Journal of Credit Risk
ISSN:
1744-6619 (print)
1755-9723 (online)
Editor-in-chief: Linda Allen and Jens Hilscher
Merton’s model with recovery risk
Albert Cohen and Nick Costanzino
Need to know
- We introduce a Stochastic Recovery Merton model where we include correlation between probability of default and loss given default to the classical Merton model by adding a recovery risk driver that is correlated to the asset value.
- Both credit and equity are priced in our framework, and we explicitly calculate correlation between PD and LGD, as well as between bond and equity returns.
- We find that the asset process solely determines default probability but not the amount recovered at default. The information asymmetry in the model leads to increased credit spreads due to the extra recovery risk.
- Finally we provide a numerical example that outlines how to apply our model to real world data.
Abstract
In this work, we model the empirically observed recovery risk premium by adding an additional correlated risk driver to Merton’s model for pricing corporate bonds. This risk driver represents the amount of the notional that would be recovered in the event of default. We compute the values of corporate bonds and their related Greeks, and we present the market price of recovery risk. By adding a structural variable, we are also able to decouple equity returns from bond returns, something that is not possible with the classical (one-factor) Merton model. To further address this decoupling, we derive a mapping between the asset–recovery correlation and the correlation of bond and equity returns, as well as a mapping between the asset–recovery correlation and the correlation between the probability of default and the loss given default. In the process of decoupling recovery risk from default risk, we are able to present a model that incorporates an asymmetry between the information set that a manager possesses and the minimal information needed by investors to price bonds and equity. Finally, we employ a numerical algorithm to calibrate to simulated market data with various outstanding notionals as an example of how users can connect this model with bond and equity data.
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