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The turmoil in the capital markets in 1997 and 1998 has highlighted the need for systematic stress testing of banks' portfolios, including both their trading and lending books. We propose that underlying macroeconomic volatility is a key part of a useful conceptual framework for stress testing credit portfolios, and that credit migration matrices provide the specific linkages between underlying macroeconomic conditions and asset quality. Credit migration matrices, which characterize the expected changes in credit quality of obligors, are cardinal inputs to many applications, including portfolio risk assessment, modeling the term structure of credit risk premia, and pricing of credit derivatives. They are also an integral part of many of the credit portfolio models used by financial institutions. By separating the economy into two states or regimes, expansion and contraction, and conditioning the migration matrix on these states, we show that the loss distribution of credit portfolios can differ greatly, as can the concomitant level of economic capital to be assigned to a bank.

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may AbstractIn this paper, we provide an overview of the subprime mortgage securitization process and the seven key informational frictions that arise. We discuss the ways that market participants work to minimize these frictions and speculate on how this process broke down. We continue with a complete picture of the subprime borrower and the subprime loan, discussing both predatory borrowing and predatory lending. We present the key structural features of a typical subprime securitization, document how rating agencies assign credit ratings to mortgage-backed securities, and outline how these agencies monitor the performance of mortgage pools over time. i Executive SummarySection numbers containing more detail are provided in [square] brackets.• Until very recently, the origination of mortgages and issuance of mortgage-backed securities (MBS) was dominated by loans to prime borrowers conforming to underwriting standards set by the Government Sponsored Agencies (GSEs) [2] − By 2006, non-agency origination of $1.480 trillion was more than 45% larger than agency origination, and non-agency issuance of $1.033 trillion was 14% larger than agency issuance of $905 billion.• The securitization process is subject to seven key frictions. The arranger has more information about the quality of the mortgage loans which creates an adverse selection problem: the arranger can securitize bad loans (the lemons) and keep the good ones. This third friction in the securitization of subprime loans affects the relationship that the arranger has with the warehouse lender, the credit rating agency (CRA), and the asset manager. Resolution: haircuts on the collateral imposed by the warehouse lender. Due diligence conducted by the portfolio manager on the arranger and originator. CRAs have access to some private information; they have a franchise value to protect. 4) Frictions between the servicer and the mortgagor: Moral hazard [2. 1.4] In order to maintain the value of the underlying asset (the house), the mortgagor (borrower) has to pay insurance and taxes on and generally maintain the property.In the approach to and during delinquency, the mortgagor has little incentive to do all that. Resolution: Require the mortgagor to regularly escrow funds for both insurance and property taxes. When the borrower fails to advance these funds, the servicer is typically required to make these payments on behalf of the investor. However, limited effort on ...

This paper presents a new approach to modeling conditional credit loss distributions. Asset value changes of firms in a credit portfolio are linked to a dynamic global macroeconometric model, allowing macroeffects to be isolated from idiosyncratic shocks from the perspective of default (and hence loss). Default probabilities are driven primarily by how firms are tied to business cycles, both domestic and foreign, and how business cycles are linked across countries. We allow for firm-specific business cycle effects and the heterogeneity of firm default thresholds using credit ratings. The model can be used, for example, to compute the effects of a hypothetical negative equity price shock in South East Asia on the loss distribution of a credit portfolio with global exposures over one or more quarters. We show that the effects of such shocks on losses are asymmetric and non-proportional, reflecting the highly nonlinear nature of the credit risk model.

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Integrated risk management in a financial institution requires an approach for aggregating risk types (market, credit, and operational) whose distributional shapes vary considerably. In this paper, we construct the joint risk distribution for a typical large, internationally active bank using the method of copulas. This technique allows us to incorporate realistic marginal distributions, both conditional and unconditional, that capture some of the essential empirical features of these risks such as skewness and fat-tails while allowing for a rich dependence structure. We explore the impact of business mix and inter-risk correlations on total risk, whether measured by value-at-risk or expected shortfall. We find that given a risk type, total risk is more sensitive to differences in business mix or risk weights than to differences in inter-risk correlations. There is a complex relationship between volatility and fat-tails in determining the total risk: depending on the setting, they either offset or reinforce each other. The choice of copula (normal versus Student-t), which determines the level of tail dependence, has a more modest effect on risk. We then compare the copula-based method with several conventional approaches to computing risk.

Market risk management under normal conditions traditionally has focussed on the distribution of portfolio value changes resulting from moves in the mid-price. Hence the market risk is really in a "pure" form: risk in an idealized market with no "friction" in obtaining the fair price. However, many markets possess an additional liquidity component that arises from a trader not realizing the mid-price when liquidating her position, but rather the mid-price minus the bid-ask spread. We argue that liquidity risk associated with the uncertainty of the spread, particularly for thinly traded or emerging market securities under adverse market conditions, is an important part of overall risk and is therefore an important component to model.We develop a simple liquidity risk methodology that can be easily and seamlessly integrated into standard value-at-risk models, and we show that ignoring the liquidity effect can produce underestimates of market risk in emerging markets by as much as 25-30%. Furthermore, we show that the BIS inadvertently is already monitoring liquidity risk, and that by not modeling it explicitly and therefore capitalizing against it, banks will be experiencing surprisingly many violations of capital requirements, particularly if their portfolios are concentrated in emerging markets.We thank Steve Cecchetti and Edward Smith for helpful comments and suggestions. All remaining errors are ours.-2-"Portfolios are usually marked to market at the middle of the bid-offer spread, and many hedge funds used models that incorporated this assumption. In late August, there was only one realistic value for the portfolio: the bid price. Amid such massive sell-offs, only the first seller obtains a reasonable price for its security; the rest loose a fortune by having to pay a liquidity premium if they want a sale. …Models should be revised to include bid-offer behaviour." Nicholas Dunbar ("Meriwether's Meltdown," Risk, October 1998, 32-36)