Autocorrelation Quant Interview Guide
Autocorrelation quant interview guide for serial dependence, lag relationships, positive and negative autocorrelation, examples, and validation.
Candidates seeing time-series dependence and signal prompts.
Autocorrelation compares a series with itself
Autocorrelation measures how values relate to past values at a lag. Positive autocorrelation means similar movements tend to persist, while negative autocorrelation can suggest reversal.
It affects inference and validation
Serial dependence reduces the independence of observations. Treating autocorrelated data as independent can overstate sample size, confidence, and validation reliability.
Concrete example
If daily returns after large moves tend to reverse, a lagged return feature may show negative autocorrelation. That pattern still needs cost, stability, and out-of-sample checks.
Ask whether it is exploitable
Autocorrelation alone is not an edge. A strong answer asks about transaction costs, timing, liquidity, regime stability, and whether the signal survives realistic execution assumptions.
Common mistakes
Candidates often spot autocorrelation and immediately propose a strategy. In interviews, distinguish statistical dependence from a robust, tradable, validated signal.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.