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版本:访谈纪要访谈记录：1．个银部整体战略：❑战略规划概要：发展目标：以市场为导向，以中心城市为主阵地，充分发挥科技、网络的作用，优化资源配置；拓展具有核心竞争力的产品与服务；建设统一的金融服务品牌。
强化质量管理和风险控制，努力提高经营效益。
市场定位：个人银行业务在行内要继续巩固支柱业务地位，在国内金融市场保持前三位的竞争优势，在银行卡、个人电子汇款、证券代理业务方面要争取做到国内前二位，居市场领导地位。
中高端客户群体也要达到前二位。
目标客户：个人银行业务的目标客户是具有收入水平中上等、收入稳定、金融往来较多、对银行贡献度较高的中高端客户。
未来业务发展：个人存款的主要地位不会变化。
未来的增长点主要应该是银行卡业务、个人支付结算，中间代理业务，个人消费贷款，个人外汇业务和一些金融衍生产品。
向中高端客户提供综合金融需求解决方案的个人理财业务是个人银行业务的重点发展方向。
❑个银业务发展面临主要问题：1．在金融产品的推出过程中并没有以客户的意见为导向做出正确的市场分析，在后期也没有及时的收集和分析客户的反馈意见，产品的推出还是以银行为驱动，不是以版本:访谈纪要客户为驱动。
2．没有产品服务渠道反馈，不能及时进行市场分析，产品推出的速度不能适应市场的发展需求，行业竞争力下降。
竞争中把握市场的能力有待提高。
3．银行资源分散，无法为客户提供整体服务。
集约化程度低，资源急需整合。
4．内部管理水平无法适应业务的快速发展。
5．技术应用及系统支持不利，制约业务发展。
没有整合的客户信息，导致产品的推出无法以客户需求为导向，资源浪费。
建行在数据集中系统的基础上构建的证券业务系统，运作情况良好；建行在清算系统的基础上构建的速汇通系统广受好评，且运营状况良好；贷记卡系统，个人理财系统，个人信贷系统的建设工作已基本完成，具体运营状况需要收集市场反馈的信息加以分析定论。
明年计划上15个项目其中7个为续建项目为了保持强势产品的市场份额，例如实现速汇通业务的实时到帐，证券业务系统的接口改造等，其余8个为新建项目例如整合客户信息的客户关系管理系统，外汇汇款系统等。
参考文献：[1]George A.Akerlof.The Market for “Lemons ”：Quality Uncertainty and the Market Mechanism[J].The Quarterly Journal of Economics ，1970，84（3）：488-500.[2]Morgan J P.Credit Metrics -technical Document[J].New York ，1997：33-37.[3]陈晓红，张泽京，王傅强.基于KMV 模型的我国中小上市公司信用风险研究[J].数理统计与管理，2008（1）：164-175.[4]张能福，张佳.改进的KMV 模型在我国上市公司信用风险度量中的应用[J].预测，2010（5）：48-52.[5]马国建，张冬华.中小企业信用再担保体系经济效益研究[J].软科学，2010，24（7）：111-120.[6]于孝建，徐维军.中小企业信用再担保各合作方的风险和收益分析[J].系统工程，2013，31（5）：33-39.[7]Friedman D.Evolutionary games in economics[J].Eomomica ，1991，（59）：637-666.[责任编辑若云]移动，收敛于模式（A 2，B 2）概率增加而收敛于模式（A 1，B 1）的概率减小。
这说明，在其他因素保持不变的情况下，增加担保放大倍数会促使主体的合作向理想方向演化。
原因是放大倍数增加，表明银行认可度高，有利于做大规模，提高担保、再担保收入，双方合作意愿提高。
图4参数n 1变化收敛方向示意图3.如图5所示，鞍点处∂y ∂λ1>0，∂y ∂g 1>0。
在其他参数不变的情况下，减少g 1、λ1会使鞍点向下平移，LKNM 的面积增加，系统向（A 1，B 1）方向收敛。
反之，LKNM 的面积减少，系统向（A 2，B 2）方向收敛。
同业管理层讨论与分析语调对股价崩盘风险的溢出效应目录1.内容概括．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．21.1 研究背景．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．3 1.2 研究意义．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．41.3 研究方法与论文结构．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．52.理论基础与文献综述．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．62.1 股价崩盘风险相关理论．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．7 2.2 同业管理层讨论与分析语调研究．．．．．．．．．．．．．．．．．．．．．．．．．．．8 2.3 溢出效应相关理论．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．102.4 文献综述与分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．113.同业管理层讨论现状分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．123.1 同业管理层讨论的重要性．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．13 3.2 同业管理层讨论的现状分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．143.3 讨论语调的识别与度量．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．154.分析语调对股价崩盘风险的直接影响．．．．．．．．．．．．．．．．．．．．．．．174.1 理论基础．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．18 4.2 实证分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．19 4.3 结果与讨论．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．215.溢出效应的路径分析．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．225.1 路径一．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．235.2 路径二．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．245.3 路径三．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．256.同业管理层讨论与分析语调的溢出效应实证研究．．．．．．．．．．．．．266.1 研究假设与模型构建．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．286.2 数据来源与处理．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．296.3 实证分析过程与结果．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．307.防范与应对策略建议．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．317.1 对上市公司的建议．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．327.2 对投资者的建议．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．347.3 对监管部门的建议．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．358.研究结论与展望．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．368.1 研究结论总结．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．378.2 研究创新点．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．398.3 研究不足与展望．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．．401. 内容概括在金融市场中，同业管理层讨论和分析的语调对于投资者判断公司股票价格的潜在崩盘风险具有重要影响。
白皮书：金融服务您的企业正在转型还是在调整21世纪零售银行模式全球银行业的变革21世纪零售银行模式白皮书目录：介绍全球银行业的变革21世纪的银行技术：促成业务战略未来之路客户管理流程企业架构信息支付沟通外包IT/业务一体化结论业务和系统一体化，增强企业竞争力作者简介3456779101111121213141515新世纪的许多重大事件——从全球化和日益增多的并购，到法规变化和技术突破——都使得银行将其注意力转移到效率与客户关系拓展上来。
在21世纪的第一个十年中，银行将如何调整以适应一个飞速变化的景象呢？毕博携手Datamonitor公司，一起访问了全球领先零售银行中的技术高层，探知各家银行如何运营转型来迎接新世纪，并阐述他们在新世纪竞争激烈的环境中取得成功的理念。
该全球研究报告揭示了全球银行业在未来5至10年中的发展方向。
这些行业领先者的意见为我们描述了21世纪银行的愿景，以及如何利用IT来实现愿景。
介绍各个银行必须寻找持续增长的方式，并且同时转型成为拥有大批超越传统银行的专业人才及资源的高效率管理的组织。
这种效率的竞争将使金融机构中的数据经纪人比银行家还多。
那些具备世界级数据存贮、管理及分析能力的组织，将能够凭借快速开发和引进下一代产品，使自己独树一帜。
