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. 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.

2. 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.

3. 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.

4. 080What is the broad range of risks a bank faces, and what is the greatest one?Risk governance and appetiteIntermediatetechnicalUBSPrivate Wealth Management · New York · 2026

   Say this

   Credit, market, liquidity and operational are the four you capitalise, then interest rate risk in the banking book, conduct, model, strategic and reputational risk on top. The greatest is liquidity, because it's the one that kills a bank in days rather than years.

   Then walk it

   1. Credit risk is the largest by capital, usually 80 to 90 percent of a commercial bank's RWA, and it's the one that does most of the slow damage. Almost every banking crisis starts as a credit cycle.
   2. Market risk is small for most commercial banks and large for a trading house. Operational risk includes conduct, which has produced some of the biggest single losses in banking history.
   3. Liquidity risk is the answer to 'greatest', and the argument is about speed and irreversibility. A capital problem gives you quarters to raise equity or shrink. A funding problem gives you a day. Northern Rock, Lehman, Credit Suisse and SVB were all liquidity events at the end, whatever started them.
   4. The nuance that makes it a better answer: the cause is usually credit or rate risk and the mechanism of death is liquidity. So the greatest risk is the interaction, not any one silo. Solvency doubts cause funding to disappear, and forced sales turn doubts into insolvency.
   5. For a specific bank the answer changes and you should say so. For an Indian public sector bank it's concentrated corporate credit. For an NBFC it's asset-liability mismatch and wholesale funding dependence. For a custodian it's operational and technology risk. For a private bank it's conduct and suitability.
   6. The risk I'd flag as most underweighted relative to its importance: third-party and technology concentration. A handful of cloud providers, core banking vendors and payment rails now underpin the system, and that exposure sits in nobody's capital calculation.
   7. So my framing: capital protects you from credit and market losses, and only liquidity and governance protect you from the failure mode that actually happens.

   Where candidates lose it

   Listing risk types with no view. The question explicitly asks which is greatest, so refusing to pick is a fail. Pick liquidity, justify it on speed, then show sophistication by saying the cause is usually credit and the mechanism is liquidity, and that the answer depends on the institution.

   Expect next

   • Why liquidity and not credit?
   • What's the greatest risk for a private wealth business specifically?
   • Which risk do you think is most underpriced today?

   Reported by candidates at UBS (Private Wealth Management, New York, 2026). 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.