TRENDS IN PORTFOLIO MANAGEMENT The State of the Art in Credit Portfolio Modeling by Charles W. Smithson and Gregory Hayt his is the second of a series of five excerpts from a forth- coming book Managing Credit Risk: Toward a Portfolio T Approach to Credit Risk Management by Charles Smithson and Gregory Hayt of Rutter Associates. Future articles will appear in the May, July/August, and October 2001 issues. quity investors have given level of return or increase an investor might wish to include enthusiastically embraced expected return for a given level a “loser” in the portfolio, as long Ethe concepts of diversifica- of risk. In marked contrast to the as that asset has a very low (or tion and asset allocation. Modern pick-winners philosophy that negative) correlation with other portfolio theory (MPT) has trans- dominated equity investing assets in the portfolio. formed equity portfolio manage- through the 1960s, this means that ment from a philosophy of “pick- ing winners” to one where Figure 1 investors strive to hold an “effi- Modern Portfolio Theory cient portfolio.” The central fea- The Efficient Frontier—The portfolios that ture of MPT is an efficient portfo- simultaneously maximize expected return for a given level of risk and minimize risk for a lio that simultaneously maximizes given level of expected return expected return for a given level of risk and minimizes risk for a given level of return. Expected Return Recognition of correlation is The “Feasible Set”—All the key to the benefits of holding possible portfolios of securities a well-diversified portfolio. If the returns of two assets are less than perfectly positively correlated, an investor can reduce the risk for a Risk (Standard Deviation) © 2001 by RMA and Rutter Associates. Smithson is managing partner and Hayt is principal at Rutter Associates, a New York-based risk management consultancy. Smithson and Hayt have both presented at RMA conferences and have co- authored other articles in The RMA Journal. 34 The RMA Journal March 2001 The State of the Art in Credit Portfolio Modeling Challenges to the credit assets have more dimen- to consolidate into a workable cen- Implementation of MPT for sions than equity as well, includ- tral database reflects the tradition- Portfolios of Credit Assets ing uncertain usage of credit lines, al originate-and-hold strategy at While MPT has driven equity uncertain losses in the event of most financial institutions in portfolio management for more default, collateral, and the ability which loans are viewed in isolation than 30 years, it is still not widely to restructure assets by changing rather than as part of a portfolio. applied to credit assets. The norm the terms of the lending agree- Another problem area for loans and other credit assets is ment. involves estimates of expected still for a financial institution to In addition, credit portfolio return, risk, and correlation. For originate the asset and then hold it management requires more than a credit assets, these parameters until it matures or defaults. One mean/variance view of the world. are, themselves, determined by reason for this is that portfolios of The credit portfolio manager the probability of default (down- credit assets present more chal- places much more emphasis on grade), the volatility of the proba- lenges to the implementation of extreme outcomes—“tail” bility of default (downgrade), loss MPT than do portfolios of equities. events—to determine economic in the event of default, and the capital and credit rating. correlation of defaults (or down- Distribution of losses. The grades). As noted earlier, there is mean-variance (that is, normally Data limitations. Arguably, limited data on the historical per- distributed) environment assumed the largest obstacle to the applica- formance of credit assets from by MPT is less justifiable for port- tion of portfolio theory in banks is which to estimate these parame- folios of credit assets than for the lack of clean, consistent data. ters. However, in the case of portfolios of equities. As is illus- One problem area involves stand-alone measures of the risk trated in Figure 2, the loss distri- the data on existing and new and return of individual credits, butions for loans and other credit loans. Financial institutions are there is an increasing array of assets are not normally distributed making significant investments in choices. and are not even symmetric. And order to locate data (often still in Figure 3 (next page) provides a there is limited data on the histor- original loan documents), to recon- listing of sources of information on ical performance of credit assets cile different systems, clean and probabilities of default for public from which the appropriate shape validate data, and develop an and private firms. Each of the of the loss distribution could be infrastructure to maintain the data default models uses proprietary estimated. Credit has strong going forward. The fact that data analytics with inputs ranging from option-like characteristics, and