Market Indicators Application for Correction of Probability of Default Estimates

Student: Leonteva Daria

Supervisor: Victor A Lapshin

Faculty: International College of Economics and Finance

Educational Programme: Financial Economics (Master)

Year of Graduation: 2019

This study is dedicated to the alternative approach for the probability of default estimation. We try to implement credit spreads as the market measures into the accounting-based model in order to increase the predictive power of the classic approach models. Generally, we want to identify which particular spread among two – Z-spread and I-spread has an advantage in the bankruptcy prediction models. Using two techniques – logistic regression and gradient boosting machine, and the sample of annual series of 80 financial ratios for 385 U.S. companies issuing corporate bonds we find evidence of the I-spread superiority in both techniques. In addition, the predictive power of chosen techniques is also compared. The up-to-date gradient boosting machine framework performs better on the test sample.

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