Modeling the Loss Distribution

Published Online:https://doi.org/10.1287/mnsc.1110.1345

References

  • Acharya V. V., Bharath S. T., Srinivasan A. Understanding the recovery rates on defaulted securities. (2003) . Working paper, London Business School, LondonCrossrefGoogle Scholar
  • Acharya V. V., Bharath S. T., Srinivasan A. Does industry-wide distress affect defaulted firms? Evidence from creditor recoveries. J. Financial Econom. (2007) 85(3):787–821CrossrefGoogle Scholar
  • Altman E. I., Hotchkiss E.Corporate Financial Distress and Bankruptcy (2005) 3rd ed. Third Edition(Wiley, New York) CrossrefGoogle Scholar
  • Altman E. I., Brady B., Resti A., Sironi A. The link between default and recovery rates: Theory, empirical evidence and implications. J. Bus. (2005) 78(6):2203–2227CrossrefGoogle Scholar
  • Andersen L., Sidenius J. Extensions to the Gaussian copula: Random recovery and random factor loadings. J. Credit Risk (2005) 1(1):29–70CrossrefGoogle Scholar
  • Basel Committee on Banking Supervision Guidance on paragraph 468 of the framework document. (2005a) July(Bank for International Settlements, Basel, Switzerland) Google Scholar
  • Basel Committee on Banking Supervision Studies on the validation of the internal rating systems. (2005b) . BCBS Working Paper 14, Bank for International Settlements, Basel, SwitzerlandGoogle Scholar
  • Berkowitz J. Testing density forecasts, with applications to risk management. J. Bus. Econom. Statist. (2001) 19(4):465–474CrossrefGoogle Scholar
  • Bharath S. T., Shumway T. Forecasting default with the Merton distance-to-default model. Rev. Financial Stud. (2008) 21(3):1339–1369CrossrefGoogle Scholar
  • Campbell J. Y., Hilscher J., Szilagyi J. In search of distress risk. J. Finance (2008) 63(6):2899–2939CrossrefGoogle Scholar
  • Chava S., Jarrow R. A. Bankruptcy prediction with industry effects. Rev. Finance (2004) 8(4):537–569CrossrefGoogle Scholar
  • Covitz D., Han S. An empirical analysis of bond recovery rates: Exploring a structural view of default. (2004) . Working paper, The Federal Reserve Board of Washington, Washington, DCGoogle Scholar
  • Crowder M.Classical Competing Risks (2001) (Chapman & Hall, New York) CrossrefGoogle Scholar
  • Dai Q., Singleton K. J., Yang W. Regime shifts in a dynamic term structure model of U.S. treasury bond yields. Rev. Financial Stud. (2007) 20(5):1669–1706CrossrefGoogle Scholar
  • Das S., Hanouna P. Implied recovery. J. Econom. Dynam. Control (2009) 33(11):1837–1857CrossrefGoogle Scholar
  • Dempster A. P., Laird N. M., Rubin D. B. Maximum likelihood from incomplete data via the EM algorithm (with discussion). J. Roy. Statist. Soc. B (1977) 39(1):1–38Google Scholar
  • Duffie D., Saita L., Wang K. Multi-period corporate failure prediction with stochastic covariates. J. Financial Econom. (2007) 83(3):635–665CrossrefGoogle Scholar
  • Duffie D., Eckner A., Horel G., Saita L. Frailty correlated default. J. Finance (2009) 64(5):2089–2123CrossrefGoogle Scholar
  • Dullmann K., Trapp M. Systematic risk in recovery rates—An empirical analysis of U.S. corporate credit exposure. (2004) . Working paper, Deutsche Bundesbank, Frankfurt, GermanyCrossrefGoogle Scholar
  • Egorov A. V., Hong Y., Li H. Validating forecasts of the joint probability density of bond yields: Can affine models beat random walk? J. Econometrics (2006) 135(1–2):255–284CrossrefGoogle Scholar
  • Frye J. Depressing recoveries. Risk Magazine (2000) 13(11):108–111Google Scholar
  • Gagliardini P., Gourieroux C. Spread term structure and default correlation. (2003) . Working paper, University of Toronto, TorontoCrossrefGoogle Scholar
  • Gilson S., John K., Lang L. Troubled debt restructurings: An empirical study of private reorganization of firms in default. J. Financial Econom. (1990) 27(2):315–355CrossrefGoogle Scholar
  • Hong Y., Li H., Zhao F. Can the random walk model be beaten in out-of-sample density forecasts? Evidence from intraday foreign exchange rates. J. Econometrics (2007) 141(2):736–776CrossrefGoogle Scholar
  • Hougaard P.Analysis of Multivariate Survival Data (2000) (Springer, New York) CrossrefGoogle Scholar
  • Lawless J. L.Statistical Models and Methods for Lifetime Data (2003) (John Wiley & Sons, Hoboken, NJ) Google Scholar
  • Li H., Li T., Yu C. No-arbitrage Taylor rules with switching regimes. (2010) . Working paper, Ross School of Business, University of Michigan, Ann ArborCrossrefGoogle Scholar
  • Merton R. C. On the pricing of corporate debt: The risk structure of interest rates. J. Finance (1974) 29(2):449–470Google Scholar
  • Moody's Moody's corporate default and recovery rates, 1920–2008. (2009) . Report, Moody's Corporation, New YorkGoogle Scholar
  • Opler T. C., Titman S. Financial distress and corporate performance. J. Finance (1994) 49(3):1015–1040CrossrefGoogle Scholar
  • Schönbucher P. J.Credit Derivatives Pricing Models (2003) (John Wiley & Sons, Hoboken, NJ) Google Scholar
  • Schuermann T., Shimko D. What do we know about loss given default? Credit Risk: Models and Management (2004) 2nd ed.(Risk Books, London) 249–274Google Scholar
  • Shumway T. Forecasting bankruptcy more accurately: A simple hazard model. J. Bus. (2001) 74(1):101–124CrossrefGoogle Scholar
  • Stefanescu C., Turnbull B. W. Multivariate frailty models for exchangeable survival data with covariates. Technometrics (2006) 48(3):411–418CrossrefGoogle Scholar
  • Varma P., Cantor R. Determinants of recovery rates on defaulted bonds and loans for North American corporate issuers: 1983–2003. J. Fixed Income (2005) 14(4):29–44CrossrefGoogle Scholar
  • Yashin A. I., Manton K. G. Effects of unobserved and partial observed covariate processes on system failures: A review of models and estimation strategies. Statist. Sci. (1997) 12(1):20–34CrossrefGoogle Scholar
  • Zmijewski M. E. Methodological issues related to the estimation of financial distress prediction models. J. Accounting Res. (1984) 22(Supplement):58–82Google Scholar
INFORMS site uses cookies to store information on your computer. Some are essential to make our site work; Others help us improve the user experience. By using this site, you consent to the placement of these cookies. Please read our Privacy Statement to learn more.