Article

Active Credit Portfolio Management in Practice

Authors:
  • NYU; Massachusetts Institute of Technology; Hebrew University
To read the full-text of this research, you can request a copy directly from the authors.

Abstract

State-of-the-art techniques and tools needed to facilitate effective credit portfolio management and robust quantitative credit analysis Filled with in-depth insights and expert advice, Active Credit Portfolio Management in Practice serves as a comprehensive introduction to both the theory and real-world practice of credit portfolio management. The authors have written a text that is technical enough both in terms of background and implementation to cover what practitioners and researchers need for actually applying these types of risk management tools in large organizations but which at the same time, avoids technical proofs in favor of real applications. Throughout this book, readers will be introduced to the theoretical foundations of this discipline, and learn about structural, reduced-form, and econometric models successfully used in the market today. The book is full of hands-on examples and anecdotes. Theory is illustrated with practical application. The authors' Website provides additional software tools in the form of Excel spreadsheets, Matlab code and S-Plus code. Each section of the book concludes with review questions designed to spark further discussion and reflection on the concepts presented. © 2009 Jeffrey R. Bohn and Roger M. Stein. All rights reserved.

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... The power of a scorecard measures the extent to which defaults are avoided when classifying good borrowers. However, even though a scorecard can have strong power, calibration is needed to match actual default rates (Bohn & Stein, 2009). The overall state of the economy during the business cycle creates a problem for the application of credit scoring from an acquisition point of view. ...
... Medema et al. (2009) indicated that a model is well calibrated if the fraction of events which actually occur is unbiasedly estimated by the estimated probability of these events (Medema et al., 2009). Bohn and Stein (2009) asserted that calibration required two steps: mapping the scores of a model to historical empirical probabilities and adjusting for the difference between historical empirical default rates and actual default rates (i.e. the probability needs to be adjusted to reflect the true prior distribution). Bohn and Stein (2009) emphasised that the first objective of credit scorecards was to obtain a high degree of predictive power and suggested simple calibration techniques to map model probabilities to empirical probabilities when econometric assumptions were invalid. ...
... Bohn and Stein (2009) asserted that calibration required two steps: mapping the scores of a model to historical empirical probabilities and adjusting for the difference between historical empirical default rates and actual default rates (i.e. the probability needs to be adjusted to reflect the true prior distribution). Bohn and Stein (2009) emphasised that the first objective of credit scorecards was to obtain a high degree of predictive power and suggested simple calibration techniques to map model probabilities to empirical probabilities when econometric assumptions were invalid. This technique -nonparametric density estimation -improves the alignment between model predictions and actual default probabilities. ...
Article
Application scorecards play a critical part in determining the creditworthiness of applicants for acquisition purposes. However, the level of bad rate in a downturn period and upturn period although monotonic are different across the scores due to procyclicality (which arises from the fluctuation of financial characteristics around a trend in an economic cycle). The procyclicality effect from an acquisition perspective on a bureau scorecard is investigated with emphasis on South African retail banking data, and performance between downturn and upturn periods is compared. This paper contributes by proposing a methodology which incorporates a Bayesian calibration approach to adjust to future expected bad rates: the comparison indicates that calibration is essential to account for procyclicality.
... The best practices of integrated risk management recommend to take into account the mutual interaction of risks. The known experts on credit risks Bohn and Stein (2009) state that the most credit risk models have underestimated credit risk, because they do not take into consideration a liquidity premium. Meantime, the world financial crisis 2007-2008 years, when liquidity of financial markets has quickly evaporated, showed importance of account of liquidity premiums. ...
... Meantime, the world financial crisis 2007-2008 years, when liquidity of financial markets has quickly evaporated, showed importance of account of liquidity premiums. Bohn and Stein (2009) assert that now there are no fully developed models for loan pricing, taking into account the liquidity risk. Therefore, this article is devoted development of approach to loan pricing, coming from the task of integrated management of credit and liquidity risk of a bank. ...
... where R is the risky interest rate, r is the risk free interest rate, el is the specific (on unit of loan sum) expected losses (Bessis, 1988;Bohn and Stein, 2009): ...
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In the paper, different approaches to pricing on loan are compared. “Cash Flow at Risk” approach to loan pricing is suggested. Application of this approach ministers to protect a bank against both credit and liquidity risks, and to receive by it interest income with interest rate that is not less than the guaranteed one. Example of interest rate on loan calculation is given. The suggested approach is easy included into RAROC approach.
