The Determinants of Market-Implied Recovery Rates

Abstract

In the presence of recovery risk, the recovery rate is a random variable whose risk-neutral expectation can be inferred from the prices of defaultable instruments. I extract market-implied recovery rates from the term structures of credit default swap spreads for a sample of 497 United States (U.S.) corporate issuers over the 2005–2014 period. I analyze the explanatory factors of market-implied recovery rates within a linear regression framework and also within a Tobit model, and I compare them with the determinants of historical recovery rates that were previously identified in the literature. In contrast to their historical counterparts, market-implied recovery rates are mostly driven by macroeconomic factors and long-term, issuer-specific variables. Short-term financial variables and industry conditions significantly impact the slope of market-implied recovery rates. These results indicate that the design of a recovery risk model should be based on specific market factors, not on the statistical evidence that is provided by historical recovery rates.

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

“…We use three sets of covariates, individual, sector‐, and market‐wide factors, to determine credit risk, based on the literature on credit risk determinants (Benkert, 2004; Doshi et al, 2013, for the default intensity; François, 2019, for the recovery rate). Market‐wide factors include interest rate factors, stock market return and volatility, and average corporate bond spreads.…”

“…We then investigate the main determinants of default intensities and recovery rates with statistical and machine learning methods, linking default risk and credit losses to market, sector, and individual variables. 2 The purposes of this analysis are to compare the determinants of the proposed implied default intensities and recovery rates with the literature and build models with high predictive power for credit risk management applications (see Benkert, 2004;Doshi et al, 2013, and references therein for the determinants of default intensity; see François, 2019, and references therein for the determinants of recovery rate). The key determinants we find with linear models and random forests are consistent with the literature.…”

“…Doshi et al (2018) extracted the term structure of recovery rates from senior and subordinated CDSs. In the same vein, Das and Hanouna (2009), Le (2015), François and Jiang (2019), and François (2019) used the term structure of CDS spreads and stock prices to extract market‐implied recovery rates. Authors have also considered using equity options to back default intensity.…”

Meta-Learning Approaches for Recovery Rate Prediction

“…Recovery rates are higher in the presence of backing guarantees, increase with coupons and decrease with maturity (Jankowitsch et al 2014). All these security-specific characteristics have been used as control variables in recent articles on bond recovery rate determinants (François 2019;Gambetti et al 2019) and are used as predictors in our study.…”