The Informational Relevance of Forward-Looking Measures of Returns and Volatility in Forecasting Defaults

Abstract

This paper proposes a variant application of the Merton distance-to-default model by employing implied volatility and implied cost of capital to forecast defaults. The proposed model's results are compared with predictions obtained from the three popular models in different setups. We find that our best model, which contains both forward-looking proxies, outperforms all other models with a default prediction accuracy rate of 89%. Additional analysis using a discrete-time hazard model indicates that the psuedo-R² values from regression models that include the two implied measures are as high as 51%. Overall, our results establish the informational relevance of implied volatility and implied cost of capital in predicting defaults.