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. 061Explain LCR and NSFR.Liquidity risk and ALMIntermediatetechnicalTreasury and ALMRegulatory reporting

   Say this

   Both are Basel III liquidity ratios with a minimum of 100 percent. LCR is a 30-day survival test: high quality liquid assets over stressed net outflows. NSFR is a one-year structural test: available stable funding over required stable funding. Short-term shock versus long-term funding mismatch.

   Then walk it

   1. LCR numerator, HQLA: Level 1 is cash, central bank reserves and most sovereign debt at no haircut. Level 2A is high-grade covered and corporate bonds at a 15 percent haircut, Level 2B is lower-rated corporates and some equities at 25 to 50 percent, and Level 2 is capped at 40 percent of the total.
   2. LCR denominator: outflows minus capped inflows, with prescribed run-off rates. Stable retail deposits 3 to 5 percent, less stable retail 10, operational wholesale deposits 25, non-operational corporate deposits 40, and financial institution deposits 100. Inflows are capped at 75 percent of outflows, so you can't rely on collecting to pay.
   3. That run-off table is the heart of it, and it encodes a real judgement: insured retail deposits are sticky, and money from other banks disappears entirely. SVB's deposits were overwhelmingly uninsured corporate money, which the table would treat as the fastest-running kind.
   4. NSFR pairs funding stability against asset liquidity over a year. Equity and long-term debt count fully as stable funding, retail deposits at 90 to 95 percent, short wholesale funding at little or nothing. On the asset side, long-dated loans need high stable funding and cash needs none.
   5. So NSFR is a structural constraint on maturity transformation, which is the business banks are in. It limits how much of a long loan book you can fund with three-month wholesale paper.
   6. Indian specifics: RBI implemented LCR from 2015 and NSFR from 2021, both at 100 percent, and has periodically adjusted the treatment of SLR securities within HQLA. The 2024 draft revisions raised run-off assumptions on retail deposits with internet and mobile banking access, which is a direct response to how fast deposits can now move.
   7. The critique to volunteer: both are point-in-time ratios with prescribed assumptions, and they can be window-dressed at reporting dates. And in a real run, the run-off rates have been far higher than the table assumes. SVB lost a quarter of its deposits in a day.

   Where candidates lose it

   Mixing up the horizons or quoting the ratios without any run-off rates. Knowing that financial institution deposits run off at 100 percent and stable retail at 3 to 5 is what shows you've seen the schedule. And the point that real runs are faster than the assumed rates is the part with judgement in it.

   Expect next

   • What counts as HQLA, and what haircuts apply?
   • Why did banks with a 100 percent LCR still fail in 2023?
   • How does NSFR constrain the lending business?
2. 062What is interest rate risk in the banking book, and how do you measure it?Liquidity risk and ALMIntermediatetechnicalTreasury and ALMIndian bank risk and treasury

   Say this

   IRRBB is the risk that rate moves hurt the banking book, and you measure it two ways that often disagree. Economic value of equity is the present value view over the full life of the balance sheet. Net interest income is the earnings view over one to three years.

   Then walk it

   1. EVE: revalue all assets, liabilities and off-balance-sheet items under a rate shock and look at the change in net present value. It's the long-horizon, economic answer, and it's where a big fixed-rate asset book shows up immediately.
   2. NII: project interest income and expense over one to three years under the shock. It's the accounting and earnings answer, and it's the one management actually cares about because it hits reported profit.
   3. They can point in opposite directions, and that's the interesting part. A bank funding long fixed-rate mortgages with short deposits looks fine on NII when rates rise slowly, because deposit rates lag, while EVE is deeply negative from day one. That gap is exactly the SVB configuration.
   4. The Basel standardised framework prescribes six shock scenarios: parallel up and down, steepener, flattener, short rate up and short rate down. Non-parallel scenarios matter because most banks are not exposed to the level so much as to the shape.
   5. Then the behavioural assumptions, which are where all the model risk lives. Non-maturity deposits have no contractual maturity so you assume one. Prepayment on fixed loans. Early withdrawal on term deposits. Pipeline commitments. Move the deposit assumption from a two-year to a five-year effective duration and the answer changes sign.
   6. The supervisory outlier test: if EVE sensitivity exceeds 15 percent of Tier 1 capital under any of the six scenarios, you attract supervisory attention under Pillar 2. That's the number to know.
   7. In India, RBI requires both the traditional gap approach and duration-based EVE reporting, and IRRBB is a core Pillar 2 item in ICAAP. Indian banks carry large SLR portfolios of government bonds, so rate risk in the banking book is structurally significant and the AFS versus HTM classification decision drives how much of it hits reported capital.
   8. How you manage it: reprice the book, use interest rate swaps to shorten effective asset duration, adjust deposit pricing, and set limits on both EVE and NII sensitivity so neither view can be ignored.

