Risk Management interview preparation

Questions

100

Traced to a firm

37

Firms

12

Updated

September 2026

Asked at

All firmsUBS14MSCI7BLBlackRock5FTFranklin Templeton3Oaktree Capital Management2Scotiabank2Jane Street1Moody's1Neuberger Berman1PIMCO1SSState Street1TSTruist Securities1

Topic

All topicsMarket risk and VaR14Tail risk and stress testing5Greeks and sensitivities5Credit risk11Counterparty risk and CVA6Operational risk5Model risk and validation6Regulatory capital7Liquidity risk and ALM6Statistics and quant foundations7Indian regulation7Risk governance and appetite4Markets and macro9Fit and career8

Level

AnyCoreIntermediateHard

Type

AnyTechnicalCaseBrainteaserMarket viewFit

1. 039What is wrong-way risk? Give me a real example.Counterparty risk and CVAIntermediatesuperdayBank credit riskDerivatives risk

   Say this

   Wrong-way risk is when your exposure to a counterparty grows at the same time as their credit deteriorates, so the two go bad together. It turns a manageable expected loss into a concentrated one, because the bad outcomes coincide by construction.

   Then walk it

   1. Specific wrong-way risk is a direct structural link. The textbook case: you buy protection on a company from a bank that is heavily exposed to that same company. When the reference entity deteriorates, your protection is worth more and your protection seller is weaker. That's the monoline insurer story in 2008 in one sentence.
   2. Another clean example: an oil producer sells you oil forward to hedge. Oil collapses, so your position with them is deeply in the money, and the same collapse is destroying their ability to pay. Energy banks lost money exactly this way in 2015 and 2020.
   3. General wrong-way risk is looser, driven by a common macro factor. Lending to emerging market banks in local currency while they've sold you dollars: a currency crisis hits your exposure and their solvency simultaneously.
   4. Also collateral correlation: taking a counterparty's own shares, or its home sovereign's bonds, as collateral. Exactly when you need to liquidate, the collateral is worth least. Basel bans own-issue collateral for this reason.
   5. How you handle it: model exposure and default jointly rather than multiplying independent expectations. Basel's standard workaround is an alpha multiplier, 1.4 by default, on the exposure input to cover correlation. That's crude and everyone knows it.
   6. The better controls are structural: don't take correlated collateral, set tighter limits on structurally linked counterparties, use break clauses, and stress the joint scenario explicitly rather than trusting the model.
   7. And the reason it matters more than its size suggests: wrong-way risk defeats diversification. You can't average it away across counterparties, because the correlation is the exposure.

   Where candidates lose it

   Giving a definition with no example. Interviewers want a named structure, and the monoline case or the oil producer case both work. The second thing they listen for is that standard CVA calculations assume independence between exposure and default, and that the Basel alpha multiplier is a crude patch for exactly this.

   Expect next

   • How does wrong-way risk affect your CVA number?
   • What collateral would you refuse to take, and why?
   • Is the Basel alpha of 1.4 adequate?
2. 040How do netting and collateral reduce counterparty exposure, and what's the difference between initial and variation margin?Counterparty risk and CVAIntermediatetechnicalBank credit riskClearing and margin

   Say this

   Netting lets you offset what you owe against what you're owed with the same counterparty, so exposure is one net figure rather than the sum of the positive trades. Collateral then covers most of that net figure. Variation margin covers today's mark-to-market; initial margin covers the move you'd suffer between their default and your close-out.