这种从银行家向数据经纪人的转型正在降低传统的准入壁垒，为新的参与者打开银行业的大门。
目睹了沃尔马最近在美国三个州及加拿大为进入银行业务所做的尝试，表面上是为了降低成本而处理借贷及电子支票业务。
银行业对沃尔马这种举动的不断抵制清楚地表明，银行对于非传统竞争对手进入市场这一全新潮流的担忧。
其二，金融交易，作为银行的传统核心业务，正在快速地日常商品化。
展望未来，银行必须将其业务视作锁定客户、服务客户及维护客户关系的一个整体。
更深入地了解客户，这对于保留现有客户和实现收入增长来说必不可少。
最后，法规要求是史无前例。
为满足不同地区严格的合规性标准，银行需要透明的系统和流程，同时需要建立更加有效的、预测性的更强的方法来进行信息的收集、存贮和处理。
Credit LimitsThis document describes an approach to calculating credit limits. The method assumes that a borrower’s limit corresponds to the first point at which additional credit exposure would make more than a maximum allowed contribution to portfolio risk. This maximum, marginal contribution would be set by credit policy, guided perhaps by regulatory limits.This approach implies that the borrower’s size, risk rating, types of credit facilities, and correlation with the bank’s entire portfolio will affect the limit. Thus, limits will be lower for smaller, higher risk borrowers, who post little collateral, and are highly correlated with the bank. Portfolio risk considerations motivate this approach. The rules described here deal only with limiting risk. A full portfolio management approach would also consider the returns from the different borrowers.Measuring Contribution to Portfolio RiskOne could use a value-at-risk (VAR) model in measuring a borrower’s total and marginal contribution to portfolio risk. But this probably would prove too cumbersome to apply on a case-by-case basis. Also, few banks have credit VAR systems sophisticated enough for an accurate assessment of limits. As an alternative, we suggest a computationally feasible method for approximating the limits that would arise from a state-of-the-art VAR analysis.Experiments with VAR systems suggest that one can approximate the credit-portfolio-risk contribution of a borrower with the following formulaRC = EDF_WT x LIED_WT x CORR_WT x EXP (1)With the assistance of a pricing model, one could use the following, closer approximationRC = SPREAD_WT x CORR_WT x EXP (2)We define the variables in (1) and (2) below (see Exhibit 1).Exhibit 1: Variables in Risk Contribution FormulasTo get the marginal risk contribution, we compute the change in (1) or (2) with respect to indebtednessMRC = ∆RC/∆D (3)Here MRC denotes the marginal risk contribution, ∆change, and D total indebtedness of the borrower.Setting Limits Using a Maximum Marginal-Risk-Contribution ThresholdUnder the approach suggested here, one would determine a borrower’s credi t limit by finding the point at which MRC reaches a ceiling (MMRC) set by policy (see Exhibit 2)Limit = EXP at which ∆RC/∆D= MMRC (4)Specific Examples of This Limit Setting ApproachWe now apply the approach just described for a couple of particular choices for default models. In conducting this experiment, we work with continuous formulas for MRC.Allowing debt changes to be arbitrarily small, we get the following expression for MRCWTSPREAD WT CORR EXP WT SPREAD DWTCORR EXP WT CORR DWTSPREAD D RC MRC _*_ *_*_ *_*_+∂∂+∂∂=∂∂=(5)Assuming that LIED doesn’t change with D and noting that spreads move about proportionally with the average EDF value over a loan’s term, we can use the followingWTCORR WT LIED WT EDF EXP WT LIED WT EDF DWTCORR EXP WT CORR WT LIED DWTEDF D RC _*_*_ *_*_*_ *_*_*_+∂∂+∂∂=∂∂(6)For the two experiments below, we use the following specificationsLIEDI m AVG LIED WT LIED m PEXP EXP WT CORR ⋅=⋅⎪⎭⎫⎝⎛+=___ρρ (7)Here PEXP denotes the bank’s total credit exposure and ρI the correlation between the value of the bank’s entire credit portfolio and the val ue of the industry component that includes the borrower. The m values represent multipliers that provide convenient scaling.Suppose we use a Merton default model. This implies EDF_WT = 2Φ(-ln(V/(kD))/σ)*m EDF where Φ represents the normal distribution function, V asset value, D total debt, and σ asset-value volatility. Then if dEXP = dD, we get the following)__)/ln(2 _)/ln(2 __)/ln(2(1WT LIED WT CORR D V EXP WT LIED D V PEXP EXP WT LIED WT CORR D V D m D RC ⋅⋅⎪⎭⎫⎝⎛-Φ+⋅⋅⎪⎭⎫ ⎝⎛-Φ⋅+⋅⋅⋅⎪⎭⎫⎝⎛-=∂∂-σσσφσ (8)The graphical representation of limit determination has the expected pattern (see Exhibit 3).1Exhibit 3: Limit Determination for the Merton Default Model0.000.250.500.751.001.251.501.752.00010203040Alternatively, suppose we assume a logistic default model. In this case, we get a different EDF weight, specifically EDF_WT = exp(λ0+λ1*ln(V/D)/σ)/(1+exp(λ0+λ1*ln(V/D)/σ))m EDF . Again we obtain the same basic pattern of limit determination (see Exhibit 4)Exhibit 4: Limit Determination Using Logistic Default Model0.000.250.500.751.001.251.501.752.000102030405060Limit1In