Bank employ rich set of scenarios to test resilience of portfolio and to deliver various measures of risk. These models span from
- Type-I: scenarios with attached probabilities (weights), like it is used in Value-at-Risk (VaR) or Monte Carlo (MC) type of models or
- Type-II: stress test based models, where possible market states are scanned in wide range, portfolio performance is inter-/extrapolated and the worse case scenarios are used as risk measure.
However, thinking more about stress tests they should serve as a model independent addition to the usual probability measure *) based tools (type-I).
Another method is to reverse engineer the risk factors of portfolio in terms of weakest points (no likelihood/probability is attached), but that will serve the purpose to some extent, because this exercise will depend on the specific in-house (pricing) model. Some model independence can be achieved by building market-wide (AI?) model which can be used to detect pockets of instability (~singularity) which scenarios have to be injected into pricing of portfolio.
Discrepancy or consistency between measures used in pricing and risk modelling is similar topic, because if singularity becomes certain (realises) it changes/shifts all pricing (via price of risk, e.g. optionality or XVA type of modelling items).
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