  This paper proposes a simple technical approach for the analytical derivation
of Point-in-Time PD (probability of default) forecasts, with minimal data
requirements. The inputs required are the current and future Through-the-Cycle
PDs of the obligors, their last known default rates, and a measurement of the
systematic dependence of the obligors. Technically, the forecasts are made from
within a classical asset-based credit portfolio model, with the additional
assumption of a simple (first/second order) autoregressive process for the
systematic factor. This paper elaborates in detail on the practical issues of
implementation, especially on the parametrization alternatives. We also show
how the approach can be naturally extended to low-default portfolios with
volatile default rates, using Bayesian methodology. Furthermore, expert
judgments on the current macroeconomic state, although not necessary for the
forecasts, can be embedded into the model using the Bayesian technique. The
resulting PD forecasts can be used for the derivation of expected lifetime
credit losses as required by the newly adopted accounting standard IFRS 9. In
doing so, the presented approach is endogenous, as it does not require any
exogenous macroeconomic forecasts, which are notoriously unreliable and often
subjective. Also, it does not require any dependency modeling between PDs and
macroeconomic variables, which often proves to be cumbersome and unstable.
