Rating Market Value CDO

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STRUCTURED FINANCE Special Report

April 3, 1998

Moodys Approach to Rating Market-Value CDOs

AUTHORS:

Yvonne Fu Falcone, Ph.D.Assistant Vice President(212) 553-1494

J eremy Gluck, Ph.D.Managing Director(212) 553-3698

CONTACTS:

Eileen MurphyManaging Director(212) 553-4061

J ulie A.CulhaneInvestor Relations(212) 553-7941

CONTENTS:

Introduction

Transaction Structure

Factors Affecting Asset Price Volatility

Volatility in a Portfolio Context

The Role of Liquidity

Measuring Portfolio Volatility

Sources of Market Data

Volatility and Correlation Adjustments and Liquidity

Applying the Simulation Approach

Role of the Collateral Manager

Legal Concerns

Summary Conclusion

Exhibits

INTRODUCTIONAs the market for collateralized debt obligations (CDOs) has mushroomed in recent

years, the range of structures has widened considerably. For example, the under-lying collateral in CDOs has evolved toward a variety of emerging market and devel-oped country bond and loan types and away from an almost exclusive reliance onspeculative-grade U.S. corporate bonds.At the same time,structures have changedto include ramp-upperiods and a greater degree of dynamism.

A minority of CDOs have been cast as market-value transactions, in which the creditenhancement is reflected in a cushion between the current market value of thecollateral and the face value1 of the structures obligations. Within this framework, thecollateral must normally be liquidated,either in whole or in part, if the ratio of themarket value of the collateral to the obligations falls below some threshold.The liqui-dated collateral is used to pay down obligations, bringing the structure back intobalance. In contrast, cash-flow transactions normally provide for the diversion of

cash flows from junior to senior classes if certain tests that relate to the structuressoundness are not met.

Since the primary risk in a market-value transaction is that of a sudden decline inthe value of the collateral pool, our analysis will focus on the price volatility of theassets that may be incorporated into these structures.We will see that this volatilitycan be reflected in a set ofadvance rates that represent adjustments to the value ofeach asset and are designed to provide a cushion against market risk.

1 To be more precise, the obligation is principal plus accrued interest.


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THE STRUCTURE OF A MARKET-VALUE TRANSACTIONIn large part,cash-flow CDOs have succeeded because they exploit the illiquidity of the high-yield markets.2The spreads on high-yield debt have historically more than compensated for thedefault risk associated with such debt,making it attractive to own such instruments on a buy-and-hold basis.3The spreads thus include a sizable liquidity premium. However, these spreadshave diminished over time, partly because CDOs have proven to be an effective arbitrage.

As soon as it is necessary to sell speculative-grade debt within a market-value structure, theliquidity premium is absorbed.The attempt to sell illiquid assets,particularly in size, will tend todrive down bid prices.Worse yet, if a fraction of a portfolio is sold in a troubled market,normal

bid-ask spreads will widen considerably, and substantial losses may be incurred.

Still Attractive for Investors and Collateral Managers

Nonetheless, market-value transactions may be attractive to some investors and collateralmanagers.The structure makes particular sense where the collateral pool consists of instru-ments that do not produce predictable cash flows, such as distressed debt. Even for pools ofconventional fixed-income instruments, the market-value structure may appeal to investors whoare most comfortable in a mark-to-market environment, such as that provided by hedge andmutual funds.

Collateral managers may prefer the greater trading flexibility that arises from the dynamic natureof the market-value tests. Market-value transactions also facilitate the purchase of assets thatmature beyond the life of the transaction because the price volatility associated with the forced

sale of such assets is explicitly considered.The capital structure of a market-value CDO resembles that ofa cash-flow transaction. Figure 1 depicts a simple market-value CDO structure.

The market-value concept is straightforward.A cushion ofexcess value protects investors from a sudden decline in thevalue of the portfolio; that is,unless the value of the assetsfalls by an amount that exceeds the cushion, the noteholderscan get out whole. Should the ratio of the value of the assetsto the liabilities fall below some threshold 1.25:1in the caseillustrated in Figure 1 then assets are sold and liabilities paidoff until the threshold can again be satisfied.Thus when rating

such an instrument, the key consideration is determining the(downside) volatility of the market value of the portfolio, as wellas any liquidity losses that might be incurred through a forcedsale of assets.

FACTORS THAT AFFECT ASSET PRICE VOLATILITYThe returns on speculative-grade bonds may vary for several reasons, among them: Changes in the rating of the instrument Actual default Changes in interest rates Changes in investor preferences (and resulting changes in credit spreads)

Changes in RatingsA speculative-grade bond is particularly vulnerable to changes in ratings.A rating change, which

is reflective of a change in the likelihood of default,affects the discount rate that the marketapplies to the cash flows promised by the bonds. The discount rate must capture the likelihoodof default, although it may also embody a liquidity premium and other factors.One can thereforethink of the price impact of a change in a bonds rating as the change in value implied by thechange in the discount factor.4The likelihood of any particular change in rating is given by aratings transition matrix.5

Moodys Approach to Rating Market-Value CDOs2

2 Both cash-flow and market-value deals may also benefit from diversification, which reduces the likelihood that losses will exceed a structures creditenhancement.

3 See, for example, Bencivenga, J oseph C., The High-Yield Corporate Bond Market inThe Handbook of Fixed Income Securities, Frank J . Fabozzi,Ed., Chapter 15.

4 This relationship is a focus of J P Morgans CreditMetrics.5 See Moodys Rating Migration and Credit Quality Correlation, 1920-1996,Moodys Special Comment, J uly 1997.

