Affiliation:
1. Department of Statistics and Actuarial Science Simon Fraser University Burnaby British Columbia Canada
2. Department of Mathematics Université du Québec à Montréal Montreal Quebec Canada
3. MAG Energy Solutions Montréal Quebec Canada
Abstract
AbstractThis study presents a firm‐specific methodology for extracting implied default intensities and recovery rates jointly from unit recovery claim prices—backed by out‐of‐the‐money put options—and credit default swap premiums, therefore providing time‐varying and market‐consistent views of credit risk at the individual level. We apply the procedure to about 400 firms spanning different sectors of the US economy between 2003 and 2019. The main determinants of default intensities and recovery rates are analyzed with statistical and machine learning methods linking default risk and credit losses to market, sector, and individual variables. Consistent with the literature, we find that individual volatility, leverage, and corporate bond market determinants are key factors explaining the implied default intensities and recovery rates. Then, we apply the framework in the context of credit risk management in applications, like, market‐consistent credit value‐at‐risk calculation and stress testing.
Funder
Natural Sciences and Engineering Research Council of Canada
Subject
Economics and Econometrics,Finance,General Business, Management and Accounting,Accounting