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.

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.