Stochastic Volatility Interview Guide
Stochastic volatility interview guide covering random volatility, mean reversion, correlation, calibration, examples, and limitations.
Candidates explaining random volatility, smiles, and model tradeoffs.
Stochastic volatility treats volatility as random
Instead of assuming one deterministic volatility path, stochastic volatility models allow volatility itself to evolve randomly.
Mean reversion is common
Many volatility models assume volatility can spike but tends to revert toward a longer-run level over time after shocks.
Concrete example
A model with negative correlation between returns and volatility can help explain why equity downside options often have higher implied volatility.
Calibration remains difficult
Parameters may fit one surface or period and fail elsewhere, especially when jumps, liquidity, and regime changes matter.
Common mistakes
Candidates often name a model without explaining intuition. Focus on random volatility, smile behavior, and calibration limits.
Practice the pattern
Use the LeetQuidity curriculum and calibration to turn this topic into a focused practice plan.