Asset volatility

Abstract

We examine whether fundamental measures of volatility are incremental to market-based measures of volatility in (i) predicting bankruptcies (out of sample), (ii) explaining cross-sectional variation in credit spreads, and (iii) explaining future credit excess returns. Our fundamental measures of volatility include (i) historical volatility in profitability, margins, turnover, operating income growth, and sales growth; (ii) dispersion in analyst forecasts of future earnings; and (iii) quantile regression forecasts of the interquartile range of the distribution of profitability. We find robust evidence that these fundamental measures of volatility improve out-of-sample forecasts of bankruptcy and help explain cross-sectional variation in credit spreads. This suggests that an analysis of credit risk can be enhanced with a detailed analysis of fundamental information. As a test case of the benefit of volatility forecasting, we document an improved ability to forecast future credit excess returns, particularly when using fundamental measures of volatility.

Management forecasts of volatility

“…Third, our paper contributes to research that incorporates accounting information, firm risk disclosures, or third-party analysts' risk assessments to assess firm risk and uncertainty (e.g., Lui et al 2007;Kravet and Muslu 2013;Campbell et al 2014;Chang et al 2020;Sridharan 2015;Konstantinidi and Pope 2016;Correia et al 2018;Goodman et al 2018). We extend these studies by providing evidence on the predictive power of a forecast of stock return volatility directly provided by managers (EVOL).…”

“…Our main specification, which includes implied volatility, historical volatility, firm characteristics, industry fixed effects, and year fixed effects, shows that EVOL has incremental information content. We also examine the robustness of our inferences to including alternative market-based, accounting-based, and disclosure-based measures of uncertainty for constrained subsamples: (1) dispersion in analyst forecasts for earnings per share (EPS) from I/B/E/S; (2) accounting characteristics following Sridharan (2015); (3) dispersion in quantile-based forecasts of return on net operating assets (RNOA) following Correia et al (2018); (4) risk factor disclosures following Campbell et al (2014); and (5) analyst risk ratings from Lui et al (2007). We find that the information content of EVOL for future stock return volatility is incremental to that of these measures.…”

Forecasting Risk in Earnings

“…However, as noted above, the set of predictor variables could be enhanced. Subsequent work by Chang, Monahan, and Ouazad () and Correia, Kang, and Richardson () has expanded our model to include a broader set of instruments using both earnings and financial policy attributes . Chang et al.…”

“…Correia et al. () use both conventional and robust quantile‐based estimates of conditional dispersion in earnings to predict bankruptcy risk and credit spreads.…”

“…() estimate quantiles of future return on equity and Correia et al. () estimate quantiles of future return on net operating assets.…”