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. 006How does Monte Carlo VaR work, and when is it worth the extra cost?Market risk and VaRHardtechnicalUBSRisk Management · Zurich · 2021

   Say this

   You specify a stochastic process for each risk factor, simulate a large number of joint paths, revalue the portfolio on every path, and take the percentile of the simulated P&L. It's worth the cost when the payoff is non-linear or path dependent, and not otherwise.

   Then walk it

   1. The inputs are a process per factor, usually a drift and volatility, plus a dependence structure, usually a correlation matrix or a copula. Then you draw correlated shocks, typically via a Cholesky decomposition.
   2. Full revaluation is the expensive part, not the random numbers. If revaluing one exotic takes a second, ten thousand paths across a thousand trades is a real overnight compute problem.
   3. It's the only method that handles path dependency properly. A barrier option, a cliquet, a callable bond, a CVA number on a swap portfolio, all of those depend on the path and not just the endpoint.
   4. It also lets you choose the distribution. You can simulate from a t distribution, or use a copula to get tail dependence that a normal correlation matrix cannot produce.
   5. Its weakness is that it is only as good as the assumed process. Historical simulation is wrong in a way you can see; Monte Carlo is wrong in a way buried in a calibration file. That's why it needs the heaviest model validation of the three.
   6. So my rule: linear portfolio, don't bother, parametric or historical is fine. Options book, structured credit, or anything with optionality in the funding, Monte Carlo earns its keep.

   Where candidates lose it

   Describing it as 'generating random scenarios' without naming the two things you have to assume, the process and the dependence structure. That's where all the model risk lives, and naming it is what separates someone who has built one from someone who read about it.

   Expect next

   • How many paths do you need, and how would you know?
   • How would you introduce tail dependence into the simulation?
   • How would you validate a Monte Carlo VaR engine?

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

2. 007Which assumption inside parametric VaR fails first, and what does that do to your number?Market risk and VaRHardsuperdayUBSRisk Management · Zurich · 2021

   Say this

   Normality fails first, and it makes VaR too small exactly when you need it. Real return distributions are leptokurtic, so the true 99th percentile sits further out than 2.33 sigma, and the deeper into the tail you go the worse the understatement gets.

   Then walk it

   1. Assumption one, normality. Equity index daily returns have kurtosis well above three. At 99% the error is modest, maybe 10 to 20 percent; at 99.9% parametric VaR can be off by a factor.
   2. Assumption two, a stable covariance matrix. Correlations rise in a sell-off, so the diversification benefit the matrix gives you evaporates in the scenario the number is supposed to protect you from.
   3. Assumption three, linearity. Parametric VaR uses deltas, so it prices an option position as if it were stock. Short gamma looks harmless and short a straddle can even show negative risk.
   4. Assumption four, independent returns. Volatility clusters, so square-root-of-time scaling understates multi-day risk during a stress period.
   5. The order matters for the answer: normality is the one people name, but linearity is the one that produces catastrophically wrong numbers, because it can be wrong by a sign rather than a percentage.
   6. Fixes in ascending order of effort: a t distribution or Cornish-Fisher adjustment for the tail, EWMA covariance for the clustering, delta-gamma for mild convexity, and full revaluation once the book has real optionality.

   Where candidates lose it

   Saying 'it assumes normality' and stopping. Every candidate says that. The differentiator is naming the linearity assumption and explaining that for an options book parametric VaR can get the direction of risk wrong, not just the magnitude.

   Expect next

   • How would you adjust it for fat tails without going to full simulation?
   • What does delta-gamma VaR fix and what does it still miss?
   • Would you ever show a board a parametric number? When?

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

3. 009What are the advantages and disadvantages of expected shortfall compared with VaR?Market risk and VaRHardtechnicalUBSRisk Management · Zurich · 2021

   Say this

   ES wins on theory and loses on practice. It's coherent, it sees the whole tail, and it can't be gamed by moving risk past the threshold. But it's harder to backtest, less stable, and more sensitive to the handful of observations that drive it.

