Discrete versus continuous state switching models for portfolio credit risk

Dynamic models for credit rating transitions are important ingredients for dynamic credit risk analyses. We compare the properties of two such models that have recently been put forward. The models mainly differ in their treatment of systematic risk, which can be modeled either using discrete states (e.g., expansion versus recession) or continuous states. It turns out that the implied asset correlations and default rate volatilities for discrete state switching models are implausibly low compared to empirical estimates from the literature. We conclude that care has to be taken when discrete state regime switching models are employed for dynamic credit risk management. As a side result of our analysis, we obtain indirect evidence that asset correlations may change over the business cycle.     

银行信贷、资本监管双重顺周期性与逆周期金融监管

商业银行信贷和资本监管具有顺周期性.银行信贷顺周期性导致经济繁荣时期的贷款扩张和经济衰退时的贷款紧缩.<巴塞尔资本协议Ⅱ>下的资本监管约束,在经济衰退时会促使银行形成信贷萎缩效应,影响和制约货币政策有效性的发挥,次贷危机为<巴塞尔资本协议Ⅱ>下的银行风险管理和监管的创新带来了新的要求和挑战.要减轻顺周期的影响,增强金融...     

浅析商业银行信贷风险管理

信贷业务是商业银行的主要收益来源,而信贷风险也是商业银行的主要风险之一,是商业银行必须重视的风险。本文针对商业银行信贷风险存在的问题进行分析,并尝试性提出改善对策,加强我国商业银行信贷风险管理的力度。     

Capital shocks, bank asset allocation, and the revised Basel Accord

In contrast to the 1988 Basel Accord (Basel I), the revised risk-based capital standards (Basel II) propose regulatory capital requirements based on credit ratings. This paper develops a theoretical model to analyze how banks will adjust their low and high credit risk commercial loans under the proposed newer standard. Capital-constrained banks respond to an adverse capital shock by reducing high credit risk loans, while under certain circumstances, low credit risk loans may actually increase. When compared to Basel I, it is shown that high-risk loans are reduced more under Basel II, but whether a bank reduces total lending more under Basel I or under the revised standards depends on a complex interaction of factors.     

Predicting SME loan delinquencies during recession using accounting data and SME characteristics: The case of Greece

The objective of this paper is the comparison of various credit‐scoring models (i.e. binomial logistic regression, decision tree, multilayer perceptron neural network, radial basis function, and support vector machine) in evaluating the risk of small and micro enterprises' (SMEs') loan delinquencies based on accounting data and applicants' specific attributes. Exploiting a representative large data set of SMEs' loans granted by a large Greek commercial bank in the expansion period, we track the evolution of SMEs' delinquencies over the recession period August 2010 to July 2012. This time frame encompasses a period of manageable levels of delays (early recession period: August 2011–July 2012) and a period when delays were increased to a very high degree (deep recession period: August 2011–July 2012). Comparison of the employed credit‐scoring models during the early recession period shows that the multilayer perceptron neural network produces the highest predicting capacity, followed by the support vector machine model. As the crisis deepens, the support vector machine model presents the highest predicting accuracy, followed by the decision tree and then the multilayer perceptron model. Generally, the predictive performance of all credit‐scoring models seems to be substantially reduced as the recession escalates. Our paper has important implications for the proper financing of SMEs given their importance for the European economy.     

Research Opportunities for Neural Networks: The Case for Credit

This article identifies research opportunities in the use of artificial neural networks in credit scoring and related business intelligence situations, particularly as they have been emerging in the global economy. In the literature review, particular attention is paid to commercial lending credit risk assessment and consumer credit scoring. Investors and auditors need models that can predict whether a customer will stay viable. Lenders must manage their credit risk to maximize profits and cash flow, while minimizing losses. As the global economic recession continues, investors are tightening their investment belts and need models that help them make better investment decisions, while lenders must strengthen lending practices and better identify both good and bad credit risks. Artificial neural networks may help firms improve their credit model development, and thereby their credit decisions and profitability. Such technology may also help improve development in emerging economies. Copyright © 2016 John Wiley & Sons, Ltd.     

Firm Size and Cyclical Variations in Stock Returns

Recent imperfect capital market theories predict the presence of asymmetries in the variation of small and large firms' risk over the economic cycle. Small firms with little collateral should be more strongly affected by tighter credit market conditions in a recession state than large, better collateralized ones. This paper adopts a flexible econometric model to analyze these mplications empirically. Consistent with theory, small firms display the highest degree of asymmetry in their risk across recession and expansion states, which translates into a higher sensitivity of their expected stock returns with respect to variables that measure credit market conditions.     

Employment in the Great Recession: How Important Were Household Credit Supply Shocks?

