Maximum likelihood estimation of first-passage structural credit risk models correcting for the survivorship bias
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Date
2019-03
Authors
Amaya, Diego
Boudreault, Mathieu
McLeish, Don L.
Journal Title
Journal ISSN
Volume Title
Publisher
Elsevier
Abstract
The survivorship bias in credit risk modeling is the bias that results in parameter estimates when the survival of a company is ignored. We study the statistical properties of the maximum likelihood estimator (MLE) accounting for survivorship bias for models based on the first-passage of the geometric Brownian motion. We find that if we neglect the survivorship bias, then the drift has a positive bias that may not disappear asymptotically. We show that correcting the survivorship bias by conditioning on survival in the likelihood function underestimates the drift. Therefore, we propose a bias correction method for non-iid samples that is first-order unbiased and second-order efficient. The economic impact of neglecting or miscorrecting for the survivorship bias is studied empirically based on a sample of more than 13,000 companies over the period 1980 through 2016 inclusive. Our results point to the important risk of misclassifying a company as solvent or insolvent due to biases in the estimates.
Description
The final publication is available at Elsevier via https://doi.org/10.1016/j.jedc.2018.11.005. © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0/
Keywords
survival bias, geometric Brownian motion, conditional estimation, default probability, inference, diffusion processes