Figure 1

A Two-Tranche Market-Value Structure

Collateral Pool CDO

$300mmAssets

$240 mmSenior Notes

$60mm Equity


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DefaultsOf course,an extreme case of a change in rating is the movement to actual default. The samerating transition matrix that suggests potential changes in rating level also gives the likelihood ofdefault for a bond with any initial rating. However, the price impact of the change is not easilyrepresented as a change in discount rate because any instrument in default can no longer bethought of as promising a well-defined set of cash flows. Instead, the price impact can beinferred from Moodys studies of the recovery value of an instrument following default.6

Changes in Interest RatesAlthough the returns on speculative-grade instruments are less closely associated with changesin interest rates than those of similar-maturity investment-grade bonds, they are nonethelessaffected by interest-rate shifts through a corresponding change in discount rate.However, thisprice sensitivity may only be observed when the rating of the bond is held constant.Variousstudies have demonstrated that yield spreads and the general level of interest rates are nega-tively correlated because high rates tend to be associated with a strong economy, an environ-ment in which firms are less likely to default.7

Changes in Investor Preferences/Credit SpreadsEven if a bonds rating and the general level of interest rates remain unchanged, the returns onspeculative-grade bonds may vary for a variety of reasons. For example, investors may growconcerned about the performance of the economy and choose to shun riskier investments. Or,regulatory actions may alter the relative appeal of speculative-grade bonds. Investors may alsoperceive that there are factors affecting the attractiveness of investments in the debt of a partic-ular company that are not reflected in the firms debt rating.

For these reasons and others, one cannot expect to fully explain the price volatility of specula-tive-grade bonds exclusively through changes in ratings and interest rates.These changes inpreferences produce changes in credit spreads that have their greatest impacts on the mostinterest-sensitive bonds.

Special Considerations for Distressed Instrument ReturnsMarket-value structures are well suited to the inclusion ofdistressed instruments. These obliga-tions of companies that are in bankruptcy proceedings do not produce a predictable stream ofcash flows. Rather, investors may find distressed instruments attractive because they offer anequity-like potential for capital gains.The instruments include distressed debt (bonds andloans), equity that has been converted from debt through a reorganization, and trade claims.

Trade claims typically represent obligations to suppliers of inputs to distressed firms.The return behavior of distressed instruments more closely resembles that of equity than debt.But unlike conventional equities where dividend levels, growth rates,and interest rates are thekey drivers of valuation the prices of distressed instruments may be subject to sudden shocksas it becomes clear during the bankruptcy process that some claimants will fare better thanothers, or as the market attempts to determine the value of the distressed firm as a goingconcern in comparison to its liquidation value.

VOLATILITY IN A PORTFOLIO CONTEXT

Portfolio EffectsThe factors we have just discussed determine the volatility of the returns on a particular specu-lative-grade or distressed instrument.When considering the volatility of the returns on a portfolio

of such instruments, one must also consider portfolio effects.The more diversified the portfolio,the less volatile will be the portfolio return, relative to the return volatility of its components.

The degree to which a portfolio is well diversified depends not only on the portfolio sharesassociated with each component, but also on the correlations among the various sources ofprice volatility for each of the components. For example, there will be a tendency for the ratingsof U.S. speculative-grade bonds to move together as the U.S. economy ebbs and flows.Thissuggests some co-movement in returns.To the extent that speculative-grade bonds areaffected by changes in interest rates,variations in rates will induce some return correlation. If

Moodys Approach to Rating Market-Value CDOs 3

6 See Historical Default Rates of Corporate Bond Issuers, 1920-1997,Moodys Special Comment, Feb. 1998.7 See, for example, Francis A. Longstaff and Eduardo S. Schwartz, A Simple Approach to Valuing Risky Fixed and Floating Rate Debt,The J ournal o

Finance, J uly 1995.


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investors suddenly decide that speculative-grade bonds are undesirable, that too will forcesome correlation in the prices of these instruments.

Sources of correlation need not be economy-wide.For instance, to the extent that firms within aparticular industry exhibit similar economic performance, there may be a correspondingtendency for the prices of bonds issued by firms in the industry to be positively correlated.Investor attitudes toward an industry may have a similar impact. In some cases, price correlationmay arise from regional, rather than industry-related factors. The relevant regions may be inter-national: the recent sharp downturn in emerging market bond prices during the Asian crisisclearly cut across national borders.

THE ROLE OF LIQUIDITYAs we have already suggested, speculative-grade bonds are illiquid relative to investment-gradeinstruments. Distressed instruments are even less liquid. Hence, if one were to sell a specula-tive-grade or distressed bond in a normalmarket, the seller would suffer some dollar lossversus fair market value due to the presence of a bid-ask spread.

But in assigning a rating to a market-value transaction,our concern is not what will happen in amore normal market. Rather, the concern is what will happen in a deteriorating market becauseit is only in such a market that investors in rated tranches stand any real chance of suffering aloss. Unfortunately, bid-ask spreads are almost certain to widen in this environment as investorstry to bail out and business is no longer characterized by two-wayflows.

Indeed, during the recent Asian crisis, some market participants remarked that they could not

get a bidfor the affected bonds. Presumably, this isnt literally true the bonds could be sold atsome price; nonetheless, its clear that liquidity had nearly dried up for certain issues. Going backeven further, defaults by several Latin American countries in the 1980s produced sudden disloca-tions in the market that made it very difficult to dispose of assets without suffering sharp losses.

To the degree that a particular portfolio manager is known to be engaged in a fire saleofassets, the tendency for spreads to widen will be exacerbated by an effective shift in the fairmarket value of the instruments that the manager is attempting to unload.This is relevant for themanager of a market-value CDO that must dispose of assets in order to ensure that theasset/liability value ratio remains above its threshold.The fact that the speculative-grade markethas performed well over the past six years may exacerbate the problem in a market downturn,because market participants have little recent experience with a broad decline in prices.

MEASURING PORTFOLIO VOLATILITYThe price volatility of a portfolio can be modeled in a number of ways.The primary alternativesare analytic and simulation approaches. Ananalytic approach would allow the calculation of the potential decline in value by applying

some formula that captures each source of price risk. Unfortunately, that is quite difficult for aportfolio that may contain both fixed-income products and those that fail to generatepredictable cash flows.