   Then walk it

   1. Close-out netting under an ISDA master with a valid netting opinion in the relevant jurisdiction is what makes it legally real. Without an enforceable opinion you have to hold gross exposure, and that's a country-by-country legal question, not a modelling one.
   2. The arithmetic is big. A portfolio of 100 trades, half positive and half negative, might have gross positive exposure of 800 crore and net exposure of 40 crore. Netting is the single most powerful mitigant there is.
   3. Variation margin: exchanged daily, equal to the change in net mark-to-market, so it keeps current exposure near zero. It's a transfer of value, and it eliminates exposure you've already suffered.
   4. Initial margin: held against future moves during the close-out period. It covers the gap between the last margin call and actually liquidating the portfolio, and it's sized off a high quantile, typically 99 percent over a 10-day margin period of risk for bilateral trades under the uncleared margin rules.
   5. So the residual risks after all that: gap risk if the market jumps between calls, margin period of risk being longer than assumed in a stressed close-out, disputes over valuation, collateral haircut adequacy, and wrong-way collateral correlation.
   6. Then the liquidity consequence, which candidates miss. Collateralisation converts credit risk into liquidity risk. You now have to fund margin calls in cash on the day, and a large adverse move means a large same-day cash outflow. That's what caused the UK gilt LDI crisis in 2022 and the 2021 nickel episode.
   7. And the Indian angle: the exchange-traded and cleared side is heavily margined under SEBI and the clearing corporations, while the bilateral OTC market is smaller and more collateral-light, so netting enforceability and CSA coverage vary a lot by counterparty type.

   Where candidates lose it

   Treating collateral as a free reduction in risk. It converts credit risk into funding liquidity risk, and the entities that blew up in 2022 were solvent and margin-called to death. Saying that out loud is what distinguishes a risk manager from someone quoting a mitigant list.

   Expect next

   • What is the margin period of risk and what would you assume for it?
   • What risk does collateralisation create?
   • What haircut would you apply to a corporate bond posted as collateral?
3. 041Why does a central counterparty reduce risk, and what new risk does it create?Counterparty risk and CVAIntermediatetechnicalClearing and marginBank credit risk

   Say this

   A CCP replaces a web of bilateral exposures with a hub and spoke, so it multilaterally nets, standardises margin and mutualises losses. In exchange you've created a single point of failure and turned counterparty risk into liquidity risk for every member.

   Then walk it

   1. Multilateral netting is the big win. If A owes B, B owes C and C owes A, bilaterally there are three exposures; through a CCP there's almost nothing. G20 reform after 2008 pushed standardised OTC derivatives to clearing for exactly this reason.
   2. It also standardises: daily variation margin, initial margin on a model everyone can see, a default fund, and a documented waterfall. That removes the dispute and delay problem that made Lehman's unwinding so slow.
   3. The default waterfall in order: the defaulter's margin, the defaulter's default fund contribution, the CCP's own skin in the game, then the surviving members' default fund, then assessments or variation margin gains haircutting. Being able to recite that is what a clearing risk interviewer wants.
   4. New risk one, concentration. The CCP is systemically critical infrastructure. If it fails, everything fails, and it has no meaningful equity relative to the exposures it faces.
   5. New risk two, mutualisation. As a clearing member you are exposed to other members' defaults through the default fund. You've swapped a known bilateral counterparty for an unknown pool of them.
   6. New risk three, procyclical margin. Margin models raise requirements when volatility rises, so the CCP demands the most cash exactly when cash is scarcest. That's a liquidity amplifier, and it's what the 2020 and 2022 episodes were about.
   7. New risk four, the member-client link. If you clear for clients, you stand between them and the CCP, so you have to fund their margin calls intraday. The LME nickel episode in 2022 showed that a CCP can also change the rules under stress, which is a governance risk you can't model.
   8. So the honest summary: clearing has reduced credit risk and increased liquidity risk, and it has concentrated tail risk into a small number of institutions. Better on balance, not free.

   Where candidates lose it

   Listing the benefits and stopping at 'it's safer'. The interviewer wants the default waterfall and at least two created risks, with procyclical margin the most important. And you should say that clearing trades credit risk for liquidity risk rather than eliminating risk.

   Expect next

   • Walk me through the default waterfall.
   • How would you stress test your exposure to a CCP?
   • Is procyclical initial margin fixable?
4. 047What is model risk?Model risk and validationIntermediatetechnicalUBSRisk Management · Zurich · 2021

   Say this

   Model risk is the risk of loss from using a model that's wrong, or from using a right model in the wrong place. Two sources, and the second is the bigger one in practice: fundamental errors in the model itself, and correct models applied outside the conditions they were built for.