... Using results by Bohn and Stein (2009), and expressing the undiscounted expected credit losses through cash flows, write it in the following form: ...
... Using results by Bohn and Stein (2009) and expressing the undiscounted unexpected credit losses through cash flows, write it in the following form: ...
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To price bank's assets correctly, it is important to know cost of funds. But funding cost calculation is complicated due to the fact that banks fund long-term assets through short-term liabilities. As a result, assets with a given time to maturity are usually financed by several liabilities with different maturities. To calculate funding cost it needs to know how cash flows are matched between assets and liabilities. For thisis used cash flow matching matrix or funding matrix. In the paper, a new algorithm of filling of a two-dimensional funding matrix that is based on the golden rule of banking and modified RAROC-approach is proposed. It provides positive definiteness and uniqueness of the matrix. The matrix shows terms to maturity and amounts of liability cash flows which fund the asset cash flow with a given term to maturity. Examples of partially and fully filled matrices are presented. It is proposed an approach to risk-adjusted pricing that is based on this funding matrix and RAROC-approach adapted to cash flows. The developed approach to pricing integrates organically credit and liquidity risks. It takes into consideration expected credit losses and economic capital (unexpected credit losses) for all lifetime of asset cash flows and not one-year period traditionally used in RAROC.
... Equity, E Scheme 1. The stylized aggregative balance sheet of the borrower (Crosbie & Bohn, 2003) As usually, a dynamics of market value of borrower's total assets is assumed to obey the geometric Brownian motion (Crosbie & Bohn, 2003;Bohn & Stein, 2009): ...
... where pd 1 is the probability of default, A 1 (t) is the market value of the borrower's assets, D is the book value of the borrower's debt at time t. Then, from the Black- Scholes model, it follows that the probability of default at time t in the future is equal to (Crosbie & Bohn, 2003;Bohn & Stein, 2009): ...
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A simple approach to explicit estimating a credit limit for a firm that is based on Moody’s KMV model is developed. It allows taking into account term to maturity of loan, quality of assets, a structure of a balance sheet and required level of default probability. The proposed approach describes such well-known intuitive phenomena as the more term to maturity, the less credit limit; the more level of confidence, the lower credit limit, and the more volatility of return on assets, the less credit limit. The result of the estimation of credit limit on an unsecured loan to a firm is given. A contribution of the approach is that it allows taking into account the fact that a firm may invest new debt in new assets with quality that differs from that of existing assets.
... In this paper, we revisit the concept of two calibration steps as used by Bohn and Stein (2009). According to Bohn and Stein (2009) the two steps are a consequence of the fact that, usually, the first calibration of a rating model is conducted on a training sample in which the proportion of good and bad might not be representative of the live portfolio. ...
... On the basis of the data presented in this section, it is also worthwhile to clarify precisely the concept of a two-step (or two-period) approach to the calibration of a rating model as mentioned by Bohn and Stein (2009). The first period is the estimation period, the second period is the calibration and forecast period. ...
Article
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PD curve calibration refers to the transformation of a set of rating grade level probabilities of default (PDs) to another average PD level that is determined by a change of the underlying portfolio-wide PD. This paper presents a framework that allows to explore a variety of calibration approaches and the conditions under which they are fit for purpose. We test the approaches discussed by applying them to publicly available datasets of agency rating and default statistics that can be considered typical for the scope of application of the approaches. We show that the popular 'scaled PDs' approach is theoretically questionable and identify an alternative calibration approach ('scaled likelihood ratio') that is both theoretically sound and performs better on the test datasets. Keywords: Probability of default, calibration, likelihood ratio, Bayes' formula, rating profile, binary classification.
... Крім моделі очікуваних кредитних збитків, що запозичена стандартами фінансової звітності з ризик-менеджменту, існує модель ціноутворення на кредити з урахуванням ризику [11]. Концепція моделі полягає в тому, що додатковий процентний дохід, генерований кредитним спредом (премією), повинен повністю покрити очікувані кредитні збитки за цим кредитом за строк його існування. ...
... Найкращі практики оцінки кредитного ризику є статистичними [11], що базуються на аналізі великої кількості подій кредитного ризику, наприклад, дефолтів. Базельській комітет рекомендує для управління кредитним ризиком використовувати саме статистичні підходи, за допомогою яких можна виокремити очікувані та неочікувані збитки [2]. ...
