内部评级体系定量验证模型及在中国银行业的应用

定量验证是运用一定的统计方法来检验商业银行已运行内部客户评级体系的准确性、审慎性及稳定性。准确性验证主要检验商业银行的内部评级模型对授信客户信用状况好坏的风险识别能力;审慎性验证主要检验银行所采用的政策与标准在辨别其内部评级和风险参数量化上的保守程度;稳定性验证则主要检验在风险不变的情况下,银行所采用政策和标准能够保持评级与估值总体上不发生变化。本文介绍了常用的定量验证模型及流程,并以中国某商业银行进行定量验证的实践为例,介绍了如何进行银行内部评级体系的定量验证及在具体实践中可能遇到的问题及解决方案。     

Tracking bond indices in an integrated market and credit risk environment

Abstract

The management of credit risky assets requires simulation models that integrate the disparate sources of credit and market risk, and suitable optimization models for scenario analysis. In this paper we integrate Monte Carlo simulation models for credit risk with scenario optimization, and develop a methodology for tracking broadly defined corporate bond indices. Testing of the models shows that the integration of the multiple risk factors improves significantly the performance of tracking models. Good tracking performance can be achieved by optimizing strategic asset allocation among broad classes of corporate bonds. However, extra value is generated with a tactical model that optimizes bond picking decisions as well. It is also shown that adding small corporate bond holdings in portfolios that track government bond indices improves the risk/return characteristics of the portfolios. The empirical results to substantiate the findings of this study are obtained by backtesting the model over a recent 30 month period.     

信息技术在商业银行信用风险管理中的应用

信用风险是我国商业银行长期以来面临的主要风险,近年来我国银行业在推进信用风险管理的历程中,整体上面临着方法论和实现路径两大课题.信息技术的应用在我国银行业信用风险管理演进中发挥了重要的支撑作用,并最终成为新型信用风险管理技术的普遍实施路径.当前商业银行的信用风险管理处在向模型化转型的关键时期,信息技术体系也正处于重构的重要阶段,在同步升级信用风险管理与重新构建信息技术体系的过程中,信用风险管理技术的应用范围要从传统产品扩充到衍生产品、形成具有可操作性的资产组合风险管理功能、实现信用风险管理在资本层面应用,最终完成新型管理技术的内化.     

Banking systemic vulnerabilities: A tail-risk dynamic CIMDO approach

This study proposes a novel framework which combines marginal probabilities of default estimated from a structural credit risk model with the consistent information multivariate density optimization (CIMDO) methodology and the generalized dynamic factor model (GDFM) supplemented by a dynamic t-copula. The framework models banks’ default dependence explicitly and captures the time-varying non-linearities and feedback effects typical of financial markets. It measures banking systemic credit risk in the three forms categorized by the European Central Bank: (1) credit risk common to all banks; (2) credit risk in the banking system conditional on distress on a specific bank or combinations of banks; and (3) the buildup of banking system vulnerabilities over time which may unravel disorderly. In addition, the estimates of the common components of the banking sector short-term and conditional forward default measures contain early warning features, and the identification of their drivers is useful for macroprudential policy. Finally, the framework produces robust out-of-sample forecasts of the banking systemic credit risk measures. This paper advances the agenda of making macroprudential policy operational.     

Granularity adjustment for mark-to-market credit risk models

The impact of undiversified idiosyncratic risk on value-at-risk and expected shortfall can be approximated analytically via a methodology known as granularity adjustment (GA). In principle, the GA methodology can be applied to any risk-factor model of portfolio risk. Thus far, however, analytical results have been derived only for simple models of actuarial loss, i.e., credit loss due to default. We demonstrate that the GA is entirely tractable for single-factor versions of a large class of models that includes all the commonly used mark-to-market approaches. Our approach covers both finite ratings-based models and models with a continuum of obligor states. We apply our methodology to CreditMetrics and KMV Portfolio Manager, as these are benchmark models for the finite and continuous classes, respectively. Comparative statics of the GA reveal striking and counterintuitive patterns. We explain these relationships with a stylized model of portfolio risk.     

