Recent research has demonstrated that structural credit risk models are capable of explaining the credit risk process for private, illiquid debt. This article extends this literature by proposing a simple and intuitive calibration approach using Bayesian inference to capture the nonlinear dynamics of debt service cover ratios using a new dataset of private cash flows collected by hand for 267 European infrastructure projects spanning 17 years. The combination of a cash flow–driven structural model with observable cash flow data and Bayesian inference enables the measurement of default risk even when few or no defaults have been or can be observed, whereas reduced-form models like the ones used by rating agencies necessarily lead to biased credit risk estimates for private debt.
Dividend yields are a determinant of asset prices, but changes in dividend growth impact both dividend yields and discount rates. As a result, dividend growth is typically treated as a known constant in most of the literature.
In this paper, we develop a dynamic approach to forecasting dividend growth using Bayesian filtering techniques, which improves markedly on standard linear methods. The resulting growth-adjusted dividend yield improves out-of-sample return predictions by several orders of magnitude. These results show that dynamic cash flow modeling can significantly improve the performance of expected return models.