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2012b. Hazardous times for monetary policy: What do twentythree million bank loans say about the effects of monetary policy on credit risktaking? Barcelona GSE working paper
"... We are grateful to Philipp Hartmann and Frank Smets for helpful comments. We thank Marco lo Duca for excellent research assistance. Ongena acknowledges the hospitality of the European Central Bank. Any views expressed are only those of the authors and should not be attributed to the Bank of Spain, t ..."
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Cited by 51 (9 self)
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We are grateful to Philipp Hartmann and Frank Smets for helpful comments. We thank Marco lo Duca for excellent research assistance. Ongena acknowledges the hospitality of the European Central Bank. Any views expressed are only those of the authors and should not be attributed to the Bank of Spain, the European We investigate the impact of the stance and path of monetary policy on the level of credit risk of individual bank loans and on lending standards. We employ the Credit Register of the Bank of Spain that contains detailed monthly information on virtually all loans granted by all credit institutions operating in Spain during the last twentytwo years – generating almost twentythree million bank loan records in total. Spanish monetary conditions were exogenously determined during the entire sample period. Using a variety of duration models we find that lower shortterm interest rates prior to loan origination result in banks granting more risky new loans. Banks also soften their lending standards – they lend more to borrowers with a bad credit history and with high uncertainty. Lower interest rates, by contrast, reduce the credit risk of outstanding loans. Loan credit risk is maximized when both interest rates are very low prior to loan origination and interest
Common failings: how corporate defaults are correlated
 Journal of Finance
, 2007
"... We test the doubly stochastic assumption under which firms ’ default times are correlated only as implied by the correlation of factors determining their default intensities. Using data on U.S. corporations from 1979 to 2004, this assumption is violated in the presence of contagion or “frailty ” (un ..."
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Cited by 44 (2 self)
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We test the doubly stochastic assumption under which firms ’ default times are correlated only as implied by the correlation of factors determining their default intensities. Using data on U.S. corporations from 1979 to 2004, this assumption is violated in the presence of contagion or “frailty ” (unobservable explanatory variables that are correlated across firms). Our tests do not depend on the timeseries properties of default intensities. The data do not support the joint hypothesis of wellspecified default intensities and the doubly stochastic assumption. We find some evidence of default clustering exceeding that implied by the doubly stochastic model with the given intensities. WHY DO CORPORATE DEFAULTS CLUSTER IN TIME? Several explanations have been explored. First, firms may be exposed to common or correlated risk factors whose comovements cause correlated changes in conditional default probabilities. Second, the event of default by one firm may be “contagious, ” in that one such event may directly induce other corporate failures, as with the collapse of Penn
An empirical analysis of the pricing of collateralized debt obligations. Working paper
, 2006
"... Abstract. We study the pricing of collateralized debt obligations (CDOs) using an extensive new data set for the activelytraded CDX credit index and its tranches. We find that a threefactor portfolio credit model allowing for firmspecific, industry, and economywide default events explains virtual ..."
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Cited by 39 (5 self)
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Abstract. We study the pricing of collateralized debt obligations (CDOs) using an extensive new data set for the activelytraded CDX credit index and its tranches. We find that a threefactor portfolio credit model allowing for firmspecific, industry, and economywide default events explains virtually all of the timeseries and crosssectional variation in CDX index tranche prices. These tranches are priced as if losses of 0.4, 6, and 35 percent of the portfolio occur with expected frequencies of 1.2, 41.5, and 763 years, respectively. On average, 65 percent of the CDX spread is due to firmspecific default risk, 27 percent to clustered industry or sector default risk, and 8 percent to catastrophic or systemic default risk. Recently, however, firmspecific default risk has begun to play a larger role.
Frailty Correlated Default
, 2008
"... This paper shows that the probability of extreme default losses on portfolios of U.S. corporate debt is much greater than would be estimated under the standard assumption that default correlation arises only from exposure to observable risk factors. At the high confidence levels at which bank loan p ..."