   Where candidates lose it

   Giving only one of the two measures. If you say EVE and not NII, or the reverse, you've described half the framework and missed the tension that makes IRRBB interesting. And the behavioural deposit assumption is the single biggest driver of the answer, so name it as the main model risk.

   Expect next

   • EVE and NII disagree. Which do you act on?
   • What is the supervisory outlier test?
   • How would you hedge a negative EVE position?
3. 063How would you build a survival horizon for a bank treasury?Liquidity risk and ALMIntermediatetechnicalTreasury and ALMIndian bank risk and treasury

   Say this

   Build a daily cash flow ladder under stress and find the first day the counterbalancing capacity runs out. That day count is the survival horizon, and the useful output isn't the number itself but which assumption drives it.

   Then walk it

   1. Start with contractual cash flows by day: maturing loans in, maturing deposits and wholesale funding out, coupon and interest flows, and known commitments. That's the easy part and the least informative.
   2. Then layer behavioural assumptions, which is where the answer is actually made. Deposit run-off rates by segment, drawdown on committed undrawn facilities, no rollover of wholesale funding, and rating-trigger-driven collateral calls.
   3. Then the counterbalancing capacity: unencumbered HQLA at stressed haircuts, central bank facilities and what collateral qualifies, committed lines you can genuinely draw, and asset sales with a realistic time-to-cash. Repo is the fast one, whole-loan sales are not.
   4. Then run at least three severities: an idiosyncratic name-specific stress where markets function but nobody will lend to you, a market-wide stress where everyone is short cash, and a combined scenario. The combined one is what regulators require and it's the one that binds.
   5. Read off the first day of negative cumulative net cash. A typical internal appetite is 30 days for a combined stress and 90 days for an idiosyncratic one, plus the regulatory 30-day LCR as a floor.
   6. Then the genuinely valuable step: sensitivity. If the horizon goes from 45 days to 12 when retail run-off moves from 10 to 20 percent, the number is an assumption, not a fact. I'd present the horizon as a range with the binding driver named.
   7. Two things people forget. Intraday and currency granularity: being liquid in rupees and short dollars on day three is a failure even if the aggregate is fine. And encumbrance, because assets already pledged in repo are not available however liquid they look.
   8. And the escalation link: each horizon threshold should map to a contingency funding plan trigger with named actions, or the measurement is an academic exercise.

   Where candidates lose it

   Building a contractual maturity ladder and calling it done. Contractual flows tell you almost nothing, because the risk lives in behaviour. And failing to split by currency is the classic error: an aggregate survival horizon can hide a dollar funding gap that kills you first.

   Expect next

   • What run-off rate would you assume on uninsured corporate deposits?
   • How do you treat central bank facilities in the counterbalancing capacity?
   • How would this differ for a non-bank finance company?
4. 064How would you model a bank's savings deposits, which have no contractual maturity?Liquidity risk and ALMIntermediatesuperdayTreasury and ALMIndian bank risk and treasury

   Say this

   Split the balance into a stable core and a volatile portion, assign a behavioural maturity to the core, and estimate a deposit beta for how much of a policy rate move you pass through. Those two parameters, core share and beta, drive the entire banking book rate risk answer.

