Stochastic Process Simulation Interview Guide
Stochastic process simulation interview guide for state updates, random draws, path metrics, convergence, validation, examples, and caveats.
Candidates discussing Monte Carlo, path simulation, and model validation.
Simulation follows the state update rule
To simulate a process, define the initial state, random input, update rule, time step, and metric to estimate. The mechanics should mirror the model assumptions.
Many paths estimate path quantities
Monte Carlo can estimate expected payoff, hitting probability, average waiting time, or distribution of outcomes by simulating many independent paths.
Concrete example
To estimate the probability a random walk hits 10 before 0, simulate many paths from the start state and count the share that hit 10 first.
Convergence needs checks
Simulation has sampling error. Use enough paths, sanity checks against simple cases, and clear random assumptions before trusting the estimate.
Common mistakes
Candidates often say simulate without specifying update rules or validation. A good simulation answer states the state, loop, stopping condition, and estimator.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.