Default prediction models: the role of forward-looking measures of returns and volatility
Date
2017-07-29
Authors
Miao, Hong, author
Ramchander, Sanjay, author
Ryan, Patricia, author
Wang, Tianyang, author
Journal of Empirical Finance, publisher
Journal Title
Journal ISSN
Volume Title
Abstract
This paper proposes a variant application of the Merton distance-to-default model by employing implied volatility and implied cost of capital to predict defaults. The proposed model's results are compared with predictions obtained from three popular models in different setups. We find that our "best" model, which contains forward-looking proxies of returns and volatility outperform other models, carries a default prediction accuracy rate of 89%. Additional analysis using a discrete-time hazard model indicates the pseudo-R² values from regression models that include the two forward-looking measures are as high as 51%. Overall, our results establish the informational relevance of implied cost of capital and implied volatility in predicting defaults.
Description
Includes bibliographical references (pages 24-26).
Published as: Journal of Empirical Finance, vol. 46, March 2018, pp. 146-162, https://doi.org/10.1016/j.jempfin.2018.01.001.
Published as: Journal of Empirical Finance, vol. 46, March 2018, pp. 146-162, https://doi.org/10.1016/j.jempfin.2018.01.001.
Rights Access
Subject
distance to default
default prediction
implied cost of capital
implied volatility