Full case study available here.
More information on the software used in the study here.
This note presents a stress testing case study for a set of banks with inter-bank obligations, illustrating how stress testing may be accomplished using Risk Control’s Stress ControllerTM software. The calculations are performed for a banking sector comprising three individual banks (labelled A, B and C). The software may accommodate any number of banks and potentially other financial intermediaries.
We show how each bank’s balance sheet, P&L and key capital planning ratios are affected by macro scenarios including (i) a domestic country recession and (ii) a US recession. Note here that the “domestic country” is a notional small, open, emerging market economy. We also demonstrate (in scenario (iii)), how the software models a ‘contagion effect’ caused by the failure to fulfil inter-bank obligations resulting in a liquidity shock to one of the banks.
Changes in the credit quality of each bank’s loan book and fluctuations in the values of mark-to-market exposures affect the value of the banks’ balance-sheet items and the banks’ income through provisions and mark-to-market asset write-offs. A liquidity shortfall from a specific bank causes changes in that bank’s possible funding resources, as well as ‘inter-bank’ losses to other related banks resulting from inter-bank exposure insolvencies.
For a base case and for each of the stressed scenarios, predictions are supplied for the banks’ key variables. The results show how the impairment provisions rise and capital, asset growth, and profitability are depressed by the different recession scenarios. A US recession has a larger and more persistent impact than the domestic-based recession scenario.
The liquidity shock results also show that the rescue triggered by the liquidity shortfall increases the capital and funding of the distressed bank, as well as causing losses and capital reductions in the other banks.
Note that the framework described here may be used either with coarse, public data to perform high-level, top-down stress testing or with more granular internal data for bottom-up stress testing purposes. Financial statement modelling is highly flexible since equations may be written first in Excel, before being converted into scripts and then imported into the software for use at run-time to perform calculations.