A simulation approachaddresses changes in portfolio values directly, or simulates the factorsthat determine portfolio value.The direct approach is simply to simulate changes in the valuesof each of the assets that comprise the portfolio.The indirect approach entails a simulation ofchanges in ratings, interest rates and, perhaps,other factors that will lead to changes in marketvalue. In combination with the characteristics of each component of the collateral pool rating,duration, and the like potential changes in portfolio value may be measured.8

Value of the Direct Simulation ApproachWe have chosen to directly simulate asset price movements because we believe that the rela-tionships between asset prices and the characteristics of each asset are unstable during theperiods of greatest interest market downturns.At such times, the noiseassociated withchanging investor preferences will dominate.Moreover, the data requirements associated withthe indirect approach, both for econometric estimation and for the collateral manager in runninga liveportfolio,are quite burdensome.


8 Note that CreditMetrics, for example, is only intended to addresses changes in value associated with credit events. Hence, neither the impact ofinterest-rate changes nor changes in investor preferences are considered. A full-blown analytic model would relate the characteristics of each asset rating, duration, convexity to the distributions that govern credit migration, interest rates and credit spreads.


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Even the choice of direct simulation of asset prices admits two possible approaches. The first the parametric approach requires assumptions about the relevant distributions and incorpo-rates the parameters of those distributions within the simulation.This method has the advantageof allowing the generation of a very large number of scenarios, including those that have notbeen observed in the past.

The second approach relies on a historical simulation. In this case, rather than generatingchanges in market variables from a theoretical distribution, one draws actual percentagechanges in price (or in the underlying factors) from history.The advantage of the historical simu-lation approach is that no assumptions need be made about the distributions of market vari-ables; the disadvantage is that only historically observed changes can be simulated.

A Blend of the Historical and the Parametric ApproachesMoodys has chosen an approach that is primarily historical, but that provides flexibility toimpose certain parametric choices where the price history is inadequate.The historicalmethod is attractive because the parameters that affect the underlying distributions are highlyunstable. Most notably, correlations can change dramatically over time, even changing sign onsome occasions.

Of particular concern is the fact that correlations often rise sharply during a period of crisis,having an enormous impact on the potential decline in portfolio value during a short period oftime.The October 1987 stock market crash or the Asian crisis are stark reminders that seem-ingly independent markets often become unexpectedly linked when a sharp downturn occurs.Because of these high correlations during stressful periods, not only is the common assumptionof normal returns at the individual asset level invalid, but the assumption is also inappropriate at

the portfolio level.9We will not,however, rely exclusively on history.The reason is simple: we dont have a sufficientprice history for each asset class to provide comfort that we have captured all the marketscenarios that could reasonably occur. It is particularly difficult to stratify the data in order toselect a meaningful sample comovements in prices during relatively rare,but critically important,market downturns.

In these cases, we will impose volatility assumptions on asset classes, and correlation assump-tions within and across asset classes, by reasoning that the price behavior should be similar tothat of assets that are close substitutes, for which we have a more extensive price history.Wewill also,where necessary, impose higher volatilities and correlations than are embedded in thehistorical data.Such adjustments are appropriate offsets to incomplete data.The more limitedour data, the greater will be the adjustment.

SOURCES OF MARKET DATAAs a rule, the less liquid an instrument, the more difficult it is to obtain historical price data forthe instrument.Therefore, it is somewhat more difficult to find price data for high-yield bondsthan for investment-grade corporate bonds. It is still more difficult to obtain data for loans.Finally, it is particularly difficult to develop a price history for distressed instruments.Table 1outlines the sources for a range of instrument types10 used in our analysis. Note that all instru-ments described in Table 1and subsequent tables are US dollar denominated domesticsecurities; thus all results presented in this article do not apply to foreign securities.


9 The tails of the return distributions precisely our focus in assigning a rating are much too fat to be consistent with normality. This is true not onlyof individual assets, but even of entire portfolios because of the high degree of correlation that prevails during market breaks. Moreover, the distribu-tions are skewed in the sense that sharp market downturns are more likely than sharp rallies. The normal assumption may be an adequate charac-terization of average portfolio behavior; however, our concern with the lower tail of the return distribution implies that the skewness and kurtosis (fattails) associated with the returns for the relevant assets cannot be neglected.

10 It is extremely difficult to obtain historical price information on certain instruments, for example trade claims. For transactions involving trade claims,we stress the reorganized equity data to obtain advance rates for trade claims. As the price information for trade claims, or other instrument types,becomes available, we will incorporate them in our historical database.

Table 1

ASSET RETURN DATA SOURCES

Asset Type Data Source Period Covered Number of AssetsHigh-yield bonds Interactive Data Corporation 1982 1997 1500

High-yield loans Loan Pricing Corporation 1991 1997 213Distressed bonds Moodys Distressed Bond Database 1987 1997 470Distressed loans Loan Pricing Corporation 1991 1997 106Distressed equity PPM America, Inc. 1992 1996 58


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Although the number of assets representing each asset type is often large, the data are sparsein the sense that many of the assets remain in the samples for a relatively short period of time.High-yield debt may be upgraded to investment-grade or may default; in either case,the instru-ment will no longer be tracked in a high-yield database. Distressed instruments either disappearor lose their distressed status following the conclusion of the bankruptcy process.

VOLATILITY AND CORRELATION ADJ USTMENTS AND LIQUIDITYASSUMPTIONS

Volatility

We have adjusted the volatility of historical returns by multiplying each return by a factor thatreflects both:

1. The length of the historical record

2. The desired rating for the CDO tranche.

Because we have the most complete record for high-yield bonds, we apply a relatively smallstress to the historical volatility for this instrument.At the other end of the spectrum, a relativelylarge stress factor is applied to distressed instruments, especially reorganized equities.