   Then walk it

   1. Advantage one, it's subadditive, so it's a coherent risk measure. Adding two books can never raise ES above the sum of their parts, which means you can allocate it down to desks and the numbers add up sensibly.
   2. Advantage two, it sees tail depth. VaR is blind beyond the quantile, so a desk can sell far out-of-the-money options and report the same VaR with vastly more real exposure. ES prices that in.
   3. Advantage three, it removes the incentive to optimise against the measure. Optimising a portfolio to minimise VaR tends to push loss into the tail; minimising ES doesn't reward that.
   4. Disadvantage one, backtesting. A VaR breach is binary and you can test the count with a Kupiec or traffic-light test. ES needs you to test conditional magnitudes, which needs far more observations, so supervisors still backtest VaR even under an ES capital regime.
   5. Disadvantage two, estimator noise. At 97.5% over 250 days, ES is the average of six observations. Change one bad day and the number jumps. It's less robust and less stable period to period, which makes limit management awkward.
   6. Disadvantage three, communication. Traders understand 'I lose more than this one day in a hundred'. 'The average of my worst six days' takes longer to land, and risk numbers nobody understands don't change behaviour.
   7. My summary line: ES is the better measure of risk and VaR is the better test of your model. Most banks now report both for exactly that reason.

   Where candidates lose it

   Giving only the coherence advantage. That's half the answer and the easy half. The interviewer is testing whether you know the practical cost, and the backtesting problem is the answer. Saying 'ES is strictly better' is the wrong answer, because if it were, Basel would have dropped VaR backtests too.

   Expect next

   • If ES is coherent and VaR is not, why do supervisors still backtest VaR?
   • How many observations would you want to estimate ES reliably?
   • Which would you set a desk limit on?

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

4. 010Why is VaR not a coherent risk measure?Market risk and VaRHardsuperdayUBSRisk Management · Zurich · 2021

   Say this

   Because it fails subadditivity. The VaR of a combined portfolio can exceed the sum of the individual VaRs, which means diversification can appear to increase risk. Coherence needs four properties, and that's the one VaR breaks.

   Then walk it

   1. The four axioms are monotonicity, translation invariance, positive homogeneity and subadditivity. VaR satisfies the first three.
   2. The classic counterexample is two independent digital or deep out-of-the-money option positions. Each has a small probability of a large loss, say 0.6 percent. Individually, at 99% confidence, the loss sits beyond the quantile, so each has near-zero VaR.
   3. Put them together and the probability of at least one blowing up is now above one percent, so the combined VaR jumps to the full loss. Two positions with almost no VaR each combine into a large one. That's the violation.
   4. Why it matters operationally: if the measure isn't subadditive, you can't safely allocate a firm limit down to desks, because desk limits summing to the firm limit no longer bound the firm's risk. And a trader can reduce measured VaR by taking on tail risk.
   5. For elliptical distributions, including the normal, VaR is subadditive, which is why the problem never shows up in a textbook example. It shows up in real books with credit and optionality, which is exactly where it matters.
   6. ES is subadditive at every confidence level and for every distribution, which is the theoretical reason Basel moved to it.

   Where candidates lose it

   Naming subadditivity without being able to construct the counterexample. The interviewer will ask for an example, and 'two out-of-the-money digital options that each blow up 0.6 percent of the time' is the one that works. Also worth avoiding: claiming VaR is never subadditive. For normal distributions it is.

   Expect next

   • Give me a concrete two-position counterexample.
   • Is VaR subadditive under any conditions?
   • What practical problem does this create for limit setting?

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

5. 022Which equities have duration?Greeks and sensitivitiesHardtechnicalBLBlackRockRisk and Quantitative Analysis · New York · 2026

   Say this

   Equity duration is how sensitive a stock's price is to the discount rate, and it's driven by how far out the cash flows sit. Long-duration equities are the ones whose value is mostly terminal value: high-growth tech, biotech with no earnings, and long-dated infrastructure and utilities.

