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Title of Journal: Methodol Comput Appl Probab

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Abbravation: Methodology and Computing in Applied Probability

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Springer US

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DOI

10.1016/0022-4049(88)90119-3

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1573-7713

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Random Survival Forests Models for SME Credit Risk

Authors: Dean Fantazzini Silvia Figini
Publish Date: 2008/05/01
Volume: 11, Issue: 1, Pages: 29-45
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Abstract

This paper extends the existing literature on empirical research in the field of credit risk default for Small Medium Enterprizes SMEs We propose a nonparametric approach based on Random Survival Forests RSF and we compare its performance with a standard logit model To the authors’ knowledge no studies in the area of credit risk default for SMEs have used a variety of statistical methodologies to test the reliability of their predictions and to compare their performance against one another As for the insample results we find that our nonparametric model performs much better that the classical logit model As for the outofsample performances the evidence is just the opposite and the logit performs better than the RSF model We explain this evidence by showing how error in the estimates of default probabilities can affect classification error when the estimates are used in a classification rule

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Other Papers In This Journal:
  1. Effect of Data Transformations on Predictive Risk Indicators
  2. Initial and Final Backward and Forward Discrete Time Non-homogeneous Semi-Markov Credit Risk Models
  3. Random Databases with Approximate Record Matching
  4. Importance and Sensitivity Analysis in Dynamic Reliability
  5. Selecting the Best Population Using a Test for Equality Based on Minimal Wilcoxon Rank-sum Precedence Statistic
  6. Saddlepoint Approximations to Tail Probabilities and Quantiles of Inhomogeneous Discounted Compound Poisson Processes with Periodic Intensity Functions
  7. On Estimation of the Intensity Function of a Point Process
  8. Call Option Prices Based on Bessel Processes
  9. Goodness-of-Fit Tests for Continuous Regression
  10. Waiting Time Distribution for the Emergence of Superpatterns
  11. Computing the Expected Markov Reward Rates with Stationarity Detection and Relative Error Control
  12. Estimating Stochastic Dynamical Systems Driven by a Continuous-Time Jump Markov Process
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