The higher the desired rating of the tranche, the greater the stress factor we apply.Thereasoning is straightforward: highly rated instruments rarely default. Therefore,an exceptionallylong data series would be required to produce a valid test of a structure when the target ratingdictates that a default should not occur more than, say, once every 1000 years. Lacking

centuries or millennia of data (and an economic environment that remained stable over theentire period!), a degree of stressing is called for.11

There is one exception to our rule of applying the greatest stress to the shortest data series.Though we have a moderately large number of observations on performing loans, there isreason to believe that the data exclude some important events.Performing loans becomedistressed loans as soon as a borrower defaults.Unfortunately, there are very few cases inwhich we can observe the price impact of default,which may be quite large.Most of the loansthat surface in the distressed data were not tracked when they were performing loans, so thatthe one-month price change cannot be observed.This lack of information warrants a harsherstress factor.

The specific factors that are applied to the historical returns for each asset class and desiredrating are outlined inTable 2.


11Note that this mirrors our approach with respect to cash-flow transactions, where instead of stressing return volatilities, we stress default rates.

Table 2

STRESS FACTORS FOR ASSET RETURNS BY ASSET TYPE AND RATING

RatingAsset Type B Ba Baa A Aa AaaPerforming Bank Loans 1.40 1.60 1.80 2.00 2.20 2.50

Performing High-yield Bonds 1.00 1.00 1.10 1.20 1.30 1.40

Distressed Bank Loans 1.05 1.10 1.20 1.30 1.40 1.60

Distressed Bonds 1.00 1.00 1.10 1.20 1.30 1.40

Distressed Equities 1.40 1.50 1.60 1.70 1.80 2.00


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In addition to boosting volatilities to reflect relatively limited data, we have also made an adjust-ment for data series where the prices do not appear to be true tradingprices. For distressedinstruments, the reported prices sometimes remain unchanged for a few consecutive months.We have removed these zero-returnobservations from the data,which has the impact ofraising overall volatility.

The stressing of asset returns directly increases the volatility of returns for the individualassets.12 By contrast, the stressing of correlations acts indirectly by raising portfolio volatility.

Correlation

Appropriate correlations are particularly difficult to infer from historical (or any other) data,because although correlations clearly rise during market downturns, there are few observationson such periods.We have imposed intra- and interindustry correlations by observing correlationsfor pairs of firms over a relatively long sample period and by comparing those longer termmeasures to correlations that prevailed in relatively stressful periods, such as 1987 and 1990-1991.

We have chosen levels that are higher than those that prevail during normalperiods to reflectour concerns about data imperfections; they are not,however, as high as those observedduring the most stressful periods. The data are not sufficiently rich to provide a basis for distin-guishing between correlations for particular pairs of industries.Rather, we use the assumptionspresented inTable 3.

Although correlations are embedded in the historical returndata, we impose these assumptions by sampling from the

database in a nonrandom fashion.We achieve this by firstranking the returns for each asset from lowest to highest,andby then selecting the individual asset returns in such a way thatthe correlation assumptions inTable 3 will be satisfied. 13

LiquidityWhenever an asset is sold, the seller must theoretically sacrifice half the bid-ask spreadthedifference between a mid-market price and the bid.For most instruments in most markets, thisis a trivial consideration in comparison to ordinary price volatility. For the instruments that typi-cally find their way into market-value transactions, liquidity becomes a key consideration duringa period of financial stress.

We have made assumptions about the loss that a seller of ahigh-yield or distressed asset would incur as a result of illiq-

uidity.Although we have tried to preserve the ordinal relation-ships among the bid-ask spreads for each instrument theassets with the largest bid-ask spreads receive the greatestliquidity haircuts the discount for illiquidity greatly exceedsnormal bid-ask spreads. Since no reliable time-series dataexist on bid-ask spreads for the relevant assets,our assump-tions follow from discussions with market participants and theirviews as to how difficult it would be to sell the assets during asharp market downturn.Table 4 contains the liquidity assump-tions for the respective asset classes.


Table 3

CORRELATION ASSUMPTIONS

Pairs of firms: Assumed Correlation (%)

Within same industry 55

In different industries 40

12 If the random return for an individual asset has a standard deviation of, then stressing the return by a factor implies that the standard deviation ofthe stressed return will be .

13Specifically, let the historical return data be arranged in a matrix of m rows by n columns, where m is the number of issues, n is the number ofperiods, and the returns for each issue are ranked from the lowest to the highest. In a historical simulation, we need to select the row and columnindices and obtain the return for each position in the portfolio. For any position in the portfolio, we select the issue (the row index) by sampling from auniform distribution. When selecting the column indices for the positions, we first sample from a correlated normal distribution based on the correla-tion parameters inTable 3; the number of samples drawn for each simulation iteration is equal to the number of positions in the portfolio and thuseach sample corresponds to a position in the portfolio. Each of these correlated normally distributed drawings is then mapped into a number rangingbetween 0 and 1 by using the inverse of the cumulative of the standard normal distribution. If we multiply this number between 0 and 1 by n (the totanumber of periods), the result gives the column index for a position in a portfolio. Because the returns are ordered for each issue, we wind up withcorrelations roughly consistent with the assumptions inTable 3. We say roughly because we have imposed rank order correlation, which maydiffer somewhat from the usual correlation measure.

Table 4

LIQUIDITY HAIRCUTSBY ASSET CLASS

Liquidity

Asset Type Haircut (%)

Performing Bank Loans 7

Performing High-yield Bonds 5

Distressed Bank Loans 12.5

Distressed Bonds 10

Reorganized Equities/ Trade Claims 20


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APPLYING THE SIMULATION APPROACH

The Calculation of Advance RatesAs in cash-flow CDOs and other structured transactions, Moodys rates market-value transac-tions on an expected loss basis. That is, the expected loss faced by the investor at a givenrating level must be consistent with the experience of investors in like-rated conventional instru-ments.The expected loss measure revolves, in turn, around a particular set ofadvance ratesthat applies to the collateral pool.

