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27
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 52 (4 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
Modelling financial high frequency data using point processes. In
 Eds.), Handbook of Financial Time Series
, 2009
"... Die ZBW räumt Ihnen als Nutzerin/Nutzer das unentgeltliche, räumlich unbeschränkte und zeitlich auf die Dauer des Schutzrechts beschränkte einfache Recht ein, das ausgewählte Werk im Rahmen der unter ..."
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Cited by 28 (2 self)
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Die ZBW räumt Ihnen als Nutzerin/Nutzer das unentgeltliche, räumlich unbeschränkte und zeitlich auf die Dauer des Schutzrechts beschränkte einfache Recht ein, das ausgewählte Werk im Rahmen der unter
Dependent credit migrations
 Journal of Credit Risk
, 2006
"... This paper examines latent risk factors in models for migration risk. We employ the standard statistical framework for ordered categorical variables and induce dependence between migrations by means of latent risk factors. By assuming a Markov process for the dynamics of the latent factors, the mode ..."
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Cited by 12 (2 self)
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This paper examines latent risk factors in models for migration risk. We employ the standard statistical framework for ordered categorical variables and induce dependence between migrations by means of latent risk factors. By assuming a Markov process for the dynamics of the latent factors, the model can be interpreted as a state space model. The paper contains an empirical study on quarterly migration data from Standard & Poor’s for the years 1981–2000, in which the ordered logit model with serially correlated latent factors is fitted by computational Bayesian techniques (Gibbs sampling). Apart from highlighting the usefulness of the Gibbs sampler for statistical inference in models of this kind, the survey in particular investigates the issues of ratingspecific factor loadings and heterogeneity among industry sectors, with emphasis on their implications in terms of implied asset correlations.
A general framework for observation driven timevarying parameter models. Unpublished paper: Tinbergen Institute Discussion Paper 108
, 2008
"... We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing timevarying parameters in a wide ..."
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Cited by 10 (1 self)
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We propose a new class of observation driven time series models that we refer to as Generalized Autoregressive Score (GAS) models. The driving mechanism of the GAS model is the scaled likelihood score. This provides a unified and consistent framework for introducing timevarying parameters in a wide class of nonlinear models. The GAS model encompasses other wellknown models such as the generalized autoregressive conditional heteroskedasticity, autoregressive conditional duration, autoregressive conditional intensity and single source of error models. In addition, the GAS specification gives rise to a wide range of new observation driven models. Examples include nonlinear regression models with timevarying parameters, observation driven analogues of unobserved components time series models, multivariate point process models with timevarying parameters and pooling restrictions, new models for timevarying copula functions and models for timevarying higher order moments. We study the properties of GAS models and provide several nontrivial examples of their application.
SYSTEMIC RISK DIAGNOSTICS COINCIDENT INDICATORS AND EARLY WARNING SIGNALS 1
, 1327
"... In 2011 all ECB publications feature a motif taken from the €100 banknote. NOTE: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. This paper can be dow ..."
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Cited by 7 (0 self)
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In 2011 all ECB publications feature a motif taken from the €100 banknote. NOTE: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. This paper can be downloaded without charge from
Systemic Risk: What Defaults Are Telling Us
, 2009
"... This paper defines systemic risk as the conditional probability of failure of a large number of financial institutions, and develops maximum likelihood estimators of the term structure of systemic risk in the U.S. financial sector. The estimators are based on a new dynamic hazard model of failure ti ..."
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Cited by 5 (0 self)
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This paper defines systemic risk as the conditional probability of failure of a large number of financial institutions, and develops maximum likelihood estimators of the term structure of systemic risk in the U.S. financial sector. The estimators are based on a new dynamic hazard model of failure timing that captures the influence of timevarying macroeconomic and sectorspecific risk factors on the likelihood of failures, and the impact of spillover effects related to missing/unobserved risk factors or the spread of financial distress in a network of firms. In and outofsample tests demonstrate that the fitted risk measures accurately quantify systemic risk for each of several risk horizons and confidence levels, indicating the usefulness of the risk measure estimates for the macroprudential regulation of the financial system.
Observation Driven MixedMeasurement Dynamic Factor Models with an Application to Credit Risk,” Tinbergen Institute Discussion Papers 11042/2/DSF16, Tinbergen Institute
, 2011
"... publications feature a motif taken from the €5 banknote. note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB. ..."
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Cited by 3 (1 self)
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publications feature a motif taken from the €5 banknote. note: This Working Paper should not be reported as representing the views of the European Central Bank (ECB). The views expressed are those of the authors and do not necessarily reflect those of the ECB.
Marketbased Credit Ratings ∗
, 2012
"... We present a methodology for rating the creditworthiness of public companies in the U.S. from the prices of traded assets. Our approach uses asset pricing data to impute a term structure of risk neutral survival functions or default probabilities. Firms are then clustered into ratings categories bas ..."
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Cited by 2 (1 self)
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We present a methodology for rating the creditworthiness of public companies in the U.S. from the prices of traded assets. Our approach uses asset pricing data to impute a term structure of risk neutral survival functions or default probabilities. Firms are then clustered into ratings categories based on their survival functions using a functional clustering algorithm. This allows all public firms whose assets are traded to be directly rated by market participants. For firms whose assets are not traded, we show how they can be indirectly rated through the use of matching estimators. We also show how the resulting ratings can be used to construct loss distributions for portfolios of bonds. Our approach has the advantages of being transparent, computationally tractable, simple to implement, and easy to interpret economically.
Exact and Efficient Simulation of Correlated Defaults
, 2009
"... Correlated default risk plays a significant role in financial markets. Dynamic intensitybased models, in which a firm default is governed by a stochastic intensity process, are widely used to model correlated default risk. The computations in these models can be performed by Monte Carlo simulation. ..."
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Cited by 1 (1 self)
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Correlated default risk plays a significant role in financial markets. Dynamic intensitybased models, in which a firm default is governed by a stochastic intensity process, are widely used to model correlated default risk. The computations in these models can be performed by Monte Carlo simulation. The standard simulation method, which requires the discretization of the intensity process, leads to biased simulation estimators. The magnitude of the bias is often hard to quantify. This paper develops an exact simulation method for intensitybased models that leads to unbiased estimators of credit portfolio loss distributions, risk measures, and derivatives prices. In a first step, we construct a Markov chain that matches the marginal distribution of the point process describing the binary default state of each firm. This construction reduces the original estimation problem to one involving a simpler Markov chain expectation. In a second step, we estimate the Markov chain expectation using a simple acceptance/rejection scheme that facilitates exact sampling. To address rare event situations, the acceptance/rejection scheme is embedded in