both examples we’ve scaled the results so that MMRC = 1. This arbitrary scaling has no effect on the results.The position of the MRC curve and thus the debt limit depends on a company’s size (debt capacity), indebtedness to others, correlation with the bank, and facility structures (loss in event of default) (see Exhibit 5).To make the above approach operational, one must establish a threshold for the MRC. In a full, portfolio risk-return analysis, one might set the threshold depending on the return associated with a borrower’s loans.Putting pricing aside, one might determine the MMRC from regulatory limits. In this case, one would select an extremely large borrower with the highest risk grade in an industry with a low correlation with the bank’s overall portfolio. One would further assume that that borrower’s entire indebtedness was with the bank. One then would solve for the MRC value corresponding to theregulatoryMRC wouldMMRCRegulatoryLimitSummaryThis limits approach presented here restricts the MRC of a borrower to a level consistent with regulatory guidelines. Under this meth od, the limit depends on a borrower’s size, risk rating, correlation with the entire bank, and indebtedness to other creditors.。
Credit Value at RiskCredit value-at-risk (VAR) models perform the calculations needed for evaluating capital adequacy and for identifying desired portfolio changes. Credit-VAR models estimate both the value distribution of an entire credit portfolio and the marginal contribution to VAR (MVAR) of each exposure within that portfolio.1Risk managers use the value distribution in assessing capital adequacy. They use the MVAR measures in guiding portfolio management.To compute timely estimates of credit VAR, one must•Maintain a current, comprehensive, accessible database on credit exposures•Develop a tool for computing the future value of exposures conditional on the borrower’s risk-rating or expected-default-frequency (EDF), and•Create a method for estimating the volatility over time of the risk rating or EDF of each borrower and the correlation coefficients among the risk ratings or EDFs of borrowers.We illustrate below the typical framework for calculating credit VAR (see Exhibit 1).Exhibit 1: Flow Diagram for Credit VAR ModelAssembling the Database on Credit ExposuresTo identify effective portfolio-management actions, one needs accurate, timely estimates of credit VAR and MVAR. To obtain such estimates, the credit-VAR application needs ready access to an up-to-date, comprehensive, credit-exposure database.This database will hold both borrower and facility information. The borrower data will include a credit risk rating or an EDF or both as well as indicators of industry and location composition and of relationships with other borrowers. These latter items affect the estimation of correlation coefficients. The facility data will describe each credit exposure to each borrower. This information will•Identify the facility type such as term loan, revolving line, bill discount facility, financial letter of credit, forward exchange contract, interest rate swap, and so on•Describe pricing including the spread, base rate, and fees•Provide detail on structural features including the committed amount, tenor, amortization schedule, seniority, collateral, and covenants•Summarize the facility’s stat us as indicated by the amount outstanding, anticipated future usage, and, for a market-risk-related exposure, the mark-to-market value.1In many cases, analysts define MVAR as the “marginal contribution to the standard deviation of the distribution.” However, one may choose another definition such as “the marginal contribution to the 99thth percentile value loss.” The ease of computing the marginal standard deviation explains its popularity.The credit-VAR model needs other information for calibration. This includes market pricing data and historical risk-rating transition rates. These data will come at least partly from external sources and may involve irregular updating.Few if any financial institutions currently maintain ready access to the information needed to drive credit VAR applications. Most institutions remain hampered by loan systems that provide little of the data needed for risk management. In many banks, however, one could obtain most of the missing data just by creating an electronic version of the memorandum already required for credit approval (see Exhibit 2).Exhibit 2: Illustration of Possible Electronic Credit Memorandum FormsDeveloping a Tool for Valuing