Financial Risk &
Banking Stress Testing

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What is financial stress testing?

Stress testing asks how a bank, portfolio or financial system would behave under unusually adverse conditions. A useful test states the shock, its timing, the affected exposures and the point at which management must act. Financial stress testing examines earnings, capital and cash flows. This article focuses on banking rather than software performance testing.

A bank may remain solvent on paper but run out of usable cash. Conversely, it may have enough cash today while loan losses erode its capital. Capital stress and liquidity stress therefore need separate calculations and a connected narrative.

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Sensitivity, scenario and reverse stress tests

A basis point (bp) equals 0.01 percentage point. A rise from 6% to 8% is +200 basis points. Sensitivity tests can be instantaneous. A scenario can be instantaneous or span several periods, depending on its purpose.

Historical replay should apply relevant shock dynamics to the bank’s current exposures. It should not simply reuse the past institution’s reported loss.

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Designing an adverse scenario

Start with the institution’s vulnerabilities. A bank concentrated in commercial property needs a scenario that stresses property values, rental income, refinancing and collateral recovery. A deposit-funded bank with long-duration securities also needs rate and withdrawal shocks.

Correlated shocks means that the assumptions fit an economic narrative, rather than every risk factor receiving its worst historical value at once. In a recession, government yields may fall while corporate spreads widen. In stagflation, both inflation and government yields may rise.

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One-factor calculations and joint shocks

Consider a hypothetical bond position with a dollar value of one basis point (DV01) of ₹0.04 crore per basis point. Here DV01 is the positive magnitude of loss from a one-basis-point rise in yield for this long position.

A separate ₹40 crore equity holding loses ₹8 crore under a 20% equity fall. If both shocks occur together, a first-order sum is ₹16 crore. The sum assumes no overlap in positions, no hedges and no additional cross-effects.

Large shocks require full instrument revaluation. Bond convexity, changing option sensitivities, basis risk and funding costs can make the linear estimate inaccurate. Sensitivity labels alone are not regulatory capital charges.

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Five groups of stress metrics

These five groups organise the discussion. They are not a universal regulatory classification. Market indices provide context and possible scenario inputs. They do not measure an individual bank’s capital or cash shortfall directly.

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Capital adequacy and solvency metrics

Additional Tier 1 (AT1) capital and Tier 2 capital have distinct eligibility and loss-absorption features. Tier 2 is not part of Tier 1. The leverage exposure measure uses prescribed treatments for derivatives, securities financing and off-balance-sheet items. It is not simply the accounting balance sheet total.

The denominator can change under stress because borrowers migrate to riskier categories, credit lines draw down or other exposures change. A ratio can deteriorate even without a proportionate fall in capital.

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Worked capital stress: a bank under pressure

All amounts are in ₹ crore. Assume opening Common Equity Tier 1 (CET1) capital of 120 and Risk-Weighted Assets (RWA) of 1,000. Opening CET1 ratio is 12%. The one-year stress uses the following simplified assumptions.

Starting-to-end depletion is 12.00% − 8.76% = 3.24 percentage points, approximately 324 basis points. Against an assumed 8% internal trigger, the capital headroom is 96.40 − (8% × 1,100) = ₹8.40 crore.

This is a teaching capital bridge. It assumes the entire stated credit loss hits the horizon’s earnings, no opening loss allowance offsets it, and no taxes, dividends, regulatory deductions or capital issuance apply. A real bank must reconcile accounting provisions, write-offs and capital adjustments to avoid counting the same loss twice.

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Reverse stress testing: starting from failure

A reverse stress test begins with an explicit adverse outcome. It can be a capital-trigger breach, inability to make payments, loss of a critical funding channel or a business model becoming unviable. Complete insolvency or total exhaustion of cash is not required.

1. Choose the failure metric, threshold and horizon.
2. Calculate the remaining loss or cash capacity.
3. Find combinations of risk-factor shocks that exhaust that capacity.
4. Challenge the economic plausibility and timing.
5. Identify early warnings and preventative actions.

Reverse testing often produces a boundary of failure combinations rather than a unique scenario. For instance, a milder credit deterioration may cause failure if it arrives alongside larger market losses and a funding run.

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Worked reverse test: the default rate at the boundary

Retain the previous bank’s assumptions, including stressed Risk-Weighted Assets (RWA) of ₹1,100 crore and an 8% internal Common Equity Tier 1 (CET1) trigger.

Holding every other input fixed, a default probability above approximately 11.11% breaches the assumed trigger. Exactly at the boundary, the CET1 ratio equals 8%.