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This paper examines ways of overcoming inconsistencies between IFRS and modern concepts of credit risk management, namely, expected loss model and risk-adjusted loan pricing. Also, it is considered an issue of acceptable levels of concentration risk in bank credit portfolio.
... They are generally grouped into two major categories: active investment management and passive investment management, with the term " passive investment " covering both index investment and portfolio insurance. A general idea of the major trends in investment management is given below (Bohn and Stein, 2009, 16) . ...
... Focus our attention on how to compute cumulative probability of default. For this, it usually used the following relationship (Jorion, 2003;Resti and Sironi, 2007;Bohn and Stein, 2009): ...
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In this paper, to estimate the credit risk spreads the interest losses are proposed to recognize immediately after default of a loan, i.e. to consider stopping accrual of interests on the defaulted loan. While a common approach supposes recognition of interest losses on defaulted loan only at maturity and, correspondently, accrual of interests on defaulted loan up to maturity. The proposed approach leads to the fact that the probable losses of bank’s interests turn out to be less than the interest losses, computed by the usually used formula. This difference is explained by the fact that the average (over loan’s lifetime) working, non-defaulted share of loan is less than the share, estimated at loan’s maturity and usually used in calculation. It is shown an importance of credit ratings migration for credit spread valuation. The credit spreads are evaluated for a bank’s fixed rate bullet loan in which both principal and interests are paid at maturity. Examples of calculating the term structure of credit spreads are given.
... [6] to convert these observed probabilities of default to risk-drift and volatility of the underlying, and Φ and Φ −1 are the cumulative and inverse cumulative normal distribution functions respectively. Weestimate the drift and volatility for the underlying from the simulated values of an all-equity structure. ...
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Financing drug development has a particular set of challenges including long development times, high chance of failure, significant market valuation uncertainty, and high costs of development. The earliest stages of translational research pose the greatest risks, which have been termed the "valley of death" as a result of a lack of funding. This thesis focuses on an exploration of financial engineering techniques aimed at addressing these concerns. Despite the recent financial crisis, many suggest that securitization is an appropriate tool for financing such large social challenges. Although securitization has been demonstrated effectively at later stages of drug development for drug royalties of approved drugs, it has yet to be utilized at earlier stages. This thesis starts by extending the model of drug development proposed by Fernandez et al. (2012). These extensions significantly influence the resulting performance and optimal securitization structures. Budget-constrained venture firms targeting high financial returns are incentivized to fund only the best projects, thereby potentially stranding less-attractive projects. Instead, such projects have the potential to be combined in larger portfolios through techniques such as securitization which reduce the cost of capital. In addition to modeling extensions, we provide examples of a model calibrated to orphan drugs, which we argue are particularly suited to financial engineering techniques. Using this model, we highlight the impact of our extensions on financial performance and compare with previously published results. We then illustrate the impact of incorporating a credit enhancement or guarantee, which allows for added flexibility of the capital structure and therefore greater access to lower costing capital. As an alternative to securitization, we provide some examples of a structured equity approach, which may allow for increased access to or efficiency of capital by matching investor objectives. Finally, we provide examples of optimizing the Sortino ratio through constrained Bayesian optimization.
... та H. S. Shin[21], активів. J.R. Bohn та R.M. Stein[35] наголошують, що більшість кредитних моделей недооцінюють кредитні спреди, оскільки не враховують премію за ліквідність. Значне падіння рівня ліквідності після 2007 р. показало важливість врахування премій за ліквідність.Те ж саме зауваження можна зробити й щодо недостатнього використання банками ціноутворення, скорегованого на ризик. ...
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The article demonstrates that the 2008-2009th financial crisis in Ukraine has had a significant and complex impact on its budget. It is shown that the banking crisis has led to a reduction in taxes revenue in the budget, diversion of public funds for the capitalization of state banks and the nationalization of systemic private banks. The main reasons for the negative impact were poor level of risk management and inefficient level of its implementation in the overall system of financial management. As a result, the risks of the banking system of Ukraine were significantly underestimated. To safeguard the state budget from unexpected increasing the expenses on the capitalization of state banks and the nationalization of private ones from reducing the taxes revenue were proposed to develop strategies of improving controllability of banks, to conduct simulation of the banking system of Ukraine on system dynamic model base and to strengthen supervision of banks by introducing reporting about projected cash flows and cash flows at risk, to develop a corresponding methodology for assessing the risk of net cash bank loss before changes in operating assets and liabilities and for stress-testing of net cash profit and loss of the bank.
