Monte Carlo Option Pricing Interview Guide
Monte Carlo option pricing interview guide for simulating paths, discounting payoffs, variance, path-dependent options, examples, and caveats.
Candidates discussing simulation, path-dependent payoffs, and pricing.
Monte Carlo prices by simulated payoffs
Monte Carlo option pricing simulates many possible paths, computes the payoff on each path, averages the payoff, and discounts under the chosen pricing assumptions.
It is useful for path dependence
Simulation is especially natural when payoff depends on the path, such as averages, barriers, or multiple dates. Closed-form formulas may be harder or unavailable.
Concrete example
For an Asian option, each simulated path can produce an average price, then a payoff from that average. The option estimate is the discounted average of those simulated payoffs.
Simulation has error
Monte Carlo estimates have sampling error. More paths, variance reduction, and diagnostics can improve precision, but they do not remove model assumptions.
Common mistakes
Candidates often say simulate and average without mentioning discounting, risk-neutral assumptions, convergence, or whether the payoff is path dependent.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.