A model that lets default rates, recoveries, earnings and risk weights deteriorate together will have a different failure boundary. The 11.11% result is a conditional threshold, not a forecast or a probability that the bank fails.

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Liquidity buffers, stable funding and survival

The Liquidity Coverage Ratio (LCR) compares eligible, unencumbered High-Quality Liquid Assets (HQLA), after applicable regulatory adjustments, with stressed 30-day net cash outflows.

The Basel normal-times minimum is 100%. During actual stress, banks may use the buffer and fall below the minimum, subject to the supervisory framework. Regulatory asset eligibility, haircuts, caps and operational availability matter.

The Net Stable Funding Ratio (NSFR) compares weighted Available Stable Funding (ASF) with weighted Required Stable Funding (RSF) over a one-year horizon. Basel requires at least 100% on an ongoing basis.

The weights depend on funding stability, maturity and asset characteristics. This is not simply deposits divided by loans.

Cash-flow projections should distinguish contractual and behavioural flows, currencies and legal entities. Report separately whether a survival horizon includes ordinary central bank facilities, emergency support or neither.

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Worked liquidity stress and a reverse run-off test

In the severe case, restoring the ratio to 100% would require ₹100 crore more eligible liquid assets if net outflows stay at 230, or an equivalent reduction in net outflows. This arithmetic does not show whether either action is executable in stress.

A separate one-year example has Available Stable Funding (ASF) of ₹850 crore and Required Stable Funding (RSF) of ₹900 crore. The Net Stable Funding Ratio (NSFR) is 94.44%. With RSF fixed, ₹50 crore more weighted stable funding is needed.

Reverse cash test

Assume ₹150 crore of usable cash resources, ₹50 crore of credible inflows and ₹80 crore of non-deposit payments over the horizon. A ₹1,000 crore deposit base can then sustain at most (150 + 50 − 80)/1,000 = 12% aggregate run-off before this simplified cash balance reaches zero.

This is a cash-budget illustration, rather than a regulatory LCR calculation. Timing can cause an earlier failure even when end-of-horizon cash is positive.

Survival under constant burn

Daily cash gaps are preferable when burn rates vary. Do not count the same asset both as sale proceeds and as collateral for borrowing.

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Credit deterioration, provisions and asset quality

For ₹600 crore exposure, 2% default probability and 40% loss given default, expected loss is ₹4.80 crore. At 8% and 45%, it is ₹21.60 crore, an increase of ₹16.80 crore. Expected loss is not a worst-case loss.

International Financial Reporting Standard 9 (IFRS 9) uses probability-weighted, discounted expected cash shortfalls, including 12-month or lifetime loss recognition as applicable. The US Current Expected Credit Losses (CECL) framework has a different recognition approach. The simple product is a teaching building block, not a complete calculation for either framework.

For lifetime calculations, default timing, survival probabilities, changing exposures, recoveries, scenarios and discounting matter. Multiplying an unconditional average default probability by unrelated average loss and exposure inputs can miss dependence.

Classification rules for non-performing loans or assets vary across jurisdictions. Such ratios are related to credit quality but are not interchangeable with modelled default probabilities.

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Market risk: stressed losses and tail measures

A scenario loss is the change in portfolio value under a specified shock. A statistical tail measure instead summarises a modelled loss distribution. Both can be useful, but they answer different questions.

For a continuous loss distribution, Expected Shortfall at confidence α is the average loss beyond the corresponding Value at Risk quantile. For discrete distributions, quantile-based tail averaging needs appropriate treatment of probability mass at the threshold.

The legacy Basel Value at Risk and stressed Value at Risk measures used a 99% confidence level and a 10-trading-day horizon. The current framework’s 97.5% Expected Shortfall specification should not be replaced casually by a 99% number. National implementation and the applicable capital method must be checked.

A transparent tail-loss illustration

Suppose 100 equally weighted simulated daily losses have worst five outcomes of ₹8, ₹10, ₹12, ₹15 and ₹20 crore. The average of the worst 5% is (8+10+12+15+20)/5 = ₹13 crore. This is a teaching 95% tail average. A Value at Risk number depends on the quantile convention and the full ordered sample.

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System-wide stress indicators

Foreign exchange (FX), money, bond, equity and financial-intermediary measures can enter a systemic-stress dashboard. Correlation across segments matters: simultaneous stress in several markets is different from an isolated equity-volatility spike.