   Then walk it

   1. The US Federal Reserve's SR 11-7 definition is the one to quote, because it splits it exactly that way: errors in design, and incorrect or inappropriate use.
   2. The error side includes bad theory, bad data, coding bugs and bad calibration. It's the side people think of and it's the side validation catches most easily.
   3. The misuse side is the one that hurts. A model calibrated on investment grade credit applied to high yield. A pricing model used for risk. A VaR model built for a linear book applied once options were added. Nothing is wrong with the model; the use is wrong.
   4. It compounds through the chain. Models feed models: a PD model feeds ECL, which feeds capital planning, which feeds the dividend decision. An error at the bottom is unrecognisable four steps up, which is why model inventories and dependency maps exist.
   5. Real examples worth naming: the Gaussian copula in structured credit, where the model was fine and the correlation assumption was not. The 2012 JPMorgan CIO losses, where a spreadsheet error and a newly approved VaR model both featured. Long-Term Capital Management, where the model was right about relationships and wrong about liquidity and leverage.
   6. How you manage it: an inventory of every model with a tier, independent validation proportionate to that tier, ongoing performance monitoring, documented limitations, and an owner. And the control that matters most is the simplest, writing down what the model may not be used for.
   7. The limitation to volunteer: you can't eliminate model risk, only bound it. The mitigant with the best return is not more validation, it's a stated range of applicability and a human who understands the model sitting between it and a decision.

   Where candidates lose it

   Defining it as 'the model being wrong'. That's half of it, and the smaller half. The answer that lands names misuse of a correct model as the larger source, and gives a concrete case. If you can cite SR 11-7, do, because it signals you've worked near a validation function.

   Expect next

   • Give me an example of a correct model used wrongly.
   • How would you tier a model inventory?
   • Can you eliminate model risk?

   Reported by candidates at UBS (Risk Management, Zurich, 2021). Source: Wall Street Oasis.

5. 049What's the difference between backtesting and benchmarking a model?Model risk and validationIntermediatetechnicalModel validationBank market risk

   Say this

   Backtesting compares the model to reality. Benchmarking compares it to another model. One tells you whether you're right, the other tells you whether you're different, and you need benchmarking precisely when reality doesn't give you enough observations to backtest.

   Then walk it

   1. Backtesting is the gold standard because the comparator is truth: forecast versus realised outcome. VaR exceptions against actual P&L, predicted default rates against observed defaults, predicted prepayment against actual.
   2. Its constraint is data. At 99% confidence you get about 2.5 exceptions a year, so a year of data can't distinguish a good model from a mediocre one. For a low-default portfolio, sovereigns or large corporates, you might have zero defaults in a decade, so backtesting is simply unavailable.
   3. Benchmarking fills that gap. Run a challenger model, a vendor model, or a simple closed-form approximation on the same portfolio and compare. Large unexplained divergence is a finding even if you can't say which one is right.
   4. It's also how you test parts of a model you can't observe. You can't observe a 20-year lifetime PD, but you can compare your curve to an agency cumulative default table or to CDS-implied hazard rates.
   5. The important limitation: benchmarking two models that share an assumption tells you nothing. If both assume a normal distribution, they'll agree and both be wrong. The benchmark has to be structurally different to be informative, and that's the part people get wrong.
   6. So they answer different questions. Backtesting: is the model calibrated? Benchmarking: is the model an outlier, and can I explain why? A full validation uses both plus sensitivity analysis, and leans on benchmarking exactly where data is thin.
   7. Practical example: for a low-default corporate portfolio, you'd benchmark the PD model against external ratings and market-implied PDs, use a binomial or Vasicek test for the little default data you have, and lean on the qualitative review. That combination is what a supervisor expects to see.

   Where candidates lose it

   Using the words interchangeably. And missing the key insight that benchmarking is only informative if the benchmark makes different assumptions. Two models with the same flawed assumption will agree beautifully, and a candidate who says that has thought about validation rather than memorised a checklist.