How rating agencies achieve rating stability

Surveys on the use of agency credit ratings reveal that some investors believe that rating agencies are relatively slow in adjusting their ratings. A well-accepted explanation for this perception on the timeliness of ratings is the through-the-cycle methodology that agencies use. According to Moody’s, through-the-cycle ratings are stable because they are intended to measure default risk over long investment horizons, and because they are changed only when agencies are confident that observed changes in a company’s risk profile are likely to be permanent. To verify this explanation, we quantify the impact of the long-term default horizon and the prudent migration policy on rating stability from the perspective of an investor – with no desire for rating stability. This is done by benchmarking agency ratings with a financial ratio-based (credit-scoring) agency-rating prediction model and (credit-scoring) default-prediction models of various time horizons. We also examine rating-migration practices. The final result is a better quantitative understanding of the through-the-cycle methodology.By varying the time horizon in the estimation of default-prediction models, we search for a best match with the agency-rating prediction model. Consistent with the agencies’ stated objectives, we conclude that agency ratings are focused on the long term. In contrast to one-year default prediction models, agency ratings place less weight on short-term indicators of credit quality.We also demonstrate that the focus of agencies on long investment horizons explains only part of the relative stability of agency ratings. The other aspect of through-the-cycle methodology – agency-rating migration policy – is an even more important factor underlying the stability of agency ratings. We find that rating migrations are triggered when the difference between the actual agency rating and the model predicted rating exceeds a certain threshold level. When rating migrations are triggered, agencies adjust their ratings only partially, consistent with the known serial dependency of agency-rating migrations.     

Credit lines as monitored liquidity insurance: Theory and evidence

We propose a theory of credit lines provided by banks to firms as a form of monitored liquidity insurance. Bank monitoring and resulting revocations help control illiquidity-seeking behavior of firms insured by credit lines. The cost of credit lines is thus greater for firms with high liquidity risk, which in turn are likely to use cash instead of credit lines. We test this implication for corporate liquidity management by identifying exogenous shocks to liquidity risk of firms in corporate bond and equity markets. Firms experiencing increases in liquidity risk move out of credit lines and into cash holdings.     

Market conditions,default risk and credit spreads

This study empirically examines the impact of the interaction between market and default risk on corporate credit spreads. Using credit default swap (CDS) spreads, we find that average credit spreads decrease in GDP growth rate, but increase in GDP growth volatility and jump risk in the equity market. At the market level, investor sentiment is the most important determinant of credit spreads. At the firm level, credit spreads generally rise with cash flow volatility and beta, with the effect of cash flow beta varying with market conditions. We identify implied volatility as the most significant determinant of default risk among firm-level characteristics. Overall, a major portion of individual credit spreads is accounted for by firm-level determinants of default risk, while macroeconomic variables are directly responsible for a lesser portion.     

The interbank market after the financial turmoil: Squeezing liquidity in a “lemons market” or asking liquidity “on tap”

After August 2007 the plumbing system that supplied banks with wholesale funding, the interbank market, failed because toxic assets obstructed the pipes. Banks were forced to squeeze liquidity in a “lemons market” or to ask for liquidity “on tap” from central banks. This paper disentangles the two components of the 3-month Euribor–Eonia swap spread, credit and liquidity risk and then evaluates the decomposition. The main finding is that credit risk increased before the key events of the crisis, while liquidity risk was mainly responsible for the subsequent increases in the Euribor spread and then reacted to the systemic responses of the central banks, especially in October 2008. Moreover, the level of the spread between May 2009 and February 2010 was influenced mainly by credit risk, suggesting that European banks were still in a “lemons market” and relied on liquidity “on tap” even before sovereign debt crisis unfolded in Europe.     

信贷违约中期预警模型的构建与分析——以医药制造企业为实证

根据新巴塞尔资本协议,基于内部评级法要求的银行客户信用等级评定可以作为测算客户信用风险违约概率的依据,但其有效期不得超过1年;构建预警模型进行中期(2-4年)预警是深化信贷风险管理的客观要求。在对当前国内外财务预警文献梳理的基础上,选择线性判别模型和logit回归模型作为信贷违约预警的基本分析工具,并利用医药制造行业样本公司实际指标数据,对各个模型的中期预测效果进行分析比较,并提出了政策建议。     

Liquidity risk in derivatives valuation: an improved credit proxy method

The models used to calculate post-crisis valuation adjustments, market risk and capital measures for derivatives are subject to liquidity risk due to severe lack of available information to obtain market implied model parameters. The European Banking Authority has proposed an intersection methodology to calculate a proxy CDS or Bond spread. Due to practical issues of this method, Chourdakis et al. introduce a cross-section approach. In this paper, we extend the cross-section methodology using equity returns, and show that our methodology is significantly more accurate compared to both existing methodologies, and produces more reliable, stable and robust market risk and capital measures, and credit valuation adjustment.     

Validation of default probability models: A stress testing approach

This study aims to evaluate the techniques used for the validation of default probability (DP) models. By generating simulated stress data, we build ideal conditions to assess the adequacy of the metrics in different stress scenarios. In addition, we empirically analyze the evaluation metrics using the information on 30,686 delisted US public companies as a proxy of default. Using simulated data, we find that entropy based metrics such as measure M are more sensitive to changes in the characteristics of distributions of credit scores. The empirical sub-samples stress test data show that AUROC is the metric most sensitive to changes in market conditions, being followed by measure M. Our results can help risk managers to make rapid decisions regarding the validation of risk models in different scenarios.     

