Markov Chain Quant Interview Guide
Markov chain quant interview guide for states, transition probabilities, Markov property, multi-step transitions, examples, and mistakes.
Candidates preparing for state-transition probability questions.
Markov chains move between states
A Markov chain has a set of states and transition probabilities between them. The next-state distribution depends on the current state, not the full past history.
Define the state carefully
The Markov property may hold only if the state includes enough information. If important history is omitted, the process may not be Markov in the chosen state.
Concrete example
A simple weather chain might have sunny and rainy states with probabilities for tomorrow based only on today. Multi-day probabilities come from repeated transitions.
Use matrices for finite chains
For finite-state chains, a transition matrix organizes one-step probabilities. Powers of the matrix can describe multi-step transition probabilities.
Common mistakes
Candidates often assume a Markov model without defining states. Good answers state the states, transition rule, matrix orientation, and any long-run question.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.