Credit ExposuresA tool for valuing credit exposures, conditional on borrower risk ratings, stands central to the credit-VAR framework. One must be able to compute value before one can compute VAR.For the most accurate results, one could integrate a full-valuation application such as KPMG’s Loan Analysis System (LAS) into the VAR engine. Suppose that we use Monte Carlo simulations in constructing the portfolio-value distribution. These simulations involve probabilistic choices of ratings at a future analysis date, often a year removed. Then, depending on the risk rating selected in a simulation trial and the terms and conditions applicable at the future date chosen for re-valuation, the tool computes a value for a facility (see Exhibit 3).Exhibit 3: Ratings Contingent Values at a One-Year Horizon6065707580859095100105-4-3-2-10123Value(% of Par)Credit Change Index BBB Five-Year Term Loans Note: Each point represents a discrete ratings category, which also is associated with a range of values for a continuous credit index.Within a simulation run, one calculates a value for each of the credit facilities. Then, by summing over all facilities, we obtain one possible value for the credit portfolio at the re-valuation horizon. To estimate the probability distribution for portfolio value, one runs many such simulations and tabulates the results.To estimate an exposure’s marginal contribution to the distribution, one excludes the exposure’s simulated values from the portfolio sums. One then compares the constructed distribution with and without that exposure. This allows one to compute an exposure’s contribution to many portfolio-risk measures including the standard deviation and various distribution quantiles. For example, one might find that an added 100,000 Won of a particular exposure increases the portfolio’s standard deviation by 1,000 Won and the 99th percentile loss amount by 5000 Won.Most credit-VAR engines use shortcuts to the full-valuation approach exemplified by LAS. Credit Metrics, for example, assumes that all exposures look like bonds. The application re-values “bond -equivalent” positions using present -value calculations that draw on a table of forward discount rates estimated for bonds of different risk grades and tenors. KMV’s Portfolio Manager similarly uses a set of standard re-valuation factors estimated from historical par spreads for loans of varying EDFs and tenor.These approaches pay little attention to the structural characteristics of credit facilities. They usually adjust only for tenor and possibly seniority. Experiments with LAS indicate that other structural features can cause an exposure’s risk contribution to vary by as much as 3-fold. By neglecting such effects, the analysis can easily make a desirable exposure appear undesirable or vice-versaA full-valuation approach avoids such errors, while increasing the run-time of the application. Other options strike a different balance between accuracy and computational speed.An appropriate compromise might use more detailed re-valuation tables computed using a full-valuation tool. These tables would distinguish not just on tenor and grade, but also on facility type, collateral, amortization provisions, call-protection, and possibly covenant strength.One would define several classes of exposures on this basis and use the full-valuation tool to create revaluation tables for each class. Then the re-valuation of a particular credit facility would involve only a look-up within the table. The efficiency would arise from using full valuation only for the generic classes not for each of the far more numerous credit facilities. Suppose, for example, one placed each of 100,000 exposures into one of 1000 categories. In this case, the look-up tables could improve the speed of the re-valuation step by nearly 100-fold.One might wish to retain full-valuation as an option. Improvements in accuracy might justify use of full valuation on some of the largest exposures.Creating a Model that Determines Variances and Correlation CoefficientsCredit VAR models view portfolio risk as arising mostly from correlated changes in risk ratings or EDFs. In bad times, the risk ratings of many borrowers fall, causing the values of many credit exposures to drop. In bad times within a particular region or industry, the ratings of many borrowers within that region or industry fall, causing the associated credit facilities to lose value. These correlated effects, therefore, may be global or regional or industry specific. Uncorrelated ratings