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Cited by 34 (2 self)
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This paper shows that the probability of extreme default losses on portfolios of U.S. corporate debt is much greater than would be estimated under the standard assumption that default correlation arises only from exposure to observable risk factors. At the high confidence levels at which bank loan portfolio and CDO default losses are typically measured for economiccapital and rating purposes, our empirical results indicate that conventionally based estimates are downward biased by a full order of magnitude on test portfolios. Our estimates are based on U.S. public nonfinancial firms existing between 1979 and 2004. We find strong evidence for the presence of common latent factors, even when controlling for observable factors that provide the most accurate available model of firmbyfirm default probabilities. ∗ We are grateful for financial support from Moody’s Corporation and Morgan Stanley, and for research assistance from Sabri Oncu and Vineet Bhagwat. We are also grateful for remarks from Torben Andersen, André Lucas, Richard Cantor, Stav Gaon, Tyler Shumway, and especially Michael Johannes. This revision is much improved because of suggestions by a referee, an associate editor, and Campbell Harvey. We are thankful to Moodys and to Ed Altman for generous assistance with data. Duffie is at The Graduate School of Business, Stanford University. Eckner and Horel are at Merrill Lynch. Saita is at Lehman
In search of distress risk
"... This paper explores the determinants of corporate failure and the pricing of financially distressed stocks whose failure probability, estimated from a dynamic logit model using accounting and market variables, is high. Since 1981, financially distressed stocks have delivered anomalously low returns. ..."
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Cited by 31 (3 self)
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This paper explores the determinants of corporate failure and the pricing of financially distressed stocks whose failure probability, estimated from a dynamic logit model using accounting and market variables, is high. Since 1981, financially distressed stocks have delivered anomalously low returns. They have lower returns but much higher standard deviations, market betas, and loadings on value and smallcap risk factors than stocks with low failure risk. These patterns are more pronounced for stocks with possible informational or arbitragerelated frictions. They are inconsistent with the conjecture that value and size e¤ects are compensation for the risk of financial distress.
The multistate latent factor intensity model for credit rating transitions
, 2005
"... A new empirical reducedform model for credit rating transitions is introduced. It is a parametric intensitybased duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semiMarkov structure designed to accommodate many of the ..."
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Cited by 23 (3 self)
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A new empirical reducedform model for credit rating transitions is introduced. It is a parametric intensitybased duration model with multiple states and driven by exogenous covariates and latent dynamic factors. The model has a generalized semiMarkov structure designed to accommodate many of the stylized facts of credit rating migrations. Parameter estimation is based on Monte Carlo maximum likelihood methods for which the details are discussed in this paper. A simulation experiment is carried out to show the effectiveness of the estimation procedure. An empirical application is presented for transitions in a 7 grade rating system. The model includes a common dynamic component that can be interpreted as the credit cycle. Asymmetric effects of this cycle across rating grades and additional semiMarkov dynamics are found to be statistically significant. Finally, we investigate whether the common factor model suffices to capture systematic risk in rating
Firm heterogeneity and credit risk diversification
 FIRM HETEROGENEITY AND CREDIT RISK DIVERSIFICATION
, 2007
"... This paper examines the impact of neglected heterogeneity on credit risk. We show that neglecting heterogeneity in firm returns and/or default thresholds leads to underestimation of expected losses (EL), and its effect on portfolio risk is ambiguous. Once EL is controlled for, the impact of neglecti ..."
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Cited by 7 (0 self)
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This paper examines the impact of neglected heterogeneity on credit risk. We show that neglecting heterogeneity in firm returns and/or default thresholds leads to underestimation of expected losses (EL), and its effect on portfolio risk is ambiguous. Once EL is controlled for, the impact of neglecting parameter heterogeneity is complex and depends on the source and degree of heterogeneity. We show that ignoring differences in default thresholds results in overestimation of risk, while ignoring differences in return correlations yields ambiguous results. Our empirical application, designed to be typical and representative, combines both and shows that neglected heterogeneity results in overestimation of risk. Using a portfolio of U.S. firms we illustrate that heterogeneity in the default threshold or probability of default, measured for instance by a credit rating, is of first order importance in affecting the shape of the loss distribution: including ratings heterogeneity alone results in a 20 % drop in loss volatility and a 40 % drop in 99.9 % VaR, the level to which the risk weights of the New Basel Accord are calibrated.
