Risk structure of interest rates: an empirical analysis for Deutschemark‐denominated bonds

This paper empirically studies the risk structure of interest rates for Deutschemark‐denominated bonds. For this purpose, we estimate term structures of interest rates using the parsimonious fitting function of Nelson and Siegel (1987) for virtually risk free Government bonds and five different rating categories classified by Moody's ratings (Aaa, Aa, A, Baa, Ba). The sample period covers the time interval from July 1990 to December 1996. We investigate the pricing errors resulting from our estimation procedure and analyse credit spreads over the term structure of Government bonds.     

国有商业银行信贷评级模型的构建及实证检验

信贷评级是信贷风险管理的前提,目前我国国有商业银行都采用这一方式管理信贷风险.本文在对国有商业银行当前信用评级方法存在问题和国内外相关研究成果进行分析的基础上,提出了构建国有商业银行内部信用评级模型,提高信贷风险管理水平的建议.作者利用贷款历史数据,通过因子分析和聚类分析等方法构建内部信用评级模型;通过因子分析方法构建的模型使评级指标体系更加科学、合理,避免了反映风险信息的冗余与遗漏;聚类分析使评级模型直接与违约概率挂钩,度量风险的准确性进一步提高.论文最后对模型进行了实证分析,使其有效性得到了检验.     

资产证券化信用评级制度与系统性风险分析

资产证券化的复杂性和高度杠杆化加剧了投资者与发行人之间的信息不对称问题,信用评级制度作为信息披露手段,在减轻信息不对称、促进风险资产定价和保护投资者利益等方面发挥着重要作用.但若缺乏有效的制度保障,资产证券化信用评级过程中隐含的对系统性风险的累积和传导危险则会对金融系统稳定产生负面作用.我国信用评级制度缺乏良好的法律监管环境和充分的市场竞争,难以支持资产证券化产品的多样化发展和规模的快速扩大趋势.对此,需从宏观审慎监管框架的确立、信用评级监管权力的统一和制度完善以及评级行业的市场化推进等方面完善信用评级制度.     

Modeling severity risk under PD–LGD correlation

In this article, a generic severity risk framework in which loss given default (LGD) is dependent upon probability of default (PD) in an intuitive manner is developed. By modeling the conditional mean of LGD as a function of PD, which also varies with systemic risk factors, this model allows an arbitrary functional relationship between PD and LGD. Based on this framework, several specifications of stochastic LGD are proposed with detailed calibration methods. By combining these models with an extension of CreditRisk+, a versatile mixed Poisson credit risk model that is capable of handling both risk factor correlation and PD–LGD dependency is developed. An efficient simulation algorithm based on importance sampling is also introduced for risk calculation. Empirical studies suggest that ignoring or incorrectly specifying severity risk can significantly underestimate credit risk and a properly defined severity risk model is critical for credit risk measurement as well as downturn LGD estimation.     

基于企业规模的信贷决策支持：运用外部信用评级的一个试验

中小企业作为一个整体,需要根据企业的风险度来确定对它的信贷规模,风险度以企业信用级别来表示,信用级别通过外部评级来确定。为此本文介绍了一个外部评级体系,利用一个地区的评级样本——59家中小企业,反映外部评级体系对中小企业风险度的独特揭示,在此基础上建立了一个对中小企业的、基于信用级别的最高授信额度测算模型。     

Determinants of Sovereign Eurobonds Yield Spread

We examine the determinants of sovereign Eurobond spread at issuance covering 1991–2000. The results of the regression models showed that yield spread increases with maturity, issue size and gross fees and decreases with credit rating and the number of managers. Higher‐grade issuers also pay a relatively higher spread to borrow long‐term funds and for smaller issues. The findings are consistent with the notion of a term structure 'liquidity premium.' Low‐grade issuers pay a higher spread than better‐rated countries. However, low‐grade countries pay high spread for larger funds. Credit rating is found to provide additional information in explaining the spread on sovereign Eurobonds beyond that provided by macroeconomic variables.     

The ordered qualitative model for credit rating transitions

Information on the expected changes in credit quality of obligors is contained in credit migration matrices which trace out the movements of firms across ratings categories in a given period of time and in a given group of bond issuers. The rating matrices provided by Moody's, Standard & Poor's and Fitch became crucial inputs to many applications, including the assessment of risk on corporate credit portfolios (CreditVar) and credit derivatives pricing. We propose a factor probit model for modeling and prediction of credit rating matrices that are assumed to be stochastic and driven by a latent factor. The filtered latent factor path reveals the effect of the economic cycle on corporate credit ratings, and provides evidence in support of the PIT (point-in-time) rating philosophy. The factor probit model also yields the estimates of cross-sectional correlations in rating transitions that are documented empirically but not fully accounted for in the literature and in the regulatory rules established by the Basle Committee.     

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.     

Analyzing the impact of credit migration in a portfolio setting

Credit migration is an essential component of credit portfolio modeling. In this paper, we outline a framework for gauging the effects of credit migration on portfolio risk measurements. For a typical loan portfolio, we find credit migration can explain as much as 51% of volatility and 35% of economic capital. We compare through-the-cycle migration effects, implied by agency rating transitions, with point-in-time migration, implied by EDF? (Expected Default Frequency) transitions, and find that migration of point-in-time credit quality accounts for a greater fraction of total portfolio risk when compared with through-the-cycle dynamics. In a stylized analytic setting, we show that, when controlling for PD term structure effects, higher likelihood of moving away from the current credit state does not necessarily imply greater risk. Finally, we review methods for generating high-frequency transition matrices, needed for analyzing instruments with cash flows or contingencies whose frequencies are asynchronous to an available transition matrix. We further demonstrate that the naïve application of such methods can result in material deviations to portfolio analytics.     

Modeling of Contagious Credit Events and Risk Analysis of Credit Portfolios

We present a new model of the occurence of credit events such as rating changes and defaults for risk analyses of some portfolio credit derivatives. The framework of our model is based on a so-called top-down approach. Specifically, we first consider modeling the point process of each type of credit event in the whole economy using a self-exciting intensity process. Next, we characterize the point processes of credit events in the underlying sub-portfolio using random thinning processes specified by the distribution of credit ratings in the sub-portfolio. One of the main features of our model is that the model can capture credit risk contagion simultaneously among several credit portfolios. We present a credit event simulation algorithm based on our model and illustrate an application of the model to risk analyses of loan portfolios.