鲁棒信用风险优化的线性锥优化模型

考虑了具有强健性的信用风险优化问题. 根据最差条件在值风险度量信用风险的方法,建立了信用风险优化问题的模型. 由于信用风险的损失分布存在不确定性,考虑了两类不确定性区间,即箱子型区间和椭球型区间. 把具有强健性的信用风险优化问题分别转化成线性规划问题和二阶锥规划问题. 最后,通过一个信用风险问题的例子来说明此模型的有效性.     

风险度量的贝叶斯估计及其统计分析

建立了贝叶斯模型,研究了在险价值及其相关风险度量的关系,给出了在险价值、期望短缺、尾条件期望、条件在险价值等风险度量的计算方法.进而研究了风险度量的贝叶斯估计和贝叶斯预测,并在指数风险模型中证明了估计的相合性和渐近正态性,最后利用数值模拟的方法验证了不同样本下估计的收敛速度.     

基于贝叶斯网络的中国电子商务信用风险因素研究

针对电子商务信用问题可能会导致的电子商务发展缓慢、电子商务市场秩序混乱以及电子商务产业崩溃等风险事件,应用贝叶斯网络理论构建中国电子商务信用风险网络模型,分别分析电子商务信用风险的敏感性因素与关键因素对风险事件的影响程度。运用GeNie仿真软件求得结果:最关键的信用风险因素是技术诈骗率提高、经济效率降低以及国家监管力度不足等三个因素,敏感性因素为新者难做、交易者的交易意愿降低、技术诈骗率提高、经济效率降低以及信息不对称等五个因素。研究认为信用风险关键因素的变化会波及信用风险敏感因素的变动,进而引发风险事件的产生。     

基于贝叶斯ARFIMA-WRV模型高频数据长记忆性研究

考虑到高频时间序列波动率的长记忆性问题,构建了赋权已实现波动分数整合自回归移动平均(ARFIMA-WRV)模型对其进行了研究.利用贝叶斯统计方法对模型做了相应的贝叶斯分析,并对我国中小板股市收益波动率的长记忆性特征进行了实证分析.实证结果表明我国中小板股市收益波动率存在长记忆性特征;采用消除日历效应影响的赋权已实现波动作为波动度量和贝叶斯参数估计方法,很大程度上提高了模型的参数精度.     

Hull-White模型在次级债定价中的应用研究

美国次级债危机引起了全球金融市场的动荡，这使得国内外风险管理者们不得不更加关注由于信用风险带来的种种问题。在我国还没有一种公认的理论或方法能够实际解决信用风险的定价问题。本文力图寻找一种不完全依赖于信用评级的信用风险定价方法，利用市场即时信息而不是历史信息对信用风险溢价进行估算，从而寻找到一套在中国市场具有操作性的信用风险定价方法。本文应用三叉树模拟的方法来构建基于Hull-White模型的信用风险定价模型，并应用市场数据对两类次级债基础衍生品进行了定价。这一研究具有较好的可操作性，对中国信用风险定价研究领域提供了有利补充，为中国证券市场动态信用风险管理提供新的思路和可能的解决渠道。     

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VaR风险度量在金融、保险中有重要的应用. 本文建立了贝叶斯模型, 在某种损失函数下研究了VaR风险度量的贝叶斯估计. 证明了指数-伽马分布下贝叶斯估计的强相合性和渐近正态性, 最后利用数值模拟的方法验证了不同样本容量下估计的收敛速度.     

信用风险相关性度量的MRS Copula模型构建及实证研究

在构建行业信用风险指数的基础上,将马尔科夫机制转换引入到信用风险相关性的度量中,建立了信用风险相关性度量的MRS Copula模型。以1990-2012年电力、煤气及水的生产和供应业,批发、零售、贸易业,石油、化学、塑胶、塑料业和信息技术业为样本的实证研究表明,行业信用风险相关性表现出较为明显的机制转换特征和非对称效应,在高风险状态,信用风险相关系数达到了0.7以上,而在低风险状态,信用风险相关系数在0.2以下.同时,信用风险"一损俱损"的特征比较明显,行业信用风险的下尾相关系数较为显著,而上尾相关系数则并不显著.商业银行可据此调整信贷资产结构,防范信用风险传染,以及优化信贷组合管理.     