   Then walk it

   1. Mechanically it's the same idea as bond duration. Discount cash flows, compute the weighted average time to those cash flows, and that's your rate sensitivity. A company earning nothing today with all the value in year fifteen has enormous duration.
   2. So the long-duration buckets: unprofitable growth software, early-stage biotech, anything valued on a distant terminal value, plus regulated utilities and infrastructure where the cash flows are bond-like and stretch for decades.
   3. The short-duration buckets: value names, banks, energy, cyclicals with high near-term free cash flow and low reinvestment. Their value is front-loaded, so the discount rate matters less.
   4. The empirical check: 2022 is the cleanest natural experiment. As real yields rose, the Nasdaq underperformed value by a huge margin even though earnings held up. That is duration doing the work, not fundamentals.
   5. There's a twist that matters for a risk seat: for financials the rate effect goes the other way through earnings. Banks' net interest margins improve with rates, so their effective duration can be negative. You can't apply a single sign to the whole market.
   6. And utilities are the interesting case, because they have long-duration cash flows and leverage, so they trade as rate proxies. Many managers hold them as bond substitutes and then get surprised when they behave like bonds.

   Where candidates lose it

   Treating this as a trick question or saying equities don't have duration. The interviewer is testing whether you can move a fixed income concept into equities and name the cohorts. And the answer that stands out mentions financials as the exception where the sign flips.

   Expect next

   • Why did long-duration equities sell off so hard in 2022?
   • Do banks have positive or negative equity duration?
   • How would you hedge the rate sensitivity of a growth equity portfolio?

   Reported by candidates at BlackRock (Risk and Quantitative Analysis, New York, 2026). Source: Wall Street Oasis.

6. 031How does IFRS 9 expected credit loss differ from Basel regulatory expected loss?Credit riskHardsuperdayBank credit riskRegulatory reporting

   Say this

   Different purposes, so different parameters. IFRS 9 is accounting: point-in-time, forward-looking, neutral, and lifetime for Stage 2 and 3. Basel is prudential: through-the-cycle PD, downturn LGD, twelve-month horizon, and deliberately conservative.

   Then walk it

   1. Horizon. Basel EL is always twelve months. IFRS 9 is twelve months in Stage 1 and lifetime in Stages 2 and 3.
   2. PD. Basel wants a long-run average, through-the-cycle PD. IFRS 9 wants a point-in-time PD conditioned on a macro forecast.
   3. LGD. Basel requires downturn LGD, a stressed recovery assumption. IFRS 9 wants a neutral, expected LGD with no prudential margin.
   4. Discounting. IFRS 9 discounts cash shortfalls at the effective interest rate. Basel EL is undiscounted.
   5. Then the reconciliation, which is where real work happens. For IRB banks, if accounting provisions exceed Basel EL, the excess counts in Tier 2 up to a cap of 0.6 percent of credit RWA. If provisions fall short, the shortfall is deducted straight from CET1. So the two frameworks meet in the capital ratio.
   6. For standardised-approach banks it's different again: general provisions can count in Tier 2 up to 1.25 percent of credit RWA, and specific provisions reduce the exposure value.
   7. And the transitional arrangements matter historically. When IFRS 9 came in, supervisors allowed a phase-in of the day-one CET1 hit precisely because the provision increase was large enough to be destabilising.

   Where candidates lose it

   Treating them as the same number with different labels. Naming the four parameter differences is table stakes; the answer that lands explains the CET1 shortfall deduction and the Tier 2 excess cap, because that's the bit that actually affects a bank's capital ratio.

   Expect next

   • What happens to CET1 if provisions are below Basel EL?
   • Why is Basel LGD downturn and IFRS 9 LGD neutral?
   • Which framework produced a bigger provision in 2020?
7. 035Explain the Merton model, and the difference between structural and reduced-form credit models.Credit riskHardtechnicalBank credit riskModel validation

   Say this

   Merton treats equity as a call option on the firm's assets with a strike equal to its debt. Default happens when asset value falls below debt at maturity, so you can back out a default probability from the equity price and its volatility. That's the structural family; reduced-form models skip the story and fit default intensity straight from market spreads.

