毕博上海银行—CreditRiskMgmtSysAnalyticsCreditVAR

1、精品文档，值得拥有Credit Value at RiskCreditvalue-at-risk(VAR)modelsperformthecalculationsneeded forevaluatingcapitaladequacy and for identifyingdesiredportfoliochanges.Credit-VARmodelsestimateboththevaluedistributionof an entire creditportfolioandthemarginalcontributionto VAR(MVAR)ofeachexposurewithinthatpo

2、rtfolio. 1Risk managersuse thevaluedistribution inassessingcapital adequacy. They use the MVAR measures in guiding portfolio management.To compute timely estimates of credit VAR, one mustMaintain a current, comprehensive, accessible database on credit exposuresDevelop a tool forcomputing the future

3、value of exposures conditional on theborrower srisk-rating or expected-default-frequency (EDF), andCreate a methodfor estimating the volatility over timeof the risk rating orEDF of eachborrower and the correlation coefficients among the risk ratings or EDFs of borrowers.We illustrate below the typic

4、al framework for calculating credit VAR (see Exhibit 1).Exhibit 1: Flow Diagram for Credit VAR ModelDrawVAR, MVAR ResultsRatingsCreditVARDatabaseEngineVariance-RecalculateCovarianceValuesStructureAssembling the Database on Credit ExposuresTo identify effective portfolio-management actions, one needs

5、 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

6、 an EDF or both as well as indicators of industry and location composition andof relationshipswithotherborrowers.Theselatteritemsaffectthe estimationof correlationcoefficients. The facility data will describe each credit exposure to each borrower. This information willIdentify the facility type such

7、 as term loan, revolving line, bill discount facility, financial letter of credit, forward exchange contract, interest rate swap, and so onDescribe pricing including the spread, base rate, and feesProvide detail on structural features including the committed amount, tenor, amortization schedule, sen

8、iority, collateral, and covenantsSummarize the facility s status as indicated by the amount outstanding, anticipated future usage, and, for a market-risk-related exposure, the mark-to-market value.1 In many cases, analysts define MVAR as the“marginal contribution to the standarddeviation ofthe distr

9、ibution.”However, one may choose another definition such asthor the 99.9thpercentile value“the marginal contribution to the 99loss. ” The ease of computing the marginal standard deviation explains its popularity.1 / 6精品文档，值得拥有The credit-VAR model needs other information for calibration. This include

10、s market pricing dataand 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 instituti

11、ons 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 P

12、ossible Electronic Credit Memorandum Forms2 / 6精品文档，值得拥有Developing 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 themostaccurat

13、e results, onecouldintegrate a full-valuationapplicationsuchas KPMG sLoan Analysis System (LAS) into the VAR engine. Suppose that we use Monte Carlo simulationsin constructing the portfolio-value distribution. These simulations involve probabilistic choices ofratingsat afuture analysisdate,often aye

14、arremoved.Then,dependingon therisk ratingselected in a simulation trial and the terms and conditions applicable at the future date chosen forre-valuation, the tool computes a value for a facility (see Exhibit 3).Exhibit 3: Ratings Contingent Values at a One-Year HorizonValueBBB Five-Year Term Loans(

15、% of Par)105Standard Loan (left axis)45100Structured Loan (left axis)4095359030852580207515701065Credit Change Probability560Density (right axis)0-4-3-2-10123Credit Change IndexNote: Each point represents a discrete ratings category, which also isassociated with a range of values for a continuous cr

16、edit 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 simul

17、ations and tabulates the results.To estimate an exposure s marginal contribution to the distribution, one excludes the exposure ssimulated values from the portfolio sums. One then compares the constructed distribution withand without that exposure.Thisallowsonetocomputean exposure scontribution toma

18、nyportfolio-risk measures includingthe standarddeviationandvarious distributionquantiles.Forexample,one mightfind thatanadded100,000Wonofaparticular exposureincreasestheportfolio s standard deviation by 1,000 Won and the 99thpercentile loss amount by 5000 Won.Most credit-VAR engines use shortcuts to

