Estimating Portfolio Credit Losses in Downturns

This paper suggests formulas able to capture potential strong connection among credit losses in downturns without assuming any specific distribution for the variables involved. We first show that the current model adopted by regulators (Basel) is equivalent to a conditional distribution derived from the Gaussian Copula (which does not identify tail dependence). We then use conditional distributions derived from copulas that express tail dependence (stronger dependence across higher losses) to estimate the probability of credit losses in extreme scenarios (crises). Next, we use data on historical credit losses incurred in American banks to compare the suggested approach to the Basel formula with respect to their performance when predicting the extreme losses observed in 2009 and 2010. Our results indicate that, in general, the copula approach outperforms the Basel method in two of the three credit segments investigated. The proposed method is extendable to other differentiable copula families and this gives flexibility to future practical applications of the model.     

Capital Allocation for Portfolio Credit Risk

Capital allocation rules are derived that maximize leverage while maintaining a target solvency rate for credit portfolios where risk is driven by a single common factor and idiosyncratic risk is fully diversified. Equilibrium conditions ensure that capital allocations depend on interest earnings as well as credits’ probability of default, endogenous loss given default, and asset correlation. Capitalization rates exceed those estimated using Gaussian credit loss models. Results demonstrate that credit risk is undercapitalized by the Basel II AIRB approach in part because of ambiguities regarding the definition of loss given default. An alternative proposed capital rule removes this bias.     

Real Activity Forecasts Using Loan Portfolio Information

To extend and monitor loans, banks collect detailed and proprietary information about the financial prospects of their customers, many of whom are local businesses and households. Therefore, banks’ loan portfolios contain potentially useful information about local economic conditions. We investigate the association between information in loan portfolios and local economic conditions. Using a sample of U.S. commercial banks from 1990:Q1 to 2013:Q4, we document that information in loan portfolios aggregated to the state level is associated with current and future changes in statewide economic conditions. Furthermore, the provision for loan and lease losses contains information incremental to leading indicators of state‐level economic activity and recessions. Loan portfolio information also helps to improve predictions of economic conditions at more granular levels, such as at the commuting zone level. We discuss the relevance of these findings for economic analysis and forecasting, and the relation of our study to prior work on the informativeness of accounting information about the macroeconomy.     

基于分层模型的银行客户信用风险预测研究

本文基于商业银行客户信贷记录数据集,通过运用拉普拉斯分层模型对客户的信用风险进行预测研究.利用客户群体存在差异化的特点,采用XGBoost机器学习算法来选择分层特征以及结合多元特征的组合形式来预测客户的违约情况.在不同分层特征结构下依次对比拉普拉斯分层模型、单独模型、共同模型和随机森林四个模型的预测效果,并建立模拟数据集来对拉普拉斯分层模型的性能进行验证.研究发现:(1)拉普拉斯分层模型的预测精度是最高的,预测性能具有稳定性;(2)本文数据集所适用的最佳分层特征是贷款金额、年龄和婚姻;(3)分层特征的选择和数量会依据不同数据而产生相应变化,并非一成不变.结合本文的研究思路和结果,以期为商业银行在客户信用风险评估实践中提供新的思考和建议.     

Saving and Portfolio Allocation Before and After Job Loss

We investigate the development of household labor income, financial wealth, and asset holdings over a 9‐year period around job loss, using unique administrative panel data from Norway. Consistent with predictions from theory, the data show additional saving and a shift toward safer assets in the years leading up to unemployment, and depletion of savings after job loss. In the years after job loss, the households' after‐tax labor income is reduced by about USD 12,500. Over the same time period, households deplete USD 3,000 of their financial assets, of which one third is accumulated prior to the job loss. This suggests that at least some households can foresee and prepare for the upcoming unemployment, which indicates that private savings can, to some extent, serve as a substitute for publicly provided unemployment insurance.     

贷款定价:从信用评级到经济资本

贷款定价是当今国际性银行全面风险管理理念的典型标志之一。本文以实地考察的美国美联银行(Wachovia Bank)为例,分析了其贷款定价的操作原理和基本理念,研究了内部信用评级、经济资本和贷款定价等相关内容。     

农村信用社经营亏损因析

经营亏损问题是制约信用社发展的重要因素，在深化改革的形势下，要认真分析造成亏损的深层次原因，进而采取有效的措施，才能更好地加快信用社发展。     

Valuation of Portfolio Credit Derivatives with Default Intensities Using the Vasicek Model

We present a methodology for valuing portfolio credit derivatives under a reduced form model for which the default intensity processes of risk assets follow the one-factor Vasicek model. A closed-form solution of joint survival time distribution is obtained. The solution is applied to value credit derivatives of a credit default swap index and collateralized debt obligation. The limitation of methods using the Vasicek model is discussed. We propose that the method is valid and efficient for a portfolio with small-scale correlated risk assets, for which the acceptable size is much greater than for the traditional method. Numerical examples and parameter analysis are also presented.     

绿色信贷“贷”动经济转型

运用信贷杠杆落实国家节能减排政策,是金融业承担调整经济结构和转变发展方式这一使命的重要手段。这其中,绿色信贷扮演了重要角色,它在依据国家产业政策和环境政策的基础上,遏制高耗能、高污染即＂两高＂行业对环境资源的破坏,运用资金有效配置各种资源,并且为生态保护、生态环境建设和绿色产业融资而进行金融创新,从而达到金融业的科学发展和可持续发展。     

Extending COLOSSUS to Cover Compensation for Future Economic Loss

COLOSSUS is a mainframe-based expert system jointly developed by Continuum Australia and GIO (General Insurance Organization) for the assessment of third-party claims. COLOSSUS has been in production for over two years and is accessible throughout all GIO regions in New South Wales, Australia. In this paper we describe the extension of COLOSSUS to cater for future economic loss to a claimant. Because of the diversity of knowledge sources in the insurance domain, we have adopted a highly interactive approach that requires successive user confirmations during a consultation. The practicality of our knowledge engineering approach is highlighted by examining several difficult issues. Design issues are also addressed with particular reference to clarity and ease of maintenance. An annotated report of a sample run is enclosed.     