I pool data from all large multimarket lenders in the United States to estimate how many of the over 7 million jobs lost in the Great Recession can be explained by reductions in the supply of mortgage credit. I construct a mortgage credit supply instrument at the county level, the weighted average (by prerecession mortgage market shares) of liquidity-driven lender shocks during the recession. The reduction in mortgage supply explains about 15% of the employment decline. The job losses are concentrated in construction and finance.     

Capital Requirements,Monetary Policy,and Aggregate Bank Lending: Theory and Empirical Evidence

Capital requirements linked solely to credit risk are shown to increase equilibrium credit rationing and lower aggregate lending. The model predicts that the bank's decision to lend will cause an abnormal runup in the borrower's stock price and that this reaction will be greater the more capital-constrained the bank. I provide empirical support for this prediction. The model explains the recent inability of the Federal Reserve to stimulate bank lending by increasing the money supply. I show that increasing the money supply can either raise or lower lending when capital requirements are linked only to credit risk.     

Internal ratings systems,implied credit risk and the consistency of banks’ risk classification policies

This paper aims at improving our understanding of internal risk rating systems (IRS) at large banks, of the way in which they are implemented, and at verifying if IRS produce consistent estimates of banks’ loan portfolio credit risk. An important property of our work is that the size of our data set allows us to derive measures of credit risk without making any assumptions about correlations between loans, by applying Carey’s [Carey, Mark, 1998. Credit risk in private debt portfolios. Journal of Finance LIII (4), 1363–1387] non-parametric Monte Carlo re-sampling method.We find substantial differences between the implied loss distributions of two banks with equal “regulatory” risk profiles; both expected losses and the credit loss rates at a wide range of loss distribution percentiles vary considerably. Such variation will translate into different levels of required economic capital. Our results also confirm the quantitative importance of size for portfolio credit risk: for common parameter values, we find that tail risk can be reduced by up to 40% by doubling portfolio size.Our analysis makes clear that not only the formal design of a rating system, but also the way in which it is implemented (e.g. a rating grade composition; the degree of homogeneity within rating classes) can be quantitatively important for the shape of credit loss distributions and thus for banks’ required capital structure. The evidence of differences between lenders also hints at the presence of differentiated market equilibria, that are more complex than might otherwise be supposed: different lending or risk management “styles” may emerge and banks strike their own balance between risk-taking and (the cost of) monitoring (that risk).     

Credit risk interconnectedness: What does the market really know?

We analyze the relation between market-based credit risk interconnectedness among banks during the crisis and the associated balance sheet linkages via funding and securities holdings. For identification, we use a proprietary dataset that has the funding positions of banks at the bank-to-bank level for 2006–2013 in conjunction with investments of banks at the security level and the credit register from Germany. We find asymmetries both cross-sectionally and over time: when banks face difficulties to raise funding, the interbank lending affects market-based bank interconnectedness. Moreover, banks with investments in securities related to troubled classes have a higher credit risk interconnectedness. Overall, our results suggest that market-based measures of interdependence can serve well as risk monitoring tools in the absence of disaggregated high-frequency bank fundamental data.     

Anomalies and financial distress

This paper explores commonalities across asset pricing anomalies. In particular, we assess implications of financial distress for the profitability of anomaly-based trading strategies. Strategies based on price momentum, earnings momentum, credit risk, dispersion, idiosyncratic volatility, and capital investments derive their profitability from taking short positions in high credit risk firms that experience deteriorating credit conditions. In contrast, the value-based strategy derives most of its profitability from taking long positions in high credit risk firms that survive financial distress and subsequently realize high returns. The accruals anomaly is an exception. It is robust among high and low credit risk firms in all credit conditions.     

Measurement,estimation and comparison of credit migration matrices

Credit migration matrices are cardinal inputs to many risk management applications; their accurate estimation is therefore critical. We explore two approaches: cohort and two variants of duration – one imposing, the other relaxing time homogeneity – and the resulting differences, both statistically through matrix norms and economically using a credit portfolio model. We propose a new metric for comparing these matrices based on singular values and apply it to credit rating histories of S&P rated US firms from 1981–2002. We show that the migration matrices have been increasing in “size” since the mid-1990s, with 2002 being the “largest” in the sense of being the most dynamic. We develop a testing procedure using bootstrap techniques to assess statistically the differences between migration matrices as represented by our metric. We demonstrate that it can matter substantially which estimation method is chosen: economic credit risk capital differences implied by different estimation techniques can be as large as differences between economic regimes, recession vs. expansion. Ignoring the efficiency gain inherent in the duration methods by using the cohort method instead is more damaging than imposing a (possibly false) assumption of time homogeneity.     

