Coming from a market risk background, I am wondering how to validate / backtest a credit risk model in practice. Here, I am specifically not asking about the PD/migration validation, but about the default aggregation parameters (, say sector correlations), i.e. those components of the model that operate on the credit risk dependence structure.
In Market Risk applications, I would usually look for time series of my risk factors (say, rates obtained from Bloomberg/ICAP); combine them with my valuation model(s), potentially add another layer of distributional assumptions (or not) and estimate desired quantities, say a VaR. At the same time, the availability of market data (seems to) allows me to perform a daily backtest of my model and offer insights into the quality of my assumptions on the joint distribution of the risk factors.
From a credit risk perspective, how do practitioners proceed with this topic, especially if your portfolio is not retail heavy (homogeneuos), but consists only of a small number of debtors, say between 1k and 10k names? In this scenario, I would assume that there simply is not a sufficient number of defaults in my portfolio so that I can do proper backtesting ... Would you then simply buy default data (as I would do with with market data from Reuters, Bloomberg / ICAP...) from somewhere else (a default data / credit quality data vendor?) and try to make sense of that data relative to your portfolio? Or do you approach your aggregate model validation from a more theoretical standpoint, say using competitor models, stressed parameters etc.?
I hope my question is not too general - effectively I am wondering how to do credit risk model backtesting - and whether that really is a thing. Thanks for any pointers.