基于特征衍生的个人信用风险评估组合模型研究

在特征选择和特征衍生的基础上,分别基于特征扰动和XGBoost与Lightgbm的算法差异建立了四个单一模型;利用单一模型性能确定权重,构建了个人信用风险评估的线性组合模型.实证分析发现,有衍生特征的四个单一模型的AUC和KS均优于无衍生特征的四个单一模型,有衍生特征组合模型的AUC和KS均优于无衍生特征组合模型.实证...     

Global Economic Divergence and Portfolio Capital Flows to Emerging Markets

This paper studies the role of global and regional variations in economic activity and policy in developed world in driving portfolio capital flows (PCFs) to emerging markets (EMs). Results suggest that PCFs to EMs depend mainly on economic activity at the global level and monetary policy in America and Asia, positively on the former and negatively on the latters. PCFs are procyclical with respect to global activity, but countercyclical to regional activity. In aggregate, regional variations contribute more than global variations. This implies that economic divergence in the developed world can have significant effects on EMs via PCFs.     

Evidence of Forward-Looking Loan Loss Provisioning with Credit Market Information

Our paper makes a fundamental contribution by studying loan loss provisioning over the credit cycle as three distinct phases. Looking at the three distinct phases of the financial crisis – the pre-crisis period, crisis period, and post-crisis period – is important as loan loss provisioning is driven by different factors in each, in part due to extensive shifts in (or in the application of) regulatory rule. Controlling for credit market information using data from the Senior Loan Officer Opinion Surveys (SLOOS) we extend the work of previous studies of forward-looking loan loss provisions using the delayed expected loss recognition approach. We contribute to the growing literature on forward-looking loan loss provisioning and early in the cycle loss recognition by incorporating a broader range of available credit information and explicitly controlling for structural breaks in the sample corresponding to the financial crisis.     

经济政策不确定性与公司商业信用供给

基于中国经济政策不确定性指数,本文使用2003–2015年上市公司季度数据考察经济政策不确定性如何影响公司商业信用供给决策。结果表明,经济政策不确定性的提高会显著减少公司提供的商业信用,外部融资环境和内部经营不确定性是这一影响的重要渠道。进一步分析发现,社会信任水平、地区金融发展程度和公司市场地位可以有效缓解二者之间的负向关系。而且,经济政策不确定性的提高还会导致公司缩短商业信用的供给期限。本文研究结果为宏观经济政策不确定条件下公司商业信用供给决策中成本和收益的权衡提供了新的经验证据,有助于政策制定者理解经济政策不确定性如何影响微观经济实体的财务决策。     

有限理性、信用机制与经济波动

本文通过建立一个个体有限理性决策与经济波动关系模型分析在市场竞争环境中，基于两种类型的有限理性，个体厂商的生产投资决策通过与其有共同利益的信用机构的传递，具有造成经济波动的可能性。为抑制个体厂商两种类型的有限理性投资决策引起经济波动，减少经济损失，可以采取相应的公共管理措施。这些公共管理措施有不同的社会成本。     

农村民间借贷发展的制度经济学分析

本运用新制度经济学理论和方法，深入分析农村民间借贷的变迁过程、特征和作用，得出以下结论：要从建立健全法律、法规体系、信用制度、农村正规金融组织积极介入等方面着手，引导农村民间借贷发展成为农村金融的重要组成部分。     

证券投资组合损失率概率评价模型的开发与实证检验

通过建立证券投资组合损失率概率评价模型,定义损失率概率为单个资产或投资组合的损失率超过市场平均损失率的概率,用于对投资组合的风险进行评价。以某基金为例,计算得到该基金的损失率概率为39.74%,表明该基金有39.74%的概率损失率超过市场平均损失率。以证券投资组合损失率概率最小为目标函数,采用遗传算法进行求解,得出该基金投资组合损失率概率最小时的投资权重系数,最小损失率概率为36.01%,与该基金公司的投资组合相比,损失率概率降低了3.73%,实例证明该模型是降低证券投资组合风险的一种实用方法。     

当前经济形势下商业银行信贷战略选择

<正> 去年,我国经济增长速度加快,商业银行信贷投放速度空前高涨。为防范系统性金融风险,央行积极采取了适度控制措施,提高准备金率,同时加强以风险控制为重点的窗口指导。进入三季度,各银行信贷增速开始回落,贷款扩张势头得到了有效控制。但从长期看,我国商业银行信贷结构不合理现象并未根本改变,各类业务中包含的信用风险、操作风险和流动性风险等仍在不断暴露。本文结合我国银行业风险最新特点和走势,提出当前形势下商业银行信贷发展的基本战略选择。     

消费信贷与经济增长的协动关系研究

经济增长是一国或地区最重要的目标,只有长期的经济发展才能提升全民的福祉和生活水平。消费是生产的出发点,同时也是最终的归宿。采用离散选择模型对经济增长与短期消费信贷和长期消费信贷的关系进行分析,结果表明,长期消费信贷对经济增长的拉动效应较明显,有效地发挥和挖掘消费信贷带动的长期消费对扩大内需和经济健康稳定发展至关重要。