For an Overnight Index Swap (OIS), specify the reference overnight rate and maturity. Comparing an overnight secured cash rate with a term swap rate also introduces maturity and market-structure differences.

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Regulatory exercises: CCAR, DFAST and EBA

Comprehensive Capital Analysis and Review (CCAR) refers to the Federal Reserve’s capital-planning review framework. Dodd–Frank Act Stress Tests (DFAST) refers to statutory stress-testing requirements. They are related concepts with different legal and supervisory roles. Terminology, coverage and mechanics have evolved over time.

The Federal Reserve’s supervisory stress test estimates losses, revenues, expenses and capital under hypothetical adverse conditions. It can include market-shock and counterparty-default components for relevant firms. The European Banking Authority (EBA) coordinates the European Union (EU)-wide stress exercise with participating authorities.

For current-year details, use the official sources linked below. An old CCAR pass/fail description should not be assumed to explain every current capital-setting mechanism.

REGULATORY CONTEXT

Dodd–Frank: why the law matters for stress testing

The Dodd–Frank Wall Street Reform and Consumer Protection Act became United States federal law on 21 July 2010, following the 2007–2008 Global Financial Crisis. It was a major overhaul of financial regulation, comparable in scope to the broad reform periods associated with the 1930s New Deal.

The Act sought to reduce systemic risk and expectations of “too big to fail” support, improve oversight and transparency in derivatives markets, strengthen consumer protection and provide a framework for resolving failing financial groups. These are policy objectives, rather than proof that bailouts or financial crises have become impossible.

Sources: Public Law 111–203, Treasury designation framework, FDIC resolution authority, Securities and Exchange Commission implementation overview and Commodity Futures Trading Commission derivatives overview. The Securities and Exchange Commission (SEC) regulates security-based swaps, while the Commodity Futures Trading Commission (CFTC) regulates swaps, with joint treatment for mixed swaps.

REGULATORY CONTEXT

The Volcker Rule: trading and covered-fund restrictions

The Volcker Rule, named after former Federal Reserve Chairman Paul Volcker, is Section 619 of Dodd–Frank and Section 13 of the Bank Holding Company Act. It generally applies to defined banking entities, including covered banks and affiliates, with statutory exclusions and regulatory exemptions.

Its purpose is to limit certain speculative activities and fund relationships within banking groups benefiting from the banking safety net. It is not a ban on every trade made for a bank’s own account, nor a rule that every bank trading loss breaches the law.

The two core restrictions

Covered-fund rules contain exclusions, permitted activities and conditions. A banking entity cannot assume it may invest in any hedge fund merely because its proposed holding is less than 3%.

Sources: Federal Reserve: Volcker Rule and Regulation VV, Section 248.12. Coverage and permitted activities depend on current law and the applicable facts.

REGULATORY CONTEXT

Permitted activities: market making, underwriting and hedging

Permitted activities remain subject to applicable limits on material conflicts of interest, high-risk exposures and threats to safety and soundness or financial stability. The particular requirements vary by activity and institution.

A hedge can still create a stress loss

Assume a customer portfolio loses $10 million in a scenario, while its hedge gains $8 million. The net loss is $2 million before funding and execution costs. The hedge’s risk-reducing purpose and compliance conditions are separate from the question of whether it eliminates every stress loss.

Source: Federal Reserve rulemaking and implementation materials. The figures above are hypothetical.

REGULATORY CONTEXT

The 3% covered-fund limits: a worked illustration

For qualifying permitted fund investment under the organising-and-offering framework, the rules generally impose a per-fund ownership limit after the permitted seeding period and an aggregate capital-based limit. Special provisions apply, including to certain securitisation vehicles and fund structures.

Example: each limit needs its own check

Assume a banking group has $10 billion of applicable Tier 1 capital. Its simplified aggregate cap is 3% × $10 billion = $300 million. A qualifying Fund A has $1 billion of outstanding ownership value, giving a simplified per-fund cap of $30 million.

These are independent hypothetical cases with fixed denominators. Actual calculations follow prescribed valuation, attribution and capital rules, and consider seeding-period treatment and applicable permissions. The limits are not a general investment allowance for all funds.

Why this belongs in a stress-testing guide

Fund value changes, ownership changes, capital depletion, redemption constraints and collateral demands can interact. A stress programme should track both economic losses and regulatory headroom. Even a legally permitted investment can create liquidity or concentration risk.

Sources: Federal Reserve: permitted investment limits and Capital treatment of covered-fund investments. This worked example illustrates the basic constraints rather than every calculation provision.