SolvencyⅡ框架下非寿险一年期准备金风险的度量

在欧盟以风险为核心的Solvency II监管框架下,非寿险准备金传统评估问题正向准备金风险管理新问题转化,准备金风险的识别、度量与控制已成为非寿险精算理论和实务重点关注的前沿问题。本文系统讨论非寿险一年期准备金风险的概念及其度量模型与方法。首先,通过实例直观阐述一年期准备金风险与索赔进展结果(CDR)的内涵;其次,基于贝叶斯对数正态模型,利用MCMC方法和R软件,随机模拟CDR的预测分布,并用CDR预测分布的统计特征来度量非寿险一年期准备金风险;最后,将欧洲保险公司实际索赔数据代入以上模型和步骤进行实证分析。研究表明,基于MCMC随机模拟方法获得的CDR预测分布,能够更加稳健和有效地度量非寿险一年期准备金风险。     

宏观因素对房地产行业信用风险的影响研究——基于BALQR模型的实证

以2008年1月至2014年9月相关数据为基础,利用贝叶斯adaptive Lasso分位数回归(BALQR)模型对影响房地产业信用风险的宏观经济因素进行了分析.结果表明,在任何分位点上,对我国房地产行业信用风险影响最大的均是GDP增长率,其次是CPI增长率和消费者信心指数增长率,其中前者为负向作用后两者为正向作用,而资本市场的景气状态则对房地产行业信用风险基本没有显著作用.不过,在不同分位点上,不同宏观因素在房地产行业的不同信用风险水平上的影响程度又存在差异性.     

我国商业银行在聚合信用风险模型下的质押贷款风险度量探讨

传统的聚合信用风险模型在度量信用风险过程中,假定违约损失是给定不变的,但是近年的实际研究表明在实际金融市场中,违约损失是变化的。针对传统模型的这个不合理性。本文充分考虑了违约时损失程度的变化,引入信用等级转移矩阵和违约风险调整的短期利率来刘画这种变化,并利用总索赔量分布的Panjer递推算法给出了信用风险的度量．改进了文献[4,5]中概率生成函数算法的不足,对模型作了发展。     

A dynamic binomial expansion technique for credit risk measurement: a Bayesian filtering approach

Credit risk measurement and management are important and current issues in the modern finance world from both the theoretical and practical perspectives. There are two major schools of thought for credit risk analysis, namely the structural models based on the asset value model originally proposed by Merton and the intensity‐based reduced form models. One of the popular credit risk models used in practice is the Binomial Expansion Technique (BET) introduced by Moody's. However, its one‐period static nature and the independence assumption for credit entities' defaults are two shortcomings for the use of BET in practical situations. Davis and Lo provided elegant ways to ease the two shortcomings of BET with their default infection and dynamic continuous‐time intensity‐based approaches. This paper first proposes a discrete‐time dynamic extension to the BET in order to incorporate the time‐dependent and time‐varying behaviour of default probabilities for measuring the risk of a credit risky portfolio. In reality, the ‘true’ default probabilities are unobservable to credit analysts and traders. Here, the uncertainties of ‘true’ default probabilities are incorporated in the context of a dynamic Bayesian paradigm. Numerical studies of the proposed model are provided.     

Enhanced decision support in credit scoring using Bayesian binary quantile regression

Fierce competition as well as the recent financial crisis in financial and banking industries made credit scoring gain importance. An accurate estimation of credit risk helps organizations to decide whether or not to grant credit to potential customers. Many classification methods have been suggested to handle this problem in the literature. This paper proposes a model for evaluating credit risk based on binary quantile regression, using Bayesian estimation. This paper points out the distinct advantages of the latter approach: that is (i) the method provides accurate predictions of which customers may default in the future, (ii) the approach provides detailed insight into the effects of the explanatory variables on the probability of default, and (iii) the methodology is ideally suited to build a segmentation scheme of the customers in terms of risk of default and the corresponding uncertainty about the prediction. An often studied dataset from a German bank is used to show the applicability of the method proposed. The results demonstrate that the methodology can be an important tool for credit companies that want to take the credit risk of their customer fully into account.     

