This paper proposes a machine learning approach to estimate physical forward default intensities. Default probabilities are computed using artificial neural networks to estimate the intensities of the inhomogeneous Poisson processes governing default process. The major contribution to previous...

Using a local adaptive Forward Intensities Approach (FIA) we investigate multiperiod corporate defaults and other delisting schemes. The proposed approach is fully datadriven and is based on local adaptive estimation and the selection of optimal estimation windows. Time-dependent model...

We propose a tractable model of a firm's dynamic debt and equity issuance policies in the presence of asymmetric information. Because "investment-grade" firms can access debt markets, managers who observe a bad private signal can both conceal this information and shield shareholders from...