基于最优负债系数的上市银行违约概率测算模型与实证

当上市银行的长期负债系数γ的取值不同时,应用KMV模型测算出的银行违约概率大相径庭。根据债券的实际信用利差可以推算出上市银行的违约概率PDi,CS,根据长期负债系数γ可以运用KMV模型确定上市银行的理论违约概率PDi,KMV。本文通过理论违约率与实际违约率的总体差异∑ni=1|PDi,KMV-PDi,cs|最小的思路建立规划模型,确定了KMV模型的最优长期负债γ系数;通过最优长期负债系数γ建立了未发债上市银行的违约率测算模型、并实证测算了我国14家全部上市银行的违约概率。本文的创新与特色一是采用KMV模型计算的银行违约概率PDi,KMV与实际信用利差确定的银行违约概率PDi,CS总体差异∑ni=1|PDi,KMV-PDi,cs|最小的思路建立规划模型,确定了KMV模型中的最优长期负债γ系数;使γ系数的确定符合资本市场利差的实际状况,解决了现有研究中在0和1之间当采用不同的长期负债系数γ、其违约概率的计算结果截然不同的问题。二是实证研究表明,当长期负债系数γ=0.7654时,应用KMV模型测算出的我国上市银行违约概率与我国债券市场所接受的上市银行违约概率最为接近。三是实证研究表明国有上市银行违约概率最低,区域性的上市银行违约概率较高,其他上市银行的违约概率居中。     

具有违约观察期公司债券的定价

借助于美国破产保护法第十一章,违约公司获得-个额外的违约观察期过程,通过纳什均衡原理对股东和债权人的利益进行重新分配,利用巴黎型期权的定价思想来刻画具有这种违约观察期过程的股票与公司债券的定价模型,并从股东权益最大化,把股票的定价模型归结为-个自由边界问题,进而通过偏微分方程方法(PDE)推出股票与公司偾券价格的闭合表达式和最佳违约边界解的显式表达式;同时文章还对公司的最优杠杆,清算概率和信用利差进行讨论.     

基于双指数跳扩散过程的公司债券定价

研究基于公司资产价值服从双指敷跳扩散过程,公司负债服从连续扩散过程的公司债券定价问题.首先用计价单位方法给出简单情况下以零息票债券为基础的公司债券定价问题的解析解;其次给出一般情况下公司债券定价问题的违约概率,并讨论信用价差的期限结构.实证分析表明该模型能较好地拟合实际情况.     

基于稀疏主成分的我国上市公司信用风险评价与预测

把我国2016-2018年沪深Ａ股上市公司中164家ST公司作为信用违约样本,492家非ST上市公司作为非违约样本进行实证研究.从营运能力、偿债能力、盈利能力和成长能力等4个方面选取了25个财务指标,然后运用稀疏主成分方法提取主成分因子,并加入公司规模、第一大股东持股比例和股权质押3个非财务指标,作为Logistic回归模型的输入参数.在此基础上构建Logistic模型进行信用风险评价和预测.     

基于混合模型的利润驱动违约判别临界点研究

违约判别临界点是金融机构是否接受客户贷款申请的重要参考,合适的违约判别临界点对减少金融机构贷款损失实现稳健经营具有重要意义。本文研究的问题是如何保证计算客户违约概率的准确性,并找到利润最大化的违约判别临界点。本文的创新与特色：一是通过将多个不同类型的违约判别模型计算的客户违约概率进行加权平均,保证了计算客户违约概率的的整体准确性,避免了使用单一模型计算客户违约概率不准确的弊端;二是通过定义金融机构从贷款中获得利润的计算公式,以利润最大为目标,求解违约判别临界点,避免了现有计算临界点的方法如广义对称点估计和经验似然法等方法得到的临界点利润不是最大的弊端。研究发现：混合模型比单一模型的准确性高,AUC值显著提高;在人人贷数据集中本文的违约判别临界点下贷款利润远高于其他方法下临界点的利润。     

基于Group-LASSO方法的广义半参数可加信用评分模型应用研究

信用风险是目前商业银行面临的风险中最为重要和最为复杂的,新巴塞尔协议要求各国条件的银行通过实施内部评级法来度量并控制信用风险,内部评级法即通过银行收集的客户相历史数据来构建数学模型,测算客户的违约概率进而对客户进行评分。文章针对信用评分模型解释变量维数较高,类型丰富,好坏客户类型数量不均衡等特点,利用广义半参数可加模型对户违约概率进行建模,并将Group LASSO方法应用于模型进行变量选择和估计。实证研究表明本文提出的模型和方法与以往常用的线性logistic回归模型相比,在模型的判别能力和预测能以及解释性和计算效率上均有较大优势。     

