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. 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?
3. 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.

4. 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?

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.