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

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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

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1. 005Calculate the one-day 99% VaR of a $100 million portfolio with 2 percent daily volatility.Market risk and VaRIntermediatetechnicalUBSRisk Management · Remote · 2020UBSRisk Management · Zurich · 2021

   Say this

   Parametric, assuming zero mean and normality: 2.33 times 2 percent times $100m, so about $4.66 million. Round it to $4.7m and say the assumptions out loud as you go.

   Then walk it

   1. The z-score at 99% one-tailed is 2.326. I'd use 2.33. At 95% it's 1.645, worth knowing cold because interviewers switch between them mid-question.
   2. Daily volatility in money terms is 2 percent of $100m, so $2m. Multiply by 2.33 and you get $4.66m.
   3. I'm assuming a zero expected return over one day, which is standard for a short horizon, and that returns are normal, which is the assumption doing all the work.
   4. To scale to ten days, multiply by the square root of ten, about 3.16, so roughly $14.7m. That scaling needs independent and identically distributed returns, so it understates the number if volatility clusters or the market trends.
   5. And the caveat I'd volunteer: because the real distribution has fatter tails than normal, this figure is probably too low at 99%. Empirically the 99th percentile of equity returns sits closer to 2.6 or 2.7 sigma than 2.33.

   Where candidates lose it

   Using 1.645 for 99% or 2.33 for 95%, or reaching for a calculator. Know both z-scores by heart, state your two assumptions before you multiply, and flag that fat tails make the answer conservative in the wrong direction.

   Expect next

   • Now give me the ten-day number.
   • What if the portfolio had a 10 percent annual expected return, does that change it?
   • How would the answer change if the returns were t-distributed with five degrees of freedom?

   Reported by candidates at UBS (Risk Management, Remote, 2020); UBS (Risk Management, Zurich, 2021). Source: Wall Street Oasis.

2. 012Your 99 percent one-day VaR model produced nine exceptions in the last 250 days. Walk me through what you do.Market risk and VaRHardsuperdayBank market riskModel validation

   Say this

   Nine is amber, one short of red, so two things happen in parallel: the capital multiplier steps up and I open a model investigation. But before either, I check that the exceptions are real and not a data or P&L-attribution problem.

   Then walk it

   1. Step one, validate the exceptions. Bad marks, a stale curve, a missing trade feed, or backtesting against actual instead of hypothetical P&L can all manufacture breaches. I've seen a whole amber month turn out to be one mispriced illiquid bond.
   2. Step two, look at clustering. Nine breaches spread evenly across the year says the model is calibrated too low. Nine in a three-week window in March says the model is fine in normal times and slow to react to a volatility regime shift. Completely different fixes.
   3. Step three, attribute. Which desk, which risk factor, which side. If eight of the nine come from one credit desk, it's not a firmwide VaR problem, it's a missing risk factor or a proxy that stopped working.
   4. Step four, size them. Breaches at 1.1 times VaR are a calibration issue. Breaches at three times VaR mean the tail shape is wrong, which points at normality or at unmodelled optionality.
   5. Step five, the regulatory and capital consequence. Under the Basel backtesting framework nine exceptions sits in the amber zone with a multiplier around 3.65 rather than 3.0, and it's a disclosable model performance issue. I'd tell the CRO and the supervisor rather than wait to be asked.
   6. Step six, the fix, and it should be the smallest defensible one: reweighting the window or moving to volatility-scaled historical simulation for clustering, adding a missing factor for a desk problem, moving to full revaluation for an optionality problem. Then re-run the backtest on the corrected model over the same period.
   7. And the interim control while the fix is validated: a VaR add-on or a tightened desk limit. You don't get to run unlimited with a broken model while the remediation is in flight.

   Where candidates lose it

   Jumping straight to 'recalibrate the model'. The first move is always to check whether the exceptions are real, and the second is to look at their pattern. A candidate who recalibrates without diagnosing has just fitted the model to a data error, and that is the exact failure the interviewer is probing for.

   Expect next

   • What if all nine were in the same fortnight?
   • What's the capital consequence of amber versus red?
   • Would you tell the regulator before or after you had a fix?

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.