Quantum Bayesian computation

Quantum Bayesian computation is an emerging field that levers the computational gains available from quantum computers. They promise to provide an exponential speed-up in Bayesian computation. Our article adds to the literature in three ways. First, we describe how quantum von Neumann measurement provides quantum versions of popular machine learning algorithms such as Markov chain Monte Carlo and deep learning that are fundamental to Bayesian learning. Second, we describe quantum data encoding methods needed to implement quantum machine learning including the counterparts to traditional feature extraction and kernel embeddings methods. Third, we show how quantum algorithms naturally calculate Bayesian quantities of interest such as posterior distributions and marginal likelihoods. Our goal then is to show how quantum algorithms solve statistical machine learning problems. On the theoretical side, we provide quantum versions of high dimensional regression, Gaussian processes and stochastic gradient descent. On the empirical side, we apply a quantum FFT algorithm to Chicago house price data. Finally, we conclude with directions for future research.     

供应链融资模式下中小企业信用风险评估研究——基于左右得分的模糊TOPSIS算法

中小企业信用风险评价是供应链融资实现的关键。本文在对已进行的研究总结的基础上,建立了一套比较完整的评价指标体系,然后运用DEA、AHP和基于左右得分的模糊TOPSIS相结合的评价方法对中小企业在供应链融资中的信用风险进行评价。最后,将方法成功应用到具体实例,结果表明将AHP方法和左右得分模糊TOPSIS结合应用到多层次指标评价问题是合理、可行的,能够为供应链融资信用风险评估提供决策依据。     

基于贝叶斯方法的低违约组合违约率估计

现代信用风险建模的核心是估计违约率,违约率估计是否准确将直接影响信用风险建模的质量。在估计违约率的众多文献中,频率法或logistic回归等统计方法的运用非常广泛,此类统计模型的基础是大样本,它客观上需要最低数量或最优数量的违约数据,而低违约组合(LDP)是指只有很少违约数据甚至没有违约数据的组合,如何估计LDP的违约率、反映违约率的非预期波动是一个值得关注的现实问题。本文针对银行贷款LDP缺乏足够历史违约数据的情况,采用贝叶斯方法估计LDP的违约率,并进一步探讨了根据专家判断或者根据同类银行LDP违约数量的历史数据来确定先验分布的方法。在贝叶斯估计中,通过先验分布的设定,不仅可以实现违约率估计的科学性和合理性,而且可以反映违约的非预期波动,有助于银行实施谨慎稳健的风险管理。     

Robust portfolios: contributions from operations research and finance

In this paper we provide a survey of recent contributions to robust portfolio strategies from operations research and finance to the theory of portfolio selection. Our survey covers results derived not only in terms of the standard mean-variance objective, but also in terms of two of the most popular risk measures, mean-VaR and mean-CVaR developed recently. In addition, we review optimal estimation methods and Bayesian robust approaches.     

Bound and collapse Bayesian reject inference for credit scoring

Reject inference is a method for inferring how a rejected credit applicant would have behaved had credit been granted. Credit-quality data on rejected applicants are usually missing not at random (MNAR). In order to infer credit-quality data MNAR, we propose a flexible method to generate the probability of missingness within a model-based bound and collapse Bayesian technique. We tested the method's performance relative to traditional reject-inference methods using real data. Results show that our method improves the classification power of credit scoring models under MNAR conditions.     

Enterprise risk management: coping with model risk in a large bank

Enterprise risk management (ERM) has become an important topic in today's more complex, interrelated global business environment, replete with threats from natural, political, economic, and technical sources. Banks especially face financial risks, as the news makes ever more apparent in 2008. This paper demonstrates support to risk management through validation of predictive scorecards for a large bank. The bank developed a model to assess account creditworthiness. The model is validated and compared to credit bureau scores. Alternative methods of risk measurement are compared.