Correlated Default Risk
, 2006
"... Recently, an unusually high number of firms in the economy defaulted, with the default rate for Moody’srated speculativegrade issuers reaching as high as 10.2 % in 2001. In their annual review, Moody’s summarized these credit events as follows, “Record defaults —unmatched in number and dollar volum ..."
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Cited by 6 (1 self)
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Recently, an unusually high number of firms in the economy defaulted, with the default rate for Moody’srated speculativegrade issuers reaching as high as 10.2 % in 2001. In their annual review, Moody’s summarized these credit events as follows, “Record defaults —unmatched in number and dollar volume since the Great Depression—have culminated in the bankruptcies of wellknown firms whose rapid collapse caught investors by surprise. ” 1 What factors cause the economywide default rate to change over time, and why does it vary as much as it does? In this article, we investigate the likelihood of joint default across
Pricing kthtodefault swaps under default contagion: the matrixanalytic approach
, 2006
"... We study a model for default contagion in intensitybased credit risk and its consequences for pricing portfolio credit derivatives. The model is specified through default intensities which are assumed to be constant between defaults, but which can jump at the times of defaults. The model is transla ..."
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Cited by 6 (2 self)
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We study a model for default contagion in intensitybased credit risk and its consequences for pricing portfolio credit derivatives. The model is specified through default intensities which are assumed to be constant between defaults, but which can jump at the times of defaults. The model is translated into a Markov jump process which represents the default status in the credit portfolio. This makes it possible to use matrixanalytic methods to derive computationally tractable closedform expressions for singlename credit default swap spreads and k thtodefault swap spreads. We ”semicalibrate” the model for portfolios (of up to 15 obligors) against market CDS spreads and compute the corresponding k thtodefault spreads. In a numerical study based on a synthetic portfolio of 15 telecom bonds we study a number of questions: how spreads depend on the amount of default interaction; how the values of the underlying market CDSprices used for calibration influence k ththto default spreads; how a portfolio with inhomogeneous recovery rates compares with a portfolio which satisfies the standard assumption of identical recovery rates; and, finally, how well k ththto default spreads in a nonsymmetric portfolio can be approximated by spreads in a symmetric portfolio.
On Computing the Distribution Function for the Sum of Independent and Nonidentical Random Indicators
, 2011
"... The Poisson binomial distribution is the distribution of the sum of independent and nonidentical random indicators. Each indicator follows a Bernoulli distribution with individual success probability. When all success probabilities are equal, the Poisson binomial distribution is a binomial distribu ..."
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Cited by 6 (3 self)
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The Poisson binomial distribution is the distribution of the sum of independent and nonidentical random indicators. Each indicator follows a Bernoulli distribution with individual success probability. When all success probabilities are equal, the Poisson binomial distribution is a binomial distribution. The Poisson binomial distribution has many applications in different areas such as reliability, survival analysis, survey sampling, econometrics, etc. The computing of the cumulative distribution function (cdf) of the Poisson binomial distribution, however, is not straightforward. Approximation methods such as the Poisson approximation and normal approximations have been used in literature. Recursive formulae also have been used to compute the cdf in some areas. In this paper, we present a simple derivation for an exact formula with a closedform expression for the cdf of the Poisson binomial distribution. The derivation uses the discrete Fourier transform of the characteristic function of the distribution. We develop an algorithm for efficient implementation of the exact formula. Numerical studies were conducted to study the accuracy of the developed algorithm and the accuracy of approximation methods. We also studied the computational efficiency of different methods. The paper is concluded with a discussion on the use of different methods in practice and some suggestions for practitioners.