In a typical market-value transaction, anovercollateralization (OC) test ensures that the market

value of assets discounted by the appropriate advance rates exceeds the liabilities.The advancerates thus represent haircutsthat provide credit enhancement for the rated notes. We presenthere examples of the calculation of the advance rates in a manner that results in an expectedloss that is consistent with the desired ratings for the debt tranches issued within the CDO.

For the sake of concreteness, we assume here that the OC test is performed biweekly and thecure period, during which assets may be liquidated, is 10 business days. In practice, theadvance rates will reflect the marking and cure periods proposed within each transaction. If theOC test is failed, assets have to be sold either to restore the balance or to unwind the deal.Aloss occurs if the deal unwinds and the proceeds from liquidating the portfolio fall short of theobligations. Since the maximum period of time during which the portfolio is subject to marketprice movements is the mark-to-market period plus the cure period (a total of one month), theconcern is whether the advance rates provide sufficient protection over a one-month period tooffset the volatility and the illiquidity of the assets in the portfolio.

We rely on our historical simulation approach to model the changes of the market value of theportfolio over the exposure period starting with the latest run of the OC test.We make theconservative assumption that in the last period, the market value of the portfolio (V), discountedby the portfolio advance rate AR,14 was exactly equal to the indebtedness (D). Hence,VAR=D,or V=D/AR. The portfolio return over the subsequent month is calculated by randomly samplingreturns from our database for each of the assets in the portfolio, which results in a return (andan end-of-month market value) for the overall portfolio.

A loss to the investors will occur whenever the end-of-month value of the portfolio falls short ofthe indebtedness. If the monthly portfolio return is rp, a loss will occur if D-V(1+ rp)>0, or D-(D/AR)(1+rp)>0, Put differently, the loss (L), relative to what is owed to investors in a particularreturn scenario, is

Notice that a loss occurs if the portfolio return is sufficiently negative that (1+rp)


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transaction. If, at any point over the five-year period, the entire portfolio must be liquidated andat least some of the investors take a loss, then the simulation stops at that point. In order toarrive at the appropriate rating for each tranche,one would compare the expected loss for thetranche over the entire 5-year period to the expected loss for a five-year conventional bond.

Examples of the Advance Rate Calculation

The Simplest Case: One Rated Tranche and Up to 100% in a Single Asset Type

In the following discussion and in the accompanying exhibits, we provide examples of thecalculation of the advance rates for specific structures.We emphasize that the particular calcula-

tion must be consistent with the structure actually proposed,which may be quite complex.Thetables will, nonetheless, provide insights as to how various factors affect the advance rates. Webegin with the simplest case of a one-tranche structure one with subordination provided onlyby the advance rates.

We make the following assumptions regarding portfolio diversification.

1. Maximum allowable investment in one issuer is 5%.

2. Maximum allowable investment in one industry is 20%.

3. Maximum allowable investment in one asset type is 100%.

The least diversified portfolio consistent with these standards thus consists of 20 issuers and 5industries.We obtain the empirical return distribution of this least diversified portfolio,with 100%invested in one asset type,by sampling the returns of individual positions from the historicalprice movements for that asset type.Assuming that the maturity of the transaction is five years,

we then evaluate expected loss with different advance rates to obtainTable 5.Table 5 provides guidance for assigning advance rates for different asset types for the givendiversification criteria of the portfolio. For specific transactions, certain structural provisions canresult in more or less favorable advance rates for some or all of the asset types.We nowconsider the impacts of these provisions.

Diversification Across Assets and Industries

One of the most important determinants of the advance rates is the extent to which the portfoliois diversified, both by asset and by industry. Between the two, asset diversification is, perhaps,more important.The data suggest that in stressful times, correlations for high-yield anddistressed assets within and across industries become somewhat similar.The assets behave asa class, rather than as members of an industry. Diversification across industries is of somevalue, but it is perhaps less critical than in the case of cash-flow transactions.


Table 5

Advance Rates for Different Asset Types and Rating Levels(20 issuers, 5 industries, 100% investment in one asset type, 5 year maturity)

Target RatingAsset Type Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1 Baa2 Baa3

Performing Bank Loans

Valued $0.90 and Above 0.870 0.890 0.895 0.900 0.905 0.910 0.915 0.930 0.935 0.940Distressed Bank LoansValued $0.85 and Above 0.760 0.780 0.790 0.795 0.810 0.815 0.820 0.830 0.840 0.870

Performing High-YieldBonds RatedBa 0.76 0.79 0.80 0.81 0.83 0.84 0.85 0.87 0.88 0.90

Performing High-YieldBonds RatedB 0.72 0.75 0.76 0.77 0.78 0.79 0.80 0.82 0.83 0.85

Distressed Bank LoansValued Below $0.85 0.58 0.62 0.63 0.64 0.67 0.68 0.69 0.71 0.72 0.74

Performing High-YieldBonds RatedCaa 0.45 0.49 0.50 0.51 0.56 0.58 0.60 0.62 0.64 0.67

Distressed Bonds 0.35 0.39 0.40 0.41 0.47 0.48 0.50 0.54 0.56 0.57

Reorganized equities 0.31 0.37 0.38 0.39 0.44 0.46 0.47 0.51 0.52 0.54


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The effect of diversification on the advance rates is reported inExhibit A.

Asset Type Limitations

The advance rates for each asset type inTable 5 are obtained under the assumption that 100%of the portfolio investment is in that asset type. In some transactions, portfolio limitations areestablished with respect to investment in certain asset classes. For those transactions,wewould need to test whether the advance rates shown inTable 5 are still appropriate for theseasset classes given the portfolio limitations. Because of the interaction between various factors,there is no theoretical conclusion as to what the outcome should be. In some instances, morefavorable advance rates might be achieved due to the restrictions. In other instances, the

advance rates are unaffected.The outcome depends on the possible asset types in the port-folio, as well as any limitations on portfolio composition.

