PD-LGD correlation for the banking lending segment: Empirical evidence from Russia

Abstract

The Bank of Russia is one of the unique banking regulators in the world as it discloses granular reporting information per the existing credit institutions with the available historical track record. Same time the number of banks dramatically declined from above two and a half thousands in 1990s to one thousand in 2010 and to around 350 in 2021. Such information stimulates designing default probability (PD) models for the Russian banks. There is a separate stream of research that studies the amount of negative capital revealed when the Russian bank got its license withdrawn. However, the existing papers have several shortcomings. First, most of them do not account for the structural breaks in data. Second, there is no search for the best fitting model, just a model is offered and the coefficients of interest are interpreted. Third, the best model is poorly interpretable. Forth, the existing models make short-term forecasts. Fifth, there is no a LGD model for Russian banks, though the amount of negative capital upon license withdrawal was considered. Thus, our research objective is to study PD-LGD correlation (PLC) for the Russian banks. To do so, we improve the existing Russian banks PD model and create a respective novel LGD model. We use the homogenous dataset from 2016 to 2021. We find that PLC for Russian banks equals to +22%.