   Expect next

   • How would you validate a PD model for a portfolio with no defaults?
   • What makes a good benchmark model?
   • Your model and the benchmark differ by 40 percent. Now what?
6. 052How do you detect overfitting in a risk model?Model risk and validationIntermediatetechnicalModel validationBuy-side risk

   Say this

   The signature is a large gap between in-sample and out-of-sample performance, and for financial models specifically between in-sample and out-of-time. If accuracy collapses on data from a later period, you've fitted the era, not the relationship.

   Then walk it

   1. Standard test: hold out data, or cross-validate. But for time series you need forward-chaining rather than random k-fold, because random folds leak the future into the training set and make everything look good.
   2. The financial-specific test is out of time. Fit on 2015 to 2019, test on 2021 to 2023. Random splits from the same period share the same regime, so they flatter the model badly.
   3. Structural warning signs: parameter count relative to observations, coefficients with implausible signs or magnitudes, a variable that only works in one sub-period, and results that change materially when you drop a single year.
   4. For strategy or signal work, the killer is multiple-testing bias. If you tried 500 specifications, the best one will look great by construction. The deflated Sharpe ratio and the idea of testing on a truly held-out period exist precisely for that.
   5. Stability diagnostics: rolling-window coefficient estimates. A genuine relationship has coefficients that wobble; an overfitted one has coefficients that flip sign. And bootstrap the performance metric to see how wide the confidence interval actually is.
   6. The cultural control, which matters more than any statistic: limit how many times you look at the holdout. Every peek at the test set converts it into training data, and in practice that's how overfitting enters a model that passed every formal test.
   7. And the counterweight to state: underfitting is also a failure, and a heavily constrained model that misses real non-linearity costs money too. The judgement is whether added complexity buys performance that survives out of time.

   Where candidates lose it

   Naming cross-validation and stopping. For financial models, random cross-validation is itself a source of false confidence because of regime and look-ahead leakage. Out-of-time testing and multiple-testing bias are the two points that show you've done this on real data.

   Expect next

   • Why is random k-fold cross-validation dangerous for time series?
   • How would you account for having tried 200 specifications?
   • How would you tell overfitting apart from a regime change?
7. 056Standardised versus internal ratings based approach. Which would you rather run?Regulatory capitalIntermediatetechnicalRegulatory reportingBank credit risk

   Say this

   IRB usually gives lower capital and better risk sensitivity, so a large bank wants it. But it costs a great deal to build, maintain and defend, and after the Basel IV output floor the capital saving is capped, so for many portfolios the honest answer is now standardised.

   Then walk it

   1. Standardised: prescribed risk weights by exposure class and external rating. Cheap, comparable across banks, transparent, and completely insensitive to whether your borrowers are good or bad within a bucket. Every unrated corporate gets 100 percent whether it's excellent or nearly insolvent.
   2. Foundation IRB: you estimate PD, the supervisor sets LGD and EAD. Advanced IRB: you estimate all three. Both need supervisory approval, years of clean data, validated models and demonstrated use in actual credit decisions, the use test.
   3. The capital saving is real, often 20 to 40 percent lower RWA on a good-quality retail or mortgage book, because the prescribed weights are calibrated conservatively for the average bank.
   4. The costs: model development and validation teams, data infrastructure with long histories, annual supervisory review, and the risk that a supervisor imposes a multiplier or pulls approval, which produces a sudden capital hit. Several European banks have taken exactly that.
   5. Then Basel IV changes the calculus. The output floor requires total RWA to be at least 72.5 percent of what the standardised approach would give, phased in. Advanced IRB has been removed for exposures to large corporates and banks, and equity IRB is gone. So the saving is bounded and the cheapest portfolios to model no longer qualify.
   6. My answer: IRB where the portfolio is large, homogeneous, data-rich and where internal models genuinely discriminate, so retail mortgages and retail lending. Standardised for low-default wholesale portfolios where you were never going to estimate PD credibly anyway. Running IRB for its own sake is a large cost for capped benefit.
   7. The point worth making that goes beyond capital: the real value of IRB was never the capital saving, it was that building the models forces a bank to understand its own credit risk. Banks that adopted IRB seriously ended up with better credit decisions, and that survives the output floor.