shifts cause offsetting value changes that mostly vanish in large portfolios.In modeling correlated ratings or EDF migrations, analysts begin by specifying a correlation structure among continuous indicators of the credit strength of borrowers. These indicators may be EDFs or calculated default distances or measures inferred from transition matrixes. As time passes, each borrower’s credit status may change. Thus, we view the value of a borrower’s credit indicator at some future date as described by a probability distribution. One typically assumes a normal distribution. Some empirical evidence supports this choice. Also it facilitates correlation analysis. Specifically, one treats future values of all of the credit indicators as being drawn from a multivariate normal distribution. The distribution will incorporate the correlation structure.Credit Metrics, for example, assumes that ratings transitions reflect an underlying continuous indicator of change in credit status over a specified period such as a year. Credit Metrics assumes that this indicator has a standard normal distribution and that discrete changes in ratings arise from the “binning” of this continuous variable. One derives the bin thresholds from observed transition rates. Suppose, for borrowers rated BBB at the start of a year, we find that 7 per cent fall to BB+ or lower by year-end and that 15 per cent fall to BBB- or lower. This implies that, for a borrower initially rated BBB, the BBB- bin extends from just above –1.476 to –1.036. The first value represents the 7th percentile point for a standard normal distribution; the second value represents the 15th percentile point (see Exhibit 4).Exhibit 4: Credit Metrics Framework for Determining Ratings Bin ThresholdsF(X)Probability Density for BBB Credit-Change Indicator0.1%0.4%1.0% 5.5% 87.0% 5.3% 0.6% 0.1%Note that the Credit Metrics indicator has no meaning separate from its use as mechanism for producing correlated migrations. We don’t actually observe the indictor as data from external sources. Rather, we assume that the indicator exists and we construct a version of it that, together with the bin thresholds, reconciles with observed transition rates.KMV’s approach resembles that of Credit Metrics, but the KMV credit indictor derives from external data. KMV assumes that one can determine EDFs from measures of “default distance” whose future values will have close to a normal probability distribution. Default distance, in turn, derives from observed indebtedness, stock prices, and price volatilities. Here, one doesn’t just fabricate the indicator so that it tautologically explains changes in credit standing. The indicator comes from actual data that could conceivably have no bearing on credit strength. KMV has provided ample evidence that its indicator helps predict default. This represents a meaningful and valuable result.Under either approach, the correlation coefficients derive from stock-price indexes. The process involves•Specifying, for each borrower, weights indicating the relative importance of various industry/regional factors and of idiosyncratic occurrences in influencing the borrower’s credit strength•Measuring the correlation coefficients among stock indexes representing the industry/regional factors•Using the above information to compute correlation coefficients for all pairs of borrowers.At the end of this process, one has the full correlation matrix needed in making appropriately correlated random draws of credit indicators from the multivariate normal distribution.Analytical Alternative to Monte Carlo SimulationOur discussion so far assumes that one uses Monte Carlo methods. Given time to perform a very large number of simulations, this approach provides reliable results that one can explain to a non-technical audience. However, one can also use analytic methods to obtain much faster results. In addition, for computing tail probabilities, these methods can be more accurate than all but extremely large numbers of Monte Carlo trials.We illustrate the analytical approach using a risk-factor formulation. We assume that these common risk factors influence ratings transition rates and through this common influence entirely account for correlation among the values of the different credit exposures. Assume that the ratings transition rates for each borrower depend on several risk factors Z1,…,Z n. Thus, conditional on , we may compute a conditional mean and standard devation for e simulation Summary。
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