The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Frailty Correlated Default
Abstract
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 collateralized debt obligation (CDO) default losses are typically measured for economic capital and rating purposes, conventionally based loss estimates are downward biased by a full order of magnitude on test portfolios. Our estimates are based on U.S. public nonfinancial firms 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 firm‐by‐firm default probabilities.
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Cites
Maximum Likelihood from Incomplete Data Via the EM Algorithm
Dempster, A. P., Laird, N. M., & Rubin, D. B. (1977) · Journal of the Royal Statistical Society Series B: Statistical Methodology · doi:10.1111/j.2517-6161.1977.tb01600.x · Cited by 50,444
The pricing of options and corporate liabilities
Black, F., & Scholes, M. (1973) · Journal of Political Economy · doi:10.1086/260062 · Cited by 29,074
Gelman, A., Carlin, J. B., Stern, H. S., & Rubin, D. B. (2003) · doi:10.1201/9780429258480 · Cited by 27,276
A tutorial on hidden Markov models and selected applications in speech recognition
Rabiner, L. R. (1989) · Proceedings of the IEEE · doi:10.1109/5.18626 · Cited by 22,743
Monte Carlo sampling methods using Markov chains and their applications.
Hastings, W. K. (1970) · Biometrika · doi:10.1093/biomet/57.1.97 · Cited by 15,195
Kass, R. E., & Raftery, A. E. (1995) · Journal of the American Statistical Association · doi:10.1080/01621459.1995.10476572 · Cited by 12,558
Returns to Buying Winners and Selling Losers: Implications for Stock Market Efficiency
JEGADEESH, N., & TITMAN, S. (1993) · The Journal of Finance · doi:10.1111/j.1540-6261.1993.tb04702.x · Cited by 11,584
Metropolis, N., & Ulam, S. (1949) · Journal of the American Statistical Association · doi:10.1080/01621459.1949.10483310 · Cited by 6,474
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Basel Committee On Banking Supervision
Rost, B. (2010) · Handbook of Transnational Economic Governance Regimes · doi:10.1163/ej.9789004163300.i-1081.238 · Cited by 1,590
GENERALIZED AUTOREGRESSIVE SCORE MODELS WITH APPLICATIONS
Creal, D., Koopman, S. J., & Lucas, A. (2013) · Journal of Applied Econometrics · doi:10.1002/jae.1279 · Cited by 981
Credit Contagion from Counterparty Risk
JORION, P., & ZHANG, G. (2009) · The Journal of Finance · doi:10.1111/j.1540-6261.2009.01494.x · Cited by 435
An Empirical Analysis of the Pricing of Collateralized Debt Obligations
LONGSTAFF, F. A., & RAJAN, A. (2008) · The Journal of Finance · doi:10.1111/j.1540-6261.2008.01330.x · Cited by 299
Campbell, J. Y., Hilscher, J., & Szilagyi, J. (2005) · SSRN Electronic Journal · doi:10.2139/ssrn.770805 · Cited by 273
Metadata
- Authors
- DUFFIE, D., ECKNER, A., HOREL, G., & SAITA, L.
- Venue
- The Journal of Finance
- Publisher
- Wiley
- Published
- 2009-09-28
- Volume
- 64
- Issue
- 5
- Pages
- 2089-2123
- ISSN
- 0022-1082 (ISSN-L)
- 1540-6261
- OpenAlex
- W2165053060
- Cited by
- 545
- References
- 8
- FWCI
- 77.87
- Type
- journal article
- Language
- en
- Open access
- closed
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Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