on credit portfolios is so difficult financial statements alone, to finan- cial statements plus equity market Figure 1 data, and to systems based entirely Losses from Loans and Other Credit Assets on spreads on traded assets. Are Not Normally Distributed Figure 4 summarizes sources for loss given default (LGD) and Observed Distribution of Losses usage of committed facilities by defaulting firms. Of course, large financial institutions have consid- erable internal data, if they can Normal Distribution organize it, on these variables. There also are important industry, academic, and commercial studies that have added to our knowledge through the pooling or collecting -7 -6 -5 -4 -3 -2 -1 0 1 2 3 4 5 6 7 of data across institutions. Promised Return 100% (No Loss) Loss 35 The State of the Art in Credit Portfolio Modeling First Generation Credit Portfolio released its CreditMetricsTM 1998. During the same period, Models methodology (and the Credit- many firms had or were develop- The public release of credit ManagerTM software package) in ing internal models for credit portfolio models began in the mid 1997. Also in 1997, Credit Suisse portfolio management. Many of 1990s. KMV released Portfolio First Boston introduced its the internal models addressed ManagerTM in 1993. The CreditRisk+TM . McKinsey intro- assets that were not covered by RiskMetrics Group (RMG) duced CreditPortfolioViewTM in the initial releases of the public models, including middle market Figure 3 corporate lending and consumer lending. Sources of Standalone Risk Estimates for Credit Assets These four models are imple- mentations of three broad Vendor Product Description approaches: KMV Credit Expected Default FrequencyTM Monitorw/ (EDF TM ) credit measures for public 1. Asset volatility CreditEdgeTM firms updated monthly/daily. Private approach. The risk of default or w Firm Model also available. downgrade is modeled in terms of Kamakura Corporation KRM—crTM Default probability derived from credit the value of a firm’s assets relative spreads to its liabilities. These models are also called structural models be- Loan Pricing Corporation Risk Rater TM Default probability and expected loss for cause the risk of default/down- commercial loans grade is derived from a formal Moody’s RiskCalcTM Default probabilities for public or private model of credit risk at the firm firms level. TM Standard & Poor’s CreditPro Historical marginal and cumulative 2. Actuarial approach. The default rates by industry and geography risk of default and the loss given Standard & Poor’s CreditModel TM Implied credit ratings for public or default are assumed to fit certain private firms distributions. This assumption EnronCredit.com Enron Cost of Default probability estimates coupled makes it possible to calculate the Credit TM with historical recovery data distribution of portfolio losses analytically. In some ways, this Figure 4 approach is closest to the original mean/variance approach of Sources of Data on Loss Given Default and Usage of Markowitz, which assumes nor- Committed Lines mally distributed asset returns and yields closed form results for Study or portfolio risk and security risk Source Database? Description of Data Altman & Kishore (96) Recovery study Recovery on bonds organized by seniority (Beta). and industry 3. Factor model approach. Carty & Lieberman (96) Default and Recovery data by seniority; Post default The probability of default is (Moody’s) recovery study default secondary market prices assumed to depend on the state of Loan Pricing Corporation Loan Loss Historical loss data for a large sample of such user-defined factors as GDP, credit assets unemployment, commodity S&P/Portfolio Loss/Recovery Historical data for a large sample of prices, and so forth. These models Management Data Database credit assets are quite general in that no under- Asarnow & Marker (95) Study of usage Usage by credit rating and loss given lying theory determines which of committed default factors are most appropriate or credit lines what weights should apply to 36 The RMA Journal March 2001 The State of the Art in Credit Portfolio Modeling changes in each factor. This lack simulated macro factors. In all of default rate volatility. of formal structure makes them the models, the user supplies the The segment allocation and very flexible and extremely trans- expected loss given default to the default rate volatility imply a cor- parent, since the risk of default is model. relation between default rates for linked explicitly to systematic Not surprisingly, the models any pair of obligors. In Credit- variables and a firm-specific com- differ in the way that they deal PortfolioView, default (down- ponent. with the correlation of defaults grade) correlation enters the Figure 5 provides an overview (downgrades). Correlation in model through the correlation of of the four widely-discussed credit Portfolio Manager is obtained by a the chosen macro factors and esti- portfolio models. The first row factor model of asset returns, mated factor weights.