From Bud Genovese, Chairman
Kruskal Hewitt, a Senor Associate based in our New York City office, has written an article below that summarizes key components used for interest rate risk (IRR) model assumptions and limit-setting. Mr. Hewitt tracked this data from the numerous IRR audits we perform each year which we’ve compiled (anonymously) into a very useful database. Please feel free to forward this informative column to any appropriate people in your financial institution. Thank you. –Bud
AUDITONE LLC’S ANALYSIS OF IRR MODEL ASSUMPTIONS 2017
AuditOne LLC is a leading provider of outsourced internal audit services for community banks, credit unions and other financial institutions. Please refer to our website for further information. Among our practice areas is interest rate risk (IRR). US financial institutions are expected to have an annual internal audit of their modeling, monitoring and control of IRR. Key to IRR modelling are various forward-looking assumptions required for the simulations of net interest income and economic value of equity under interest rate shock scenarios.
AuditOne has compiled (anonymously) data from 94 of our IRR clients on IRR limits and assumptions. These are institutions where we have used data from the most recent AuditOne IRR audit, no further back than 2014. AuditOne believes this database is relevant to AuditOne clients because it covers a relatively narrow range of asset size, geography and business lines.
NII: Net interest income (NII) is a current period (generally, one-year and two-year) estimate of interest-sensitive revenues and expenses under alternate interest rate scenarios. (The tables beginning on page two all apply to one-year horizons.)
EVE: Economic value of equity (EVE) is a theoretic valuation of the institution where cash flows from all assets and liabilities are discounted to their net present value, then summed together.
INSTANTANEOUS vs. RAMPED CHANGES: The figures showing in the tables below are mostly for instantaneous (or immediate) rate shocks (85 clients). These assume rates change instantly by the full shock amount, as opposed to a gradual rate rise (ramp) over time, typically a 12-month ramp.
BETA: This represents the assumed percent of a market rate change that is reflected in administered rates – most importantly, deposit rates. If the driver rate is Fed Funds and the beta for saving accounts is 45, then for every 100 basis point rise in Fed Funds, savings account rates are assumed to rise 45 basis points. There may also be assumptions about lags in administered rate changes, but we have not captured these in our database.
AVERAGE LIFE: Non-maturity deposits (NMDs) have no contractual maturity and therefore form a stable, longer-term funding source. In order to get a meaningful estimate of EVE, NMDs are assigned an assumed average life by account type.
PARALLEL vs. NON-PARALLEL RATE SHOCKS:
The standard rate shock set-up assumes the yield curve shifts in parallel fashion over the entire maturity spectrum. However, many institutions also run simulations based on flatteners, steepeners and other non-parallel shocks. These can be helpful for assessing specific, balance sheet vulnerabilities. But we advise against basing IRR limits on non-parallel shocks because shock details are too hard to define in policy.
STATIC vs. DYNAMIC BALANCE SHEET: For NII simulations, the balance sheet can either be static (constant), with like replacement of run-off assets or liabilities, or it can incorporate growth (e.g., budgeted balances). The 2010 Interagency Guidance specified that a static balance sheet must be used, though simulations can also be run off a dynamic balance sheet.
2017 DATABASE ANALYSIS
The following results have been presented here across the whole database. However, we would be happy to recalculate any of the results for subsets of institutions based on asset size, primary regulator and/or model vendor. Please contact either Jeremy Taylor at 949-981-0420 or Kevin Watson at 562-802-3581. See the database mix summary section below for the key identifiers.
We have presented only average (mean) figures in the tables below. We also computed medians, but these were very close to the corresponding average for all but one data set. The one exception was NMD average life where the average exceeded the median by a meaningful amount for each of the four deposit categories. This means that the top half of institutions (in terms of their average life assumptions) kewed more, or had more extreme values than, the bottom half.
NII-at-risk (one-year) simulation policy limits
EVE-at-risk simulation policy limits
Average life assumptions (in months)
Interest rate shock application (for limits)
Parallel versus non-parallel shock assumptions
Balance sheet growth assumptions
DATABASE MIX SUMMARY
The following tables describe the 94 institutions in the database. All dollar figures are in millions.
Database mix by asset size
Database mix by primary regulator
Database mix by model vendor
Kruskal has been a Senior Associate with AuditOne since 2014, specializing in ALM (asset/liability management) audit and consulting work. He has considerable experience in the treasury and trading areas, including derivatives, investments and foreign exchange, in addition to interest rate and liquidity risk. Prior to AuditOne, he was with a Japanese utility, managing market and credit risk. Before that his background included market risk management with a large US regional bank and with multinational banks in the US, Asia and Europe. Kruskal holds a BA in Mathematics and an MBA from Northeastern University. His certifications include PRM (Professional Risk Manager), FRM (Financial Risk Manager), and CALMS (Certified ALM Specialist).