We present a stochastic simulation model for estimating forward-looking corporate probability of default and loss given default. We formulate the model in a discrete time frame, apply capital-budgeting techniques to define the relationships that identify the default condition and solve the model by Monte Carlo simulation. First, we present the model; then we show how to extend the model to estimate company-specific loss given default, expected loss and unexpected loss as well. Subsequently, we compare the risk analysis probability of default (RAPD) model with option/contingent models, inasmuch as both models use the same definition of the event of default. The focus of this paper is on the theoretical and modeling aspects of the new methodological approach proposed; however, we also present an application of the method, and the results of a comparative test (covering RAPD, Altman Z-score, two option-contingent models and Standard & Poor's ratings) which, in our opinion, positively empirically support the validity of the RAPD approach.