   Where candidates lose it

   Answering 'IRB because it's lower capital'. Post-Basel IV that's only partly true, and a candidate who hasn't registered the output floor and the withdrawal of advanced IRB for large corporates is working from pre-2017 knowledge. Mention the use test too; supervisors care more about it than about the maths.

   Expect next

   • What is the use test?
   • Which portfolios can no longer use advanced IRB?
   • What happens if a supervisor withdraws IRB permission?
8. 058Why does a leverage ratio exist alongside risk-weighted capital?Regulatory capitalIntermediatetechnicalRegulatory reportingBank market risk

   Say this

   Because risk weights are model outputs and models can be wrong or gamed. The leverage ratio is a non-risk-based backstop: Tier 1 over total exposure, minimum 3 percent, and it doesn't care what you think the risk is.

   Then walk it

   1. The pre-crisis evidence is the whole argument. Banks entered 2008 with comfortable risk-based ratios and leverage of 30 or 50 to one, because sovereign debt, AAA tranches and repo books all carried tiny weights and turned out not to be riskless.
   2. So the design intent is a floor that survives being wrong about risk. It binds when a bank holds a lot of assets it has judged safe, which is exactly the situation that has historically preceded trouble.
   3. The exposure measure is deliberately broad: on-balance-sheet assets, derivative exposures including a potential future exposure add-on, securities financing transactions, and off-balance-sheet commitments converted at credit conversion factors. You can't shrink it by netting the way you can for RWA.
   4. Who it binds: banks with large low-risk-weight books. Custodians, repo intermediaries, and banks holding large government bond portfolios. For those, leverage rather than RWA is the constraint that drives the business decision.
   5. Its own weakness, and you should say it: it's risk-insensitive by construction, so it treats a treasury bill and an unsecured emerging market loan identically. That creates an incentive to shift toward higher-yielding, higher-risk assets once the ratio binds, which is the opposite of what you want.
   6. So the two measures are deliberate complements. Risk weights give you sensitivity and can be gamed; leverage gives you robustness and rewards risk-taking at the margin. Neither alone is adequate, which is the point of having both.
   7. Live example: in 2020 several jurisdictions temporarily excluded central bank reserves from the exposure measure, because deposit inflows and QE were inflating the denominator and constraining lending. That's a good illustration of the ratio binding for reasons unrelated to risk.

   Where candidates lose it

   Stating the definition without the pre-crisis motivation, and without the downside. A complete answer says the leverage ratio pushes banks toward riskier assets at the margin, because the interviewer wants to see you can criticise a rule you also support.

   Expect next

   • Which kinds of bank does the leverage ratio bind?
   • What perverse incentive does it create?
   • Why did supervisors exclude central bank reserves in 2020?
9. 061Explain LCR and NSFR.Liquidity risk and ALMIntermediatetechnicalTreasury and ALMRegulatory reporting

   Say this

   Both are Basel III liquidity ratios with a minimum of 100 percent. LCR is a 30-day survival test: high quality liquid assets over stressed net outflows. NSFR is a one-year structural test: available stable funding over required stable funding. Short-term shock versus long-term funding mismatch.

   Then walk it

   1. LCR numerator, HQLA: Level 1 is cash, central bank reserves and most sovereign debt at no haircut. Level 2A is high-grade covered and corporate bonds at a 15 percent haircut, Level 2B is lower-rated corporates and some equities at 25 to 50 percent, and Level 2 is capped at 40 percent of the total.
   2. LCR denominator: outflows minus capped inflows, with prescribed run-off rates. Stable retail deposits 3 to 5 percent, less stable retail 10, operational wholesale deposits 25, non-operational corporate deposits 40, and financial institution deposits 100. Inflows are capped at 75 percent of outflows, so you can't rely on collecting to pay.
   3. That run-off table is the heart of it, and it encodes a real judgement: insured retail deposits are sticky, and money from other banks disappears entirely. SVB's deposits were overwhelmingly uninsured corporate money, which the table would treat as the fastest-running kind.
   4. NSFR pairs funding stability against asset liquidity over a year. Equity and long-term debt count fully as stable funding, retail deposits at 90 to 95 percent, short wholesale funding at little or nothing. On the asset side, long-dated loans need high stable funding and cash needs none.
   5. So NSFR is a structural constraint on maturity transformation, which is the business banks are in. It limits how much of a long loan book you can fund with three-month wholesale paper.
   6. Indian specifics: RBI implemented LCR from 2015 and NSFR from 2021, both at 100 percent, and has periodically adjusted the treatment of SLR securities within HQLA. The 2024 draft revisions raised run-off assumptions on retail deposits with internet and mobile banking access, which is a direct response to how fast deposits can now move.
   7. The critique to volunteer: both are point-in-time ratios with prescribed assumptions, and they can be window-dressed at reporting dates. And in a real run, the run-off rates have been far higher than the table assumes. SVB lost a quarter of its deposits in a day.

