Stationarity Quant Interview Questions
Stationarity quant interview questions for stable distributions, changing means and variances, time-series assumptions, examples, and caveats.
Candidates discussing stable distributions and model assumptions.
Stationarity means stable behavior over time
A stationary process has statistical properties that do not shift across time in the relevant sense. For interviews, focus on whether mean, variance, and dependence structure appear stable enough for the model.
Why it matters
Many models and validation arguments assume the future resembles the past. If the process changes, historical estimates can become stale, overconfident, or directionally wrong.
Concrete example
A signal that worked in calm markets may fail during a volatile regime. The average effect and variance can both change, weakening a model trained on the earlier period.
Testing is not the whole answer
Formal stationarity tests can be useful, but small samples, structural breaks, and market context matter. In interviews, explain both the diagnostic and the practical implication.
Common mistakes
Candidates often assume stationarity because it makes formulas easier. A stronger answer asks what would show the process changed and how validation should respond.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.