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Management actions, governance and model risk

Results matter only if the institution can act on them. Management should see the unmitigated outcome first, then the outcome after credible actions. An asset sale that assumes normal liquidity during a crisis can overstate resilience.

Stressed correlations can change, models can extrapolate poorly and historical data can omit plausible future shocks. Reverse testing helps expose gaps, but it also depends on the chosen failure definition.

A practical report identifies the main vulnerability, the failure boundary, leading indicators and the date by which action must start. It should not present a single ratio as proof that every material risk is under control.

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Interactive bank stress laboratory

Change the inputs to reproduce the capital bridge, calculate a reverse default-rate boundary and assess a separate 30-day liquidity ratio. Amounts are in ₹ crore. Percentages are entered as percentage numbers, so 8 means 8%.

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Excel setup and classroom questions

Excel percentage cells use decimal values internally. Enter 8% or 0.08 in B5, rather than 8. Check that the reverse boundary lies between 0% and 100%. A negative value means the threshold already fails before credit loss. A value above 100% means default probability alone cannot reach it under the fixed assumptions.

EXTENDED LEARNING

Core and peripheral variables: completing the scenario

A scenario usually specifies a few core variables directly, such as an equity-index decline or a rate shock. Other peripheral variables, such as related equity indices or sector spreads, must also receive consistent shocks. Leaving every unspecified variable unchanged can materially understate loss.

A simple linear conditional-mean model for changes in two variables is:

Here E denotes expectation, Δ denotes a change, ρ (rho) is correlation and σ (sigma) is standard deviation. With negligible mean changes over the chosen interval:

Suppose the core equity index falls 30%, its volatility is 18%, a related mid-cap index has volatility 24%, and correlation is 0.85. The conditional estimate is 0.85 × (24/18) × (−30%) = −34%.

Volatility and correlation must use compatible definitions, units and sampling horizons. The result is a conditional mean, not a worst-case peripheral shock. Under a joint normal linear model, conditional residual standard deviation is σY√(1−ρ²), about 12.64% with these inputs. That normal assumption may be unreliable for an extreme shock.

Use stress-period relationships, alternative correlations and residual shocks to challenge the estimate. With several core variables, multiple regression or a covariance-based conditional model can help, but unstable relationships still need judgement.

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Static, dynamic and path-dependent stress tests

Daily liquidity can fail before a quarterly capital trough

The end-of-week arithmetic is positive, but the institution faces a ₹15 crore shortfall on day 2. A later inflow cannot pay an earlier obligation without bridging finance.

A second-round loss illustration

Suppose a bank must raise ₹18 crore by selling securities that initially carry a marked value of ₹20 crore. A 10% sale discount raises ₹18 crore but adds a ₹2 crore loss. If similar sales lower remaining collateral values, new margin calls may follow. Model the sale proceeds, realised loss and reduced asset stock consistently.

The same idea applies to dynamic option hedging: after a shock changes delta, rebalancing creates trades, funding needs and execution costs. Repeated shocks can be costly even when the final market price returns near its starting value.

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Bank Kaveri: an integrated multi-risk case

This case extends the capital bridge to bonds, equities, currency exposure and incremental credit provisions. All amounts are hypothetical ₹ crore. Opening Common Equity Tier 1 (CET1) capital is 1,200 and Risk-Weighted Assets (RWA) are 10,000, so the opening ratio is 12%.

Against assumed teaching comparison levels of 5.5% and 8%, capital remains above the first but below the second. The result does not establish survival: liquidity, total capital and institution-specific requirements must also pass.

Two important conventions

Currency quotation: the ₹50 crore loss assumes the rupee price of a dollar rises 10%. If instead “the rupee falls 10%” means dollars per rupee fall 10%, the inverse exchange rate rises 11.111%, producing a ₹55.56 crore liability increase. Define the quote before calculating.

Opening provisions: this case deducts only the ₹192 crore increase above an assumed ₹72 crore existing allowance. The earlier ₹120 crore capital example assumed no opening allowance. Do not transfer one case’s provision convention into the other.

Bond loss uses a modified-duration approximation and omits convexity. The bridge also omits tax, distributions and regulatory adjustments. Profit must be measured consistently before the additional loss charges.

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Combined-shock reverse testing: the failure frontier

Retain Bank Kaveri’s opening capital of ₹1,200 crore, but use a separate reverse-test experiment: fixed Risk-Weighted Assets of ₹10,000 crore, no profit, only credit and bond shocks, and an assumed 5.5% Common Equity Tier 1 trigger. Loss capacity is 1,200 − 0.055 × 10,000 = ₹650 crore.