消费信贷与家庭人力资本投资

已有丰富的文献考察了消费信贷对家庭消费和投资行为的影响，但少有研究关注其对家庭人力资本投资的影响。家庭人力资本投资对于人力资本积累、经济高质量发展至关重要。基于此，本文考察了信用卡使用对家庭人力资本投资的影响及其影响机制，主要发现如下：信用卡使用显著增加了家庭人力资本投资，且这一效应具有长期动态影响并对城市、高收入、高教育程度家庭影响更为显著，这意味着信用卡消费信贷可能会增加人力资本不平等。进一步研究发现，家庭会增加劳动力供给来应对人力资本支出的增加。机制分析表明，信用卡使用主要通过增加家庭消费投资、促进消费升级、缓解家庭预算约束三种途径促进家庭人力资本投资。在风险可控的前提下，引导消费信贷流向有利于实体经济发展的领域，可助力于消费升级与人力资本积累，从而为经济发展提供新动能。     

Collateral,debt capacity,and corporate investment: Evidence from a natural experiment

This paper examines how a shock to collateral value influences firms’ debt capacities and investments. Using a source of exogenous variation in collateral value provided by the land market collapse in Japan, I find that collateral has a statistically and economically significant impact on corporate investments. I also provide direct evidence on the workings of such a collateral channel. Exploiting a unique dataset of matched bank-firm lending, I show that firms with greater collateral losses are less likely to sustain their banking relationships and tend to obtain a smaller amount of bank credit.     

Investment Decisions in Anticipation of Recessions and Outperformance of Pre-Acting Firms

We empirically examine whether firms make investment decisions in anticipation of recessions and subsequently perform better. Using a large quarterly dataset of fixed asset investments for U.S. firms during 1984–2012, we show that not all firms efficiently adjust their investment decisions in anticipation of a recession. However, we find that pre-acting firms that properly adjust their investment decisions (i.e., underinvest) before a recession outperform re-acting firms that fail to make proper investment decisions (i.e., overinvest) before a recession in subsequent returns on assets, returns on investments, and market-adjusted return measures.     

绿色信贷政策得不偿失还是得偿所愿？——基于资源配置视角的PSM-DID成本效率分析

党的十九大报告聚焦生态文明体制改革,明确提出了发展绿色金融的战略要求。商业银行作为实施绿色信贷政策的主体,更加关注因此而产生的成本效率问题,即考量银行自身是得不偿失抑或得偿所愿？本文基于2005—2017年间73家中国商业银行的数据,首先,运用SFA模型测算商业银行的成本效率;其次,运用倾向得分匹配—双重差分法(PSM-DID)实证分析绿色信贷政策对银行成本效率影响的净效应;最后,采用边际动态检验方法考察绿色信贷政策净效应的影响趋势。文章发现绿色信贷政策的实施会通过成本效应机制降低银行成本效率,但同时因其改善了银行的信贷风险管理、提升了银行的声誉,从而对银行成本效率施加正向影响。此外,文章还发现绿色信贷政策的净效应呈现U型趋势,表现为2007—2013年绿色信贷政策净效应负向影响加深,2014年后出现转好信号,现已越过U型谷底。因此,从长期看,绿色信贷政策将有利于银行成本效率提升。     

利率市场化背景下商业银行利率风险管理

随着利率市场化的推进,我国商业银行面临的利率风险日益凸显。本文分析提出商业银行应进一步加强对利率风险的识别、度量和管理。还同时分析利率风险和信用风险之间的关系,并指出利率风险和信用风险都影响着银行的经营,应纳入银行的全面风险管理之中。     

关于出口信用体系建设的三个重要问题

中国入世以后 ,出口信用具有了更重大的现实意义。尽管我国出口信用体系建设已初见成效 ,但仍面临三个重要问题。首先 ,法律的缺位 ,导致了出口信用组织体系的含混不清 ,加剧了不必要竞争 ,不利于引导和鼓励商业性金融服务的广泛介入。因而出口信用体系法规建设的必要性甚为紧迫。其次 ,面对入世后对外经济发展环境的根本性变化以及WTO因素的影响 ,出口信用有必要调整政策导向 ,从而有利于WTO义务的履行。第三 ,为了适应我国对外直接投资的发展以及出口贸易运作方式的变化 ,出口信用服务亦应多元化、国际化     

银行信用风险评估模型的选择及应用策略分析

相比于巴塞尔新资本协议的现代化管理要求,我国银行在信用风险管理方面的能力,基础相对薄弱,无法全面适应银行业未来经营发展、市场竞争、业务持续发展等需求.为此,文章将通过银行信用风险评估模型的构建,从中提出强化信用风险管理体系的方法,进而为银行信用风险管理水平的提高,提供有效的依据.