Going beyond regulatory compliance - Making ALM and FTP work for you (part 2)
Karl Rubach, CFA
Managing Director at Integrated Balance Sheet Management Solutions Inc.
Please see Making ALM and FTP work for you (part 1) for an initial discussion / background of our fictional "IBSM-Bank".
How much attention and resources should you devote to develop the required capabilities to understand and manage NII sensitivity?
Given its relevance in driving PnL and potential long term impact on future profitability (in the extreme non-viability), compounded by the complexity of carrying out meaningful calculations, one would expect a proportional effort in terms of developing these capabilities, and for Senior Management and ALCO's to focus on forward looking balance sheet related performance metrics (vs. backward looking financial performance & analysis). However, as so many ALCO participants can attest to, this is not always the case, with IRRBB taking a backseat in relation to the more "interesting" trading books and/or backward looking ALCO meetings (e.g. taking a Financial Review role)
NII variations will have a more than proportional impact on PnL: For most Financial Institutions net interest income (NII) represents a very large portion of total revenue, often in the 75+% range. After factoring in credit losses and a cost-to-income ratio of 60+%, a 10% variation in NII can easily translate into a 25% impact to net income (e.g. NII 80 plus Other Revenue 20 minus PCL 5 minus expenses 65 results in a base case net income before taxes of 30, a 10% volatility in NII=8 represents 26% of base case net income). A lower initial profitability (e.g. higher credit losses, or higher cost-income ratio) will exacerbate this impact.
Where, when and for how long will risk materialize?
These basic questions for ALM are, more often than not, left entirely out of the conversation. Many ALCOs and/or Board of Directors accept a sensitivity measure (e.g. +/-100bp) as an assurance that risks are under control. This might be a result of model limitations (e.g. gap derived measures vs. full balance sheet and net interest income simulation), not understanding resulting measures and model/assumption limitations, and in some cases even neglect (i.e. meeting only outdated minimum regulatory requirements)
- Timing is important: Aggregated NII measures can hide timing differences. Can your institution survive a large initial shock until projected future benefits materialize? When setting up risk limits the timing of any potential NII variation (including any MTM impact of securities / derivatives portfolio through OCI and/or PnL) needs to be considered.
- As displayed in the following table, measured over a 36 month interval under the BCBS scenarios, IBSM-Bank's NII seems to be relatively stable, fluctuating around the CAD 96.8M (~32M per year) base case scenario by approximately +/- 2.5% under the different scenarios. Taking a closer look, we can see that for most scenarios the net impact over this time horizon is an offset between positive and negative variations. As an example, the short-rate up scenario has a negative total impact of (2.5) MM, this is ~(2.5%) of total, but in year one the fluctuation is (2.9) M, almost (9.3)%. Zoom-in again and in two quarters we would have to explain a (1.1) M or (14.2)% decline in NII. Using the revenue / cost assumptions outlined above this would imply a 40% reduction in PnL.
- When looking at the Linear +400 bp scenario there is a clear negative impact during the first year and as assets start to reprice NII starts to recover resulting in a net gain in the third year. On the contrary, in the falling rate scenarios there is no such recovery and the negative NII impact will continue if not accelerate as high yielding assets are repriced to lower rate levels.
Results are path dependent
Regulators seem to favor instantaneous shocks (parabp llel or not), while Institutions with the capabilities to model them tend to favor rate ladders (rising/falling rate scenarios over time, e.g. around each Central Bank meeting). Instantaneous shocks tend to be difficult to relate to (i.e. discredited by the "that has never happened" argument) and may in fact produce very different results (even when arriving at the same end point, say +200 bp). What explains this difference and why is it relevant?
The following graph shows the evolution on the average yield for A/L over time. Dotted lines represent the +200 bp scenario (simulation) and the continuous lines the +25 bp/quarter scenario (base, 8x = +200 bp).
- Under the +200 bp scenario yield on assets is consistently much higher, reflecting that every new transaction (reinvestment) will incorporate the full impact of the rate shock. In contrast under the +25 bp ladder scenario new volumes will be booked without fully reflecting the full shock (during the first quarter reflecting +25 bp, second quarter +50 bp, etc.).
- On the liability side, while a difference exists, lines move closer over time. The difference is due to a much shorter repricing tenor on the liability side, resulting in a faster and full recognition of the impact. In contrast on the asset side, fixed rate loans (e.g. mortgages) booked after the initial +25 bp will keep this rate during all the simulation horizon (36 months)
Implications for setting up your IRRBB risk metrics & limits:
Bottom-line is that you need to understand if your institution has the risk taking capacity to survive any initial shock (instantaneous shifts as well as those evolving over time) and wait for your balance sheet to reprice (hopefully it will!) while taking remedial actions to adjust to the new market conditions. To make up for our limited rate forecasting abilities (evidenced by the fact that we are still working), we need to explore a wide range of scenarios (magnitude and rate evolution) to uncover potential effects. In setting up your metrics, limits and risk appetite, a couple of relevant questions:
- How much volatility can you afford over a specific time horizon?
- For how long can you survive with a depressed NII before your business mix and/or growth targets need to be adjusted due to this lower internal capital generation?
More on IRRBB next week … (link to part 3)
If you would like to learn more about how to measure and benefit from implementing an ALM and/or FTP framework please contact us at: IBSM Solutions
#alm #irrbb #ftp #lcr #nsfr #nccf #morssoftware #treasurymanagement #osfi #fsra
Exe. Director- Market Risk/ALM/Treasury | Risk Strategy, Enterprise Risk Management, Market Risk
5 年Karl Rubach, CFA : Continuing our previous discussion, I do recognize the benefit of splitting NII into specific tenor but it is bit dependent on how we hedge our portfolio and timing of our hedge. Generally, we use volume projections which are monthly averages but they are usually different from actual volumes at the time of hedge which leads to fluctuation in income figures. Another thing is the optionality in our portfolio which is highly path dependent and impact our forecasts heavily. So, indeed path dependency should be considered in our scenario setting and it might even explain monthly fluctuations mentioned in the article. One thing I usually want to see is the impact of fixing the liquidity spread on asset side which impacts the pass through rate and thus, the average asset yields. If you look at liquidity metrics, the difference between timing of hedge and volume projection does have quite an impact on LCR during specific months. One more thing we can add here is the Hedge Accounting capacity because the liabilites and asset don't offset each other exactly and thus, we see P&L fluctuation. This should ideally be part of risk appetite limit. I am curious to know how this is handled at other institutions!
Founding Partner, Sackville Partners
5 年Very well put Karl - another great edition of this series