Let p be the stressed Probability of Default (PD), with Loss Given Default fixed at 55%. Retain the opening credit allowance of ₹72 crore. Let Δy be the bond-yield rise in decimal units.

At a 10% default probability and a 300-basis-point yield rise, loss is 368 + 360 = ₹728 crore. Closing capital is ₹472 crore, or 4.72% of fixed risk-weighted assets, below the assumed 5.5% trigger.

The 2% row still produces a ₹16 crore incremental loss because loss given default has increased from 45% in the opening allowance to 55%. Every row uses that same stressed loss fraction.

The boundary identifies combinations that reach the trigger. It is not a probability distribution. Very large yield shocks make duration approximations unreliable, so full bond revaluation should replace the linear formula in implementation.

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Reverse liquidity testing: a refinancing freeze

A hypothetical Non-Banking Financial Company (NBFC) has ₹800 crore usable cash and liquid investments, ₹300 crore credible collections and ₹2,000 crore maturing short-term debt over 30 days. Assume no other payments, no asset haircuts and inflows available before obligations fall due.

At 55% non-renewal, the simplified end-of-horizon cash buffer is exactly exhausted. Above 55%, the company has a shortfall. A 60% non-renewal rate requires ₹1,200 crore but resources are ₹1,100 crore, leaving a ₹100 crore gap.

Commercial Paper (CP) is one form of short-term borrowing. Rollover means replacing maturing debt with new funding. A capital-adequate lender can still fail to pay when lenders refuse renewal.

If only half the expected collections arrive, the threshold becomes (800 + 150)/2,000 = 47.5%. If asset sales incur discounts or other payments occur, survival capacity falls further. This cash test is separate from a bank’s regulatory Liquidity Coverage Ratio.

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Reverse scenario search with constraints

When several risk factors can change, a spreadsheet or numerical optimiser can search for a large loss or for a threshold breach. Begin with an explicit objective, economic constraints and a reliable valuation model.

Excel Solver is an optimisation tool. Use changing cells for risk factors, a calculated loss or capital cell as the objective, and explicit bounds. A worst-loss result under arbitrary bounds is not automatically a reverse-failure scenario.

For the frontier above, fix the default probability at 10%, set a target loss of ₹650 crore and use the yield change as the changing cell. Solver or direct algebra gives 2.35%, or 235 basis points.

For nonlinear option books, try several initial points, inspect a coarse grid and check the candidate by full repricing. A local optimum can miss a worse combination. Principal Component Analysis (PCA) can reduce the number of search dimensions, but omitted factors and residual shocks still require testing.

Scaling a historical shock vector by 1.5 magnifies its moves. It does not, by itself, create a new correlation structure or capture a liquidity feedback loop.

EXTENDED LEARNING

Expected loss, unexpected loss and concentration

Stress losses complement statistical credit measures. With fixed Exposure at Default (EAD) and Loss Given Default (LGD), let D equal 1 on default and 0 otherwise, with Probability of Default (PD) equal to p.

For two such loan-loss variables with correlation ρ:

Assume loan X has exposure ₹100 crore, default probability 1% and loss fraction 50%. Loan Y has exposure ₹60 crore, default probability 3% and loss fraction 40%. Expected losses are ₹0.50 crore and ₹0.72 crore. Individual unexpected losses are approximately ₹4.975 crore and ₹4.094 crore.

At 99.9% confidence, a common economic-capital definition is loss quantile minus expected loss. Multiplying standard deviation by a normal quantile is only a shortcut and can misrepresent skewed credit tails. Some sources use “Credit Value at Risk” for the total loss quantile, so state the convention.

The correlation here is between default indicators or resulting fixed-severity losses. It is not interchangeable with the latent asset correlation used in a one-factor credit model. Concentration also includes common sectors, collateral, guarantors and funding sources.

EXTENDED LEARNING

Scenario probabilities and integration with tail measures

Most stress tests ask “what happens if this scenario occurs?” without estimating its probability. An optional modelling extension assigns explicit probabilities and combines stress outcomes with a historical or simulated loss distribution.

For example, assign a combined 2% probability to two mutually exclusive stress outcomes, 1% each. Allocate the remaining 98% to 100 equally weighted ordinary scenarios, so each ordinary scenario receives 0.98% probability.