   Then walk it

   1. Volume modelling first. Look at the historical balance series per segment, strip out trend and seasonality, and take a low percentile of the remaining distribution as the stable core. Many banks use something like the balance exceeded 95 percent of the time over five years.
   2. Assign a repricing or behavioural maturity to the core. If it has been sticky for a decade, you might treat it as five to seven years of effective duration, capped by supervisory limits. Basel caps the average repricing maturity of core retail non-maturity deposits at five years in its standardised framework, and that cap exists because banks were assuming longer and flattering their rate risk.
   3. Deposit beta: regress the rate you actually paid against the policy rate. Indian savings rates have historically been very sticky, so betas on savings accounts are low, maybe 0.2 to 0.4, while term deposits and bulk deposits run much higher, 0.6 to 0.9. Segment or the average is meaningless.
   4. Betas are asymmetric and non-linear, and that's the point most candidates miss. They're low on the way up until competition bites and then they jump, and they're low on the way down because you can't pay less than zero. Modelling a single symmetric beta understates the squeeze in a rising cycle.
   5. Segmentation matters more than technique: retail versus corporate, insured versus uninsured, digitally active versus branch-only, relationship versus rate-shopping. A digitally active uninsured corporate depositor behaves nothing like a pensioner with a branch passbook.
   6. Then validate the assumptions against a real episode rather than trusting the regression. What actually happened to your balances and your pass-through in the 2022 to 2023 hiking cycle? That's the out-of-time test.
   7. The limitation to volunteer, and it's the important one: these models are all calibrated on a world where moving money was slow. Mobile banking and instant transfers have shortened behavioural maturities in a way the history doesn't contain. SVB lost a quarter of its deposits in a day, which no core-stability model would have produced. So I'd stress the assumption hard rather than trust it.
   8. RBI's 2024 draft LCR revisions add a run-off add-on for internet and mobile banking enabled deposits for exactly this reason, which is a good example of supervisors updating faster than the models.

   Where candidates lose it

   Treating it as purely a statistical problem. The regression is the easy part; the judgement is in segmentation, in the asymmetry of beta, and in recognising that the historical data predates instant digital withdrawal. A candidate who says 'the history no longer applies' and then says what they'd do about it stands out.

   Expect next

   • What deposit beta would you assume for Indian savings accounts?
   • How would you stress the core assumption?
   • Why does Basel cap the assumed maturity at five years?
5. 065A bank funds long-dated fixed-rate securities with uninsured corporate deposits. Walk me through everything that can go wrong.Liquidity risk and ALMHardcase studyTreasury and ALMBank market risk

   Say this

   That's the Silicon Valley Bank structure, and it fails in a specific sequence: rates rise, the asset side loses economic value without showing it in the accounts, depositors leave because they have better options, and selling the assets to pay them crystallises the loss and destroys the capital.

   Then walk it

   1. Step one, the duration mismatch. Long fixed-rate assets and overnight liabilities means economic value of equity falls hard when rates rise, even while net interest income looks fine for a while because deposit rates lag.
   2. Step two, the accounting shield that becomes a trap. Securities classified as held-to-maturity aren't marked through capital, so the loss is invisible in the reported ratios. SVB had roughly $15bn of unrealised HTM losses against about $16bn of equity at the end of 2022. The capital ratio said nothing was wrong.
   3. Step three, the depositor incentive. Uninsured corporate treasurers are rate-sensitive and professional. When T-bills yield 5 percent and your account pays 0.5, they leave for economic reasons before there's any fear. That's a slow outflow that forces asset sales.
   4. Step four, the crystallisation. Selling HTM securities to fund outflows moves the loss from a footnote into the income statement and the capital ratio. Worse, selling any of the portfolio can force reclassification of the whole HTM book under the accounting rules, which is why banks resist it until they can't.
   5. Step five, the run. Once the loss is public, uninsured depositors with a 100 percent loss-given-failure have every incentive to leave first, and they can now do it in an afternoon from a phone with a group chat coordinating them. SVB lost about $42bn in a single day.
   6. Step six, concentration as the accelerant. A depositor base drawn from one industry with shared advisers and shared venture investors is not a diversified funding book. It's one depositor with many accounts.
   7. What the risk function should have done: report EVE alongside NII and escalate the gap, treat unrealised HTM losses as economic capital regardless of accounting, model uninsured deposits with far faster run-off, set a concentration limit on depositor type, and hedge the duration with swaps. The last one is the cheapest and SVB had almost none on.
   8. And the governance point: SVB had no chief risk officer for part of 2022 and its interest rate stress scenarios had reportedly been changed to be less severe. The measurement failure was downstream of a governance failure, which is almost always the case.

