1、 外文翻译 原文 Listed companies in real estate credit risk measurement based on KMV model Material Source: Journal of Banking & Finance Author: Michel Crouhy BIS 1998 is now in place, with internal models for market risk, both general and specific risk, implemented at the major G-10 banks, and used every
2、day to report regulatory capital for the trading book. The next step for these banks is to develop a VaR framework for credit risk. The current BIS requirements for “specific risk” are quite loose, and subject to broad interpretation. To qualify as an internal model for specific risk, the regulator
3、should be convinced that “concentration risk”, “spread risk”, “downgrade risk” and “default risk” are appropriately captured, the exact meaning of “appropriately” being left to the appreciation of both the bank and the regulator. The capital charge for specific risk is then the product of a multipli
4、er, whose minimum volume has been currently set to 4, times the sum of the VaR at the 99% confidence level for spread risk, downgrade risk and default risk over a 10-day horizon. There are several issues with this piecemeal approach to credit risk. First, spread risk is related to both market risk a
5、nd credit risk. Spreads fluctuate either, because equilibrium conditions in capital markets change, which in turn affect credit spreads for all credit ratings, or because the credit quality of the obligor has improved or deteriorated, or because both conditions have occurred simultaneously. Downgrad
6、e risk is pure credit spread risk. When the credit quality of an obligor deteriorates then the spread relative to the Treasury curve widens, and vice versa when the credit quality improves. Simply adding spread risk to downgrade risk may lead to double counting. In addition, the current regime assim
7、ilates the market risk component of spread risk to credit risk, for which the regulatory capital multiplier is 4 instead of 3. Second, this issue of disentangling market risk and credit risk driven components in spread changes is further obscured by the fact that often market participants anticipate
8、 forthcoming credit events before they actually happen. Therefore, spreads already reflect the new credit status when the rating agencies effectively downgrade an obligor, or put him on “credit watch”. Third, default is just a special case of downgrade, when the credit quality has deteriorated to th
9、e point where the obligor cannot service anymore its debt obligations. An adequate credit-VaR model should therefore address both migration risk, i.e. credit spread risk, and default risk in a consistent and integrated framework. Finally, changes in market and economic conditions, as reflected by ch
10、anges in interest rates, the stock market indexes, exchange rates, unemployment rates, etc. may affect the overall profitability of firms. As a result, the exposures of the various counterparts to each obligor, as well as the probabilities of default and of migrating from one credit rating to anothe
11、r. In fact, the ultimate framework to analyze cred it risk calls for the full integration of market risk and credit risk. So far no existing practical approach has yet reached this stage of sophistication. During the last two years a number of initiatives have been made public. Credit Metrics from J
12、P Morgan, first published and well publicized in 1997, is reviewed in the next section. Credit Metrics approach is based on credit migration analysis, i.e. the probability of moving from one credit quality to another, including default, within a given time horizon, which is often taken arbitrarily a
13、s 1 year. Credit Metrics models the full forward distribution of the values of any bond or loan portfolio, say 1 year forward, where the changes in values are related to credit migration only, while interest rates are assumed to evolve in a deterministic fashion. Credit-VaR of a portfolio is then de
