Net Stable Funding Ratio
Executive Summary
Key Takeaways
- ✓ The net stable funding ratio tests whether a bank's longer-term assets are backed by a stable enough funding profile over a one-year horizon, expressed as available stable funding divided by required stable funding.
- ✓ Available stable funding should be built by applying an available stable funding factor to each funding segment, weighted by its behavioural stability — equity and long-term funding weighted highest, short-term wholesale funding weighted lowest.
- ✓ Required stable funding should be built by applying a required stable funding factor to each asset segment, weighted by tenor and liquidity — longer-dated, less liquid loans require more stable funding support than short-term, highly liquid assets.
- ✓ The NSFR is a structural, one-year-horizon metric, complementing rather than duplicating the short-term liquidity coverage ratio, and the two should be modelled and reported as distinct outputs.
- ✓ A model does not itself perform or validate the regulatory NSFR calculation; it should represent the available and required stable funding factor assumptions as visible, sourced model inputs.
Objective¶
This guide covers how to model the net stable funding ratio (NSFR), within the Banking Financial Modelling pillar, complementing the short-term Liquidity Coverage Ratio with a longer-term structural funding view.
What the NSFR Tests¶
The NSFR tests whether a bank's longer-term assets are backed by a sufficiently stable funding profile over a one-year horizon.
NSFR = Available Stable Funding ÷ Required Stable Funding
A minimum ratio, typically 100%, is generally required to be maintained under the applicable regulatory framework.
Modelling Available Stable Funding¶
Available stable funding should be built by applying a stability factor to each funding segment, drawing on the same behavioural stability assumptions already established in Deposit Modelling:
| Funding Source | Typical Relative Stability Factor |
|---|---|
| Capital and long-term funding (maturity ≥ 1 year) | Highest |
| Stable retail and small business deposits | Intermediate-high |
| Less stable retail deposits and operational deposits | Intermediate |
| Short-term wholesale funding | Lowest |
Segment Available Stable Funding = Segment Balance × Segment Stability Factor
Total Available Stable Funding = Σ (Segment Available Stable Funding)
Modelling Required Stable Funding¶
Required stable funding should be built by applying a required funding factor to each asset segment, weighted by tenor and liquidity — drawing on the same segmentation used in Loan Portfolio Modelling:
Segment Required Stable Funding = Segment Asset Balance × Segment Required Funding Factor
Total Required Stable Funding = Σ (Segment Required Stable Funding)
Short-term, highly liquid assets require comparatively little stable funding support; long-dated, illiquid loans require substantially more, since they cannot readily be converted to cash if funding availability tightened.
Relationship to the Liquidity Coverage Ratio¶
The NSFR is a structural, one-year-horizon metric, distinct from the short-term, 30-day Liquidity Coverage Ratio. The two test different things over different time horizons and should be modelled and reported as distinct outputs rather than treated as interchangeable liquidity indicators — see LCR vs. NSFR.
Scope of This Guide¶
This guide describes how the available and required stable funding factor assumptions should be represented as visible, sourced model inputs. It does not describe FMAE performing or validating the underlying regulatory NSFR calculation itself.
Common Construction Pitfalls¶
- Applying a single blended stability or funding factor rather than segment-specific factors reflecting actual funding source and asset tenor.
- Building the NSFR without connecting it to the same segmentation already used in deposit and loan portfolio modelling.
- Treating the NSFR and LCR as interchangeable liquidity metrics rather than distinct outputs testing different horizons.
- Presenting the model as validating the regulatory NSFR calculation rather than representing its assumptions transparently.
Continue Reading¶
Prerequisites¶
- Banking Financial Modelling — the parent pillar
- Deposit Modelling
- Loan Portfolio Modelling
Related Technical Guides¶
Related Comparisons¶
How OXXON tests thisRun a free structural check with FMAE
Frequently Asked Questions
What does the net stable funding ratio measure?
Whether a bank's longer-term assets are backed by a stable enough funding profile over a one-year horizon, expressed as available stable funding divided by required stable funding.
How is the NSFR calculated?
NSFR = Available Stable Funding ÷ Required Stable Funding, with a minimum ratio typically required to be maintained at or above 100%.
How should available stable funding be modelled?
By applying an available stable funding factor to each funding segment, weighted by its behavioural stability — capital and long-term funding weighted at or near 100%, more stable retail deposits at an intermediate factor, and short-term wholesale funding weighted lowest, reflecting how reliably each source would remain available over a one-year horizon.
How should required stable funding be modelled?
By applying a required stable funding factor to each asset segment, weighted by tenor and liquidity — short-term, highly liquid assets require little stable funding support, while long-dated, illiquid loans require substantially more, since they cannot readily be converted to cash if funding became scarce.
How does the NSFR differ from the liquidity coverage ratio?
The NSFR is a structural, one-year-horizon metric testing funding stability, while the LCR is a short-term, 30-day stress metric testing immediate liquidity — the two test different things over different horizons and should be modelled and reported as distinct outputs — see Liquidity Coverage Ratio and LCR vs. NSFR.
Does a model validate the actual regulatory NSFR calculation?
No — a model represents the available and required stable funding factor assumptions as visible, sourced inputs; it does not itself perform or validate the underlying regulatory NSFR calculation, which remains outside the structural audit engine's scope.
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Deposit modelling is the liability-side counterpart to loan portfolio modelling: deposits should be segmented by product type — transactional, savings, and term — each carrying its own volume, cost, and behavioural assumptions. Behavioural modelling matters more on the deposit side than almost anywhere else in a bank model, since a deposit's contractual maturity (or lack of one, for transactional accounts) frequently does not match its actual behavioural stickiness, and that gap is central to both funding and liquidity risk management.
Liquidity Coverage Ratio
The liquidity coverage ratio (LCR) tests whether a bank holds enough high-quality liquid assets to survive a defined 30-day acute stress scenario. This guide covers how to model the LCR's two components — the stock of high-quality liquid assets and net cash outflows under the stress scenario — and how the deposit and funding behavioural assumptions built elsewhere in the model feed directly into the outflow calculation.
LCR vs. NSFR
The liquidity coverage ratio (LCR) and net stable funding ratio (NSFR) are the two Basel III liquidity standards, but they test fundamentally different things: the LCR tests short-term survival under a 30-day acute stress scenario, while the NSFR tests structural funding stability over a one-year horizon. This comparison sets out the differences a modeller needs to understand to build and report both correctly, as distinct outputs rather than a single blended liquidity metric.
Balance Sheet Forecasting
Balance sheet forecasting is the central forward-looking exercise in a bank model: forecasting segmented asset volumes (loans, securities) and liability volumes (deposits, wholesale funding) period by period, then reconciling the two through an explicit funding plan. This guide covers how to structure that forecast, how to build the funding plan that closes any gap between asset growth and deposit growth, and how the forecast should be checked against capital adequacy and liquidity constraints rather than produced in isolation from them.