Risk Management interview preparation
Market, credit and operational risk, plus model validation, regulatory capital, liquidity and ALM, the statistical foundations and the Indian regulatory syllabus. Every question is either traced to a named firm from a public candidate report, or tagged at desk level when we could not trace it — and answers lead with the point, then the mechanism, then the limitation.
100 questions, mapped to the firms that asked them
- Questions
- 100
- Traced to a firm
- 37
- Firms
- 12
- Updated
- September 2026
016Design a stress scenario for a book that is long Indian corporate bonds and short interest rate futures.Indian bank risk and treasuryBank market risk
Say this
The scenario has to break the hedge, not just move the market. The position is long credit and short duration, so the pain case is spreads widening while the risk-free curve rallies, which is precisely what a flight to quality does.
Then walk it
- Start by naming the real exposures. Net duration is small by design, so a parallel shift is not the risk. The live risks are credit spread, the government-bond-to-swap basis, the futures-to-cash basis, and liquidity in the corporate leg.
- So the core shock: AAA and AA corporate spreads widen 150 to 250 basis points, while the ten-year G-sec yield falls 75 basis points. You lose on both legs at once. That is the textbook flight-to-quality asymmetry and it happened in March 2020.
- Layer in the basis. The bond futures may not track the cash bond you hold, and the cheapest-to-deliver can switch. Add 25 to 50 basis points of adverse basis independent of the spread move.
- Layer in liquidity. Indian corporate bond secondary volumes are thin outside the top names, so add a bid-offer widening of two to four times normal and assume you can only exit 20 percent of the position in a week. Then mark the rest at the stressed exit price, not the matrix price.
- Layer in funding. Repo haircuts on corporate paper rise, margin on the futures short goes up as volatility spikes, and both hit the same day. That is the mechanism that turns a mark-to-market loss into a forced sale.
- Add a name-specific tail: one issuer in the book is downgraded below investment grade, which triggers forced selling by mandate-constrained funds and moves the whole rating bucket. The IL&FS episode in 2018 is the live Indian precedent, and the credit-fund redemption spiral that followed is the second-round effect.
- Then report it properly: P&L by leg, the funding call in rupees, days to unwind, and which limits break. A scenario that produces one aggregate number is not decision-useful.
Where candidates lose it
Designing a parallel rate shock. The book is deliberately hedged against that, so the scenario shows nothing and you have proved you didn't look at the position. A good stress scenario attacks the assumption the hedge relies on, which here is spread-to-rate correlation, and it must include liquidity and funding, not just price.
Expect next
- How would you calibrate the size of the spread move?
- What second-round effects would you add?
- How would you present this to a treasurer who says the book is hedged?
019Your model says that was a one-in-ten-thousand-year event, and it has now happened twice this decade. What is wrong?Model 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
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.

