使用信用违约互换产品化解信贷集中风险

信贷集中风险已经成为我国银行业亟待解决的问题,本文在分析了我国银行业信贷集中风险的现状和危害后,提出了使用信用违约互换产品化解银行信贷集中风险的建议。并对信用违约互换产品的基本原理以及在我国的实施方法提出了建议。     

Forecasting recovery rates on non-performing loans with machine learning

We compare the performance of a wide set of regression techniques and machine-learning algorithms for predicting recovery rates on non-performing loans, using a private database from a European debt collection agency. We find that rule-based algorithms such as Cubist, boosted trees, and random forests perform significantly better than other approaches. In addition to loan contract specificities, predictors that refer to the bank recovery process — prior to the portfolio’s sale to a debt collector — are also shown to enhance forecasting performance. These variables, derived from the time series of contacts to defaulted clients and client reimbursements to the bank, help all algorithms better identify debtors with different repayment ability and/or commitment, and in general those with different recovery potential.     

信息不对称理论下中小企业信用担保风险分析

信用担保是国际公认的高风险行业,信用担保市场上的信息不对称是信用担保风险客观存在且居高不下的主要原因。本文从信息不对称理论的视角,分析了中小企业信用担保风险的表现形式,并提出了解决信用担保过程中信息不对称问题的对策。     

Modeling frailty-correlated defaults using many macroeconomic covariates

We propose a novel time series panel data framework for estimating and forecasting time-varying corporate default rates subject to observed and unobserved risk factors. In an empirical application for a U.S. dataset, we find a large and significant role for a dynamic frailty component even after controlling for more than 80% of the variation in more than 100 macro-financial covariates and other standard risk factors. We emphasize the need for a latent component to prevent a downward bias in estimated default rate volatility and in estimated probabilities of extreme default losses on portfolios of U.S. debt. The latent factor does not substitute for a single omitted macroeconomic variable. We argue that it captures different omitted effects at different times. We also provide empirical evidence that default and business cycle conditions partly depend on different processes. In an out-of-sample forecasting study for point-in-time default probabilities, we obtain mean absolute error reductions of more than forty percent when compared to models with observed risk factors only. The forecasts are relatively more accurate when default conditions diverge from aggregate macroeconomic conditions.     

What does money and credit tell us about real activity in the United States?

We analyse the forecasting power of different monetary aggregates and credit variables for US GDP. Special attention is paid to the influence of the recent financial market crisis. For that purpose, in the first step we use a three-variable single-equation framework with real GDP, an interest rate spread and a monetary or credit variable, in forecasting horizons of one to eight quarters. This first stage thus serves to pre-select the variables with the highest forecasting content. In a second step, we use the selected monetary and credit variables within different VAR models, and compare their forecasting properties against a benchmark VAR model with GDP and the term spread (and univariate AR models). Our findings suggest that narrow monetary aggregates, as well as different credit variables, comprise useful predictive information for economic dynamics beyond that contained in the term spread. However, this finding only holds true in a sample that includes the most recent financial crisis. Looking forward, an open question is whether this change in the relationship between money, credit, the term spread and economic activity has been the result of a permanent structural break or whether we might return to the previous relationships.     

浅析国家助学贷款毕业生违约原因与对策

文中透过分析学生贷款违约原因,找出减少贷款毕业生违约的途径和对策,让国家助学贷款工作朝着健康、有序、可持续性方向发展。     

银行不良贷款违约损失率结构特征研究

本文对中国银行业面临的信用风险违约损失率(LGD)展开研究,以温州某商业银行不良贷款数据为样本,通过描述性统计,对LGD的结构特征:信用风险暴露规模特征、期限特征、地域特征以及担保特征等进行了详细分析。结果表明LGD与风险暴露规模呈负相关,LGD与贷款期限呈正相关,不同地域、不同担保方式的违约贷款其LGD差异性显著。以上这些结论可为商业银行信用风险管理、信贷投放导向以及信用风险监管提供现实帮助。     

CREDIT DEFAULT SWAPS: HAS THE GFC INFLUENCED PERCEPTIONS OF THEIR UTILITY FOR BANKS?

Credit Default Swaps (CDSs) are considered as one of the most versatile financial innovations of the 21^st century. Since its inception, the credit derivatives market has grown to a peak of $64 trillion in 2008 in terms of gross notional values (Cont, 2010 ). However, after the onset of the GFC, this market has decreased to a large extent. While there is evidence that promotes the risk reduction properties of CDSs, there is a growing body of research post GFC that identifies destabilising effects of these instruments. In this paper, I review the relevant scholarly literature and provide evidence of how the views on CDSs have changed post GFC due a deeper investigation of issues such as systemic risk, regulatory arbitrage, information asymmetry and risk‐taking, among others, in the banking industry. This paper provides a survey of the literature of views ‘for’ and ‘against’ the use of CDSs during pre and post GFC periods and finally concludes that in comparison to the pre GFC period, post GFC there is a significant increase in the literature that highlights the detrimental nature of CDSs.     

Predicting loss given default (LGD) for residential mortgage loans: A two-stage model and empirical evidence for UK bank data

With the implementation of the Basel II regulatory framework, it became increasingly important for financial institutions to develop accurate loss models. This work investigates the loss given default (LGD) of mortgage loans using a large set of recovery data of residential mortgage defaults from a major UK bank. A Probability of Repossession Model and a Haircut Model are developed and then combined to give an expected loss percentage. We find that the Probability of Repossession Model should consist of more than just the commonly used loan-to-value ratio, and that the estimation of LGD benefits from the Haircut Model, which predicts the discount which the sale price of a repossessed property may undergo. This two-stage LGD model is shown to perform better than a single-stage LGD model (which models LGD directly from loan and collateral characteristics), as it achieves a better R² value and matches the distribution of the observed LGD more accurately.     

信息不对称下保险信用缺失分析及防范

当前,保险信用缺失已成为制约保险市场进一步发展的瓶颈。文中从投保人、保险人的逆向选择及道德风险三个方面阐述了保险信用缺失的原因,同时提出了健全我国保险信用体系的对策。     

Consumer credit on American Indian reservations

This paper provides the first encompassing quantitative picture of consumer credit in Indian Country. Drawing on a unique large-scale consumer credit database, we find that Equifax Risk Scores and the use of certain forms of credit, especially mortgages, are low on reservations. However, usage of other forms of credit on reservations is not always low. Moreover, the gaps in credit usage on versus off reservations differ significantly across states and can change notably over time. Among predictors of consumer credit outcomes, the percentage of American Indian residents is robustly negatively associated with favorable credit outcomes. Furthermore, once controlling for racial composition, the effect of an area's location vis-à-vis a reservation often becomes statistically insignificant. Other socio-economic variables are generally poor predictors of credit outcomes on reservations. State jurisdiction over legal matters is, at least on average, associated with favorable credit outcomes on reservations.