Stochastic Processes Quant Interview Guide
Stochastic processes quant interview guide for indexed random variables, dependence, Markov property, Poisson processes, examples, and caveats.
Candidates preparing for probability, modeling, and derivatives prompts.
A stochastic process is a sequence of random variables
A stochastic process tracks random quantities indexed by time, steps, or another ordering. Interview prompts usually care about dependence, state, and how the process evolves.
Process assumptions drive solutions
Markov property, independent increments, stationarity, and memorylessness are not interchangeable. Name which assumption the prompt gives before choosing a formula.
Concrete example
A random walk updates by adding a random step each period. A Markov chain moves between states with transition probabilities. A Poisson process counts random arrivals over time.
Connect to quant use cases
Processes appear in price models, queues, arrivals, option pricing, hitting probabilities, and risk models. The interview version rewards clear setup more than advanced notation.
Common mistakes
Candidates often memorize named processes without checking assumptions. A stronger answer defines state, transition rule, time index, and what quantity is being asked.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.