Monte Carlo Error Quant Interview Guide
Monte Carlo error quant interview guide for estimators, variance, standard error, convergence, confidence intervals, examples, and mistakes.
Candidates using simulation for probability, pricing, risk, or expected value estimates.
Monte Carlo estimates have sampling error
A simulation average is an estimator, not the exact answer. Its uncertainty depends on payoff variance and number of independent trials.
Error often falls slowly
Standard error typically decreases at a square-root rate with sample size. Ten times more accuracy can require far more than ten times more samples.
Concrete example
If a payoff simulation has high variance, two runs with the same sample size may differ noticeably. A confidence interval communicates that uncertainty.
Validation is separate from precision
More paths reduce sampling error but do not fix a wrong model, biased random generator, bad payoff code, or lookahead in inputs.
Common mistakes
Candidates often trust one simulation printout. Strong answers report estimator, standard error, convergence checks, and simple sanity tests.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.