19、 the full-valuation approach exemplified by LAS. CreditMetrics,for example,assumesthatall exposureslooklike bonds.Theapplicationre-values“ bond-equivalent ”positions using present -value calculations that draw on a table of forwarddiscount rates estimated for bonds of different risk grades and tenor

20、s. KMV s Portfolio Managersimilarly uses a set of standard re-valuation factors estimated from historical par spreads for loans of varying EDFs and tenor.3 / 6精品文档，值得拥有These approaches pay little attention to the structural characteristics of credit facilities. They usually adjust only for tenor and

21、 possibly seniority. Experiments with LAS indicate that otherstructural 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-valuationapproach avoidssuche

22、rrors, while increasingthe run-timeofthe 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-valuationtool.Thesetableswould distinguishnot just on tenor and grade,but

23、also onfacilitytype, collateral, amortization provisions, call-protection, and possibly covenant strength.One would define several classes ofexposureson this basis and use thefull-valuationtooltocreate revaluation tables for each class. Then the re-valuation of a particular credit facility wouldinvo

24、lve only a look-up within the table. The efficiency would arise from using full valuation onlyfor thegenericclassesnotforeachof the farmore numerouscredit facilities.Suppose,forexample, one placed each of 100,000 exposures into one of 1000 categories. In this case, the look-up tables could improve t

25、he 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 portf

26、olio 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 creditexposures to drop. In bad times within a particular region or industry, the ratings of many borrowers within that region or industry fa

27、ll, 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

28、 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 timepasses, each borrower scredit status may change. Thus, we view the value of

29、 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 a

30、s beingdrawn from a multivariate normal distribution. The distribution will incorporate the correlation structure.Credit Metrics,forexample,assumes thatratings transitionsreflectan underlyingcontinuousindicatorof changeincreditstatusover aspecifiedperiodsuch as ayear. CreditMetricsassumesthatthisind

31、icatorhas astandardnormaldistribution andthatdiscretechanges inratings arise from the“ binning ” of this continuous variable. One derives the bin thresholds fromobserved transition rates. Suppose, for borrowers rated BBB at the start of a year, we find that 7per cent fall to BB+ or lower by year-end

32、 and that 15 per cent fall to BBB- or lower. This impliesthat, for a borrower initially rated BBB, the BBB- bin extends from just above1.476 to1.036.The first value represents the 7th percentile point for a standard normal distribution; the secondvalue represents the 15th percentile point (see Exhib

33、it 4).4 / 6精品文档，值得拥有Exhibit 4: Credit Metrics Framework for Determining Ratings Bin ThresholdsF(X)Probability Density for BBB Credit-Change Indicator0.4X=00.3Bin0.2ThresholdsBinBBBThresholds0.1BBABCCCAA0DAAA0.1%0.4%1.0% 5.5%87.0%5.3%0.6% 0.1%XNote that the Credit Metrics indicatorhas no meaning sepa

34、rate from its use as mechanism forproducingcorrelatedmigrations.We don actuallyobservetheindictor asdatafromexternalsources.Rather,weassume thatthe indicatorexistsandweconstructaversionof itthat,together with the bin thresholds, reconciles with observed transition rates.KMV s approachresemblesthatof

35、CreditMetrics,buttheKMV creditindictorderivesfromexternal 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 v

36、olatilities. Here, one doesn t justfabricate the indicator so that it tautologically explains changes in credit standing. The indicatorcomes from actual data that could conceivablyhavenobearingon creditstrength.KMVhasprovidedample evidence thatitsindicatorhelpspredictdefault.This representsa meaning

37、fuland valuable result.Under either approach, the correlation coefficients derive from stock-price indexes. The process involvesSpecifying, for each borrower, weights indicating the relative importance of various industry/regional factors and of idiosyncratic occurrences in influencing the borrower s credit strengthMeasuring the correlation coefficients among stock indexes representing the