Multi-Period Corporate Default Prediction With Stochastic Covariates

By Darrell Duffie, Leandro Saita, Ke Wang. 2007

1,012 citations recorded by the source corpus.

We provide maximum likelihood estimators of term structures of conditional probabilities of corporate default, incorporating the dynamics of firm-specific and m acroeconomic covariates. For U.S. Industrial firms, based on over 390,000 firm-months of data spanning 1979 to 2004, the level and shape of the estimated term structure of conditional future default probabilities depends on a firm's distance to default (a volatility-adjusted measure of leverage), on the firm's trailing stock return, on trailing S& P 500 returns, and on U.S. interest rates, among other covariates. Variation in a firm's distance to default has a substantially greater effection the term structure of future default hazard rates than does a comparatively significant change in any of the other covariates. Default intensities are estimated to be lower with higher short-term interest rates. Theout-of-sample predictive performance of the model is an improvement over that of other available models.

Subfields: Accounting, Economics and Econometrics, Finance

View Paper: https://www.semanticscholar.org/paper/126dcc78bebe25698b710066da6e1c908b9537f4

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Citations in the corpus

Estimation in the Mixture of Markov Chains Moving With Different Speeds (2005, neutral): “But as the recent literature indicates (see, e.g., Hamilton and Cantor 2004; Duffie and Wang 2003), the behavior of credit ratings may not be Markovian; the present rating may not be sufficient for predicting a future rating.”
Competition and the Cost of Debt (2012, neutral): “Duffie, Saita, and Wang (2007) show that the expected default frequency is economically important for explaining the term structure of default probabilities. and is significant at the 1% confidence level.”
Deep belief networks for predicting corporate defaults (2015, methodology): “Different from statistical models, the machine learning models are non-parametric techniques for the prediction, so they can overcome some constraints of the traditional statistical models [4], [5], [6].”
Multiperiod Corporate Default Prediction - A Forward Intensity Approach (2012, positive): “This approach has been widely applied in the literature, for example, Duffie, et al (2007).”
A framework for assessing the systemic risk of major financial institutions (2009, background): “The realized correlation coefficient for the i’th period between stock k and l is ρ̂(kl),j = ∑M j=1 r(k)j,ir(l)j,i √ ∑M j=1 r 2 (k)j,i ∑M j=1 r 2 (l)j,i (7) Barndorff-Nielsen and Shephard (2004) proposed the asymptotic theory underlying the above realized correlation measure.”
Modeling the Effect of Macroeconomic Factors on Corporate Default and Credit Rating Transitions (2012, positive): “…reduced form approach has been widely used for credit risk analysis in both academic and real world research, e.g., Jarrow, Lando, and Turnbull (1995) and Jarrow, Lando, and Turnbull (1997), Lando and Skødeberg (2002), Duffie, Saita, and Wang (2007), Koopman et al. (2008) and Koopman et al. (2009).”
Bank competition and financial stability in Asia Pacific (2014, background): “…For example, see Hillegeist et al. (2004), Vassalou and Xing (2004), Gropp et al. (2004, 2006), Akhigbe et al. (2007), ChanLau and Sy (2007), Duffie et al. (2007), Bharath and Shumway (2008), and Campbell et al. (2008). u = the expected return δ = the dividend rate estimated as total…”
The Determinants of Operational Risk in U.S. Financial Institutions (2011, methodology): “…management oversight and accountability in enforcing risk management controls.2 Following the recent literature on corporate default prediction (Duffie, Saita, and Wang (2007)), we treat the arrival of operational risk events as a conditional Poisson process, whose intensity is driven by…”