For a fully specified teaching distribution, suppose every ordinary scenario has a ₹2 crore loss and the stress outcomes have losses of ₹10 crore and ₹20 crore. Using the smallest loss whose cumulative probability reaches the confidence level:

For discrete data, include only the required fraction of any probability mass at a tail boundary when calculating Expected Shortfall. Avoid double counting stress events already represented in the ordinary scenarios.

Empirical probabilities come from observed frequencies under modelling assumptions. Subjective probabilities reflect judgement. Rare-event weights are especially uncertain. Recalculate results over alternative weights and avoid presenting a judgemental probability as an established crisis frequency.

A joint scenario can use conditional probabilities: P(A and B) = P(A) × P(B given A). Multiplying two unconditional probabilities requires independence, which may be a poor crisis assumption. Probability-weighted integration is an optional risk-model technique, not a requirement that every regulatory stress scenario receive an occurrence probability.

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Stress Capital Buffer: worked calculation and current context

The US Stress Capital Buffer (SCB) connects supervisory stress results with capital requirements. A familiar single-exercise teaching representation is:

The dividend add-on uses specified projected common dividends and the prescribed Risk-Weighted Assets (RWA) denominator. This shorthand omits detailed regulatory calculation and transition provisions.

For Bank A, a simplified capital stack of a 4.5% Common Equity Tier 1 minimum plus a 3.8% buffer is 8.3%, before any applicable systemic surcharge or other requirements. Bank B’s raw 1.2% calculation is below the 2.5% floor.

Sources: Federal Reserve capital-planning rule and 30 September 2026 final-rule announcement. Exercise horizons, effective dates and institution categories should be checked in the applicable year’s official documents.

EXTENDED LEARNING

India: macro stress tests and bank-level capital assessment

The Reserve Bank of India (RBI) assesses system resilience through macro-financial stress analysis, including its Financial Stability Report (FSR). System-level results do not replace a bank’s own Internal Capital Adequacy Assessment Process (ICAAP), the forward-looking assessment of capital needed for its material risks.

An ICAAP should connect to risk appetite, capital allocation and the Supervisory Review and Evaluation Process (SREP). The RBI’s commercial-bank capital directions contain stress-testing and scenario-analysis requirements. The applicable requirements depend on bank type and current directions.

In the Bank Kaveri case, 5.5% and 8% remain explicitly stated teaching comparison levels. A real assessment must also consider Tier 1 and total capital, how Common Equity Tier 1 is allocated to meet those minima, conservation and systemic buffers, and supervisory requirements. A breach of a buffer is not the same as immediate insolvency.

For the Gross Non-Performing Asset (GNPA) ratio, use the report’s observation date, scenario horizon and institution coverage. This guide does not copy headline ratios from an older slide or a textbook as if they were current data.

Source: RBI Commercial Banks: Prudential Norms on Capital Adequacy Directions, 2025, updated 7 October 2026. Consult the official directions and latest Financial Stability Report for numerical requirements and projections.

EXTENDED LEARNING

A complete stress programme: additional exposures and decisions

A useful board summary identifies the scenario, pre-action capital and liquidity results, failure boundaries, proposed actions, execution lead times and remaining vulnerabilities. Present the result after action separately so that planned mitigation does not conceal the underlying exposure.

Reading and teaching references

John C. Hull, Risk Management and Financial Institutions, fifth edition, Chapter 22, printed pages 497–511, provides the conceptual background on conditional scenarios, feedback effects, reverse searches and probability integration. Regulatory thresholds described in that 2018 edition require current-source verification.

The integrated Kaveri, combined-shock and refinancing examples align with the Session 12 teaching material, with quotation conventions and allowance assumptions stated explicitly here. The figures are hypothetical, and the extensions use original explanatory text.

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Sources and further learning

• Basel Committee: stress testing principles and reverse testing
• Basel Framework: Liquidity Coverage Ratio
• Basel Framework: Net Stable Funding Ratio
• Basel Framework: leverage ratio
• Federal Reserve: 2026 stress-test materials
• Federal Reserve: capital planning and stress-test questions
• European Banking Authority: EU-wide stress testing
• Cboe: Volatility Index methodology
• New York Fed: Secured Overnight Financing Rate
• European Central Bank: Composite Indicator of Systemic Stress
• IFRS Foundation: IFRS 9 Financial Instruments
• Basel market-risk standards

Updated 1 November 2026. All bank examples and internal thresholds are hypothetical teaching assumptions. The guide summarises concepts and does not specify the applicable regulatory requirement for a particular institution.

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