一种与市场预期误差最小的信用风险评价模型及实证

利用KMV测算的违约距离,反映市场预期的违约风险.引入R平方构建信息比率测算指标体系的信息含量,解决指标筛选过程中的信息含量测算问题,筛选出的财务指标体系既满足分散化原则,又符合信息含量最大原则.基于与市场预期违约风险一致原则,通过构建与市场预期违约风险误差最小的有约束优化模型确定指标权重,解决没有公开和完备贷款违约数据库时的信用风险评价问题.以中小企业板块上市公司作为样本进行了实证分析,评价结果表明,建立的与市场预期风险误差最小的信用风险评价体系能够准确反映我国中小企业违约风险的现状.     

地方政府债券信用风险缓释工具定价研究

随着地方政府债券发行规模的扩大,地方政府债务的信用风险日益凸出。本研究以企业债信用风险缓释工具的推出为契机,借鉴结构化模型的思路和KMV模型求解违约概率的逻辑,通过Monte Carlo方法模拟地方政府的违约过程,直接测算地方政府的整体违约概率;结合简约化模型的思路测算地方政府债券的具体违约概率,计算信用风险缓释工具的理论价格,从而构建了地方政府债券信用风险缓释工具的混合定价模型。研究发现,以企业债券为标的测算出的模型理论价格与市场报价基本一致,参数的敏感性检验进一步验证了模型的理论自洽性和实证可靠性。上述结论或将为新《预算法》实施过程中地方政府债务的治理与掌控及中国区域性、系统性金融风险的防范提供新思路。     

成也萧何,败也萧何？——高管金融经历与公司债券融资

基于印记理论和高层梯队理论,本文以2008至2018年发行公司债券的上市公司为研究对象,考察高管金融经历对公司债券融资的影响机理。结果表明:首先,高管金融经历在提高公司债券发行成功率的同时也会增加债券融资成本和违约风险,而对发债规模与期限并不会产生显著影响,在控制可能的内生性问题后结论依然成立;其次,具有金融经历的高管所任职公司会通过盈余管理来提高债券发行成功率,但也会增加债券的融资成本和违约风险;最后,具有证券公司或商业银行工作经历的高管对公司债券融资的影响更为显著。本文有助于实务界和理论界理解高管异质性特征对公司债券融资的影响机制,为监管部门制定防范金融风险的政策提供可靠的理论依据。     

一种利用Pls提取因子的财务危机预警模型

为了克服直接使用财务指标建立Logit模型存在的估计和检验不可靠问题,在所有的财务比率指标基础上,通过删除完全共线、意义相同和缺失数据指标,建立海选指标集.利用t检验筛选指标,删除t检验不显著的指标,保留t检验显著的指标,构建财务危机预警指标体系.通过提取互不相关的Pls因子,估计Pls-logit模型对因子赋权.将Pls因子回归代入Pls-logit模型对指标赋权,建立基于Pls-logit模型的财务危机预警模型.以2010年381家制造业上市公司作为建模样本进行了实证分析,表明Pls-logit模型中31个系数在5%的显著性水平下显著,说明建立的财务预警指标体系能够显著解释财务危机.以2010年和2011年随机抽取的制造业公司为检验样本进行回溯检验,检验结果表明预警总准确率达到91.7%以上.2010年危机预警准确率为66.7%;2011年危机预警率为100%.Pls-logit模型的总体预警准确率明显优于财务指标logit模型.     

Chinese companies distress prediction: an application of data envelopment analysis

Bankruptcy prediction is a key part in corporate credit risk management. Traditional bankruptcy prediction models employ financial ratios or market prices to predict bankruptcy or financial distress prior to its occurrence. We investigate the predictive accuracy of corporate efficiency measures along with standard financial ratios in predicting corporate distress in Chinese companies. Data Envelopment Analysis (DEA) is used to measure corporate efficiency. In contrast to previous applications of DEA in credit risk modelling where it was used to generate a single efficiency—Technical Efficiency (TE), we assume Variable Returns to Scale, and decompose TE into Pure Technical Efficiency and Scale Efficiency. These measures are introduced into Logistic Regression to predict the probability of distress, along with the level of Returns to Scale. Effects of efficiency variables are allowed to vary across industries through the use of interaction terms, while the financial ratios are assumed to have the same effects across all sectors. The results show that the predictive power of the model is improved by this corporate efficiency information.     