   Where candidates lose it

   Describing it as a liquidity problem only, or a rate problem only. It's the interaction, plus an accounting classification that hid the loss, plus a concentrated and professional depositor base. Candidates who name the HTM accounting treatment and the depositor concentration show they've read the post-mortem rather than the headline.

   Expect next

   • Why didn't the capital ratio show the problem?
   • What single hedge would have changed the outcome?
   • How would you set a depositor concentration limit?
6. 066What does it mean if an estimator is BLUE?Statistics and quant foundationsHardtechnicalUBSRisk Management · Zurich · 2021

   Say this

   Best Linear Unbiased Estimator. Among all estimators that are linear in the data and unbiased, it has the smallest variance. That's the Gauss-Markov result: ordinary least squares is BLUE provided a specific set of assumptions holds.

   Then walk it

   1. Unpack each word, because that's what the question is testing. Linear in the observations. Unbiased, so its expected value equals the true parameter. Best, meaning minimum variance within that class.
   2. The Gauss-Markov conditions: correct linear specification, errors with zero conditional mean, homoskedasticity, no autocorrelation, and no perfect multicollinearity. Notice normality is not required for BLUE. You need normality for the t and F tests in small samples, not for OLS to be efficient.
   3. The restriction that matters is 'linear'. A biased or non-linear estimator can easily beat OLS on mean squared error. Ridge and lasso are deliberately biased and often predict better, and James-Stein shrinkage famously dominates the sample mean. So BLUE is optimality within a box, not optimality.
   4. In finance the assumptions fail routinely. Returns are heteroskedastic and volatility clusters, so OLS stays unbiased but the standard errors are wrong, which means your t-statistics lie. That's the practical consequence and it's the one to lead with when asked what breaks.
   5. The fixes: White or Newey-West robust standard errors for heteroskedasticity and autocorrelation, generalised least squares if you know the error structure, and instrumental variables if the regressor is endogenous. Endogeneity is the serious one, because it destroys unbiasedness rather than just efficiency.
   6. The distinction to keep straight: heteroskedasticity and autocorrelation cost you efficiency and valid inference. Omitted variables, measurement error in a regressor and simultaneity cost you unbiasedness. Those are different problems needing different fixes.
   7. So the answer I'd close with: BLUE is a useful benchmark and a weak guarantee. In a risk model I care more about whether the specification is right and whether the relationship is stable than about being efficient within the linear unbiased class.

   Where candidates lose it

   Expanding the acronym and stopping, or claiming normality is a Gauss-Markov requirement. It isn't. The two answers that separate candidates are that 'best' is only within linear unbiased estimators, so biased shrinkage estimators can beat it, and that in finance the binding violation is heteroskedasticity making your standard errors wrong.

   Expect next

   • Is normality required for OLS to be BLUE?
   • Which Gauss-Markov assumption fails most often in financial data?
   • Can a biased estimator ever be preferable?

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

7. 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?
8. 068Correlation versus dependence. Why does correlation fail exactly when you need it?Statistics and quant foundationsHardsuperdayBank market riskModel validation

   Say this

   Pearson correlation measures only the linear component of dependence, and it's a single average number. Dependence is the whole joint distribution. So two variables can be uncorrelated and completely dependent, and correlation tells you nothing about whether they move together in the tail.