   Then walk it

   1. The Merton set-up: firm assets follow a lognormal process, equity holders own a call with strike equal to the debt face value, and default probability is the chance the asset value ends below that strike. Distance to default is how many asset standard deviations you are above the barrier.
   2. The clever part is that it's forward-looking and uses market data. Equity prices update every second, so a structural PD reacts long before a rating agency does. That's what Moody's KMV commercialised as EDF.
   3. Its weaknesses are specific. It underpredicts short-term default because the asset process is continuous and can't jump. It needs asset value and asset volatility, neither of which is observable. It assumes one debt maturity. And it produces credit spreads well below observed ones, the credit spread puzzle.
   4. Reduced-form, or intensity models like Jarrow-Turnbull and Duffie-Singleton, take default as an exogenous Poisson-type event with a hazard rate calibrated from CDS or bond spreads. No story about why the firm defaults, just a fit to prices.
   5. So the trade-off: structural models explain and give you economic intuition and a link to the equity market. Reduced-form models fit market prices and are what you use to value and hedge credit derivatives.
   6. In practice a bank uses both and for different jobs. Structural or hybrid models for wholesale PD estimation and early warning; reduced-form for pricing and for CVA. And the risk-management caveat: a structural model's PD spikes whenever equity vol spikes, so it's cyclical and noisy, which is fine as an early warning signal and bad as a provisioning input.

   Where candidates lose it

   Describing equity as a call option and stopping. The interviewer will ask what's wrong with Merton, and 'it underpredicts short-horizon default because assets can't jump' plus 'asset value and asset volatility aren't observable' are the answers. Also be able to say which model you'd use for pricing versus for PD estimation.

   Expect next

   • What is distance to default?
   • Why does Merton understate short-term default risk?
   • Which would you use to price a CDS?
8. 050Explain what a Kalman filter is.Model risk and validationHardtechnicalUBSRisk · London · 2022

   Say this

   It's a recursive estimator for a hidden state you can only observe with noise. Each period you predict the state forward with your model, then correct that prediction with the new observation, weighting the two by how much you trust each. Under linear-Gaussian assumptions it's the optimal estimator.

   Then walk it

   1. Two equations. A state equation for how the unobserved thing evolves, and a measurement equation linking the state to what you actually see, each with its own noise.
   2. Two steps per period. Predict: roll the state and its uncertainty forward. Update: compute the surprise, the difference between the observation and what you expected, and move your estimate toward it by the Kalman gain.
   3. The gain is the whole intuition. If measurement noise is large relative to state uncertainty, the gain is small and you mostly trust your model. If your state uncertainty is large, the gain is large and you mostly trust the new data. It's Bayesian updating with the arithmetic done for you.
   4. Where it's used in finance: extracting a time-varying beta or hedge ratio, estimating a stochastic volatility or unobserved factor, filtering a fair-value or pairs-trading spread, term structure models where the factors are latent, and nowcasting a macro variable from noisy high-frequency data.
   5. Why a risk function cares: it gives you an estimate that adapts without the jumpiness of a rolling window. A 60-day rolling beta lurches when an old observation drops out; a Kalman-filtered beta moves smoothly and quantifies its own uncertainty.
   6. The assumptions and their cost: linear dynamics and Gaussian noise. For non-linear problems you need the extended or unscented variants or a particle filter. And you have to specify the two noise covariances, which are rarely known, so in practice you estimate them by maximum likelihood and the result is sensitive to them.
   7. The limitation to volunteer: it's optimal given the model, and it has no way to tell you the state equation is wrong. Feed it a misspecified process and it will produce confident, smooth, wrong estimates, which is a particularly dangerous failure mode.

   Where candidates lose it

   Reciting matrix equations. Nobody wants the algebra; they want the predict-then-correct intuition, the gain as a trust weighting, and one concrete financial use. If you can't name a use case, the answer reads as memorised from a signal-processing course.

   Expect next

   • How would you use it to estimate a time-varying hedge ratio?
   • What happens if the noise covariances are misspecified?
   • How does it compare to a simple exponentially weighted estimate?

   Reported by candidates at UBS (Risk, London, 2022). Source: Wall Street Oasis.

9. 057What is Basel IV, and what changed for market risk?Regulatory capitalHardsuperdayRegulatory reportingBank market risk

   Say this

   Basel IV, formally the finalisation of Basel III, is about comparability rather than more capital. The headline is the 72.5 percent output floor on internally modelled RWA. For market risk it's FRTB, which replaced VaR with expected shortfall and drew a much harder line between trading and banking book.

