Til Schuermann

Macroeconomic Dynamics and Credit Risk: A Global Perspective

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 nonproportional, reflecting the highly nonlinear nature of the credit risk model.

Modeling Liquidity Risk with Implications for Traditional Market Risk Measurement and Management

Market risk management traditionally has focussed on the distribution of portfolio value changes resulting from moves in the midpoint of bid and ask prices. 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.

Credit Rating Dynamics and Markov Mixture Models

Despite mounting evidence to the contrary, credit migration matrices, used in many credit risk and pricing applications, are typically assumed to be generated by a simple Markov process. Based on empirical evidence, we propose a parsimonious model that is a mixture of (two) Markov chains, where the mixing is on the speed of movement among credit ratings. We estimate this model using credit rating histories and show that the mixture model statistically dominates the simple Markov model and that the differences between two models can be economically meaningful. The non-Markov property of our model implies that the future distribution of a firms ratings depends not only on its current rating but also on its past rating history. Indeed we find that two firms with identical current credit ratings can have substantially different transition probability vectors. We also find that conditioning on the state of the business cycle or industry group does not remove the heterogeneity with respect to the rate of movement. We go on to compare the performance of mixture and Markov chain using out-of-sample predictions.

Confidence Intervals for Probabilities of Default

In this paper we conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD) using several analytical approaches as well as parametric and nonparametric bootstrap methods. We do so for two different PD estimation methods, cohort and duration (intensity), with 22 years of credit ratings data. We find that the bootstrapped intervals for the duration based estimates are relatively tight when compared to either analytic or bootstrapped intervals around the less efficient cohort estimator. We show how the large differences between the point estimates and confidence intervals of these two estimators are consistent with non-Markovian migration behavior. Surprisingly, even with these relatively tight confidence intervals, it is impossible to distinguish notch-level PDs for investment grade ratings, e.g. a PDAA- from a PDA+. However, once the speculative grade barrier is crossed, we are able to distinguish quite cleanly notch-level estimated PDs. Conditioning on the state of the business cycle helps: it is easier to distinguish adjacent PDs in recessions than in expansions.

Macroprudential Supervision of Financial Institutions: Lessons from the SCAP

A fundamental conclusion drawn from the recent financial crisis is that the supervision and regulation of financial firms in isolation - a purely microprudential perspective - are not sufficient to maintain financial stability. Rather, a macroprudential perspective, which evaluates and responds to the financial system as a whole, seems necessary, and the ongoing discussions of regulatory reform in the United States underscore this view. The recently concluded Supervisory Capital Assessment Program (SCAP), better known as the bank “stress test,” is one example of how the macro- and microprudential perspectives can be joined to create a stronger supervisory framework that addresses a wider range of supervisory objectives. This paper reviews the key features of the SCAP and discusses how they can be leveraged to improve bank supervision in the future.

Understanding the Securitization of Subprime Mortgage Credit

In this survey we provide an overview of the subprime mortgage securitization process and the seven key informational frictions which arise. We discuss how 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 the rating agencies assign credit ratings to mortgage-backed securities, and outline how the agencies monitor the performance of mortgage pools over time. Throughout the survey, we draw upon the example of a mortgage pool securitized by New Century during 2006.

Robust Capital Regulation

Banks’ leverage choices represent a delicate balancing act. Credit discipline argues for more leverage, while balance-sheet opacity and ease of asset substitution argue for less. Meanwhile, regulatory safety nets promote ex post financial stability, but also create perverse incentives for banks to engage in correlated asset choices and to hold little equity capital. As a way to cope with these distorted incentives, we outline a two-tier capital framework for banks. The first tier is a regular core capital requirement that helps deter excessive risk-taking incentives. The second tier, a novel aspect of our framework, is a special capital account that limits risk taking but preserves creditors’ monitoring incentives.

Estimating Probabilities of Default

We conduct a systematic comparison of confidence intervals around estimated probabilities of default (PD), using several analytical approaches from large-sample theory and bootstrapped small-sample confidence intervals. We do so for two different PD estimation methods-cohort and duration (intensity)-using twenty-two years of credit ratings data. We find that the bootstrapped intervals for the duration-based estimates are surprisingly tight when compared with the more commonly used (asymptotic) Wald interval. We find that even with these relatively tight confidence intervals, it is impossible to distinguish notch-level PDs for investment grade ratings-for example, a PDAA- from a PDA+. However, once the speculative grade barrier is crossed, we are able to distinguish quite cleanly notch-level estimated default probabilities. Conditioning on the state of the business cycle helps; it is easier to distinguish adjacent PDs in recessions than in expansions.

Credit Migration Matrices

This article provides a brief overview of credit migration or transition matrices, which characterize past changes in credit quality of obligors (typically firms). They are cardinal inputs to many risk management applications, including portfolio risk assessment, the pricing of bonds and credit derivatives, and the assessment of regulatory capital as is the case for the New Basel Accord. I address questions of how to estimate these matrices, how to make inference and compare them, and provide two examples of their use: the pricing of a derivative called a yield spread option, and the calculation of the value distribution for a portfolio of credit assets. The latter is especially useful for risk management of credit portfolios. Keywords: credit risk; credit portfolios; credit derivatives; Markov; probabilities of default

The Role of Industry, Geography and Firm Heterogeneity in Credit Risk Diversification

In theory the potential for credit risk diversification for banks could be substantial. Portfolio diversification is driven broadly by two characteristics: the degree to which systematic risk factors are correlated with each other and the degree of dependence individual firms have to the different types of risk factors. We propose a model for exploring these dimensions of credit risk diversification: across industry sectors and across different countries or regions. We find that full firm-level parameter heterogeneity matters a great deal for capturing differences in simulated credit loss distributions. Imposing homogeneity results in overly skewed and fat-tailed loss distributions. These differences become more pronounced in the presence of systematic risk factor shocks: increased parameter heterogeneity greatly reduces shock sensitivity. Allowing for regional parameter heterogeneity seems to better approximate the loss distributions generated by the fully heterogeneous model than allowing just for industry heterogeneity. The regional model also exhibits less shock sensitivity.