Virtually any set of asset-class restrictions might apply.To focus on a particular case,supposethat the collateral manager wishes to invest mainly in performing high-yield bonds rated B3andabove, as well as in distressed bonds, and is willing to limit the investment in distressed bondsto be at most 30% of the portfolio.Given this limitation, our simulation produces advance ratesfor distressed bonds that are higher than those reported inTable 5. In fact, for a transaction inwhich anA2rating is sought, the advance rate for distressed bonds rises from 0.48 to 0.54; fora transaction geared toward aBaa2rating, the advance rate rises from 0.56 to 0.61.Theadvance rates associated with these two target ratings for the possible asset types in this trans-action are presented inTable 6.

To demonstrate that such limitations do not always result in higher advance rates, consider acase in which the collateral manager wishes to invest in bank loans and that up to 30% of theportfolio will be invested in distressed bank loans. Simulation results reveal that the advancerates for distressed bank loans shown inTable 5 are in fact not affected by the portfolio limita-tion in this case.

Further examples of the impact of portfolio limitations are presented in Exhibit B.

Subordination

If there are multiple debt tranches and there is a set of advance rates for each tranche, subordi-nation might provide credit enhancement beyond that associated with the advance rates forsenior tranches.This, however, will depend on the relative sizes of the debt tranches, thedesired ratings and the possible portfolio composition limitations imposed by the structure.

The impact of subordination is explored in Exhibit C.

A Minimum Net Worth Test May Permit Higher Advance Rates

Some structures contain a minimum net worth test to ensure that a certain percentage of theinitial equity level must be maintained; the test is often performed with a different frequencyfrom the monthly period that is otherwise relevant.The impact of the test is to introduce asecond relevant horizon say, one quarter over which changes in portfolio value must beevaluated.When the minimum net worth test is performed with a lower frequency than the OCtest,we need to run simulations to evaluate the effect of this impact; indeed, it would be verydifficult to incorporate such a test without running simulations.

The introduction of a quarterly minimum net worth standard may serve to raise the advancerates by assuring that at the beginning of each quarter, equity will exceed some level. Even


Table 6ADVANCE RATES WITH A RESTRICTION ON THE PROPORTION OF DISTRESSED BONDS(20 issuers, 5 industries, maximum 30% in Distressed Bonds)

RatingAsset Type A2 Baa2Performing High-yield Bonds RatedBa1-Ba3 0.84 0.88Performing High-yield ratedB1-B3 0.79 0.83Distressed Bond 0.54 0.61


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though the level may fall during the quarter, a decline in value is by no means assured, poten-tially leaving a bit of extra cushion beyond what the advance rates provide.

The interplay between a minimum net worth test and the advance rates is explored in Exhibit D.

THE ROLE OF THE COLLATERAL MANAGERWhatever the price history for high-yield and distressed assets may suggest, it is ultimately theresponsibility of the collateral manager to make intelligent choices with regard to the buying andselling of assets.Thus, a rigorous analysis of portfolio price volatility must be supplemented by asubjective evaluation of the manager.

Obviously, a manager that selects undervalued assets and sells prior to a deterioration in priceis superior to one that lacks these instincts.However, Moodys ratings are not based on a judg-ment that a particular manager will outperform the market. Rather, we seek comfort that amanager understands and can modify, where appropriate, the risks embedded in the assetportfolio.Hence, a track record of high absolute returns would be less reassuring than one ofsolid risk-adjusted returns.

The credit analysts employed by the management firm should have an in-depth knowledge ofthe firms represented within the CDO portfolio.Credit analysis should be systematic and welldocumented.A process of seeking first-hand information,rather than relying exclusively onindustry research, also gives reassurance. Of immediate concern in a market-value transactionis the ability to understand correlations in prices so that diversification will limit the downsiderisk in the portfolio.

In evaluating a particular manager, Moodys draws comfort from depth of experience in managingthe types of assets that will constitute the CDO collateral pool. By depth,we refer to both thenumber of years of experience that the management team can claim and to the size of firmsstaff.The fate of the CDO should not be dependent on a single individual. Redundant manage-ment skills, credit analysis and systems expertise characterize better collateral managers.

The back office environment is no less important than the activities of the front office.Thoughthe trustee for the transaction will have a role in verifying transactions and the status of the port-folio, the collateral manager should be in a position to track the portfolio and to verify that anytests embodied in the transactions documents are met.The presence of independent auditorsand control personnel, that do not report to the portfolio manager, give additional comfort.

A particular concern in the market-value context is the marking to market of the collateral. Manyof the instruments are illiquid, making it difficult to obtain meaningful mid-market price quotes.Despite the expertise of the manager in valuing the instruments, marks by competent, indepen-dent sources provide greater reassurance. For instruments whose prices are not typicallyquoted by dealers, it is often possible to obtain marks with some frequency from other sources,such as independent auditors or investment banks.The haircutsthat we have applied to themore illiquid instruments reflect, in part, the concern that the market prices of such assets maybe difficult to establish prior to an actual sale.

LEGAL CONCERNSThough a thorough review of the documents governing a market value CDO is an absoluteprerequisite to assigning a rating, the legal issues for these transactions are similar to those rele-vant to cash-flow transactions. In particular, the structure must mitigate concerns about(1) bankruptcy remoteness (so that the SPVwill not face claimants other than the senior note-

holders that might force a filing for bankruptcy), (2) true sale (i.e., the assets sold to the SPVarepurchased in a legitimate fashion), and (3) the enforceability of the underlying agreements.15

SUMMARY CONCLUSIONWe have mapped out a procedure for evaluating market-value transactions and providedvarious examples of its implementation.As with all of our rating approaches, we will reevaluatethe procedure over time. In particular, we will update our database of asset returns at leastannually, and more often should there appear to be a fundamental shift in the environment.16


15For a detailed discussion of these issues, see Rating Cash-Flow Transactions Backed by Corporate Debt: 1998 Update,Moodys SpecialComment, April 1998.

