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. 019Your model says that was a one-in-ten-thousand-year event, and it has now happened twice this decade. What is wrong?Tail risk and stress testingHardsuperdayModel validationBank market risk

   Say this

   The model is wrong, not the world. Two ten-thousand-year events in ten years is overwhelming evidence against the distribution, and the usual culprit is a normal assumption applied to a market that isn't normal.

   Then walk it

   1. First, the arithmetic. Under the model, the probability of two such events in a decade is vanishingly small. Bayes says you should abandon the model long before you conclude you got unlucky twice.
   2. Most likely cause one, the wrong distribution. Normal tails decay far faster than real financial tails. A move that is 6 sigma under a normal is roughly a 1-in-500-million-day event; under a t distribution with four degrees of freedom it's something you see every few years.
   3. Cause two, non-stationarity. The model was calibrated on a regime that no longer applies. Volatility clusters and regimes shift, so an unconditional distribution fitted over twenty years will call a high-volatility regime impossible.
   4. Cause three, a dependence assumption. Individually plausible moves become impossible jointly if you've assumed low correlation. In a crisis correlations go to one and the joint event is far more likely than the model thinks.
   5. Cause four, the mundane one that is often the real answer: the event was outside the model's domain entirely. A sovereign default, a currency peg breaking, a negative oil price. The factor wasn't allowed to do that, so the model assigned it probability zero rather than a small number.
   6. And the professional answer to 'what do you do': stop quoting return periods you can't support. Report the scenario and the loss, drop the implied probability, and say the model is uninformative beyond the range where you have data.

   Where candidates lose it

   Defending the model by saying markets got unusual. That's the answer a regulator hears from a bank that is about to fail. The point of the question is whether you will update your beliefs against a model you built, and the credible answer names fat tails, regime change and the correlation assumption specifically.

   Expect next

   • How would you re-estimate the tail with so little data?
   • Would extreme value theory help here?
   • How would you communicate this to a board that has been shown the old number for three years?

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.