Active Credit Portfolio Management in Practice (book)

“This is an excellent book written by two authors who have a wealth of credit modeling experience.”

—John Hull, Maple Financial Group Professor of Derivatives and Risk Management, Joseph L. Rotman School of Management, University of Toronto

 

“Bohn and Stein, both accomplished theorists and practitioners, present an accessible collection of complex models to assist the modern financial institution to manage credit risk effectively in what we all now understand to be the most critical financial risk concept in the world today. Now it is up to the major stakeholders, especially senior management and boards, to embrace these models and make them an integral part of their firms’ culture.”

—Professor Edward I. Altman, Max L. Heine Professor of Finance, Stern School of Business, NYU

 

“This book, by authors who have worked very closely with the theory and practical implementation of credit models, is an excellent field guide to credit risk modeling . . . The authors do not shrink from explaining difficult models but, instead of providing lengthy proofs, focus on explaining the intuition behind the models, and their practical limitations, in simple, accessible language. This is a must-read for practitioners in the area of credit risk, risk management, and banking, and for students and faculty in finance.”
—Raghuram G. Rajan, Eric J. Gleacher Distinguished Service Professor ofFinance, University of Chicago Booth School of Business, and former Chief Economist, International Monetary Fund

 

“Bohn and Stein have created a wonderful guide to state-of-the-art credit risk modeling and credit risk portfolio construction. The book will provide the reader with a clear understanding of the theoretical foundation for the most useful credit models, and with a wealth of practical information on implementing these models. This book provides key insights on how to use and, perhaps more importantly, how to not misuse creditrisk models.”
—Kent D. Daniel, Director of Equity Research, Quantitative Investment Strategies, Goldman Sachs Asset Management

 

“Suffice it to say that this is a rich book on matters of historic proportions.”

—Peter Carr, PhD, Head of Quantitative Financial Research, Bloomberg; Director of the Masters in Math Finance Program, Courant Institute, NYU

 

“A breakthrough piece and so timely! Bohn and Stein take us through this complex topic in a clearly articulated rational progression.”

—Loretta M. Hennessey, First Chairperson of the International Association of Credit Portfolio Managers

 

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Commercializing biomedical research through securitization techniques

Commercializing biomedical research through securitization techniques

Fernandez, JM, R. M. Stein and A. W. Lo. 2012. nature biotechnology. 30. 10.

 

Biomedical innovation has become riskier, more expensive and more difficult to finance with traditional sources such as private and public equity. Here we propose a financial structure in which a large number of biomedical programs at various stages of development are funded by a single entity to substantially reduce the portfolio’s risk. The portfolio entity can finance its activities by issuing debt, a critical advantage because a much larger pool of capital is available for investment in debt versus equity. By employing financial engineering techniques such as securitization, it can raise even greater amounts of more-patient capital. In a simulation using historical data for new molecular entities in oncology from 1990 to 2011, we find that megafunds of $5–15 billion may yield average investment returns of 8.9–11.4% for equity holders and 5–8% for ‘research-backed obligation’ holders, which are lower than typical venture-capital hurdle rates but attractive to pension funds, insurance companies and other large institutional investors.

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Can Financial Engineering Cure Cancer?

Can Financial Engineering Cure Cancer?

Fernandez, J-M, D. Fagnan, A. W. Lo &  R. M. Stein. 2013.  American Economic Review. 103. 3.

 

In this paper, we describe a new approach to financing biomedical innovation that we first proposed in Fernandez, Stein, and Lo (2012) and extend in several ways here: using portfolio theory and securitization to reduce the risk of translational medicine. By combining a large number of drug-development projects within a single portfolio, a “megafund,” it becomes possible to reduce the investment risk to such an extent that issuing bonds backed by these proj- ects becomes feasible. Debt financing is a key innovation because the cost of each drug-development project can be several hundred million dollars; hence, a sufficiently diversified portfolio may require tens of billions of dollars of investment capital, and debt markets have much greater capacity than either private or public equity markets. If these bonds are structured to have different priorities, the most senior class or “tranche” may be rated by credit-rating agencies, opening up a much larger pool of institutional investors who can purchase such instruments, e.g., pension funds, sovereign wealth funds, endowments, and foundations. Our results also suggest that even a small (in expected value) third-party guarantee can materially improve the economics of an RBO transaction.

