Through-the-Cycle PD Estimation Under Incomplete Data -- A Single Risk Factor Approach

TL;DR

This paper introduces a method for estimating long-term default probabilities across multiple portfolios using incomplete data, leveraging a single risk factor model suitable for institutions with limited data availability.

Contribution

It presents a novel calibration approach that works with incomplete data and does not require a complete dataset for any sub-portfolio, aligning with Basel guidelines.

Findings

Effective calibration with incomplete data

Applicable to small and large institutions

Aligns with Basel recommendations

Abstract

Banks are required to use long-term default probabilities (PDs) of their portfolios when calculating credit risk capital under internal ratings-based (IRB) models. However, the calibration models and historical data typically reflect prevailing market conditions. According to Basel recommendations, averaging annual PDs over a full economic cycle should yield the long-term PD. In practice, the available data are often temporally incomplete - even for high-risk portfolios. In this paper, we present a method for the simultaneous calibration of long-term PDs across all sub-portfolios, based on the single risk factor model embedded in the Basel framework. The method is suitable even for smaller, budget-constrained institutions, as it relies exclusively on the bank's own default data. A complete dataset is not required - not even for any individual sub-portfolio - as the only prerequisite is the presence of overlapping data before and after the missing values, a mild condition that is typically met in practical situations.