... The traditional approach to estimating the future expected cash flows from loans assumes that each individual payment on the loan has only two states being paid or default. The default is presumed to occur immediately after the event of failure of individual payment on loan, neglecting its overdue term (see, for example, Bohn and Stein, 2009;Jorion, 2003;Resti and Sironi, 2007). ...
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A new model for predicting the future expected cash flows from a loan is developed. It is based on a detailed analysis of the events of fulfilling, delinquency and default of each individual payment on the loan. The proposed model has significantly less uncertainty compared with the Markov chain model with the same detailing. The model is expected to have greater predictive power in comparison to the traditional models, and its usage will allow reducing the interest rate on the loan. The results of the estimation of the probabilities of payments over time and the future expected cash flows from the loan with monthly equal principal repayment are given.
... In addition, is the US 1-year Treasury rate, obtained from St. Louis Fed (FRED); T = 1 year is the time horizon under consideration in our model; Junior debt (equity) volatility ( ) is calculated from the time series data of L. By solving the above equations, we can extract the implied asset value A and asset volatility ( ) of each government entity. Lastly, it is worth noting that another approach to back out the implied government asset is the Vasicek-Kealhofer model (Bohn & Stein, 2009); this method solves for the asset value and volatility in a recursive fashion. ...
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The current European debt crisis has made sovereign credit risk a popular topic. In this paper we adapt an established structural model for assessing sovereign credit risk and expand it to evaluate the cases of California and Greece. Specifically, major political events such as a bailout or a breakout from a monetary union are not accounted for in current models despite that they may introduce non-linearities in the behavior of the default probability. In this paper, we attempt to account for these extra factors and solve the problem numerically. We rely on a 2-D finite differences method, modeling both the risky assets of our target sovereignty and the ones of its encompassing monetary union. Finally, we detail a method for hedging sovereign credit risk using tradable securities.
... Of course, this could be interpreted as evidence of incompatibility as in the case of violation of the likelihood ratio condition in Theorem 2.5 (i). Bohn and Stein (2009) present an alternative approach which uses the 'change of base rate' theorem (Elkan, 2001, Theorem 2). However, the solution by that approach in general does not solve (2.10) because often the outcome is ...
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The law of total probability may be deployed in binary classification exercises to estimate the unconditional class probabilities if the class proportions in the training set are not representative of the population class proportions. We argue that this is not a conceptually good approach and suggest an alternative based on the new law of total odds. The law of total odds can also be used for transforming the conditional class probabilities if exogenous estimates of the unconditional class probabilities of the population are given.
... Bohn et Stein (2009) ont souligné que le risque est la possibilité que la valeur de l'actif subisse des oscillations sur une période donnée. Cependant, ces définitions ne sont pas les seules, car certains experts ont défini le risque en fonction de la probabilité de défaillance. ...
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... While the actual nature of an individual obligor's debt is considerably more complex, with default possible at several times, the preceding assumptions do provide us with a quality, widely used starting point for credit risk modelling. There have been extensions in terms of asset value modelling (see Bluhm et al. (2010); Bohn and Stein (2009);McNeil et al. (2015) for an overview), but the Merton model remains the "prototype" of many credit risk models, such as Bluhm and Overbeck (2003); Frei and Wunsch (2018); Gordy (2000). In particular, the Merton model is at the basis of the capital requirement described by the Basel Committee on Banking Supervision (2005), whose framework Miu and Ozdemir (2017) suggest to employ for IFRS 9 purposes. ...
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... Therefore, one of the key factors in determining whether a pool of assets can be securitized is whether the stochastic properties of the underlying assets' returns over time can be measured and managed. In the multi-trillion-dollar mortgage-backed securities market, the answer was (and still is) yes, as is the case for corporate debt and several other asset classes 29 . We believe the same may be true for biomedical research. ...
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... 5 The quality of rating systems is a multidimensional measure, which, for example comprises characteristics such as unbiasedness of PD estimates, predictive power and size, i.e. the ability to separate future defaulting firms from non-defaulting firms, timeliness of information or adjustments, transparency, and others. For an in-depth discussion, seeBohn/Stein (2009). 6 SeeDas et al. 2009 for a description of CDS instruments. ...
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