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dc.contributor.authorAmaya, Diego
dc.contributor.authorBoudreault, Mathieu
dc.contributor.authorMcLeish, Don L. 18:17:50 (GMT) 18:17:50 (GMT)
dc.descriptionThe final publication is available at Elsevier via © 2019. This manuscript version is made available under the CC-BY-NC-ND 4.0 license
dc.description.abstractThe 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.en
dc.subjectsurvival biasen
dc.subjectgeometric Brownian motionen
dc.subjectconditional estimationen
dc.subjectdefault probabilityen
dc.subjectdiffusion processesen
dc.titleMaximum likelihood estimation of first-passage structural credit risk models correcting for the survivorship biasen
dcterms.bibliographicCitationDiego Amaya, Mathieu Boudreault, Don L. McLeish, Maximum likelihood estimation of Þrst-passage structural credit risk models correcting for the survivorship bias, Journal of Economic Dynamics & Control (2019), doi:
uws.contributor.affiliation1Faculty of Mathematicsen
uws.contributor.affiliation2Statistics and Actuarial Scienceen

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