Statistical merging of rating models

In this paper we introduce and discuss statistical models aimed at predicting default probabilities of Small and Medium Enterprises (SME). Such models are based on two separate sources of information: quantitative balance sheet ratios and qualitative information derived from the opinion mining process on unstructured data. We propose a novel methodology for data fusion in longitudinal and survival duration models using quantitative and qualitative variables separately in the likelihood function and then combining their scores linearly by a weight, to obtain the corresponding probability of default for each SME. With a real financial database at hand, we have compared the results achieved in terms of model performance and predictive capability using single models and our own proposal. Finally, we select the best model in terms of out-of-sample forecasts considering key performance indicators.     

Bayesian corporate bond pricing and credit default swap premium models for deriving default probabilities and recovery rates

This paper develops a Bayesian method by jointly formulating a corporate bond (CB) pricing model and credit default swap (CDS) premium pricing models to estimate the term structure of default probabilities and the recovery rate. These parameters are formulated by incorporating firm characteristics such as industry, credit rating and Balance Sheet/Profit and Loss information. A cross-sectional model valuing all given CB prices and CDS premiums is considered. The quantities derived are regarded as what market participants infer in forming CB prices and CDS premiums. We also develop a statistical significance test procedure without any distributional assumptions for the specified model. An empirical analysis is conducted using Japanese CB and CDS market data.     

Modelling credit risk of portfolio of consumer loans

One of the issues that the Basel Accord highlighted was that, though techniques for estimating the probability of default and hence the credit risk of loans to individual consumers are well established, there were no models for the credit risk of portfolios of such loans. Motivated by the reduced form models for credit risk in corporate lending, we seek to exploit the obvious parallels between behavioural scores and the ratings ascribed to corporate bonds to build consumer-lending equivalents. We incorporate both consumer-specific ratings and macroeconomic factors in the framework of Cox Proportional Hazard models. Our results show that default intensities of consumers are significantly influenced by macro factors. Such models then can be used as the basis for simulation approaches to estimate the credit risk of portfolios of consumer loans.     

A comparison of strategies to develop a customer default scoring model

Behavioural scoring models are generally used to estimate the probability that a customer of a financial institution who owns a credit product will default on this product in a fixed time horizon. However, one single customer usually purchases many credit products from an institution while behavioural scoring models generally treat each of these products independently. In order to make credit risk management easier and more efficient, it is interesting to develop customer default scoring models. These models estimate the probability that a customer of a certain financial institution will have credit issues with at least one product in a fixed time horizon. In this study, three strategies to develop customer default scoring models are described. One of the strategies is regularly utilized by financial institutions and the other two will be proposed herein. The performance of these strategies is compared by means of an actual data bank supplied by a financial institution and a Monte Carlo simulation study.     

On the simulation of portfolios of interest rate and credit risk sensitive securities

We discuss extensions of reduced-form and structural models for pricing credit risky securities to portfolio simulation and valuation. Stochasticity in interest rates and credit spreads is captured via reduced-form models and is incorporated with a default and migration model based on the structural credit risk modelling approach. Calculated prices are consistent with observed prices and the term structure of default-free and defaultable interest rates. Three applications are discussed: (i) study of the inter-temporal price sensitivity of credit bonds and the sensitivity of future portfolio valuation with respect to changes in interest rates, default probabilities, recovery rates and rating migration, (ii) study of the structure of credit risk by investigating the impact of disparate risk factors on portfolio risk, and (iii) tracking of corporate bond indices via simulation and optimisation models. In particular, we study the effect of uncertainty in credit spreads and interest rates on the overall risk of a credit portfolio, a topic that has been recently discussed by Kiesel et al. [The structure of credit risk: spread volatility and ratings transitions. Technical report, Bank of England, ISSN 1268-5562, 2001], but has been otherwise mostly neglected. We find that spread risk and interest rate risk are important factors that do not diversify away in a large portfolio context, especially when high-quality instruments are considered.