The integrated impact of credit and interest rate risk on banks: A dynamic framework and stress testing application

Credit and interest rate risk are the two most important risks faced by commercial banks in their banking book. In this paper we derive a consistent and comprehensive framework to measure the integrated impact of both risks. By taking account of the repricing characteristics of assets, liabilities and off balance sheet items, we assess the integrated impact of credit and interest rate risk on banks’ economic value and capital adequacy. We then stress test a hypothetical but realistic bank using our framework and show that it is fundamental to measure the impact of credit and interest rate risk jointly.     

Credit cycle coherence in the eurozone: Was there a euro effect?

This paper examines effects of the euro introduction on credit cycle coherence in the eurozone through six channels. We construct and describe credit cycles for total bank credit, household mortgages and non-financial business loans for 16 EMU economies over 1990–2015. Credit cycle coherence is measured by synchronicity of cycle movements and similarity of their amplitudes. We find that the effect of euro introduction runs through elimination of currency risk and higher capital flows, which decrease coherence of total credit and mortgage credit cycles, but increase coherence of business credit cycles. Falling interest rates contribute to the convergence of total and mortgage credit cycles. Financial deregulation and legal harmonization are associated with lower coherence of all credit cycles, while trade openness has the opposite impact. The findings impinge on monetary policy effectiveness in the eurozone, with implications for macroprudential policy.     

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.     

On long-term credit risk assessment and rating: towards a new set of models

Institutional investors are supposed to assess credit risk by using a combination of quantitative information such as option models and qualitative assessments. Although option models can be easily constructed, they are not so suitable for the assessment of long-term credit risk that is required by institutional investors. This is mainly because the probability of bankruptcy varies so widely depending on the timing of assessment. We propose a new set of assessment models for long-term credit risk which does not necessarily use stock prices and may incorporate business cycles. The new grand model consists of the two pillars: a long-term cash flow prediction model and a credit risk spread assessment model. The calculated values derived from these models are effectively usable for reasonable calculation of risk spreads. It is quite interesting to see that our investigation indicates that rating bias may exist in the credit risk assessment of the market.     

The Impact of the Federal Reserve's Large‐Scale Asset Purchase Programs on Corporate Credit Risk

Estimating the effect of Federal Reserve's announcements of Large‐Scale Asset Purchase (LSAP) programs on corporate credit risk is complicated by the simultaneity of policy decisions and movements in prices of risky financial assets, as well as by the fact that both interest rates of assets targeted by the programs and indicators of credit risk reacted to other common shocks during the recent financial crisis. This paper employs a heteroskedasticity‐based approach to estimate the structural coefficient measuring the sensitivity of market‐based indicators of corporate credit risk to declines in the benchmark market interest rates prompted by the LSAP announcements. The results indicate that the LSAP announcements led to a significant reduction in the cost of insuring against default risk—as measured by the CDX indexes—for both investment‐ and speculative‐grade corporate credits. While the unconventional policy measures employed by the Federal Reserve to stimulate the economy have substantially lowered the overall level of credit risk in the economy, the LSAP announcements appear to have had no measurable effect on credit risk in the financial intermediary sector.     

Credit Markets and the Propagation of Monetary Policy Shocks

This paper analyzes the propagation of monetary policy shocks through the creation of credit in an economy. Models of the monetary transmission mechanism typically feature responses that last for a few quarters contrary to what the empirical evidence suggests. To propagate the impact of monetary shocks over time, these models introduce adjustment costs by which agents find it optimal to change their decisions slowly. This paper presents another explanation that does not rely on any sort of adjustment costs or stickiness. In our economy, agents own assets and make occupational choices. Banks intermediate between agents demanding and supplying assets. Our interpretation is based on the way banks create credit and how the monetary authority affects the process of financial intermediation through its monetary policy. As the central bank lowers the interest rate by buying government bonds in exchange for reserves, high productive entrepreneurs are able to borrow more resources from low-productivity agents. We show that this movement of capital among agents sets in motion a response of the economy that resembles an expansionary phase of the cycle.     

Credit risk management at retail in Mexico: An econometric improvement in the selection of variables and changes in their characteristics

The early prediction of bad debtors for revolving credits in Mexico is a relevant issue today. The credit behavior econometric model proposed considers the changes in the characteristics of the consolidated accredited and provides better results than those obtained with the methodology utilized by the CNBV on provision matters. The results obtained show that the possibility of replacing the current model, minimizing the expected loss and increasing the ROA per financial institution at a national level by 2.20%, complies with the methodological criteria and the statistical tests in accordance with the Compiled Banking Regulation and Basel II guidelines on credit risk issues.     

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.