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Evaluating discrete choice prediction models when the evaluation data is corrupted: analytic results and bias corrections for the area under the ROC

 

Validating default models when the validation data are corrupted: Analytic results and bias corrections

There has been a growing recognition that issues of data quality, which are routine in practice, can materially affect the assessment of learned model performance. In this paper, we develop some analytic results that are useful in sizing the biases associated with tests of discriminatory model power when these are performed using corrupt (“noisy”) data. As it is sometimes unavoidable to test models with data that are known to be corrupt, we also provide some guidance on interpreting results of such tests. In some cases, with appropriate knowledge of the corruption mechanism, the true values of the performance statistics such as the area under the ROC curve may be recovered (in expectation), even when the underlying data have been corrupted. We also provide estimators of the standard errors of such recovered performance statistics. An analysis of the estimators reveals interesting behavior including the observation that “noisy” data does not “cancel out” across models even when the same corrupt data set is used to test multiple candidate models. Because our results are analytic, they may be applied in a broad range of settings and this can be done without the need for simulation.multiple default models. Because our results are analytic, they may be applied in a broad range of settings and this can be done without the need for simulation experiments.

 

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Financing drug discovery for orphan diseases

 

Financing drug discovery for orphan diseases

David E. Fagnan, Austin A. Gromatzky, Roger M. Stein, Jose-Maria Fernandez and Andrew W. Lo

 

Recently proposed ‘megafund’ financing methods for funding translational medicine and drug development require billions of dollars in capital per megafund to de-risk the drug discovery process enough to issue long-term bonds. Here, we demonstrate that the same financing methods can be applied to orphan drug development but, because of the unique nature of orphan diseases and therapeutics (lower development costs, faster FDA approval times, lower failure rates and lower correlation of failures among disease targets) the amount of capital needed to de-risk such portfolios is much lower in this field. Numerical simulations suggest that an orphan disease megafund of only US$575 million can yield double-digit expected rates of return with only 10–20 projects in the portfolio.

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Differences in Tranching Methods: Some Results and Implications

Differences in Tranching Methods: Some Results and Implications

We review the mathematics of evaluating the credit risk of tranches of structured transactions with simple loss-priority structure for two common tranching approaches: PD-based tranching where the probability of default of a tranche is the quantity of interest; and EL- based tranching where the expected loss on a tranche is the quantity of interest. While comparing the attributes of these two tranching approaches, we examine their relative (level of credit enhancement) conservatism. While the mathematics is simple some implications of the results are interesting. We discuss the impact of the collateral LGD assumption on attachment points; show that, all else equal, lowering attachment point or the detachment point necessarily increases the tranche EL; and provide upper-bounds on senior-most tranche LGD under reasonable distributional assumptions. One implication of the low LGDs associated with the senior tranches is that under some EL definitions, it may be impossible to create a tranche with a given EL under the EL-based approach, even though they are always possible under the PD-based approach.

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Aggregate and firm-level measures of systemic risk from a structural model of default

Aggregate and firm-level measures of systemic risk from a structural model of default

Reyngold, A., K. Shnyra. and R. M. Stein. 2013. MIT LFE Working Paper LFE-0501-13.

 

The breadth and dynamics of the recent financial crisis have led to efforts to develop forward-looking tools to monitor systemic risk. In this paper we propose a new measure which is an extension of the absorption ratio (AR) introduced in Kritzman et al. (2010). Using principal component analysis (as in the original AR methodology) in conjunction with a structural model of default we develop a measure of systemic risk that may be calculated using only publicly available data. We call our new measure the credit absorption ratio (CAR). We find that increases in the CAR preceded periods of financial distress during the recent crisis. The CAR may be interpreted economically: it highlights states of the financial system during which the credit fundamentals of institutions and markets exhibit heightened coupling and higher potential for cascading distress. We also demonstrate that a byproduct of CAR analysis provides a measure of the degree to which specific financial institutions are exposed to systemic risk factors at any point in time. We find that a number of the institutions that exhibited high potential exposure under our measure during the lead-up to the recent crisis subsequently experienced higher levels of distress or required external assistance.

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