   Where candidates lose it

   Mixing up the horizons or quoting the ratios without any run-off rates. Knowing that financial institution deposits run off at 100 percent and stable retail at 3 to 5 is what shows you've seen the schedule. And the point that real runs are faster than the assumed rates is the part with judgement in it.

   Expect next

   • What counts as HQLA, and what haircuts apply?
   • Why did banks with a 100 percent LCR still fail in 2023?
   • How does NSFR constrain the lending business?
10. 062What is interest rate risk in the banking book, and how do you measure it?Liquidity risk and ALMIntermediatetechnicalTreasury and ALMIndian bank risk and treasury

    Say this

    IRRBB is the risk that rate moves hurt the banking book, and you measure it two ways that often disagree. Economic value of equity is the present value view over the full life of the balance sheet. Net interest income is the earnings view over one to three years.

    Then walk it

    1. EVE: revalue all assets, liabilities and off-balance-sheet items under a rate shock and look at the change in net present value. It's the long-horizon, economic answer, and it's where a big fixed-rate asset book shows up immediately.
    2. NII: project interest income and expense over one to three years under the shock. It's the accounting and earnings answer, and it's the one management actually cares about because it hits reported profit.
    3. They can point in opposite directions, and that's the interesting part. A bank funding long fixed-rate mortgages with short deposits looks fine on NII when rates rise slowly, because deposit rates lag, while EVE is deeply negative from day one. That gap is exactly the SVB configuration.
    4. The Basel standardised framework prescribes six shock scenarios: parallel up and down, steepener, flattener, short rate up and short rate down. Non-parallel scenarios matter because most banks are not exposed to the level so much as to the shape.
    5. Then the behavioural assumptions, which are where all the model risk lives. Non-maturity deposits have no contractual maturity so you assume one. Prepayment on fixed loans. Early withdrawal on term deposits. Pipeline commitments. Move the deposit assumption from a two-year to a five-year effective duration and the answer changes sign.
    6. The supervisory outlier test: if EVE sensitivity exceeds 15 percent of Tier 1 capital under any of the six scenarios, you attract supervisory attention under Pillar 2. That's the number to know.
    7. In India, RBI requires both the traditional gap approach and duration-based EVE reporting, and IRRBB is a core Pillar 2 item in ICAAP. Indian banks carry large SLR portfolios of government bonds, so rate risk in the banking book is structurally significant and the AFS versus HTM classification decision drives how much of it hits reported capital.
    8. How you manage it: reprice the book, use interest rate swaps to shorten effective asset duration, adjust deposit pricing, and set limits on both EVE and NII sensitivity so neither view can be ignored.

    Where candidates lose it

    Giving only one of the two measures. If you say EVE and not NII, or the reverse, you've described half the framework and missed the tension that makes IRRBB interesting. And the behavioural deposit assumption is the single biggest driver of the answer, so name it as the main model risk.

    Expect next

    • EVE and NII disagree. Which do you act on?
    • What is the supervisory outlier test?
    • How would you hedge a negative EVE position?

Firm tags come from public, anonymous candidate reports on Wall Street Oasis: strong signal, not sworn testimony. Firms are named as the places a question was reported, not as partners of Fin Maverick. Answers are written for this page to show how to think out loud; they are not scripts to recite.