* Modified from the background paper for the BARa Forum on IRRBB, 6 September 2018
One of many relevant topics that needs to be addressed under the new IRRBB framework of Indonesia's Financial Services Authority (OJK) is the requirements for banks to enhance their dynamic balance sheet projections and stress tests. The emphasis on dynamic analysis is a key reason why the new IRRBB framework should not be approached as a mere compliance exercise, but as great opportunity to optimize balance sheet strategies supported by more solid understanding of customer behavior in the face of interest rate shocks.
A good quality implementation of the IRRBB stress tests framework is instrumental for credible IRRBB measurement and limits setting. It is an effective tool to optimize interest rate tenor mismatch strategy in view of, most probably, increasing interest rate environment.
It seems that a wide range of banks welcomes the IRRBB guideline for stress test scenarios and dynamic balance sheet projections.
In a survey of FIS and d-fine GmbH, banks were asked on their approaches for IRRBB implementation. It is notable from the survey results that majority of banks intend to enhance their methodological framework for IRRBB identification and measurement and for performing interest dependent simulation of future volume, margin, repricing period, and maturity.
To get meaningful results, such simulations need to be based on very granular data and in coherence with banks’ business planning (i.e., where maturing contracts are replaced by a new-business simulation logic). Based on this, forward-looking ΔEVE metrics can be analyzed too, which allows banks to look at future developments of these metrics under different business and tenor mismatch strategies.
Due to the maximum operator inherent to the ΔEVE metric, ALM infrastructure is a major consideration, particularly when implementing a sophisticated tenor mismatch strategy or in an environment of hefty interactions between interest rate movements and customer behavior.
It is important to note that the two IRRBB quantification, namely ΔEVE and ΔNII is significantly impacted by not only the shocks to the possible changes of the shape of interest rate yield curves, but also ‘by the economic stress scenarios that would be consistent with these shocks’ (SEOJK IRRBB, Annex II, B.4.a).
Hence, when assessing the earnings and economic value impacts from the interest rate shocks, banks should also consider possible correlations with loans quality affecting margin, change of customer behavior affecting banks’ liquidity risk profile, and changes in macroeconomic environment affecting profitability and/or capital adequacy.
The new IRRBB rule will bring significant changes to IRRBB modeling in banks and requires a robust ALM platform to support more dynamic balance sheet management and robust integrated stress tests. All this is expected to support OJK's campaigns to mandate more rigorous stress testing in banks’ capital, liquidity and contingency/recovery planning.
IRRBB management with a dynamic perspective created added burden on bank resources and will need a greater cooperation among Risk Management, ALM and Planning departments to come up with sensible unified business-as-usual and stress scenarios.
But the benefits from proper dynamic ALM projections and stress tests are considerable. Even without regulations or supervisory expectations for (bottom-up) stress tests, all banks would want and need to know the valuable information and insights from these exercises for better decision making.