models with an application to operational risk

ABSTRACT

Modeling multivariate time-series aggregate losses is an important actuarial topic that is very challenging due to the fact that losses can be serially dependent with heterogeneous dependence structures across loss types and business lines. In this paper, we investigate a flexible class of multivariate Cox Hidden Markov Models for the joint arrival process of loss events. Some of the nice properties possessed by this class of models, such as closed-form expressions, thinning properties and model versatility are discussed in details. We provide the expectation-maximization (EM) algorithm for efficient model calibration. Applying the proposed model to an operational risk dataset, we demonstrate that the model offers sufficient flexibility to capture most characteristics of the observed loss frequencies. By modeling the log-transformed loss severities through mixture of Erlang distributions, we can model the aggregate losses. Finally, out-of-sample testing shows that the proposed model is adequate to predict short-term future operational risk losses.     

Business risk auditing: A regressive evolution?—A research note

Business risk auditing (BRA) has been much publicised as revolutionary. The essence of the phenomenon, and the actual impact on practice, however, are unclear. This note revisits some pre-BRA interview evidence investigating auditor engagement with business risk. The evidence suggests that, pre-BRA, big-six auditors were already familiar with concepts of business risk although they were uncertain as to how precisely business risk informed the audit process. This suggests that BRA was evolutionary, rather than revolutionary, change and that the engagement of recent international standards with business risk is not significantly different from that of big-six auditors pre-BRA. The BRA era in audit methodology might be conceptualized as one of regressive evolution.