   Then walk it

   1. The output floor: total RWA can't fall below 72.5 percent of the standardised calculation, phased in over several years. It caps the benefit of internal models and restores comparability between banks, which was the central complaint after 2008.
   2. Credit risk: advanced IRB removed for large corporates and financial institutions, IRB removed for equities, input floors on PD and LGD, and a more granular standardised approach with real loan-to-value sensitivity on mortgages.
   3. Operational risk: internal models abolished entirely, replaced by the standardised measurement approach driven by business indicators and your own loss history.
   4. FRTB for market risk, and the four things that changed. Expected shortfall at 97.5 percent replaces 99 percent VaR, so tail depth is captured. Liquidity horizons vary by risk factor from 10 to 120 days, so illiquid risk costs more capital.
   5. Third, non-modellable risk factors. If a factor lacks enough real price observations, you can't model it and it attracts a stress-based add-on. That was a large and unwelcome surprise for exotic and emerging market desks.
   6. Fourth, the trading and banking book boundary became prescriptive with restrictions on reclassification, ending the pre-crisis practice of moving positions to whichever book carried less capital. And the internal models approval is now at desk level with P&L attribution tests, so one desk can fail and lose modelled treatment while others keep it.
   7. Implementation dates have slipped repeatedly and differ by jurisdiction, with the US, UK and EU all on different timelines and different versions, especially for FRTB internal models. That fragmentation is itself a live commercial issue for global banks.
   8. The fair criticism: the aggregate capital impact is modest but very unevenly distributed, falling hardest on European banks with big IRB books and on trading desks in illiquid products. And the complexity it adds runs against the original goal of simplicity.

   Where candidates lose it

   Treating Basel IV as just higher capital requirements. The theme is comparability and constraining internal models, not level. And for market risk you need FRTB's specifics: expected shortfall, liquidity horizons, non-modellable risk factors and the desk-level P&L attribution test. Naming only the first shows shallow reading.

   Expect next

   • Why did FRTB move to expected shortfall?
   • What is a non-modellable risk factor and what does it cost?
   • What happens when a desk fails P&L attribution?
10. 066What does it mean if an estimator is BLUE?Statistics and quant foundationsHardtechnicalUBSRisk Management · Zurich · 2021

    Say this

    Best Linear Unbiased Estimator. Among all estimators that are linear in the data and unbiased, it has the smallest variance. That's the Gauss-Markov result: ordinary least squares is BLUE provided a specific set of assumptions holds.

    Then walk it

    1. Unpack each word, because that's what the question is testing. Linear in the observations. Unbiased, so its expected value equals the true parameter. Best, meaning minimum variance within that class.
    2. The Gauss-Markov conditions: correct linear specification, errors with zero conditional mean, homoskedasticity, no autocorrelation, and no perfect multicollinearity. Notice normality is not required for BLUE. You need normality for the t and F tests in small samples, not for OLS to be efficient.
    3. The restriction that matters is 'linear'. A biased or non-linear estimator can easily beat OLS on mean squared error. Ridge and lasso are deliberately biased and often predict better, and James-Stein shrinkage famously dominates the sample mean. So BLUE is optimality within a box, not optimality.
    4. In finance the assumptions fail routinely. Returns are heteroskedastic and volatility clusters, so OLS stays unbiased but the standard errors are wrong, which means your t-statistics lie. That's the practical consequence and it's the one to lead with when asked what breaks.
    5. The fixes: White or Newey-West robust standard errors for heteroskedasticity and autocorrelation, generalised least squares if you know the error structure, and instrumental variables if the regressor is endogenous. Endogeneity is the serious one, because it destroys unbiasedness rather than just efficiency.
    6. The distinction to keep straight: heteroskedasticity and autocorrelation cost you efficiency and valid inference. Omitted variables, measurement error in a regressor and simultaneity cost you unbiasedness. Those are different problems needing different fixes.
    7. So the answer I'd close with: BLUE is a useful benchmark and a weak guarantee. In a risk model I care more about whether the specification is right and whether the relationship is stable than about being efficient within the linear unbiased class.

    Where candidates lose it

    Expanding the acronym and stopping, or claiming normality is a Gauss-Markov requirement. It isn't. The two answers that separate candidates are that 'best' is only within linear unbiased estimators, so biased shrinkage estimators can beat it, and that in finance the binding violation is heteroskedasticity making your standard errors wrong.

    Expect next

    • Is normality required for OLS to be BLUE?
    • Which Gauss-Markov assumption fails most often in financial data?
    • Can a biased estimator ever be preferable?

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

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.