Leveraging prices from credit and equity option markets for portfolio risk management

This 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 a...

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Bibliographic Details
Published inThe journal of futures markets Vol. 44; no. 1; pp. 122 - 147
Main Authors Bégin, Jean‐François, Boudreault, Mathieu, Thériault, Mathieu
Format Journal Article
LanguageEnglish
Published Hoboken Wiley Periodicals Inc 01.01.2024
Subjects
Bond markets
Corporate bonds
Credit risk
credit risk management
default intensity
machine learning
Money
Premiums
Prices
Recovery
recovery rates
Risk management
unit recovery claim
Volatility
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Summary:This 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.
ISSN:0270-7314
1096-9934
DOI:10.1002/fut.22465