14、rived in a similar fashion as for market risk. It is simply the percentile of the distribution corresponding to the desired confidence level. KMV Corporation, a firm specialized in credit risk analysis, has developed over the last few years a credit risk methodology, as well as an extensive database
15、, to assess default probabilities and the loss distribution related to both default and migration risks. KMVs methodology differs somewhat from Credit Metrics as it relies upon the “Expected Default Frequency”, or EDF, for each issuer, rather than upon the average historical transition frequencies p
16、roduced by the rating agencies, for each credit class. Both approaches rely on the asset value model originally proposed by Merton (1974), but they differ quite substantially in the simplifying assumptions they require in order to facilitate its implementation. How damaging are, in practice, these c
17、ompromises to a satisfactory capture of the actual complexity of credit measurement stays an open issue. It will undoubtedly attract many new academic developments in the years to come. KMVs methodology is reviewed in Section 3. At the end of 1997, Credit Suisse Financial Products (CSFP) released a
18、new approach, Credit Risk+, which only focuses on default. Section 4 examines briefly this model. Credit Risk+ assumes that default for individual bonds, or loans, follows a Poisson process. Credit migration risk is not explicitly modeled in this analysis. Instead, Credit Risk+ allows for stochastic
19、 default rates which partially account, although not rigorously, for migration risk. Finally, McKinsey, a consulting firm, now proposes its own model, Credit Portfolio View, which, like Credit Risk+, measures only default risk. It is a discrete time multi-period model, where default probabilities ar
20、e a function of macro-variables such as unemployment, the level of interest rates, the growth rate in the economy, government expenses, foreign exchange rates, which also drive, to a large extent, credit cycles. Credit Portfolio View is examined in Section 5. From the actual comparison of these mode
21、ls on various benchmark portfolios, it seems that any of them can be considered as a reasonable internal model to assess regulatory capital related to credit risk, for straight bonds and loans without option features. 1 All these models have in common that they assume deterministic interest rates an
22、d exposures. While, apparently, it is not too damaging for simple “vanilla” bonds and loans, these models are inappropriate to measure credit risk for swaps and other derivative products. Indeed, for these instruments we need to propose an integrated framework that allows to derive, in a consistent
23、manner, both the credit exposure and the loss distribution. Currently, none of the proposed models offers such an integrated approach. In order to measure credit risk of derivative securities, the next generation of credit models should allow at least for stochastic interest rates, and possibly defa
24、ult and migration probabilities which depend on the state of the economy, e.g. the level of interest rates and the stock market. According to Standard & Poors, only 17 out of more than 6700 rated corporate bond issuers it has rated defaulted on US $4.3 billion worth of debt in 1997, compared with 65
25、 on more than US $20 billion in 1991. In Fig. 1 we present the record of defaults from 1985 to 1997. It can be seen that in 1990 and 1991, when the world economies were in recession, the frequency of defaults was quite large. In recent years, characterized by a sustained growth economy, the default