   Then walk it

   1. The clean counterexample: Y equals X squared with X symmetric around zero. Perfectly dependent, zero linear correlation. Any non-monotonic relationship defeats Pearson.
   2. It's also not invariant to non-linear transforms, which matters because option payoffs are non-linear transforms of the underlying. Rank measures like Kendall's tau and Spearman's rho are invariant and are what you should use for dependence.
   3. The failure that costs money is that correlation is an average over the whole distribution, dominated by the many ordinary days. Tail dependence, the probability that both variables are extreme together, is a separate property that Pearson does not capture at all.
   4. Two distributions can share the same correlation matrix and have utterly different joint tails. A Gaussian copula has zero asymptotic tail dependence: extreme joint events are asymptotically independent. A t copula has positive tail dependence. Same correlation, completely different stress behaviour.
   5. Then correlations are non-stationary and they rise in a crisis. Equity pair correlations that sit around 0.3 in calm markets go to 0.8 in a sell-off. Diversification built on the calm number evaporates precisely when you were counting on it.
   6. Part of that rise is a statistical artefact worth knowing about: conditioning on large moves mechanically increases measured correlation even with a stable underlying joint distribution. So not all of the observed increase is a regime change, and that subtlety is a genuinely strong thing to say.
   7. What I'd do instead: use rank correlations for dependence, look at exceedance correlations conditional on large moves, use a copula with tail dependence when I need a joint distribution, and stress correlations to one in the scenario rather than trusting the estimate.
   8. Real cost: the 2007 quant equity deleveraging and the 2008 structured credit losses were both failures of dependence assumptions rather than of the individual marginal distributions.

   Where candidates lose it

   Saying 'correlation goes to one in a crisis' as the whole answer. That's true and it's the easy half. The deeper point is that correlation and dependence are different objects, that two joint distributions can share a correlation matrix and differ entirely in the tail, and that part of the observed crisis rise is a conditioning artefact.

   Expect next

   • What is tail dependence?
   • Why do Gaussian and t copulas differ if they have the same correlation?
   • How would you stress correlations in a scenario?
9. 069What is a copula, and what went wrong with the Gaussian copula in 2008?Statistics and quant foundationsHardsuperdayModel validationBank credit risk

   Say this

   A copula separates the marginal distributions from the dependence structure. Sklar's theorem says any joint distribution can be written as a copula applied to its marginals, so you can model each variable's tail properly and then choose how they move together. The 2008 failure was choosing a Gaussian copula, which has no tail dependence, for a problem that is entirely about tail dependence.

   Then walk it

   1. Mechanically: transform each variable to a uniform through its own cumulative distribution, then model the joint behaviour of those uniforms. That's the copula. It's a clean separation of 'how fat is each tail' from 'do they go bad together'.
   2. Gaussian copula: parameterised by a correlation matrix, and its asymptotic tail dependence is zero for any correlation below one. So as you go further into the tail, extreme joint events become asymptotically independent. That's the mathematical fact behind the failure.
   3. t copula: has positive tail dependence controlled by the degrees of freedom, so extremes cluster. Gumbel gives you upper tail dependence only, Clayton lower tail only, which is useful for credit where you care about joint defaults and not joint survivals.
   4. What actually happened with CDOs. David Li's Gaussian copula model let you price a tranche off a single correlation number, calibrated to historical data from a benign period, often 0.3 for mortgage pools. The senior tranche's value depends almost entirely on the probability that many defaults happen together, which is exactly the quantity the Gaussian copula sets too low.
   5. So the model said AAA was safe because widespread simultaneous default across regions was nearly impossible. In a national housing downturn, correlation went towards one and losses blew through tranches the model said were remote.
   6. The market knew before the maths admitted it. The base correlation skew, needing a different correlation for each tranche to fit observed prices, was the model telling you it was wrong, and it was read as a market quirk instead.
   7. The honest lesson, and the one to say: the failure was not really the copula. It was calibrating a tail parameter to data with no tail in it, using one number to describe dependence across a whole system, and treating a pricing convention as a risk model. A t copula with a bad correlation input would have failed too.
   8. Practical use today: copulas are standard in economic capital, portfolio credit models and multi-asset VaR. You'd use a t copula, calibrate dependence with attention to stress periods, and stress the dependence parameter rather than point-estimating it.

   Where candidates lose it

   Blaming 'the formula that killed Wall Street' without being able to say what property was missing. The specific answer is zero asymptotic tail dependence, plus a correlation parameter calibrated on benign data. And the mature closing point is that a better copula with the same bad calibration would also have failed.

   Expect next

   • Why does the Gaussian copula have zero tail dependence?
   • What was base correlation telling the market?
   • Would a t copula have prevented it?
10. 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.

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.