16At least on a temporary basis, such shifts can also be addressed by adjusting the appropriate stressing factors.


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We will also extend the database to other asset classes, such as emerging market debt. In fact,there is little in the way of a theoretical limit on the type of instrument that can be incorporatedinto these structures, so long as the appropriate data are available.

It should be apparent from our discussion that the interactions between the various compo-nents of market-value structures are complex, so that it is difficult to imagine that anything otherthan a simulation approach will do justice to these structures. Nonetheless, we hope that wehave given some guidance as to the impact of various structural choices on the advance rates.

The exhibits that follow should further clarify these impacts.


Table A1

Advance Rates For a Portfolio with 10 Issuers,5 Industries(100% investment in one asset type, 5 year maturity)

Target RatingAsset Type Aaa Aa1 Aa2 Aa3 A1 A2 A3 Baa1 Baa2 Baa3Performing Bank LoansValued $0.90 and Above 0.845 0.870 0.875 0.880 0.890 0.895 0.900 0.910 0.935 0.940

Distressed Bank LoansValued $0.85 and Above 0.730 0.765 0.770 0.775 0.790 0.795 0.800 0.815 0.825 0.860







Table A2

Advance Rates For a Portfolio with 20 Issuers, 5 Industries(100% investment in one asset type, 5 year maturity)









Exhibit AImpact of Industry/Issuer Diversification on Advance Rates


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Table A3



Distressed Bank Loans

Valued $0.85 and Above 0.780 0.795 0.800 0.805 0.815 0.820 0.825 0.835 0.850 0.870Performing High-YieldBonds RatedBa 0.78 0.82 0.83 0.84 0.86 0.87 0.88 0.89 0.90 0.91






Table A4











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Exhibit CImpact of Subordination on the Advance Rates

Suppose, for example,that the capital structure consists of two debt tranches: x% of seniordebt and y% of subordinated debt, plus the unrated equity.Two sets of advance rates arenormally used to provide protection for (i) the senior debt and (ii) the senior and the subordi-nated debt, collectively. In essence, two OC tests ensure that (i) the market value of assetsdiscounted by senior portfolio advance rate (AR1) is at least as great as the senior debt,and (ii)the market value of assets discounted by the subordinated portfolio advance rate (AR2) is atleast as great as the senior debt plus the subordinated debt.As a result of these two OC tests,the market value (V%, expressed as the percentage of the total capitalization) of the portfolio inthe last period in which the OC tests were met will be:

The effective senior portfolio advance rate (ARE1) is then given by,

The intuition behind formula (4) is that, when

the OC test for the subordinated debt can provide additional protection for the senior debt. If

equation (5)holds for all possible portfolio compositions, the rating of the senior debt implied bya) the rating of the subordinated debt and b) the relative size of the senior and subordinatedtranches, is higher than the rating associated with AR1, the nominalsenior advance rate.

Notice that AR1 and AR2 are portfolio advance rates (see footnote 14) and the above result ishighly sensitive to the portfolio composition, as well as the relative sizes of the debt tranches.Given the dynamic nature of the portfolio composition, the positive effect of the subordinationhas to be viewed in the context of the overall structure.

Consider a case where the collateral manager wishes to invest in high-yield bonds rated B3and better and that up to 50% of the investment is in distressed bonds, if the capital structure


Table B1

Advance Rates With Portfolio Limitations(Investment Limited to Performing High-Yield Bonds

(ratedBa3and above) and Distressed Bonds)

Target Limitation on Advance Rate For

Rating Distressed Bonds Distressed BondsA2 No Limit 0.48A2 Up to 50% 0.54A2 Up to 30 % 0.56Baa2 No Limit 0.56Baa2 Up to 50% 0.61Baa2 Up to 30% 0.62

Table B2

Advance Rates With Portfolio Limitations(Investment Limited to Performing High-yield Bonds

(ratedB3and above) and Distressed Bonds)

Target Limitation on Advance Rate For

Rating Distressed Bonds Distressed BondsA2 No Limit 0.48A2 Up to 50% 0.52A2 Up to 30 % 0.54Baa2 No Limit 0.56Baa2 Up to 50% 0.60Baa2 Up to 30% 0.61

(3) V=max (x

AR,

x+y

AR)

1 2

(4) ARE =x

AR,

x+y

AR)

max(

x

1 2

1

(5) xAR

x+y

AR> ,

1 2

Exhibit BImpact of Portfolio Limitations on Advance Rates (20 issuers,5 industries)


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dictates that the senior and subordinated debt account for 75% and 5% of liabilities, respec-tively, and if the ratings sought for the senior and subordinated debt are A2 and Baa2, respec-tively, then the effective senior advance rate for performing high-yield bonds ratedB3or above(based on Equation (4)) is 0.78,which corresponds to a higher rating (A1) than the desired rating(A2). This does not imply that we should assign a rating ofA1 to the senior debt, because thisrating applies only if 100% of the portfolio investment is in performing high-yield bonds rated B3or above. Simulation shows that for other possible portfolio compositions, e.g.,70% investmentin performing high-yield bonds and 50% investment in distressed bonds, A2andBaa2areindeed the appropriate ratings for the senior and subordinated debt, respectively.

If, however, senior and subordinated debt account for 70% and 10% of liabilities and the port-folio limitation assumptions still hold, then the senior rating consistent with the rating of thesubordinated debt (Baa2) and the relative sizes of the tranches (70% senior,10% subordinateddebt) is in factA1, one notch higher than the initially desired ratingA2.