26、rate has declined dramatically. KMV and Credit Portfolio View base their approach on the same empirical observation that default and migration probabilities vary over time. KMV adopts a microeconomic approach which relates the probability of default of any obligor, to the market value of its assets.
27、 Credit Portfolio View proposes a methodology which links macroeconomics factors to default and migration probabilities. The calibration of this model necessitates reliable default data for each country, and possibly for each industry sector within each country. Another limitation of the model is th
28、e ad-hoc procedure to adjust the migration matrix. It is not clear that the proposed methodology performs better than a simple Bayesian model where the revision of the transition probabilities would be based on the internal expertise accumulated by the credit department of the bank, and the internal
29、 appreciation of where we are in the credit cycle given the quality of the banks credit portfolio. These two approaches are somewhat related since the market value of the firms assets depends on the shape of the economy. It would then be interesting to compare the transition matrices produced by bot
30、h models. 译文 基于 KMV模型的房地产业上市公司信用风险度量 资料来源：银 行与金融杂 志 作者：迈 克尔 .克 劳西 1988 年国际清算银行已经为市场风险建立包括一般风险和特殊风险的内部模型， 10 个国家的银行实行了这一模型，并每天对贸易协议报告监管资本。对这些银行来说，下一步就是为信贷风险建立一个风险价值范围。目前国际清算银行对“特殊风险”的要求是相当松散的，而且也广受争议。为了让特殊风险有合格的内部模型，监管机构应当考虑使“集中性风险”、“分散风险”、“降级风险”、“违约风险”能够被适当的控制，“适当的”的 确切的含义是同时能被银行和监管机构所了解。特殊风险的资本支出是收
31、益增值率的产物，其最小成交量现在已经调整到 4。 资本支出乘以最大置信水平为 99%的价值评估能够能使转移风险、降级风险和违约风险刚好过了 10 天的水平。这样微小的趋近信用风险有一些问题。 首先，分散风险同时与市场风险和信用风险相联系。利差波动可能由于平衡条件在资本市场的变化反过来影响到所有信贷评级的期权价差，或者因为信贷人的信贷品质有所改善或者恶化，更或者是因为这两个情况同时发生而变动。降级风险是纯粹的信用利差风险。当债务人的信贷品质恶化时，政府的财 政曲线扩大，而当信贷品质有所改善时，情况相反。简单的增加分散风险来降低风险可能会导致重复计算。此外，现行制度吸收市场风险的组成部分，以分散风
32、险的信贷风险，而监管资本乘数从 3 调整为 4。 第二，清理市场风险和信用风险驱使竞争者有更广泛的改变，这个问题进一步的被以下事实所掩盖，那就是，市场参与者总是预期即将发生地信贷事件在时间发生以前。因此，利差能够反映新的信用状况当评级机构已经降级了债务人的信用或者把债务人的信用进行“监视”。 第三，违约是信用降级的一种特殊情况，即当信贷品质恶化到一个点时债务人就无法再对任何人 履行其偿债义务。一个适用的信用评估模型因此应该能在一个统一和完整的体系里解决转移风险（也就是信用分散风险）和违约风险。 最后，变化的市场和经济状况，反映了利率变化、股市指数、汇率、失业率等变化，这些可能会影响公司的整体盈
33、利能力。其结果是，每一个债务人接触到不同的对手，以及违约的概率从一个客户信贷分类转移到另一个。事实上，需要全面一体化的市场风险和信用风险来作为最终的框架分析信贷风险。到目前为止还没有任何切实可行的办法到达这个复杂的阶段。 在过去的两年里已经公开了一些信用风险的创新。 1997 年 JP 摩根公司开发 的信用度量术模型首次公布并广为人知，该模型将在下一节进行讨论。信用度量术的方法是基于信用转移分析，即概率从一个信贷品质转移到另一个信贷品质，包括违约情况下，并在一个给定时间范围内，这个给定时间范围经常采取任意的 1 年。 信用度量术模型是关于债券和贷款投资组合的价值的完全期货分配，比如说一年期货，
34、价值的改变仅跟风险的转移有关，与此同时假定利率多少是被确定的。信用风险估价模型的投资组合运用了相似的方法衡量市场风险，这百分比的分配完全和理想的可信度水平相一致。 KMV 公司是一家专业的信用风险研究公司，它发展了过去几年中的信用风险度量方法，以建立广泛的数据库来评估违约风险和转移风险中违约的可能性和损失的分配。和信用度量术模型不尽相同的是， KMV 公司的方法依赖于“预期违约概率”（ EDF），它针对于每一个发行者而得出，而不注重于由评级机构得出的基于平均历史波动率的企业信用评级。 这两种方法都依赖于最初由默顿（ 1974）提出的的资产定价模型，但是他们相当大的不同之处是简化了不同的假定条件
35、以便于自身的使用。 在实践中，这些妥协方法在多少程度上破坏了信贷评估的灵活性，仍然是一个 待解决的问题。 这无疑在今后的几年里会引起许多新学术的发展。 KMV公司的模型将在第三节进行讨论。 1997 年末，瑞士信贷银行开发了一个新的信用风险度量方法：信用风险附加法模型，该模型只考虑违约风险，第四节简要的介绍了该模型。信用风险附加法模型假定个别证券和贷款的违约频率遵循布朗运动。信用风险转移并没有在这一模型里明确的被提及，相反的，信用风险附加法模型允许违约概率受随机因素的影响，而对转移风险来说并不受影响。 最后，麦肯基顾问公司提出了自己的信用风险模型 宏观经济模型（ CPV），这个模型和信用附加法
36、模型相似 ，只考虑违约风险。它是一个离散时间多期模型，违约的可能性是宏观经济变量，如失业率、利率水平、经济增长率、政府支出、外汇汇率、甚至信贷周期在一个很大程度上也是驱动因素。宏观经济模型在第五节进行介绍。 从实际的情况比较这些模型的不同的基准投资组合，似乎任何一个模型都可以被认为合理的内部模型，为没有期权特性的直接债券和贷款评估监管资本的信贷风险。这些模型的共同点是他们假设利率固定并面临财政损失风险。很显然的，虽然这并没有对“纯”债券和贷款造成很大的破坏，但是这些模型并不合适于计量信贷风险的掉期交易和其他衍生品。现 在，所有的模型都提供了这样一个综合的方法。为了度量衍生证券的信用风险，新的信
37、贷模型应该至少允许随机的利率，并且认为可能性违约和风险转移取决于经济状况。比如：利率水平和股市。根据标准普尔得出， 1997年一共只有 17家债券发行超过 6700的公司债券发行商拖欠了 43亿美金，和其相比， 1991年有 65家公司共 200亿美元的拖欠。表一是目前记录的 1985-1997年间的欠款。从图中可以看出， 1990-1991年间，由于世界经济处于衰退状态，此时的违约频率相当大。而在近几年，经济呈现持续增长的特征，随着违约频率急剧下降 。 KMV模型和 CPV模型以实证为基础得出结论，认为违约概率和风险转移概率都随着时间的改变而改变。 KMV模型通过联系任何债务人发生违约的可能性这样一种微观的经济方法来计算出资产的市场价值。 CPV模型则提供一种通过宏观经济变量的变动计算违约风险和信用转移风险的模型方法。为了使模型具有准确性，国家乃至国家内的工业领域，必须有可靠的信用违约数据库。另一个模型的限制条件是必须通过特定的程序调整其迁移矩阵。目前还不清楚新被提议的方法论是否优于一个简单的贝叶斯模型，这个模型的修改转移概率将基于银行信贷部门积累的内部专业 知识，以及在给定银行信贷投资组合质量的信用循环中的内部鉴定。 KMV 和 CPV 这两个模型又稍微有所关联，因为公司资产的市场价值都取决于经济的发展状况。对这两个模型的迁移矩阵进行比较是非常有意思的。
