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. 067Would you model equity returns as normal or Student's t, and what difference does it make?Statistics and quant foundationsIntermediatetechnicalBank market riskBuy-side risk

   Say this

   Student's t, with something like four to six degrees of freedom for daily equity returns. Real returns are leptokurtic, so a normal assumption systematically understates the tail, and the understatement gets worse the further out you go.

   Then walk it

   1. The evidence is simple and worth quoting: daily equity index returns have excess kurtosis well above zero, often 3 to 8, against zero for a normal. The 1987 crash was more than 20 standard deviations under a normal fitted to prior data, which that distribution says should never happen in the age of the universe.
   2. A t with low degrees of freedom has polynomial rather than exponential tail decay, which fits observed extremes far better. Around four to six degrees of freedom is the usual empirical range for daily equity data, and it converges to normal as the degrees of freedom rise.
   3. The practical consequence for VaR: at 95 percent the two distributions give similar answers, and at 99.9 percent the normal can understate by a large multiple. So the choice barely matters for routine reporting and matters enormously for capital and stress.
   4. The second issue is that the unconditional fat tail is partly a mixture effect. Returns are closer to normal conditional on the current volatility regime, and it's volatility clustering that produces fat unconditional tails. So a GARCH model with normal innovations gets you a long way, and GARCH with t innovations gets you further.
   5. For a multivariate problem it gets harder. A multivariate t gives you tail dependence, which a multivariate normal does not, and tail dependence is the property that matters in a crisis. That's the bridge to copulas.
   6. Where the t is not enough: extreme value theory, fitting a generalised Pareto distribution to the exceedances above a threshold, is the right tool if you genuinely only care about the far tail. It uses less data more efficiently for that specific job.
   7. The caveat I'd give: a t distribution has more parameters and the degrees of freedom estimate is unstable in small samples. And skew matters too, since equity downside tails are fatter than upside ones, so a skewed t or an empirical distribution is often the pragmatic choice.

   Where candidates lose it

   Saying 'returns aren't normal' without knowing what to use instead or how big the error is. Naming a plausible degrees-of-freedom range, and saying that the choice matters at 99.9 percent but hardly at 95, shows you've fitted these to real data rather than repeating a slogan.

   Expect next

   • How would you estimate the degrees of freedom?
   • Does GARCH with normal innovations solve the problem?
   • When would you use extreme value theory instead?
2. 070What's the tracking error formula?Statistics and quant foundationsIntermediatetechnicalMSCIFinancial Tools · Monterrey · 2013

   Say this

   Tracking error is the standard deviation of the difference between portfolio and benchmark returns. Compute active return each period, take its standard deviation, then annualise by multiplying by the square root of the number of periods in a year.

   Then walk it

   1. Formula in words: active return equals portfolio return minus benchmark return each period. Tracking error is the standard deviation of that series. On monthly data you annualise by root twelve, on daily by root 252.
   2. The ex-post version uses realised returns. The ex-ante version uses a risk model: active weights transposed times the covariance matrix times active weights, then square root. Those two numbers routinely disagree, and explaining the gap is a real part of a buy-side risk job.
   3. Be careful about mean adjustment. Some definitions use the standard deviation of active returns and others use the root mean square of active returns, which includes the average outperformance. They differ, and you should say which one you mean.
   4. Feel for the numbers: an index fund runs 5 to 50 basis points, an enhanced index strategy 0.5 to 2 percent, an active core equity fund 3 to 6 percent, and a concentrated high-conviction fund 8 percent or more. Quoting a range is what shows you've looked at real funds.
   5. What it's used for: mandate limits, since most institutional mandates cap tracking error. And the information ratio, active return divided by tracking error, which is the risk-adjusted measure of skill and the number that actually matters.
   6. Decomposition is the useful part. Break tracking error into factor and specific contributions: how much comes from a systematic style or sector tilt versus stock selection. A manager paid for stock picking who is running most of their tracking error on an unintended country bet has a problem, and that's exactly the conversation a risk analyst has with a PM.
   7. Limitations to volunteer: it's symmetric, so it penalises outperformance identically to underperformance. It assumes a stable covariance structure, so realised tracking error jumps in a crisis. And it's backward-looking, so a manager who has just changed their positioning has a stale number.

   Where candidates lose it

   Giving the formula and nothing else. This question is a screen: the formula takes five seconds, and the rest of your answer is what's being assessed. Knowing typical ranges by strategy, the ex-ante versus ex-post gap, and factor decomposition is what converts a definition into an answer.

   Expect next

   • What tracking error would you expect from an index fund?
   • Why do ex-ante and ex-post tracking error differ?
   • What is the information ratio?

   Reported by candidates at MSCI (Financial Tools, Monterrey, 2013). Source: Wall Street Oasis.

3. 072A fraud test is 99 percent accurate and fraud occurs in one transaction in ten thousand. The test flags a transaction. What is the chance it's really fraud?Statistics and quant foundationsIntermediatetechnicalOperational riskModel validation

   Say this

   About one percent. Out of a million transactions, 100 are fraud and the test catches 99 of them, but it also falsely flags one percent of the 999,900 clean ones, which is about 9,999. So 99 true positives against roughly 10,000 flags means a 1 percent hit rate.

   Then walk it

   1. Set it up with counts rather than Bayes' formula. A million transactions, 100 frauds, 999,900 clean. It's faster and you won't fumble the algebra out loud.
   2. True positives: 99 percent of 100, so 99. False positives: 1 percent of 999,900, so 9,999. Total flags about 10,098, of which 99 are real. That's 0.98 percent.
   3. The lesson is the base rate. When the event is rare, even a very accurate test produces overwhelmingly false alarms, because the clean population is so much larger. This is the base rate fallacy and it's the entire point of the question.
   4. This is not a puzzle, it's the daily reality of transaction monitoring and sanctions screening. Real AML alert systems run false positive rates above 95 percent, which is why banks employ thousands of people to clear alerts, and it's a genuine operational risk and cost problem.
   5. So the design conclusion: for rare events, headline accuracy is the wrong metric. You care about precision and recall, and about the cost asymmetry between a missed fraud and an investigated false alarm. Then you tune the threshold to that cost, not to accuracy.
   6. How you improve it in practice: raise the prior before you apply the test by segmenting on risk, so you're testing a population with a much higher base rate. Or stack models so an expensive accurate check only runs on things a cheap screen flagged. Both raise precision far more than improving the test itself would.
   7. And the number worth remembering as a reasonableness check: with a 1-in-10,000 base rate you need a false positive rate around 1 in 10,000 to get to a 50 percent hit rate. That is a far harder test than 99 percent accurate.

   Where candidates lose it

   Answering 99 percent. That's the reflex answer and it's what the question is designed to catch. Use counts on a million, and then draw the operational conclusion about alert volumes, because in a risk interview the business implication is worth as much as the arithmetic.

   Expect next

   • What accuracy would you need for a 50 percent hit rate?
   • How would you reduce false positives in practice?
   • How do you set the threshold if a missed fraud costs 500 times an investigation?

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.