Exhibit DImpact of a Minimum Net Worth Test on the Advance Rates

To quantify the effect of a minimum net worth test on the expected loss and advance rate calcu-lation, let us first assume that the test is performed with the same frequency as the OC test.Under this assumption,the value of the assets the last time the OC test was run, relative to theinitial capitalization, must be at least x+y+p*(1-x-y); hence, the effective portfolio advance ratesfor senior and subordinated debt are:

Let us consider a case where investments are limited to performing high-yield bonds rated Ba3and above.Assume that we have a structure with 80% senior debt,10% subordinated debt, and10% equity and that the desired ratings for the senior and the subordinated debt are A2andBaa2, respectively. If the minimum net worth test dictates that 50% of the initial equity levelmust be maintained,then the effective advance rates for senior and subordinated debt are 84%and 88% respectively. Since these effective advance rates are the same as the advance rates


Table C1

Implied Senior Rating For Various Subordination Levels(20 issuers, 5 industries; Investment Limited to Performing High-Yield Bonds rated Ba3and above;

up to 50% in Distressed Bonds)

Rating of Implied SeniorSenior Debt Subordinated Debt Subordinated Debt Debt Ratings75% 5% Baa2 A2

70% 10% Baa2 A165% 15% Baa2 Aa3

Table C2

Implied Senior Rating For Various Subordination Levels(20 issuers, 5 industries; Investment Limited to Performing Bank Loans and Up to 30% in Distressed

Bank Loans Valued Below $0.85)

Rating of Implied SeniorSenior Debt Subordinated Debt Subordinated Debt Debt Ratings85% 5% Baa2 A280% 5% Baa2 A1

80% 7% Baa2 Aa3

(6a) ARE =min (AR ,x

x+y+p*(1-x-y)) , and

(6b) ARE =min (AR ,x

x+y+p*(1-x-y))

2 2

1 1


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Moodys Approach to Rating Market-Value CDOs

Copyright 1998 by Moodys Investors Service, Inc., 99 Church Street, New York, New York 10007.All rights reserved. ALL INFORMATION CONTAINED HEREIN IS COPYRIGHTED IN THE NAME OF MOODYS INVESTORS SERVICE, INC. (MOODYS), AND NONE OF SUCH INFORMATION MAY BE COPIED OROTHERWISE REPRODUCED, REPACKAGED, FURTHER TRANSMITTED, TRANSFERRED, DISSEMINATED, REDISTRIBUTED OR RESOLD, OR STORED FOR SUBSEQUENT USE FOR ANY SUCH PURPOSE, INWHOLE OR IN PART, IN ANY FORM OR MANNER OR BY ANY MEANS WHATSOEVER, BY ANY PERSON WITHOUT MOODYS PRIOR WRITTEN CONSENT. All information contained herein is obtained byMOODYS from sources believed by it to be accurate and reliable. Because of the possibility of human or mechanical error as well as other factors, however, such information is provided as is without warranty of anykind and MOODYS, in particular, makes no representation or warranty, express or implied, as to the accuracy, timeliness, completeness, merchantability or fitness for any particular purpose of any such information.Under no circumstances shall MOODYS have any liability to any person or entity for (a) any loss or damage in whole or in part caused by, resulting from, or relating to, any error (negligent or otherwise) or other circum-stance or contingency within or outside the control of MOODYS or any of its directors, officers, employees or agents in connection with the procurement, collection, compilation, analysis, interpretation, communica-tion, publication or delivery of any such information, or (b) any direct, indirect, special, consequential, compensatory or incidental damages whatsoever (including without limitation, lost profits), even if MOODYS isadvised in advance of the possibility of such damages, resulting from the use of or inability to use, any such information. The credit ratings, if any, constituting part of the information contained herein are, and must beconstrued solely as, statements of opinion and not statements of fact or recommendations to purchase, sell or hold any securities. NO WARRANTY, EXPRESS OR IMPLIED, AS TO THE ACCURACY, TIMELINESS,COMPLETENESS, MERCHANTABILITY OR FITNESS FOR ANY PARTICULAR PURPOSE OF ANY SUCH RATING OR OTHER OPINION OR INFORMATION IS GIVEN OR MADE BY MOODYS IN ANY FORM ORMANNER WHATSOEVER. Each rating or other opinion must be weighed solely as one factor in any investment decision made by or on behalf of any user of the information contained herein, and each such usermust accordingly make its own study and evaluation of each security and of each issuer and guarantor of, and each provider of credit support for, each security that it may consider purchasing, holding orselling. Pursuant to Section 17(b) of the Securities Act of 1933, MOODYS hereby discloses that most issuers of debt securities (including corporate and municipal bonds, debentures, notes and commercialpaper) and preferred stock rated by MOODYS have, prior to assignment of any rating, agreed to pay to MOODYS for appraisal and rating services rendered by it fees ranging from $1,000 to $550,000.

presented inTable 5, the minimum net worth test does not have a direct impact on the advancerates in this structure.

If, by contrast, the structure provides 60% senior debt,10% subordinated debt,and 30% equity,then the effective advance rates implied by the minimum net worth test are 71% and 82%,respectively, and are therefore lower than the advance rates for performing high-yield bondspresented inTable 5. For this example, a simulation reveals that due to the additional protectionprovided by the equity and the minimum net worth test,we can increase the subordinatedadvance rate for performing high-yield bonds rated Ba3 from 0.88 to 0.89.

16

Table D1

Impact of Minimum Net Worth Test (20 issuers,5 industries)(Investment Limited to Performing High-Yield Bonds ratedBa3and above)

Advance RatesSenior Debt Subordinated Debt Equity Target Rating High-Yield Bonds

70% 10% 20% Baa2 0.88

60% 10% 30% Baa2 0.89

50% 10% 40% Baa2 0.90

Documents

Rating Market Value CDO